tag:blogger.com,1999:blog-29098270599620628522024-03-29T07:20:29.833+00:00Molecular DesignControlling the behavior of compounds and materials by manipulation of molecular properties.Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.comBlogger145125tag:blogger.com,1999:blog-2909827059962062852.post-85799413904494335842024-03-27T06:02:00.023+00:002024-03-29T07:19:56.552+00:00Leadeth me unto Truth and delivereth me from those who have already found it<div style="text-align: center;">A theory has only the alternative of being true or false.</div><div><div style="text-align: center;">A model has a third possibility: it may be true, but irrelevant.</div><div style="text-align: center;">With apologies to Manfred Eigen (1927 - 2019)</div><div style="text-align: center;">******************</div><div style="text-align: center;"><span style="text-align: left;"><br /></span></div><div style="text-align: justify;"><span style="text-align: left;">I've just returned to Cheshire from the Caribbean and, to kick off blogging from 2024 I'll share a photo of the orchids at Berwick-on-Sea on the north coast of Trinidad.</span></div><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgbw8Fld8Bi5OUCtgqSc7VbJVNgiekNBW4b0BW1zdHgF9v3oxNdGAafNg18ppaNNKCIGU6ZeU0sRkvqJVkEDsCajixQfqXA4ECS2rs6RA5THMaDak4OniIwgQWJq6f4Hk7O510u2dCc8QCSdb04JJ3NoWMpTDzsElNs8ENdz6FE4G-fTLJ6NIbMGSp3f1Ep/s4000/20240209_134338.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1800" data-original-width="4000" height="144" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgbw8Fld8Bi5OUCtgqSc7VbJVNgiekNBW4b0BW1zdHgF9v3oxNdGAafNg18ppaNNKCIGU6ZeU0sRkvqJVkEDsCajixQfqXA4ECS2rs6RA5THMaDak4OniIwgQWJq6f4Hk7O510u2dCc8QCSdb04JJ3NoWMpTDzsElNs8ENdz6FE4G-fTLJ6NIbMGSp3f1Ep/s320/20240209_134338.jpg" width="320" /></a></div><br /><div style="text-align: justify;">Encountering words like “truth” and “beauty” (here's a good <a href="https://doi.org/10.1038/nchem.1243" target="_blank">example</a>) in the titles of scientific articles always sets off warning bells for me and I’ll kick off blogging for 2024 with a look at <a href="https://doi.org/10.1016/j.cell.2024.01.003" target="_blank">FM2024</a> (Structure is beauty, but not always truth) that was recently published in Cell (and has already been <a href="https://www.science.org/content/blog-post/john-keats-would-word" target="_blank">reviewed</a> by Derek). The authors have highlighted important issues: we typically use single conformations of targets in design and the experimentally-determined structures used for design may differ substantially from the structures of targets as they exist in vivo. These points do need be stressed given the expanding range of modalities being exploited by drug designers and the increasing use of AI/ML in drug design. That said, it’s my view that the authors have allowed themselves to become prisoners of their article’s title. Specifically, I see “beauty” as a complete red herring and suggest that it would have been much better to have discussed structure in terms of accuracy and relevance rather than truth. Here’s the abstract for <a href="https://doi.org/10.1016/j.cell.2024.01.003" target="_blank">FM2024</a>:</div><div style="text-align: justify;"><br /></div></div><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><div><div style="text-align: justify;">Structural biology, as powerful as it is, can be misleading. We highlight four fundamental challenges: interpreting raw experimental data; accounting for motion; addressing the misleading nature of in vitro structures; and unraveling interactions between drugs and “anti-targets.” Overcoming these challenges will amplify the impact of structural biology on drug discovery.</div></div></blockquote><div><div><br /></div><div style="text-align: justify;">I'll start by taking a look at the introduction and my view is that the authors do need to be much clearer about what they mean by “this hydrogen bond is better than that one” when using terms like “ground truth”. For example, we can infer that the geometry of one target-ligand hydrogen bond is closer to optimal than the geometry of another target-ligand hydrogen bond. However, the energetic cost of breaking a target-ligand hydrogen bond is not something that can generally be measured and, as noted in <a href="https://doi.org/10.1186/s13321-019-0330-2" target="_blank">NoLE</a>, the contribution of an intermolecular contact to affinity is not actually an experimental observable. Ligands associate with their targets (and anti-targets) in aqueous media and this means that intermolecular contacts, for example between polar and non-polar atoms, can destabilize the target-ligand complex without being inherently repulsive. What I’m getting at here is that structures of ligand-target complexes are relatively simple and well-defined entities within the broader context of drug discovery and yet it doesn’t appear useful to discuss them in terms of truth.</div><div><br /></div><div>The remainder of the post follows the <a href="https://doi.org/10.1016/j.cell.2024.01.003" target="_blank">FM2024</a> section headings. </div><div><div><br /></div><div><b>A structure is a model, not experimental reality</b></div><div><br /></div><div style="text-align: justify;">The term “structure” can have a number of different meanings in structure-based drug design. First, drug targets (and anti-targets) have structures that exist regardless of whether they have been experimentally determined. Second, models are built for drug targets by fitting nuclear coordinates to experimental data such as electron density (these are often referred to as experimental structures although they should strictly be called models because they are abstractions of the experimental data). Third, the structure could have been predicted using computational tools such as AlphaFold2 (here's an <a href="https://doi.org/10.1038/s41592-023-02087-4" target="_blank">article</a>, cited by <a href="https://doi.org/10.1016/j.cell.2024.01.003" target="_blank">FM2024</a>, on why we still need experimentally-determined structures). </div><div><br /></div><div style="text-align: justify;">In the abstract the authors identify “interpreting raw experimental data” as one of “four fundamental challenges”. However, the actual focus of this section appears to be evaluation of predicted structures rather than interpretation of raw experimental data. While I’m sure that we can find better ways to interpret raw experimental data, and indeed to evaluate predicted structures, I don’t see either as representing a fundamental challenge. </div></div><div style="text-align: justify;"><br /></div><div><div><b>Representing wiggling and jiggling is hard</b></div><div><br /></div><div style="text-align: justify;">My view is that it’s actually the ensemble of conformations rather than the wiggling and jiggling that we actually need to represent. Simulation of the wiggling and jiggling is one way to generate an ensemble of conformations but it’s not the only way (nor is it necessarily the best way). That said, it's a lot easier to sell protein motion to venture capitalists than it is to sell ensembles of conformations. <br /></div><div><br /></div><div>The authors state:</div><div><br /></div></div></div><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><div><div><div style="text-align: justify;">Analogous to how structure-based drug design is great for optimizing “surface complementarity” and electrostatics, future protein modeling approaches will unlock ensemble-based drug design with an ability to predictably tune new and important aspects of design, including entropic contributions [<a href="https://doi.org/10.7554/eLife.74114" target="_blank">7</a>] and residence times [<a href="https://doi.org/10.1038/nrd.2015.18" target="_blank">8</a>] of bound ligands.</div></div></div></blockquote><div><div><br /></div><div style="text-align: justify;">The term “entropic contributions” does come across as arm-waving (especially in a drug design context) and my view is that entropy should be seen as an effect rather than a cause. Thermodynamic signatures for binding are certainly of scientific interest but I would argue that they are essentially irrelevant to drug design (it can be instructive to consider how patients might sense the benefits of enthalpically-driven drug binding). The case for increasing residence time might not be quite as solid as many believe it to be (see the <a href="https://doi.org/10.1016/j.drudis.2017.07.016" target="_blank">F2018</a> study and this blog <a href="https://fbdd-lit.blogspot.com/2023/04/a-clear-demonstration-of-benefits-of.html" target="_blank">post</a>).</div><div><br /></div><div><div><b>In vitro can be deceiving</b></div><div><br /></div><div style="text-align: justify;">The authors identify “addressing the misleading nature of in vitro structures” as a fundamental challenge and they state:</div><div><br /></div></div></div><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><div><div><div style="text-align: justify;">While purifying a protein out of its cellular context can be enabling for in vitro drug discovery, it can also provide a false impression. Recombinant expression can lead to missing post-translational modifications (e.g., phosphorylation or glycosylation) that are critical to understanding the function of a protein.</div></div></div></blockquote><div><div><br /></div><div style="text-align: justify;">To this I’d add that we often don’t use the full-length proteins in design and recombinant proteins may have been engineered to make them easier to crystallize or more robust for soaking experiments. Furthermore, target engagement may require the drug to interact with two or more proteins (see <a href="https://doi.org/10.1042/EBC20170041" target="_blank">HC2017</a>) which will probably be more amenable individually to structure determination than the their complex. I fully agree that it is important for drug designers to be aware that the experimentally-determined structures that they're using differ from the structures of the targets as they exist in vivo. However, I don't believe that it makes any sense to talk about “the misleading nature of in vitro structures” (or indeed about “in vitro drug discovery”) because target structures are never experimentally determined in vivo and are only misleading to the extent that users overinterpret them. As a more general point users of experimental data do need to very careful about describing the experimental data that they’re using as “misleading” or "deceiving". </div><div><br /></div><div style="text-align: justify;">When we use structures to represent targets the issue is much less about the truth of the structures that we’re using and much more about their relevance to the targets that we’re trying to represent. This is not just an issue for structural biology and we might, for example, use the catalytic domain of an enzyme as a model for the full-length protein when running biochemical assays. We have to make assumptions in these situations and we also need to check that these assumptions are reasonable. For example, we might examine the structure-activity relationship in a cell-based assay for consistency with the structure-activity relationship that we’ve observed in the enzyme inhibition assay. It's also worth pointing out that what we observe in cells is usually a coarse approximation to what actually happens in vivo and we can't even measure the intracellular concentration of a drug in vivo. </div><div style="text-align: justify;"><br /></div><div><div><div><b>Drugs mingle with many different receptors</b></div><div><br /></div><div style="text-align: justify;">Drugs do indeed mingle with many receptors in vivo but it’s important to be aware that the consequences of this mingling depend on the drug concentration (a spatiotemporal quantity) at the site of action. Drug discovery scientists use the term exposure when talking about drug concentration at the site of action and one underappreciated challenge in drug design is that intracellular drug concentration cannot generally be measured in vivo (here’s an open access <a href="https://doi.org/10.1124/dmd.118.085951" target="_blank">article</a> that I recommend to everybody working drug discovery). I argue in <a href="https://doi.org/10.1186/s13321-019-0330-2" target="_blank">NoLE</a> that controllability of exposure should be seen as a drug design objective although the current impossibility of measuring intracellular concentration means that we can only assess how effectively the objective has been achieved in an indirect manner. Alternatively, drug design can be seen in terms of minimization of the dose at which therapeutically beneficial effects can be observed. </div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">One assumption often made in drug design is that the drug concentration at the site of action is equal to the unbound concentration in plasma and this assumption is referred to as the free drug hypothesis (FDH) although the term “free drug theory” is also used. The basis for the FDH is the assumption that the drug can move freely between plasma and the target compartment. In reality the drug concentration at the site of action will generally lag behind its unbound plasma concentration and the lag time is inversely related to the ease with which the drug permeates through the barriers which separate the target from the plasma. There are a couple of scenarios under which you can’t assume that the drug concentration in the target compartment will be the same as its unbound plasma concentration. The first of these is when active transport is significant and this is a scenario with which drug designers tackling targets within the central nervous system (CNS) are familiar with. The second scenario is that there is an ionizable functional group (as is the case for amines) in the molecular structure of the drug and the pH at the site of action differs significantly from plasma pH (as is the case for lysosomes).</div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">There are two general types of undesirable outcome that can result when a drug encounters receptors with which it mingles. First, the receptor is an anti-target and the encounter results in binding of the drug, leading to toxicity (patients are harmed). Second, the receptor is a metabolic enzyme or a transporter and the encounter leads to the drug either being turned over or pumped from where it needs to function (patients do not benefit from the treatment). <br /></div></div></div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">I've inserted some comments (italicised in red) into the following quoted text: </div><div style="text-align: justify;"><br /></div></div><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><div><div style="text-align: justify;">The sad reality that all drug discoverers must face is that however well designed we may believe our compounds to be, they will find ways to interact with many other proteins or nucleic acids in the body and interfere with the normal functions of those biomolecules. While occasionally, the ability of a medicine to bind to multiple biomolecules will increase a drug’s efficacy, such polypharmacology is far more likely to produce undesirable effects. These undesirable outcomes take two forms. Obviously, the direct binding to an anti-target can lead to a bewildering range of toxicities, many of which render the drug too hazardous for any use. <i><span style="color: red;">[While there are well-known anti-targets such as hERG that must be avoided, my understanding is that those responsible for drug safety generally prefer not to see any off-target activity given the difficulties in prediction of toxicity. Here are a couple of relevant articles (<a href="https://doi.org/10.1038/nrd3845" target="_blank">B2012</a> | <a href="https://doi.org/10.1016/j.vascn.2020.106869" target="_blank">J2020</a>) and a <a href="https://www.eurofinsdiscovery.com/solution/safety-panels" target="_blank">link</a> to some information about in vitro safety pharmacology profiling panels from Eurofins.]</span></i> More subtly, the binding to anti-targets reduces the ability of the drug to reach the desired target. A drug that largely avoids binding to anti-targets will partition more effectively through the body, enabling it to accumulate at high enough concentrations in the disease-relevant tissue to effectively modulate the function of the target. <i><span style="color: red;">[I consider it unlikely that binding to an anti-target could account for a significant proportion of the dose. In any case, I’d expect binding of a drug to anti-targets to cause unacceptable toxicity long before it results in sequestration of a significant proportion of the dose.] </span></i></div></div></blockquote><div><div style="text-align: justify;"><br /></div></div><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><div><div><div style="text-align: justify;">A particular challenge results from the interaction of drugs with the enzymes, transporters, channels, and receptors that are largely responsible for controlling the metabolism and pharmacokinetic properties (DMPK) of those drugs—their absorption, distribution, metabolism, and elimination. Drugs often bind to plasma proteins, preventing them from reaching the intended tissues; <i><span style="color: red;">[A degree of binding to plasma proteins is not a problem and, in the case of warfarin, is probably essential for the safe use of the drug.]</span></i> they can block or be substrates for all manner of pumps and transporters, changing their distribution through the body;<i><span style="color: red;"> [Transporters can indeed prevent drugs from getting to their sites of action at therapeutically effective concentrations and limited brain exposure resulting from active efflux is a common issue for CNS drug discovery programs (see <a href="https://doi.org/10.1021/jm201136z" target="_blank">H2012</a> and <a href="https://doi.org/10.1021/jm501535r" target="_blank">R2015</a>). I am not aware of any transporters that are definitely considered to be anti-targets from the safety perspective (I'm happy to be corrected on this point) and inhibition of efflux pumps is a recognized tactic (see <a href="https://doi.org/10.1016/j.ejphar.2021.174151" target="_blank">T2021</a> and <a href="https://doi.org/10.1007/164_2020_403" target="_blank">H2020</a>) in drug discovery.]</span></i> xenobiotic sensors such as PXR that turn on transcriptional programs recognizing foreign substances; and they often block enzymes like cytochrome P450s, thereby changing their own metabolism and that of other medicines. <i><span style="color: red;">[Inhibition of CYPs is generally considered undesirable from the safety perspective because of the potential for drug-drug interactions (see <a href="https://doi.org/10.1007/s00204-020-02936-7" target="_blank">H2020</a>). That said, the CYP3A inhibitor ritonavir (see <a href="https://doi.org/10.2165/00003495-200363080-00004" target="_blank">CG2003</a>) is used in the COVID-19 treatment <a href="https://doi.org/10.1126/science.abl4784" target="_blank">Paxlovid</a> to slow metabolism of SARS-CoV-2 main protease nirmatrelvir.]</span></i> They are themselves substrates for P450s and other metabolizing enzymes and, once altered, can no longer carry out their assigned, life-saving function. <i><span style="color: red;">[Medicinal chemists are well aware of the challenges presented by drug-metabolizing enzymes although it must be stressed that any drug that was cleared too slowly would be considered to be an unacceptable safety risk.] </span></i></div></div></div></blockquote><div><div><div><br /></div></div></div><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><div><div><div><div style="text-align: justify;">Taken together, we refer to these DMPK-related proteins, somewhat tongue-in-cheek, as the “avoidome” (Figure 2). <i><span style="color: red;">[</span><span style="color: red;">It is unclear why the authors have chosen to only include DMPK-related proteins in the avoidome (hERG is not a DMPK-related protein but is an anti-target that every drug discovery scientist would wish to avoid blocking). For reasons outlined in the previous paragraph I would actually argue against the inclusion of DMPK-related proteins in the avoidome.]</span></i> Unfortunately, the structures of the vast majority of avoidome targets have not yet been determined. Further, many of these proteins are complex machines that contain multiple domains and exhibit considerable structural dynamism. Their binding pockets can be quite large and promiscuous, favoring distinct binding modes for even closely related compounds. <i><span style="color: red;">[</span><span style="color: red;">It is not clear whether this assertion is based on experimental observations.]</span></i> As a consequence, multiple structures spanning a range of bound ligands and protein conformational states will be required to fully understand how best to prevent drugs from engaging these problematic anti-targets. </div></div></div></div></blockquote><div><div><div><div><br /></div></div></div></div><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><div><div><div><div><div style="text-align: justify;">We believe the structural biology community should “embrace the avoidome” with the same enthusiasm that structure-based design has been applied to intended targets.<i><span style="color: red;"> [My view is that the authors need to clearly articulate their reasons for only including DMPK-related proteins in the avoidome before seeking to direct the activities of structural biology community. I presume that the <a href="https://doi.org/10.1039/D1MD00228G" target="_blank">Target 2035 initiative</a>, which aims to “to create by year 2035 chemogenomic libraries, chemical probes, and/or biological probes for the entire human proteome”, will also cover anti-targets. Having chemical and/or biological probes available for anti-targets should lead to better understanding of toxicity in humans.]</span></i> The structures of these proteins will shed considerable light on human biology and represent exciting opportunities to demonstrate the power of cutting-edge structural techniques.<i><span style="color: red;"> [Experimental structures of target-ligand complexes do indeed provide valuable direct evidence that a ligand is binding to a protein but the structures themselves are not particularly informative from the perspective of understanding human biology. It is actually high-quality chemical probes that are needed to shed light on human biology and here’s a <a href="https://www.chemicalprobes.org/" target="_blank">link</a> to the Chemical Probes Portal. Structures at atomic resolution for protein-ligand complexes are certainly useful for chemical probe design but are not strictly necessary for effective use of chemical probes.]</span></i> Crucially, a detailed understanding of the ways that drugs engage with avoidome targets would significantly expedite drug discovery. <i><span style="color: red;">[Experimentally-determined structures of anti-targets complexed with ligands are certainly informative when elucidating structure-activity relationships for binding to anti-targets. However, structural information of this nature is much less directly useful for addressing problems such as metabolic lability and active efflux.]</span></i> This information holds the potential to achieve a profound impact on the discovery of new and enhanced medicines.</div></div></div></div></div></blockquote><div><div><div><br /></div><div><b>Conclusion</b></div><div><br /></div><div>The authors assert: </div><div><br /></div></div></div><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><div><div><div style="text-align: justify;">In drug discovery, truth is a molecule that transforms the practice of medicine. <i><span style="color: red;">[I disagree with this assertion. In drug discovery truth may also be a compound that, despite an excellent pharmacokinetic profile, chokes comprehensively in phase 2.]</span></i></div></div></div></blockquote><div><div><div><br /></div><div style="text-align: justify;">It's been been a long post and this is a good place to leave things. While the authors have raised some valid points I found the 'Drugs mingle with many different receptors' section to be rather confused and I don't think that the drug discovery and structural biology communities are in desperate need of yet another 'ome' word. I hope that my review of <a href="https://doi.org/10.1016/j.cell.2024.01.003" target="_blank">FM2024</a> will be useful for readers of the article while providing helpful feedback for the authors and for the Editors of Cell. </div></div></div>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-66292485507935961922023-12-31T17:20:00.015+00:002024-01-03T10:00:03.124+00:00Chemical con artists foil drug discovery<p style="text-align: justify;">One piece of general advice that I offer to fellow scientists is to not let the fact that an article has been published in Nature (or any other ‘elite’ journal for that matter) cause you to switch off your critical thinking skills while reading it and the <a href="https://doi.org/10.1038/513481a" target="_blank">BW2014</a> article (Chemistry: Chemical con artists foil drug discovery) that I’ll be reviewing in this post is an excellent case in point. My main criticism of <a href="https://doi.org/10.1038/513481a" target="_blank">BW2014</a> that is that the rhetoric is not supported by data and I’ve always seen the article as something of a propaganda piece.</p><p style="text-align: justify;">One observation that I’ll make before starting my review of <a href="https://doi.org/10.1038/513481a" target="_blank">BW2014</a> is that what lawyers would call ‘standard of proof’ varies according to whether you’re saying something good about a compound or something bad. For example, I would expect a competent peer reviewer to insist on measured IC50 values if I had described compounds as inhibitors of an enzyme in a manuscript. However, it appears to be acceptable, even in top journals, to describe compounds as PAINS without having to provide any experimental evidence that they actually exhibit some type of nuisance behavior (let alone pan-assay interference). I see a tendency in the ‘compound quality’ field for opinions to be stated as facts and reading some of the relevant literature leaves me with the impression that some in the field have lost the ability to distinguish what they know from what they believe. </p><p style="text-align: justify;"><a href="https://doi.org/10.1038/513481a" target="_blank">BW2014</a> has been heavily cited in the drug discovery literature (it was cited as the first reference in the ACS assay interference <a href="https://doi.org/10.1021/acs.jmedchem.7b00229" target="_blank">editorial</a> which I reviewed in <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">K2017</a>) despite providing little in the way of practical advice for dealing with nuisance behavior. <a href="https://doi.org/10.1038/513481a" target="_blank">B2014</a> appears to exert a particularly strong influence on the Chemical Probes Community having been cited by the <a href="https://doi.org/10.1038/nchembio.1867" target="_blank">A2015</a>, <a href="https://doi.org/10.1016/j.ccell.2017.06.005" target="_blank">BW2017</a>, <a href="https://doi.org/10.1038/s41467-022-31271-x" target="_blank">AW2022</a> and <a href="https://doi.org/10.1093/nar/gkac909" target="_blank">A2022</a> articles as well as in the Toxicophores and PAINS Alerts <a href="https://www.chemicalprobes.org/info/pains" target="_blank">section</a> of the <a href="https://www.chemicalprobes.org/" target="_blank">Chemical Probes Portal</a>. Given the commitment of the Chemical Probes Community to open science, their enthusiasm for the PAINS substructure model introduced in <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a> (New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays) is somewhat perplexing since neither the assay data nor the associated chemical structures were disclosed. My advice to the Chemical Probes Community is to let go of PAINS filters. </p><p style="text-align: justify;">Before discussing <a href="https://doi.org/10.1038/513481a" target="_blank">BW2014</a>, I’ll say a bit about high-throughput screening (HTS) which emerged three decades ago as a lead discovery paradigm. From the early days of HTS it was clear, at least to those who were analyzing the output from the screens, that not every hit smelt of roses. Here’s what I wrote in <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">K2017</a>: </p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">Although poor physicochemical properties were partially blamed (<a href="https://doi.org/10.1016/S0169-409X(96)00423-1" target="_blank">3</a>) for the unattractive nature and promiscuous behavior of many HTS hits, it was also recognized that some of the problems were likely to be due to the presence of particular substructures in the molecular structures of offending compounds. In particular, medicinal chemists working up HTS results became wary of compounds whose molecular structures suggested reactivity, instability, accessible redox chemistry or strong absorption in the visible spectrum as well as solutions that were brightly colored. While it has always been relatively easy to opine that a molecular structure ‘looks ugly’, it is much more difficult to demonstrate that a compound is actually behaving badly in an assay.</p></blockquote><p style="text-align: justify;">It has long been recognized that it is prudent to treat frequent-hitters (compounds that hit in multiple assays) with caution when analysing HTS output. In <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">K2017</a> I discussed two general types of behavior that can cause compounds to hit in multiple assays: Type 1 (assay result gives an incorrect indication of the extent to which the compound affects target function) and Type 2 (compound acts on target by undesirable mechanism of action (MoA)). Type 1 behavior is typically the result of interference with the assay read-out and the hits in question can be accurately described as ‘false positives’ because the effects on the target are not real. Type 1 behaviour should be regarded as a problem with the assay (rather than with the compound) and, provided that the activity of a compound has been established using a read-out for which interference is not a problem, interference with other read-outs is irrelevant. In contrast, Type 2 behavior should be regarded as a problem with the compound (rather than with the assay) and an undesirable MoA should always be a show-stopper.</p><p style="text-align: justify;">Interference with read-out and undesirable MoAs can both cause compounds to hit in multiple assays. However, these two types of bad behavior can still cause big problems whether or not the compounds are observed to be frequent-hitters. Interference with read-out and undesirable MoAs are very different problems in drug discovery and the failure to recognize this point is a serious deficiency that is shared by<a href="https://doi.org/10.1038/513481a" target="_blank"> BW2014</a> and <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a>.</p><p style="text-align: justify;">Although I’ve criticized the use of PAINS filters there is no suggestion that compounds matching PAINS substructures are necessarily benign (many of the PAINS substructures look distinctly unwholesome to me). I have no problem whatsoever with people expressing opinions as to the suitability of compounds for screening provided that the opinions are not presented as facts. In my view the chemical con-artistry of PAINS filters is not that benign compounds have been denounced but the implication that PAINS filters are based on relevant experimental data.</p><p style="text-align: justify;">Given that the PAINS filters form the basis of a cheminformatic model that is touted for prediction of pan-assay interference, one could be forgiven for thinking that the model had been trained using experimental observations of pan-assay interference. This is not so, however, and the data that form the basis of the PAINS filter model actually consist of the output of six assays that each use the AlphaScreen read-out. As noted in <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">K2017</a>, a panel of six assays using the same read-out would appear to be a suboptimal design of an experiment to observe pan assay interference. Putting this in perspective, <a href="https://doi.org/10.1021/ci050504m" target="_blank">P2006</a> (An Empirical Process for the Design of High-Throughput Screening Deck Filters) which was based on analysis of the output from 362 assays had actually been published four years before <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a>.</p><p style="text-align: justify;">After a bit of a preamble, I need to get back to reviewing BW2014 and my view is that readers of the article who didn’t know better could easily conclude that drug discovery scientists were completely unaware of the problems associated with misleading HTS assay results before the re-branding of frequent-hittters as PAINS in <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a>. Given that <a href="https://doi.org/10.1021/jm030266r" target="_blank">M2003</a> had been published over a decade previously. I was rather surprised that <a href="https://doi.org/10.1038/513481a" target="_blank">BW2014</a> had not cited a single article about how colloidal aggregation can foil drug discovery. Furthermore, it had been known (see <a href="https://doi.org/10.1038/nprot.2006.77" target="_blank">FS2006</a>) for years before the publication of <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a> that the importance of colloidal aggregation could be assessed by running assays in the presence of detergent.</p><p style="text-align: justify;">I'll be commenting directly on the text of <a href="https://doi.org/10.1038/513481a" target="_blank">BW2014</a> for the remainder of the post (my comments are italicized in red).</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">Most PAINS function as reactive chemicals rather than discriminating drugs.<i><span style="color: red;"> [It is unclear here whether “PAINS” refers to compounds that have been shown by experiment to exhibit pan-assay interference or simply compounds that share structural features with compounds (chemical structures not disclosed) claimed to be frequent-hitters in the <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a> assay panel. In any case, sweeping generalizations like this do need to be backed with evidence. I do not consider it valid to present observations of frequent-hitter behavior as evidence that compounds are functioning as reactive chemicals in assays.]</span></i> They give false readouts in a variety of ways. Some are fluorescent or strongly coloured. In certain assays, they give a positive signal even when no protein is present. <span style="color: red;"><i>[The <a href="https://doi.org/10.1038/513481a" target="_blank">BW2014</a> authors appear to be confusing physical phenomena such as fluorescence with chemical reactivity.]</i></span></p></blockquote><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">Some of the compounds that should ring the most warning bells are toxoflavin and polyhydroxylated natural phytochemicals such as curcumin, EGCG (epigallocatechin gallate), genistein and resveratrol. These, their analogues and similar natural products persist in being followed up as drug leads and used as ‘positive’ controls even though their promiscuous actions are well-documented (<a href="https://doi.org/10.2174/138161213805289228" target="_blank">8</a>,<a href="https://doi.org/10.1021/cb500086e" target="_blank">9</a>). <span style="color: red;"><i>[Toxoflavin is not mentioned in either <a href="https://doi.org/10.2174/138161213805289228" target="_blank">Ref8</a> or <a href="https://doi.org/10.1021/cb500086e" target="_blank">Ref9</a> although <a href="https://doi.org/10.1016/j.bmcl.2003.12.014" target="_blank">T2004</a> would have been a relevant reference for this compound. <a href="https://doi.org/10.2174/138161213805289228" target="_blank">Ref8</a> only discusses curcumin and I do not consider that the article documents the promiscuous actions of this compound. Proper documentation of the promiscuity of a compound would require details of the targets that were hit, the targets that were not hit and the concentration(s) at which the compound was assayed. The effects of curcumin, EGCG (epigallocatechin gallate), genistein and resveratrol on four membrane proteins were reported in <a href="https://doi.org/10.1021/cb500086e" target="_blank">Ref9</a> and these effects would raise doubts about activity for any of these compounds (or their close structural analogs) that had been observed in a cell-based assay. However, I don’t consider that it would be valid to use the results given in <a href="https://doi.org/10.1021/cb500086e" target="_blank">Ref9</a> to cast doubt on biological activity measured in an assay that was not cell-based.]</i> </span></p></blockquote><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">Rhodanines exemplify the extent of the problem. <span style="color: red;"><i>[Rhodanines are specifically discussed in <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">K2017</a> in which I suggest that the most plausible explanation for the frequent-hitter behavior observed for rhodanines in the <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a> panel of six AlphaScreen assays is that the singly-connected sulfur reacts with singlet oxygen (this reactivity has been reported for compounds with thiocarbonyl groups in their molecular structures).]</i></span> A literature search reveals 2,132 rhodanines reported as having biological activity in 410 papers, from some 290 organizations of which only 24 are commercial companies. <span style="color: red;"><i>[Consider what the literature search would have revealed if the target substructure had been ‘benzene ring’ rather than ‘rhodanine’? As discussed in this <a href="https://fbdd-lit.blogspot.com/2023/12/are-fused-tetrahydroquinolines.html" target="_blank">post</a> the <a href="https://doi.org/10.1021/acs.jmedchem.3c01277" target="_blank">B2023</a> study presented the diversity of targets hit by compounds incorporating a fused tetrahydroquinolines in their molecular structures as ‘evidence’ for pan-assay interference by compounds based on this scaffold.]</i></span> The academic publications generally paint rhodanines as promising for therapeutic development. In a rare example of good practice, one of these publications (<a href="https://doi.org/10.1016/S0960-894X(02)00941-1" target="_blank">10</a>) (by the drug company Bristol-Myers Squibb) warns researchers that these types of compound undergo light-induced reactions that irreversibly modify proteins. <span style="color: red;"><i>[The <a href="https://doi.org/10.1073/pnas.211178398" target="_blank">C2001</a> study (Photochemically enhanced binding of small molecules to the tumor necrosis factor receptor-1 inhibits the binding of TNF-α) is actually a more relevant reference since it focuses of the nature of the photochemically enhanced binding. The structure of the complex of TNFRc1 with one of the compounds studied (IV703; see graphic below) showed a covalent bond between one of carbon atoms of the pendant nitrophenyl and the backbone amide nitrogen of A62. The structure of the IV703–TNFRc1 complex shows that a covalent bond between pendant aromatic ring must also be considered as a distinct possiblity for the rhodanines reported in <a href="https://doi.org/10.1016/S0960-894X(02)00941-1" target="_blank">Ref10</a> and <a href="https://doi.org/10.1073/pnas.211178398" target="_blank">C2001</a>.]</i></span> It is hard to imagine how such a mechanism could be optimized to produce a drug or tool. Yet this paper is almost never cited by publications that assume that rhodanines are behaving in a drug-like manner. <i><span style="color: red;">[It would be prudent to cite <a href="https://doi.org/10.1021/jm201243p" target="_blank">M2012</a> (Privileged Scaffolds or Promiscuous Binders: A Comparative Study on Rhodanines and Related Heterocycles in Medicinal Chemistry) if denouncing fellow drug discovery scientists for failure to cite <a href="https://doi.org/10.1016/S0960-894X(02)00941-1" target="_blank">Ref10</a>.]</span></i></p></blockquote><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBB0Pj__I58J9Q1vQKx5-QnwhnUq3ox7NmvRiFJd2RQjnUkBPZuLnzlaCQONSJYk_YBlzFo0blhK7JPysckHjUuvwWlprd0xu6vGbsh5q0y4RA94PYmKaXHQjlMb_A4AxV7IsyhCtRZDkcWbY5xc2ioIJIWyzwdcTD13ich9M8aOaASTFMAIV-xbaMv1BH/s997/nature_pains.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="549" data-original-width="997" height="176" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBB0Pj__I58J9Q1vQKx5-QnwhnUq3ox7NmvRiFJd2RQjnUkBPZuLnzlaCQONSJYk_YBlzFo0blhK7JPysckHjUuvwWlprd0xu6vGbsh5q0y4RA94PYmKaXHQjlMb_A4AxV7IsyhCtRZDkcWbY5xc2ioIJIWyzwdcTD13ich9M8aOaASTFMAIV-xbaMv1BH/s320/nature_pains.jpg" width="320" /></a></div><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">In a move partially implemented to help editors and manuscript reviewers to rid the literature of PAINS (among other things), the Journal of Medicinal Chemistry encourages the inclusion of computer-readable molecular structures in the supporting information of submitted manuscripts, easing the use of automated filters to identify compounds’ liabilities. <span style="color: red;"><i>[I would be extremely surprised if ridding the literature of PAINS was considered by the JMC Editors when they decided to implement a requirement that authors include computer-readable molecular structures in the supporting information of submitted manuscripts. In any case, claims such as this do need to be supported by evidence.]</i></span> We encourage other journals to do the same. We also suggest that authors who have reported PAINS as potential tool compounds follow up their original reports with studies confirming the subversive action of these molecules.<span style="color: red;"><i> [I’ve always found this statement bizarre since the <a href="https://doi.org/10.1038/513481a" target="_blank">BW2014</a> authors appear to be suggesting that that authors who have reported PAINS as potential tool compounds should confirm something that they have not observed and which may not even have occurred. When using the term “PAINS” do the <a href="https://doi.org/10.1038/513481a" target="_blank">BW2014</a> authors mean compounds that have actually been shown to exhibit pan-assay interference or compounds that that share structural features with compounds that were claimed to exhibit frequent-hitter behavior in the <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a> assay panel? Would interference in with the AlphaScreen read-out by a singlet oxygen quencher be regarded as a subversive action by a molecule in situations when a read-out other than AlphaScreen had been used?]</i></span> Labelling these compounds clearly should decrease futile attempts to optimize them and discourage chemical vendors from selling them to biologists as valid tools. <span style="color: red;"><i>[The real problem here is compounds being sold as tools in the absence of the measured data that is needed to support the use of the compounds for this purpose. Matches with PAINS substructures would not rule out the use of a compound as a tool if the appropriate package of measured data is available. In contrast, a compound that does not match any PAINS substructures cannot be regarded as an acceptable tool if the appropriate package of measured data is not available. Put more bluntly, you’re hardly going to be able to generate the package of measured data if the compound is as bad as PAINS filter advocates say it is.]</i></span></p></blockquote><p>Box: PAINS-proof drug discovery</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">Check the literature.<i><span style="color: red;"> [It’s always a good idea to check the literature but the failure of the <a href="https://doi.org/10.1038/513481a" target="_blank">BW2014</a> authors to cite a single colloidal aggregation article such as <a href="https://doi.org/10.1021/jm030266r" target="_blank">M2003</a> suggests that perhaps they should be following this advice rather than giving it. My view is that the literature on scavenging and quenching of singlet oxygen was treated in a cursory manner in <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a> (see earlier comment in connection with rhodanines).]</span></i> Search by both chemical similarity and substructure to see if a hit interacts with unrelated proteins or has been implicated in non-drug-like mechanisms. <i><span style="color: red;">[Chemical similarity and substructure search will identify analogs of hits and it is actually the exact match structural search that you need do in order to see if a particular compound is a hit in assays against unrelated proteins.</span></i>] Online services such as SciFinder, Reaxys, BadApple or PubChem can assist in the check for compounds (or classes of compound) that are notorious for interfering with assays. <i><span style="color: red;">[I generally recommend <a href="https://www.ebi.ac.uk/chembl/" target="_blank">ChEMBL</a> as a source of bioactivity data.] </span></i> </p></blockquote><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">Assess assays. For each hit, conduct at least one assay that detects activity with a different readout. <i><span style="color: red;">[This will only detect problems associated with interference with read-out. As discussed in <a href="https://doi.org/10.1177/1087057106286653" target="_blank">S2009</a> it may be possible to assess and even correct for interference with read-out without having to run an assay with a different read-out.]</span></i> Be wary of compounds that do not show activity in both assays. If possible, assess binding directly, with a technique such as surface plasmon resonance. <i><span style="color: red;">[SPR can also provide information about MoA since association, dissociation and stoichiometry can all be observed directly using this detection technology.]</span></i> </p></blockquote><p style="text-align: justify;">That concludes blogging for 2023 and many thanks to anybody who has read any of the posts this year. For too many people Planet Earth is not a very nice place to be right now and my new year wish is for a kinder, happier and more peaceful world in 2024. </p>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-41118030520484470472023-12-19T10:02:00.007+00:002023-12-20T01:30:14.554+00:00On quality criteria for covalent and degrader probes<p style="text-align: justify;">I’ll be taking a look at <a href="https://doi.org/10.1021/acs.jmedchem.3c00550" target="_blank">H2023</a> (Expanding Chemical Probe Space: Quality Criteria for Covalent and Degrader Probes) in this post and this article has also been discussed <a href="https://www.science.org/content/blog-post/chemical-probes-used-and-misused" target="_blank">In The Pipeline</a>. I’ll primarily be discussing the quality criteria for covalent probes in this post although I’ll also comment briefly on chemical matter criteria proposed for degrader probes. The post is intended as a contribution to the important scientific discussion that the <a href="https://doi.org/10.1021/acs.jmedchem.3c00550" target="_blank">H2023</a> Perspective is intended to jumpstart:</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">We are convinced that now is the time to initiate similar efforts to achieve a consensus about quality criteria for covalently acting and degrader probes. This Perspective is intended to jumpstart this important scientific discussion.</p></blockquote><p style="text-align: justify;">Covalent bond formation between ligands and targets is a drug design tactic for exploiting molecular recognition elements in targets that are difficult to make beneficial contacts with. Cysteine SH has minimal capacity to form hydrogen bonds with polar ligand atoms and the exposed nature of catalytic cysteine SH reduces its potential to make beneficial contacts with non-polar ligand atoms. One common misconception in drug discovery is that covalent bond formation between targets and ligands is necessarily irreversible and it wasn’t clear from my reading of <a href="https://doi.org/10.1021/acs.jmedchem.3c00550" target="_blank">H2023</a> whether the authors were aware that covalent bond formation between targets and ligands can also be reversible. In any case, it needed to be made clear that the quality criteria proposed by the authors for covalently acting small-molecule probes only apply to probes that act irreversibly.</p><p style="text-align: justify;">Irreversible covalent bond formation is typically used to target non-catalytic residues and design is lot more complicated than for reversible covalent bond formation. First, IC50 values are time-dependent (there are two activity parameters: affinity and inactivation rate constant) which makes it much more difficult to assess selectivity or to elucidate SAR. Second, the transition state structural models required for modelling inactivation cannot be determined experimentally and therefore need to be calculated using computationally intensive quantum mechanical methods.</p><p style="text-align: justify;">I’ll start my review with a couple of general comments. Intracellular concentration is factor that is not always fully appreciated in chemical biology and I generally recommend that people writing about chemical probes demonstrate awareness of <a href="https://doi.org/10.1124/dmd.118.085951" target="_blank">SR2019</a> (Intracellular and Intraorgan Concentrations of Small Molecule Drugs: Theory, Uncertainties in Infectious Diseases and Oncology, and Promise). One a more pedantic note I cautioned against using ‘molecule’ as a synonym for ‘compound’ in my <a href="https://fbdd-lit.blogspot.com/2023/11/on-misuse-of-chemical-probes.html" target="_blank">review</a> of <a href="https://doi.org/10.1038/s41467-023-38952-1" target="_blank">S2023</a> (Systematic literature review reveals suboptimal use of chemical probes in cell-based biomedical research) and I suggest that “covalent molecule” might be something that you don't want to see in the text of an article in a chemistry journal.</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">However, significant efforts need to be invested into characterizing and validating covalent molecules as a prerequisite for conclusive use in biomedical research and target validation studies.</p></blockquote><p style="text-align: justify;">The proposed quality criteria for covalently acting small-molecule probes are given in Figure 2 of <a href="https://doi.org/10.1021/acs.jmedchem.3c00550" target="_blank">H2023</a> although I’ll be commenting on the text of the article. Subscripting doesn't work well in blogger and so I'll use K.i and k.inact respectively throughout the post to denote the inhibition constant and the first order inactivation rate constant. </p><p style="text-align: justify;">I’ll start with Section 2.1 (Criteria for Assessing Potency of Covalent Probes) and my comments are italicised in red. </p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">When working with irreversible covalent probes, it is important to consider that target inhibition is time-dependent and therefore IC50 values, while frequently used, are a suboptimal descriptor of potency. (<a href="https://doi.org/10.1002/cpz1.419" target="_blank">21</a>) Best practice is to use k.inact (the rate of inactivation) over K.i (the affinity for the target) values instead. (<a href="https://doi.org/10.1073/pnas.1313733111" target="_blank">22</a>) <i><span style="color: red;">[I recommend that values of both k.inact and K.i be reported since because this enables the extent of non-covalent target engagement by the chemical probe to be assessed. Regardless of whether binding to target is covalent or non-covalent, the concentration and affinity of substrates (as well as cofactors such as ATP) need be properly accounted for when interpreting effects of chemical probes in cell-based assays. This is a significant issue for ATP-competitive kinase inhibitors (as discussed in my <a href="https://fbdd-lit.blogspot.com/2023/11/on-misuse-of-chemical-probes.html" target="_blank">review</a> of <a href="https://doi.org/10.1038/s41467-023-38952-1" target="_blank">S2023</a>) and I recommend this <a href="https://krhornberger.substack.com/p/tweetorials-receptor-occupancy-and" target="_blank">tweetorial</a> from Keith Hornberger.]</span></i></p></blockquote><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">As measurement of k.inact/K.i values can be labor-intensive (or in certain cases technically impossible), IC50 values (or target engagement TE50 values) are often reported for covalent leads and used to generate structure–activity relationships (SARs). <i><span style="color: red;">[The labor-intensive nature of the measurements is not a valid justification for a failure to measure k.inact and K.i values for a covalent chemical probe.] </span></i> Carefully designed biochemical assays used in determining IC50 values can be well-suited as surrogates for k.inact/K.i measurements. (<a href="https://doi.org/10.1016/j.bmc.2020.115865" target="_blank">24</a>) <span style="color: red;"><i>[It is my understanding that the primary reason for doing this is to increase the throughput of irreversible inhibition assays for SAR optimization and I would generally be extremely wary of any IC50 value measured for an irreversible inhibitor if it had not been technically impossible to measure k.inact or K.i values for the inhibitor.]</i></span></p></blockquote><p>2.2. Criteria for Assessing Covalent Probe Selectivity</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">We propose a selectivity factor of 30-fold in favor of the intended target of the probe compared to that of other family members or identified off-targets under comparable assay conditions. <i><span style="color: red;">[The authors need to be clearer as to which measure of ‘activity’ they propose should be used for calculating the ratio and some justification for the ratio (why 30-fold rather than 50-fold or 25-fold?) should be given. Regardless of whether binding to target is covalent or non-covalent, the concentration and affinity of substrates (as well as cofactors such as ATP) need to be properly accounted for when assessing selectivity. It is not clear how the selectivity factor should be defined to quantify selectivity of an inhibitor that binds covalently to the target but non-covalently to off-targets. My comments on the <a href="https://www.chemicalprobes.org/thz1?q=THZ1" target="_blank">THZ1</a> probe in my <a href="https://fbdd-lit.blogspot.com/2023/11/on-misuse-of-chemical-probes.html" target="_blank">review</a> of the <a href="https://doi.org/10.1038/s41467-023-38952-1" target="_blank">S2023</a> study may be relevant.]</span></i></p></blockquote><p>2.3. Chemical Matter Criteria for Covalent Probes</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">Ideally, the on-target activity of the covalent probe is not dominated by the reactive warhead, but the rest of the molecule provides a measurable reversible affinity for the intended target. <span style="color: red;"><i>[My view is that the reversible affinity of the probe should be greater than simply what is measurable and I suggest, with some liberal arm-waving, that a K.i cutoff of ~100 nM might be more useful (a K.i value of 10 μM is usually measurable provided that the inhibitor is adequately soluble in assay buffer).]</i></span> Seeing SARs over 1–2 log units of activity resulting from core, substitution, and warhead changes is an important quality criterion for covalent probe molecules. <i><span style="color: red;">[The authors need to be clearer about which ‘activity’ they are referring to (differences in K.i and k.inact values between compounds are likely to be greater than the corresponding differences in k.inact/K.i values). The criterion “SAR for covalent and non-covalent interactions” shown in Figure 2 is nonsensical.]</span></i></p></blockquote><p>3.3. Chemical Matter Criteria for Degrader Probes</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">When selecting chemical degrader probes, it is recommended that a chemist critically assesses the chemical structure of the degrader for the presence of chemical groups that impart polypharmacology or interfere with assay read-outs (PAINs motifs). (<a href="https://doi.org/10.1021/acschembio.7b00903" target="_blank">78</a>) <i><span style="color: red;">[I certainly agree that chemists should critically assess chemical structures of probes and, if performing a critical assessment of this nature for a degrader probe, I would be taking a look in <a href="https://www.ebi.ac.uk/chembl/" target="_blank">ChEMBL</a> to see what’s known for structurally-related compounds. I consider the risk of discarding acceptable chemical matter on the basis of matches with PAINS substructures to be low although there’s a lot more to critical assessment of chemical structures than simply checking for matches against PAINS substructures. My view is that genuine promiscuity (as opposed to frequent hitter behavior resulting from interference with read-out) cannot generally be linked to chemical groups. As noted in <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">K2017</a> the PAINS substructure model introduced in <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a> was actually trained on the output of six AlphaScreen assays and the applicability domain of the model should be regarded as prediction of frequent-hitter behavior in this assay panel rather than interference with assay read-outs (that said the most plausible explanation for frequent-hitter behavior in the PAINS assay panel is interference with the AlphaScreen read-out by compounds that quench or react with singlet oxygen). My recommendation is that chemical matter criteria for chemical probes should be specified entirely in terms of measured data and the models used to select/screen potentially acceptable chemical matter should not be included in the chemical matter criteria.]</span></i><span style="text-align: left;"> </span></p></blockquote><p style="text-align: justify;"><span style="text-align: left;">This is a good point to wrap up my contribution to the important scientific discussion that <a href="https://doi.org/10.1021/acs.jmedchem.3c00550" target="_blank">H2023</a> is intended to jumpstart. While some of what I've written might be seen as nitpicking please bear in mind that quality criteria for chemical probes need to be defined precisely in order to be useful to the chemical biology and medicinal chemistry communities.</span></p>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-69995848534844938512023-12-06T18:42:00.002+00:002023-12-07T11:11:45.972+00:00Are fused tetrahydroquinolines interfering with your assay?<p style="text-align: justify;">I’ll be taking a look at <a href="https://doi.org/10.1021/acs.jmedchem.3c01277" target="_blank">B2023</a> (Fused Tetrahydroquinolines Are Interfering with Your Assay) in this post. The article has already been discussed in posts at <a href="https://practicalfragments.blogspot.com/2023/11/beware-of-fused-tetrahydroquinolines.html" target="_blank">Practical Fragments</a> and <a href="https://www.science.org/content/blog-post/another-class-compounds-watch-out" target="_blank">In The Pipeline</a>. In anticipation of the stock straw man counterarguments to my criticisms of PAINS filters, I must stress that there is absolutely no suggestion that compounds matching PAINS filters are necessarily benign. The authors have shown that fusion of cyclopentene at C3-C4 of the tetrahydroquinoline (THQ) ring system is associated with a risk of chemical instability and I consider this to be extremely useful information for anybody thinking about using this scaffold. However, the authors do also appear to be making a number of claims that are not supported by evidence and, in my view, have not demonstrated that the chemical instability leads to pan-assay interference or even frequent-hitter behavior. </p><p style="text-align: justify;">The term ‘PAINS’ crops up frequently in <a href="https://doi.org/10.1021/acs.jmedchem.3c01277" target="_blank">B2023</a> (the authors even refer to <i>“the PAINS concept”</i> although I think that’s pushing things a bit) and I’ll start by saying something about two general types of nuisance behavior of compounds in assays and these points are discussed in more detail in <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">K2017</a> (Comment on The Ecstasy and Agony of Assay Interference Compounds). From the perspective of screening libraries of compounds for biological activity, the two types of nuisance behavior are very different problems that need to be considered very differently. One criticism that can be made of both <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a> (original PAINS study) and <a href="https://doi.org/10.1038/513481a" target="_blank">BW2014</a> (Chemical con artists foil drug discovery) is that neither study considers the differing implications for drug discovery of these two types of nuisance behavior.</p><p style="text-align: justify;">The first type of nuisance behavior in assays is interference with assay read-out and when ‘activity’ in an assay is due to assay interference hits can accurately be described as ‘false positives’ (this should be seen as a problem with the assay rather than the compound). Interference with assay read-outs is certainly irksome when you’re analysing output from screens because you don’t know if the ‘activity’ is real or not. However, if you’re able to demonstrate genuine activity for a compound using an assay with a read-out for which interference is not an issue then interference with other assay read-outs is irrelevant and would not rule out the compound as a viable starting point for further investigation. Interference with assay read-outs generally increases with the concentration of the compound in the assay (this is why biophysical methods are often favored for screening fragments) and I’ll direct readers to a helpful <a href="https://doi.org/10.1177/1087057106286653" target="_blank">article</a> by former colleagues. It’s also worth noting that interference with read-out can also lead to false negatives. </p><p style="text-align: justify;">The second type of nuisance behavior is that the compound acts on a target by an undesirable mechanism of action (MoA) and it is not accurate to describe hits behaving in this manner as ‘false positives’ because the effect on the target is real (this should be seen as a problem with the compound rather than the assay). In contrast to interference with read-out, an undesirable MoA is a show-stopper. An undesirable MoA with which many drug discovery scientists will be familiar is colloidal aggregate formation (see <a href="https://doi.org/10.1021/jm030266r" target="_blank">M2003</a>) and the problem can be assessed by running the assay in the absence and presence of detergent (see <a href="https://doi.org/10.1038/nprot.2006.77" target="_blank">FS2006</a>). In some cases patterns in screening output may point to an undesirable MoA. For example, cysteine reactivity might be indicated by compounds hitting in multiple assays for inhibition of enzymes that use feature cysteine in their catalytic mechanisms.</p><p style="text-align: justify;">I’ll make some comments on PAINS filters before I discuss <a href="https://doi.org/10.1021/acs.jmedchem.3c01277" target="_blank">B2023</a> in detail and much of what I’ll be saying has already been said in <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">K2017</a> and <a href="https://doi.org/10.1021/acs.jcim.6b00465" target="_blank">C2017</a> (Phantom PAINS: Problems with the Utility of Alerts for Pan-Assay INterference CompoundS) although you shouldn’t need to consult these articles in order to read the blog post unless you want to get some more detail. The PAINS filter model introduced in <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a> consists of number of substructures which are claimed (I say “claimed” because the assay results and associated chemical structures are proprietary) to be associated with frequent hitter behavior in a panel of six assays that all use the AlphaScreen read-out (compounds that react with or quench singlet oxygen have the potential of interfere with this read-out). I argued in <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">K2017</a> that six assays, all using the same read-out, do not constitute a credible basis for the design of an experiment to detect pan-assay interference. Put another way, the narrow scope of the data used to train the PAINS filter model restricts the applicability domain of this model to prediction of frequent-hitter behavior in these six assays. The <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a> study does not appear present a single example of a compound that has been actually been demonstrated by experiment to exhibit pan-assay interference.</p><p style="text-align: justify;">The <a href="https://doi.org/10.1021/acs.jmedchem.3c01277" target="_blank">B2023</a> study reports that tetrahydroquinolines (THQs) fused at C3-C4 with cyclopentene (1) are unstable. This is valuable information for anybody who may be have the misfortune to be working with this particular scaffold and the observed instability implies that drug discovery scientists should also be extremely wary of any biological activity reported for compounds that incorporate this scaffold. Furthermore, the authors show that the instability can be linked to the presence of the carbon-carbon double bond in the ‘third ring’ since <b>2</b>, the dihydro analog of <b>1</b>, appears to be stable. I would certainly mention the chemical instability reported in <a href="https://doi.org/10.1021/acs.jmedchem.3c01277" target="_blank">B2023</a> if reviewing a manuscript that reported biological activity for compounds based on this scaffold. However, I would not mention that <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a> has stated that the scaffold matches the <i>anil_alk_ene</i> (SLN: C[1]:C:C:C[4]:C(:C:@1)NCC[9]C@4C=CC@9 ) PAINS substructure because the nuisance behavior consists of hitting frequently in a six-assay panel of questionable relevance and the PAINS filters were based on analysis of proprietary data.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhX8bojQgLFHSxNMr4dEdFIsoq46EV9jkbxR8dHkh65s77ZIE7KqHDlnnFauLsqFUjCHbkeL-n4gUnpw5dFIjsYDCoMa_QMkJqSWCdLKiPWZjfVO6Q8iw9wj38dEozmcscEmqhksTysG7cYS2hcAkPwQmtkuXaCitBuMXp8KwLpzEIRvFNhYkuG96tcCGUT/s1280/thq.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="326" data-original-width="1280" height="82" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhX8bojQgLFHSxNMr4dEdFIsoq46EV9jkbxR8dHkh65s77ZIE7KqHDlnnFauLsqFUjCHbkeL-n4gUnpw5dFIjsYDCoMa_QMkJqSWCdLKiPWZjfVO6Q8iw9wj38dEozmcscEmqhksTysG7cYS2hcAkPwQmtkuXaCitBuMXp8KwLpzEIRvFNhYkuG96tcCGUT/s320/thq.jpg" width="320" /></a></div><p style="text-align: justify;">Although I wouldn’t have predicted the chemical instability reported for <b>1</b> by <a href="https://doi.org/10.1021/acs.jmedchem.3c01277" target="_blank">B2023</a>, this scaffold is certainly not a structural feature that I would have taken into lead optimization with any enthusiasm (a hydrogen that is simultaneously benzylic and allylic does rather look like a free lunch for the CYPs). I would still be concerned about instability even if methylene groups were added to or deleted from the aliphatic parts of <b>1</b>. I suspect that the electron-releasing nitrogen of <b>1</b> contributes to chemical instability although I don’t think that changing nitrogen for another atom type would eliminate the risk of chemical instability. Put another way, the instability observed for <b>1</b> should raise questions about the stability of a number of structurally-related scaffolds. Chemical instability is (or at least should be) a show-stopper in the context of drug discovery even if doesn't lead to interference with assay read-out, an undesirable MoA or pan-assay interference.</p><p style="text-align: justify;">I certainly consider the instability observed for <b>1</b> to be of interest and relevant to a number of structurally-related chemotypes. However, I have a number of concerns about <a href="https://doi.org/10.1021/acs.jmedchem.3c01277" target="_blank">B2023</a> and one specific criticism is that the authors use <i>“tricyclic/fused THQ”</i> as a synonym throughout the text as a synonym for <i>“tricyclic/fused THQ with a carbon-carbon double bond in the ‘third’ ring”</i>. At best this is confusing and it could lead to groundless criticism, either publicly or in peer review, of a study that reported assay results for compounds based on the scaffold in <b>2</b>. <span style="text-align: left;">A more general point is that the authors make a number of claims that, in my view, are not adequately supported by evidence. I’ll start with the significance section and my comments are italicized in red:</span></p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">Tricyclic tetrahydroquinolines (THQs) are a family of lesser studied pan-assay interference compounds (PAINS) <i><span style="color: red;">[The authors need to provide specific examples of tricyclic THQs that have been actually been shown to exhibit pan-assay interference to support this claim.]</span></i> These compounds are found ubiquitously throughout commercial and academic small molecule screening libraries.<i> [<span style="color: red;">The authors do not appear to have presented evidence to support this claim and the presence of compounds in vendor catalogues does not prove that the compounds are actually being screened. In my view, the authors appear to be trying to ‘talk up’ the significance of their findings by making this statement.]</span> </i>Accordingly, they have been identified as hits in high-throughput screening campaigns for diverse protein targets. We demonstrate that fused THQs are reactive when stored in solution under standard laboratory conditions and caution investigators from investing additional resource into validating these nuisance compounds.</p></blockquote><p>Continuing with the introduction</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">Fused tetrahydroquinolines (THQs) are frequent hitters in hit discovery campaigns. <span style="color: red;"><i>[In my view the authors have not presented sufficient evidence to support this statement and I don’t consider claims made in the BH2010 for frequent-hitter behavior by compounds matching the </i>anil_alk_ene<i> PAINS substructure to be admissible as evidence simply because they are based on proprietary data. In any case the numbers of compounds matching the </i>anil_alk_ene <i>PAINS substructure and reported in BH2010 to hit in zero (17) or one (11) assays in the PAINS assay panel suggest that 28 compounds (of a total of 51 substructural matches) cannot be regarded as frequent-hitters in this assay panel.]</i></span> Pan-assay interference compounds (PAINS) have been controversial in the recent literature. While some literature supports these as nuisance compounds, other papers describe PAINS as potentially valuable leads. (<a href="https://doi.org/10.1038/513481a" target="_blank">1</a> | <a href="https://doi.org/10.1021/acs.jcim.6b00465" target="_blank">2</a> | <a href="https://doi.org/10.1021/acs.jcim.8b00385" target="_blank">3</a> | <a href="https://doi.org/10.4155/fmc.10.237" target="_blank">4</a>) <i><span style="color: red;">[The <a href="https://doi.org/10.1021/acs.jcim.6b00465" target="_blank">C2017</a> study referenced as 2 is actually a critique of PAINS filters and I’m assuming that the authors aren’t suggesting that it “supports these [PAINS] as nuisance compounds”. However, I would consider it a gross misrepresentation of <a href="https://doi.org/10.1021/acs.jcim.6b00465" target="_blank">C2017</a> to imply that the study describes “PAINS as potentially valuable leads”.]</span></i> There have been descriptions of many different classes of PAINS that vary in their frequency of occurrence as hits in the screening literature. <i><span style="color: red;">[In my view, the number of articles on PAINS appears to greatly exceed the number of compounds that have actually been shown to exhibit pan-assay interference.]</span></i></p></blockquote><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">The number of papers that selected this scaffold during hit discovery campaigns from multiple chemical libraries supports the idea that fused THQs are frequent hitters. <span style="color: red;"><i>[Let’s take a closer look at what the authors are suggesting by considering a selection of compounds, each of which has a benzene ring in its molecular structure. Now let’s suppose that each of a large number of targets is hit by at least one of the compounds in this selection (I could easily satisfy this requirement by selecting marketed drugs with benzene rings in their molecular structures). Applying the same logic as the authors, I could use these observations to support the idea that compounds incorporating benzene rings in their molecular structures are frequent-hitters. In my view the <a href="https://doi.org/10.1021/acs.jmedchem.3c01277" target="_blank">B2023</a> study doesn’t appear to have presented a single example of a fused THQ that has actually been shown experimentally to exhibit frequent-hitter behavior. As mentioned earlier in this post less than half of the compounds matching the </i>anil_alk_ene<i> PAINS substructure that were evaluated in the <a href="https://doi.org/10.1021/jm901137j" target="_blank">BH2010</a> assay panel can be regarded as frequent-hitters.]</i></span> At first glance, these compounds appear to be valid, optimizable hits, with reasonable physicochemical properties. Although micromolar and reproducible activity has been reported for multiple THQ analogues on many protein targets, hit-to-lead optimization programs aimed at improving the initial hits (Supporting Information (SI), Table S1) have resulted in no improvement in potency or no discernible structure–activity relationships (SAR) <i><span style="color: red;">[Achieving increased potency and establishing SARs are certainly important objectives in hit-to-lead studies. However, assertions that hit-to-lead optimizations “have resulted in no improvement in potency or no discernible structure–activity relationships” do need to be supported with appropriate discussion of specific hit-to-lead optimization studies.] </span></i><span style="text-align: left;"> </span></p></blockquote><p style="text-align: justify;"><span style="text-align: left;">Examples of Fused THQs as “Hits” Are Pervasive</span></p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">The diversity of protein targets captured below supports the premise that the fused THQ scaffold does not yield specific hits for these proteins but that the reported activity is a result of pan-assay interference. [<i><span style="color: red;">I could use an argument analogous to the one that I’ve just used for frequent-hitters to ‘prove’ that compounds with benzene rings in their molecular structure do not yield specific hits and that any reported activity is due to pan-assay interference. The authors do not appear to have presented a single example of a fused THQ that has been shown by experiment to exhibit pan-assay interference.]</span></i></p></blockquote><p style="text-align: left;">Concluding remarks</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">Our review and evidence-based experiments solidify the idea that tricyclic THQs are nuisance compounds that cause pan-assay interference in the majority of screens rather than privileged structures worthy of chemical optimization. <i><span style="color: red;">[</span></i><span style="color: red;"><i>While I certainly agree that chemical instability would constitute a nuisance, I would consider it wildly extravagant to claim that tricyclic THQs can “cause pan assay interference” since nobody appears to have actually observed pan-assay interference for even a single tricyclic THQ.] </i></span>Their widespread micromolar activities on a broad range of proteins with diverse assay readouts support our assertion that they are unlikely to be valid hits. <i><span style="color: red;">[As stated previously, I do not consider that “widespread micromolar activities on a broad range of proteins” observed for compounds that share a particular structural feature implies that all compounds with the particular structural feature are unlikely to be valid hits.]</span></i></p></blockquote><p style="text-align: justify;">So that concludes my review of the <a href="https://doi.org/10.1021/acs.jmedchem.3c01277" target="_blank">B2023</a> study. I really liked the experimental work that revealed the instability of <b>1</b> and linked it to the presence of the double bond in the 'third' ring. Furthermore, these experimental results would (at least for me) raise questions about the chemical stability of some scaffolds that are structurally-related to <b>1</b>. However, I found the analysis of the bioactivity data reported in the literature for fused THQs to be unconvincing to the extent that it significantly weakened the <a href="https://doi.org/10.1021/acs.jmedchem.3c01277" target="_blank">B2023</a> study. </p>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com2tag:blogger.com,1999:blog-2909827059962062852.post-52546136640685347992023-11-19T17:36:00.008+00:002024-01-29T13:07:24.149+00:00On the misuse of chemical probes<p style="text-align: justify;">It’s now time to get back to chemical probes and I’ll be taking a look at <a href="https://doi.org/10.1038/s41467-023-38952-1" target="_blank">S2023</a> (Systematic literature review reveals suboptimal use of chemical probes in cell-based biomedical research) which has already been reviewed in blog posts from <a href="https://practicalfragments.blogspot.com/2023/07/a-rule-of-two-for-using-chemical-probes.html" target="_blank">Practical Fragments</a>, <a href="https://www.science.org/content/blog-post/chemical-probes-used-and-misused" target="_blank">In The Pipeline</a> and the <a href="https://www.icr.ac.uk/blogs/the-drug-discoverer/page-details/as-research-tools-improve-our-use-of-them-must-evolve-too-we-re-here-to-help" target="_blank">Institute of Cancer Research</a>. Readers of this blog are aware that <a href="https://doi.org/10.1021/jm901137j" target="_blank">PAINS filters</a> usually crop up in posts on chemical probes but there are other things that I want to discuss and, in any case, references to <a href="https://doi.org/10.1021/jm901137j" target="_blank">PAINS</a> in <a href="https://doi.org/10.1038/s41467-023-38952-1" target="_blank">S2023</a> are minimal. Nevertheless, I’ll still stress that a substructural match of a chemical probe with a <a href="https://doi.org/10.1021/jm901137j" target="_blank">PAINS filter</a> does not constitute a valid criticism of a chemical probe (it simply means that the chemical structure of the chemical probe shares structural features with compounds that have been claimed to exhibit frequent-hitter behaviour in a panel of six AlphaScreen assays) and one is more likely to encounter a bunyip than a compound that has actually been shown to exhibit pan-assay interference.</p><p style="text-align: justify;">The authors of <a href="https://doi.org/10.1038/s41467-023-38952-1" target="_blank">S2023</a> claim to have revealed <i><span style="color: #666666;">“suboptimal use of chemical probes in cell-based biomedical research”</span></i> and I’ll start by taking a look at the abstract (my annotations are italicised in red):</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">Chemical probes have reached a prominent role in biomedical research, but their impact is governed by experimental design. To gain insight into the use of chemical probes, we conducted a systematic review of 662 publications, understood here as primary research articles, employing eight different chemical probes in cell-based research. [<i><span style="color: red;">A study such as <a href="https://doi.org/10.1038/s41467-023-38952-1" target="_blank">S2023</a> that has been claimed by its authors to be systematic does need to say something about how the eight chemical probes were selected and why the literature for this particular selection of chemical probes should be regarded as representative of chemical probes literature in general.]</span></i> We summarised (i) concentration(s) at which chemical probes were used in cell-based assays, (ii) inclusion of structurally matched target-inactive control compounds and (iii) orthogonal chemical probes. Here, we show that only 4% of analysed eligible publications used chemical probes within the recommended concentration range and included inactive compounds as well as orthogonal chemical probes. <i><span style="color: red;">[I would argue that failure to use a chemical probe within a recommended concentration range is only a valid criticism if the basis for the recommendation is clearly articulated.] </span></i>These findings indicate that the best practice with chemical probes is yet to be implemented in biomedical research. <i><span style="color: red;">[My view is that the best practice with chemical probes is yet to be defined.] </span></i>To achieve this, we propose ‘the rule of two’: At least two chemical probes (either orthogonal target-engaging probes, and/or a pair of a chemical probe and matched target-inactive compound) to be employed at recommended concentrations in every study.<i><span style="color: red;"> [The authors of <a href="https://doi.org/10.1038/s41467-023-38952-1" target="_blank">S2023</a> do seem to moving the goalposts since the they’ve criticized studies for not using structurally matched target-inactive control compounds but are saying that using an additional orthogonal target-engaging probe makes it acceptable not to use a structurally matched target-inactive control compound. This suggestion does appear to contradict the Chemical Probes Portal <a href="https://www.chemicalprobes.org/info/classical-modulators" target="_blank">criteria</a> for 'classical' modulators which do require the use of a control compound defined as having a "similar structure with similar physicochemistry, non-binding against target".]</span></i></p></blockquote><p>The following sentence does set off a few warning bells for me:</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">The term ‘chemical probe’ distinguishes compounds used in basic and preclinical research from ‘drugs’ used in the clinic, from the terms ‘inhibitor’, ‘ligand’, ‘agonist’ or ‘antagonist’ which are molecules targeting a given protein but are insufficiently characterised, and also from the term ‘probes’ which is often referring to laboratory reagents for biophysical and imaging studies.</p></blockquote><p style="text-align: justify;">First, the
terms 'compound' and 'molecule' are not interchangeable and I would generally
recommend using 'compound' when talking about biological activity or affinity. A
more serious problem is that the authors of <a href="https://doi.org/10.1038/s41467-023-38952-1" target="_blank">S2023</a> seem to be getting into
homeopathic territory by suggesting that chemical probes are not ligands and
this might have caused Paul Ehrlich (who died 26 years before Kaiser Wilhelm
II) to spit a few feathers. Drugs and
chemical probes are ligands for their targets by virtue of binding to their
targets (the term 'ligand' is derived from the Latin 'ligare' which means 'to
bind' and a compound can be a ligand for one target without necessarily being a
ligand for another target) while the terms 'inhibitor', 'agonist' and 'antagonist' specify the consequences of ligand binding. I was also concerned by the
use of the term 'in cell concentration' in <a href="https://doi.org/10.1038/s41467-023-38952-1" target="_blank">S2023</a> given that uncertainty in
<a href="https://doi.org/10.1124/dmd.118.085951" target="_blank">intracellular concentration</a> is an issue when working with chemical probes (as
well as in <a href="https://doi.org/10.1021/acs.jmedchem.3c00514" target="_blank">PK-PD modelling</a>). Although my
comments above could be seen as nit-picking these are not the kind of errors
that authors can afford to make if they’re going to claim that their <i><span style="color: #666666;">“findings
indicate that the best practice with chemical probes is yet to be implemented
in biomedical research”</span></i>.</p><p style="text-align: justify;">Let’s take a look at the criteria by which the authors of <a href="https://doi.org/10.1038/s41467-023-38952-1" target="_blank">S2023</a> have assessed the use of chemical probes. They assert that <i><span style="color: #666666;">“Even the most selective chemical probe will become non-selective if used at a high concentration”</span> </i>although I think it’d be more correct to state that the functional selectivity of a probe depends on binding affinity of the probe for target and anti-targets as well as the concentration of the probe (at its site of action). Selectivity also depends on the concentration of anything that binds competitively with the probe and, when assessing kinase selectivity, it can be argued that assays for ATP-competitive kinase inhibitors should be run at a typical intracellular ATP concentration (here’s a recent open access <a href="https://doi.org/10.3390/biology10111166" target="_blank">review</a> on intracellular ATP concentration). The presence of serum in cell-based assays should also be considered when setting upper concentration limits since chemical probes may bind to serum proteins such as albumin which means that the concentration of a compound that is ‘seen’ by the cells is lower than the total concentration of the compound in the assay. In my experience binding to albumin tends to increase with lipophilicity and is also favored by the presence of an acidic group such as carboxylate in a molecular structure.</p><p style="text-align: justify;">I’m certainly not suggesting that chemical probes be used at excessive concentrations but if you’re going to criticise other scientists for exceeding concentration thresholds then, at very least, you do need to show that the threshold values have been derived in an objective and transparent manner. My view that it would not be valid to criticise studies publicly (or in peer review of submitted manuscripts) simply because the studies do not comply with recommendations made by the <a href="https://www.chemicalprobes.org/" target="_blank">Chemical Probes Portal</a>. It is significant that the recommendations from different groups of chemical probe experts with respect to the maximum concentration at which <a href="https://www.chemicalprobes.org/unc1999?q=unc1999" target="_blank">UNC1999</a> should be used differ by almost an order of magnitude:</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;">As the recommended maximal in-cell concentration for UNC1999 varies between the Chemical Probes Portal and the Structural Genomics Consortium sites (400 nM and 3 μM, respectively), we analysed compliance with both concentrations.</p></blockquote><p style="text-align: justify;">One of the eight chemical probes featured in <a href="https://doi.org/10.1038/s41467-023-38952-1" target="_blank">S2023</a> is <a href="https://www.chemicalprobes.org/thz1?q=THZ1" target="_blank">THZ1</a> which is reported to bind covalently to CDK7 and the electrophilic warhead is acrylamide-based, suggesting that binding is irreversible. Chemical probes that form covalent bonds with their targets irreversibly need to be considered differently to chemical probes that engage their targets reversibly (see this <a href="https://doi.org/10.1021/acs.jcim.2c00466" target="_blank">article</a>). Specifically, the degree of target engagement by a chemical probe that binds irreversibly depends on time as well as concentration (if you wait long enough then you’ll achieve 100% inhibition). This means that it’s not generally possible to quantify selectivity or to set concentration thresholds objectively for chemical probes that bind to their targets irreversibly. It’s not clear (at least to me) why an irreversible covalent inhibitor such as <a href="https://www.chemicalprobes.org/thz1?q=THZ1" target="_blank">THZ1</a> was included as one of the eight chemical probes covered by the <a href="https://doi.org/10.1038/s41467-023-38952-1" target="_blank">S2023</a> study so I checked to see what the <a href="https://www.chemicalprobes.org/" target="_blank">Chemical Probes Portal</a> had to say about <a href="https://www.chemicalprobes.org/thz1?q=THZ1" target="_blank">THZ1</a> and something doesn’t look quite right. The on-target potency is given as a Kd (dissociation constant which is a measure of affinity) value of 3.2 nM and the potency assay is described as <span style="color: #666666;">“<i>time-dependent binding established supporting covalent mechanism”</i></span>. However, Kd is a measure of affinity (and therefore not a time-dependent) and my understanding is that it is generally difficult to measure Kd for irreversible covalent inhibitors which are typically characterized by kinact (inactivation rate constant) and Ki (inhibition constant) values obtained from analysis of enzyme inhibition data. The off-target potency of <a href="https://www.chemicalprobes.org/thz1?q=THZ1" target="_blank">THZ1</a> is summarized as <i><span style="color: #666666;">“KiNativ profiling against 246 kinases in Loucy cells was performed showing >75% inhibition at 1 uM of: MLK3, PIP4K2C, JNK1, JNK2, JNK3, MER, TBK1, IGF1R, NEK9, PCTAIRE2, and TBK1, but in vitro binding to off-target kinases was not time dependent indicating that inhibition was not via a covalent mechanism”</span></i>. The results from the assays used to measure on-target and off-target potency for <a href="https://www.chemicalprobes.org/thz1?q=THZ1" target="_blank">THZ1</a> do not appear to be directly comparable.</p><p style="text-align: justify;"><span style="text-align: left;">It’s now time to wrap up and I suggest that it would not be valid to criticise (either publicly or in peer review) a study simply on the grounds that it reported results of experiments in which a chemical probe was used at a concentration exceeding a recommended maximum value. The </span><a href="https://doi.org/10.1038/s41467-023-38952-1" style="text-align: left;" target="_blank">S2023</a><span style="text-align: left;"> authors assert that an additional orthogonal target-engaging probe can be substituted for a matched target-inactive control compound but this appears to contradict </span><a href="https://www.chemicalprobes.org/info/classical-modulators" style="text-align: left;" target="_blank">criteria</a><span style="text-align: left;"> for classical modulators given by the </span><a href="https://www.chemicalprobes.org/" style="text-align: left;" target="_blank">Chemical Probes Portal</a><span style="text-align: left;">.</span></p>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com3tag:blogger.com,1999:blog-2909827059962062852.post-66413363078472874772023-09-27T21:27:00.012+01:002023-10-02T15:34:47.791+01:00Five days in Vermont<p style="text-align: justify;">A couple of months ago I enjoyed a visit to the US (my first for eight years) on which I caught up with old friends before and after a few days in Vermont (where a trip to the golf course can rapidly become a National Geographic Moment). One highlight of the trip was randomly <a href="https://fbdd-lit.blogspot.com/2023/07/blogger-meets-blogger.html" target="_blank">meeting</a> my friend and fellow blogger Ash Jogalekar for the first time in real life (we’ve actually known each other for about fifteen years) on the Boston T Red Line. Following a couple of nights in green and leafy Belmont, I headed for the Flatlands with an old friend from my days in Minnesota for a <a href="https://www.startribune.com/obituaries/detail/0000386674/" target="_blank">Larry Miller</a> group reunion outside Chicago before delivering a short <a href="https://doi.org/10.6084/m9.figshare.24157377.v1" target="_blank">harangue</a> on polarity at Ripon College in Wisconsin. After the harangues, we enjoyed a number of most excellent <a href="https://newglarusbrewing.com/pages/year-round-beers" target="_blank">Spotted Cattle</a> (Only in Wisconsin) in Ripon. I discovered later that one of my Instagram friends is originally from nearby Green Lake and had taken classes at Ripon College while in high school. It is indeed a small world.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjCVIArW_qntoCQQ7wpYIzL5m55HXzDbCu_oM0poxVFnzNe2XXWv1JMA0mFaG-Rlhgu6AavRJcLqU8HKME_QzAh0PxGSWHbu15Hm19CW_jDDDkDrCDDPYaKMxNiRI9H1uv_vWIRgVnrIxzcx2jIHkKHsc78iM1owa8L6CIWb7nmHl8zutNOh-DUEOPVHNKY/s1072/737.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="538" data-original-width="1072" height="161" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjCVIArW_qntoCQQ7wpYIzL5m55HXzDbCu_oM0poxVFnzNe2XXWv1JMA0mFaG-Rlhgu6AavRJcLqU8HKME_QzAh0PxGSWHbu15Hm19CW_jDDDkDrCDDPYaKMxNiRI9H1uv_vWIRgVnrIxzcx2jIHkKHsc78iM1owa8L6CIWb7nmHl8zutNOh-DUEOPVHNKY/s320/737.jpg" width="320" /></a></div><p style="text-align: justify;">The five days spent discussing computer-aided drug design (CADD) in Vermont are what I’ll be covering in this post and I think it’s worth saying something about what drugs need to do in order to function safely. First, drugs need to have significant effects on therapeutic targets without having significant effects on anti-targets such as hERG or CYPs and, given the interest in new modalities, I’ll be say “effects” rather than “affinity”, although Paul Ehrlich would have reminded us that drugs need to bind in order to exert effects. Second, drugs need to get to their targets at sufficiently high concentrations for their effects to be therapeutically significant (drug discovery scientists use the term ‘exposure’ when discussing drug concentration). Although it is sometimes believed that successful drugs simply reduce the numbers of patients suffering from symptoms it has been known from the days of Paracelsus that it is actually the dose that differentiates a drug from a poison.</p><p style="text-align: justify;">Drug design is often said to be multi-objective in nature although the objectives are perhaps not as numerous as many believe (this point is discussed in the introduction section of <a href="https://doi.org/10.1186/s13321-019-0330-2" target="_blank">NoLE</a>, an article that I'd recommend to insomniacs everywhere). The first objective of drug design can be stated in terms of minimization of the concentration at which a therapeutically useful effect on the target is observed (this is typically the easiest objective to define since drug design is typically directed at specific targets). The second objective of drug design can be stated in analogous terms as maximization of the concentration at which toxic effects on the anti-targets are observed (this is a more difficult objective to define because we generally know less about the anti-targets than about the targets). The third objective of drug design is to achieve controllability of exposure (this is typically the most difficult objective to define because drug concentration is a dose-dependent, spaciotemporal quantity and intracellular concentration cannot generally be measured for drugs in vivo). Drug discovery scientists, especially those with backgrounds in computational chemistry and cheminformatics, don’t always appreciate the importance of controlling exposure and the uncertainty in <a href="https://doi.org/10.1124/dmd.118.085951" target="_blank">intracellular concentration</a> always makes for a good stock question for speakers and panels of experts.</p><p style="text-align: justify;">I <a href="https://fbdd-lit.blogspot.com/2023/07/ill-start-this-post-by-stressing-that.html" target="_blank">posted</a> previously on artificial intelligence (AI) in drug design and I think it’s worth highlighting a couple of common misconceptions. The first misconception is that we just need to collect enough data and the drugs will magically condense out of the data cloud that has been generated (this belief appears to have a number of adherents in Silicon Valley). The second misconception is that drug design is merely an exercise in prediction when it should really be seen in a Design of Experiments framework. It’s also worth noting that genuinely categorical data are rare in drug design and my view is that many (most?) "global" machine learning (ML) models are actually ensembles of local models (this heretical view was expressed in a 2009 <a href="https://doi.org/10.1016/j.bmcl.2008.12.003" target="_blank">article</a> and we were making the point that what appears to be an interpolation may actually be an extrapolation). Increasingly, ML is becoming seen as a panacea and it’s worth asking why quantitative structure activity relationship (QSAR) approaches never really made much of a splash in drug discovery.</p><p style="text-align: justify;">I enjoyed catching up with old friends [ <a href="https://doi.org/10.1021/ja00226a005" target="_blank">D</a> | <a href="https://doi.org/10.1021/jm00002a012" target="_blank">K</a> | <a href="https://doi.org/10.1021/ci700332k" target="_blank">S</a> | <a href="https://doi.org/10.1021/ci960471y" target="_blank">R/J</a> | <a href="https://doi.org/10.1016/S1359-6446(97)01163-X" target="_blank">P/M</a> ] as well as making some new ones [ <a href="https://doi.org/10.1021/ci700052x" target="_blank">G</a> | <a href="https://doi.org/10.1038/s41598-020-77033-x" target="_blank">B/R</a> | <a href="https://doi.org/10.1021/jacs.3c06674" target="_blank">L</a> ]. However, I was disappointed that my beloved Onkel Hugo was not in attendance (I continue to be inspired by Onkel’s laser-like focus on the hydrogen bonding of the ester) and I hope that Onkel has finally forgiven me for asking (in 2008) if Austria was in Bavaria. There were many young people at the gathering in Vermont and their enthusiasm made me greatly optimistic for the future of CADD (I’m getting to the age at which it’s a relief not to be greeted with: "How nice to see you, I thought you were dead!"). Lots of energy at the posters (I learned from one that Voronoi was Ukrainian) although, if we’d been in Moscow, I’d have declined the refreshments and asked for a room on the ground floor (left photo below). Nevertheless, the bed that folded into the wall (centre and right photos below) provided plenty of potential for hotel room misadventure without the ‘helping hands’ of NKVD personnel.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg68rSfe7TTOHjNW9TeD1nD7say18VqtDgvQo_FPdvt4c_a91HS0_Z8fHfY4uOXM_ReaasIi40EilhlVnQaI9XMnhKpJKP-sctteduSmzutRMyy4BoxVT7TED5GdBDYt9qBhxVF21DIQEB2mKdqTn6VI6gmCTr_Ghu7ct5yeVzJAWkESNz3jiIRPkHtkv0e/s1186/bed.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="391" data-original-width="1186" height="105" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg68rSfe7TTOHjNW9TeD1nD7say18VqtDgvQo_FPdvt4c_a91HS0_Z8fHfY4uOXM_ReaasIi40EilhlVnQaI9XMnhKpJKP-sctteduSmzutRMyy4BoxVT7TED5GdBDYt9qBhxVF21DIQEB2mKdqTn6VI6gmCTr_Ghu7ct5yeVzJAWkESNz3jiIRPkHtkv0e/s320/bed.jpg" width="320" /></a></div><p style="text-align: justify;">It'd been four years since CADD had been discussed at this level in Vermont so it was no surprise to see COVID-19 on the agenda. The COVID-19 pandemic led to some very interesting developments including the <a href="https://doi.org/10.26434/chemrxiv-2021-585ks-v2" target="_blank">Covid Moonshot</a> (a very different way of doing drug discovery and one I was happy to contribute to during my 19 month sojourn in Trinidad) and, more tangibly, <a href="https://doi.org/10.1126/science.abl4784" target="_blank">Nirmatrelvir</a> (an antiviral medicine that has been used to treat COVID-19 infections since early 2022). Looking at the molecular structure of <a href="https://doi.org/10.1126/science.abl4784" target="_blank">Nirmatrelvir</a> you might have mistaken trifluoroacetyl for a protecting group but it’s actually a important feature (it appears to be beneficial from the permeability perspective). My view is that the alkane/water logP (alkane is a better model than octanol for the hydrocarbon core of a lipid bilayer) for a trifluoroacetamide is likely to be a couple of log units greater than for the corresponding acetamide.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgraSDF-DJ7xC_1fPU4IMikd33bPPsSSph6TUrai00VmXkZD6j5nyJoGqbLNWliB5Gs4_gke2i5kjpW_dC4GHSaICZ3IimVDPlEINGc9-983NBLKlCiONHSZCPgt474RYNs-I53Hw795Fg4yUv4k-DcPyzk9hpyZiVxaLWhB9Jq-gxBg08oyJqY70kdHDKS/s1058/nirmatrelvir_vermont.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="595" data-original-width="1058" height="180" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgraSDF-DJ7xC_1fPU4IMikd33bPPsSSph6TUrai00VmXkZD6j5nyJoGqbLNWliB5Gs4_gke2i5kjpW_dC4GHSaICZ3IimVDPlEINGc9-983NBLKlCiONHSZCPgt474RYNs-I53Hw795Fg4yUv4k-DcPyzk9hpyZiVxaLWhB9Jq-gxBg08oyJqY70kdHDKS/s320/nirmatrelvir_vermont.jpg" width="320" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div><br /></div><div><div style="text-align: justify;">I’ll take you through how the alkane/water logP difference between a trifluoroacetamide and corresponding acetamide can be estimated in some detail because I think this has some relevance to using AI in drug discovery (I tend to approach pKa prediction in an analogous manner). Rather than trying to build an ML model for making the prediction, I’ve simply made connections between measurements for three different physicochemical properties (alkane/water logP, hydrogen bond basicity and hydrogen bond acidity) which is something that could easily be accommodated within an AI framework. I should stress that this approach can only be used because it is a difference in alkane/water logP (as opposed to absolute values) that is being predicted and these physicochemical properties can plausibly be linked to substructures.</div><div><br /></div><div style="text-align: justify;">Let’s take a look at the triptych below which I admit that is not quite up to the standards of Hieronymus Bosch (although I hope that you find it to be a little less disturbing). The first panel shows values of polarity (q) for some hydrogen bond acceptors and donors (you can find these in Tables 2 and 3 in <a href="https://doi.org/10.1021/acs.jmedchem.2c01147" target="_blank">K2022</a>) that have been derived from alkane/water logP measurements. You could, for example, use these polarity values to predict that reducing the polarity of an amide carbonyl oxygen to the extent that it looks like a ketone will lead to a 2.2 log unit increase in alkane/water logP. The second panel shows measured hydrogen bond basicity values for three hydrogen bond acceptors (you can find these in this freely available <a href="https://doi.org/10.6084/m9.figshare.12084183.v1" target="_blank">dataset</a>) and the values indicate that a trifluoroacetamide is an even weaker hydrogen bond acceptor than a ketone. Assuming a linear relationship between polarity and hydrogen bond basicity, we can estimate that the trifluoroacetamide carbonyl oxygen is 2.4 log units less polar than the corresponding acetamide. The final panel shows measured hydrogen bond acidity values (you can find these in Table 1 of <a href="https://doi.org/10.1021/acs.jmedchem.2c01147" target="_blank">K2022</a>) that suggest that an imide NH (q = 1.3; 0.5 log units more polar than typical amide NH) will be slightly more polar than the trifluoroacetamide NH of Nirmatrelvir. So to estimate he difference in alkane/water logP values you just need to subtract the additional polarity of trifluoroacetamide NH (0.5 log units) from the lower polarity of the trifluoroacetamide carbonyl oxygen (2.4) to get 1.9 log units.</div><div><br /></div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjsu0Ok9hPRgv1w-S6URS_7J28Jbt-o0PFTACyzz9nwNWmV6L8gdn5pbCNDPokS75lHOOn-1B9ltl02dhNx_Hq3wSl2ixrQcquBbUsy3KmcQFVFgM5MIHN2sCYRCvcuUXvHhPRO6KtMsGyBNTqMh7mVfiUmoe5l75Rs2-c_qjztuUpxKeV9l3fGJephAflE/s1280/tript.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="720" data-original-width="1280" height="180" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjsu0Ok9hPRgv1w-S6URS_7J28Jbt-o0PFTACyzz9nwNWmV6L8gdn5pbCNDPokS75lHOOn-1B9ltl02dhNx_Hq3wSl2ixrQcquBbUsy3KmcQFVFgM5MIHN2sCYRCvcuUXvHhPRO6KtMsGyBNTqMh7mVfiUmoe5l75Rs2-c_qjztuUpxKeV9l3fGJephAflE/s320/tript.jpg" width="320" /></a></div><div><br /></div><div style="text-align: justify;">Chemical space is a recurring theme in drug design and its vastness, which defies human comprehension, has inspired much navel-gazing over the years (it’s actually tangible chemical space that’s relevant to drug design). In drug discovery we need to be able to navigate chemical space (ideally without having to ingest huge quantities of Spice) and, given that Ukrainian chemists have revolutionized the world's idea of <a href="https://enamine.net/" target="_blank">tangible chemical space</a> (and have also made it a whole lot larger), it is most appropriate to have a Ukrainian <a href="https://doi.org/10.1021/acs.jcim.2c00509" target="_blank">guide</a> who is most ably assisted by a trusty Transylvanian sidekick. I see benefits from considering molecular complexity more explicitly when mapping chemical space. </div><div> </div><div style="text-align: justify;">AI (as its evangelists keep telling us) is quite simply awesome at generating novel molecular structures although, as noted in a previous <a href="https://fbdd-lit.blogspot.com/2023/07/ill-start-this-post-by-stressing-that.html" target="_blank">post</a>, there’s a little bit more to drug design than simply generating novel molecular structures. Once you’ve generated a novel molecular structure you need to decide whether or not to synthesize the compound and, in AI-based drug design, molecular structures are often assessed using ML models for biological activity as well as absorption, distribution, metabolism and excretion (ADME) behaviour. It’s well-known that you need a lot of data for training these ML models but you also need to check that the compounds for which you’re making predictions lie within the chemical space occupied by the training set (one way to do this is to ensure that close structural analogs of these compounds exist in the training set) because you can’t be sure that the big data necessarily cover the regions of chemical space of interest to drug designers using the models. A panel discusses the pressing requirement for more data although ML modellers do need to be aware that there’s a huge difference between assembling data sets for <a href="https://practicalcheminformatics.blogspot.com/2023/08/we-need-better-benchmarks-for-machine.html" target="_blank">benchmarking</a> and covering chemical space at sufficiently high resolution to enable accurate prediction for arbitrary compounds. </div><div><br /></div><div style="text-align: justify;">There are other ways to think about chemical space. For example, differences in biological activity and ADME-related properties can also be seen in terms of structural relationships between compounds. These structural relationships can be defined in terms of molecular similarity (Tanimoto coefficient for the molecular fingerprints of X and Y is 0.9) or substructure (X is the 3-chloro analog of Y). Many medicinal chemists think about structure-activity relationships (SARs) and structure-property relationships (SPRs) in terms of matched molecular pairs (MMPs: pairs of molecular structures that are linked by specific substructural relationships) and free energy perturbation (FEP) can also be seen in this framework. Strong <a href="https://doi.org/10.1021/acs.jcim.5b00018" target="_blank">nonadditivity</a> and <a href="https://doi.org/10.1021/acs.jcim.2c01073" target="_blank">activity cliffs</a> (large differences in activity observed for close structural analogs) are of considerable interest as SAR features in their own right and because prediction is so challenging (and therefore very useful for testing ML and physics-based models for biological activity). One reason that drug designers need to be aware of activity cliffs and nonadditivity in their project data is that these SAR features can potentially be exploited for selectivity.</div><div> </div><div style="text-align: justify;">Cheminformatic approaches can also help you to decide how to synthesize the compounds that you (or your AI Overlords) have designed and automated synthetic route planning is a prerequisite for doing drug discovery in ‘self-driving’ laboratories. The key to success in cheminformatics is getting your data properly organized before starting analysis and the Open Reaction Database (<a href="https://doi.org/10.1021/jacs.1c09820" target="_blank">ORD</a>), an open-access schema and infrastructure for structuring and sharing organic reaction data, facilitates training of models. One area that I find very exciting is the use of high-throughput experimentation in the search for <a href="https://doi.org/10.1021/jacs.2c11563" target="_blank">new synthetic reactions</a> which can led to better coverage of unexplored chemical space. It’s well known in industry that the process chemists typically synthesize compounds by routes that differ from those used by the medicinal chemists and data-driven multi-objective <a href="https://doi.org/10.1021/jacs.2c08513" target="_blank">optimization</a> of catalysts can lead to more efficient manufacturing processes (a higher conversion to the desired product also makes for a cleaner crude product). </div><div><br /></div><div style="text-align: justify;">It’s now time to wrap up what’s been a long post. Some of what is referred to as AI appears to already be useful in drug discovery (especially in the early stages) although non-AI computational inputs will continue to be significant for the foreseeable future. I see a need for cheminformatic thinking in drug discovery to shift from big data (global ML models) to focused data (generate project specific data efficiently for building local ML models) and also see advantages in using atom-based descriptors that are clearly linked to molecular interactions. One issue for data-driven approaches to prediction of biological activity such as ML and QSAR modelling is that the need for predictive capability is greatest when there's not much relevant data and this is a scenario under which physics-based approaches have an advantage. In my view, validation of ML models is not a solved problem since clustering in chemical space can cause validation procedures to make optimistic assessments of model quality. I continue to have significant concerns about how relationships (which are not necessarily linear) between descriptors are handled in ML modelling and remain generally skeptical of claims for interpretability of ML models (as noted in <a href="https://doi.org/10.1186/s13321-019-0330-2" target="_blank">NoLE</a>, the contribution of a protein–ligand contact to affinity is not, in general, an experimental observable).</div><div><br /></div><div style="text-align: justify;">Many thanks for staying with me to the end and hope to see many of you at <a href="https://www.euroqsar.org/" target="_blank">EuroQSAR</a> in Barcelona next year. I'll leave you with a memory from the early days of chemical space navigation.</div><div><br /></div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjzqv_n4JmWkmt3vX8Lj65ugH0YGgXlfhC_gM10V0p9ZDQ3ugaKGZU8LIqJcArsnbPLuHYd4CXTvLZM3b8SwVRqzqDksSMjkUWDqyL4aumtVN0riDN7UVrCHNsI1c_5Q0z_hnt5nwTd_pCf-V9WX_Eq5ydI8r4joiV5x7KQ7LgG6a8D7Rr-anvK13-8LQTa/s959/tudor2.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="634" data-original-width="959" height="212" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjzqv_n4JmWkmt3vX8Lj65ugH0YGgXlfhC_gM10V0p9ZDQ3ugaKGZU8LIqJcArsnbPLuHYd4CXTvLZM3b8SwVRqzqDksSMjkUWDqyL4aumtVN0riDN7UVrCHNsI1c_5Q0z_hnt5nwTd_pCf-V9WX_Eq5ydI8r4joiV5x7KQ7LgG6a8D7Rr-anvK13-8LQTa/s320/tudor2.jpg" width="320" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div></div></div><div><br /></div>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-82170660102511811872023-07-26T16:29:00.004+01:002023-08-05T18:08:57.281+01:00Blogger Meets Blogger<p style="text-align: justify;">Over the years I’ve had had some cool random encounters (some years ago I bumped into a fellow member of the Macclesfield diving club in the village of Pai in the north of Thailand) but the latest is perhaps the most remarkable (even if it's not quite in the league of Safecracker Meets Safecracker in Surely You’re Joking). I was riding the Red Line on Boston’s T en route to Belmont from a conference in Vermont when my friend Ash Jogalekar, well known for The Curious Wavefunction <a href="http://wavefunction.fieldofscience.com/" target="_blank">blog</a>, came over and introduced himself. Ash and I have actually known each other for about 15 years but we’d never before met in real life.</p><p style="text-align: justify;">The odds against such an encounter would appear to be overwhelming since Ash lives in California while this was my first visit to the USA since 2015. I had also explored the possibility of getting a ride to Boston (some of those attending had driven to the conference from there) because the bus drops people off at the airport. Furthermore, I was masked on the T which made it more difficult for Ash to recognize me. However, I was carrying my poster tube (now re-purposed for the transport of unclean underwear) and, fortuitously, the label with my name was easy for Ash to spot. Naturally, we discussed the physics of ligand efficiency.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj1aqs-UINAAXWM7cllSWQ3tig6A0CylbXc0abAK7U1OYczOX879yyzsqjHmhqTEw7xTD7WgRajoNGf98kG3BH85Q5api5KO7ira11XxugrbtUJCQdaZ7a3cBU0FIprPG_tqwWRT1D4arK7R7QTnpyTYfE3B9TltAFoA-BqD3UbwLktNZ3xn5uZOhU5xNyK/s1280/ash.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="720" data-original-width="1280" height="180" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj1aqs-UINAAXWM7cllSWQ3tig6A0CylbXc0abAK7U1OYczOX879yyzsqjHmhqTEw7xTD7WgRajoNGf98kG3BH85Q5api5KO7ira11XxugrbtUJCQdaZ7a3cBU0FIprPG_tqwWRT1D4arK7R7QTnpyTYfE3B9TltAFoA-BqD3UbwLktNZ3xn5uZOhU5xNyK/s320/ash.jpg" width="320" /></a></div>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-59234549113605752862023-07-18T20:20:00.003+01:002024-01-18T14:36:52.729+00:00AI-based drug design?<p><br /></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhRSuaADEwrqroqvGftiNaj-TGzSQYhQIu2v1hM3zqOIVVcpl2THO0rV-dQJvuiJJhYR59cOXVv7WMcYhjHgMNo6woxuB_q1KmzB5UTuk7ZN9imDDVm1aqaxUlIN31pJcVqxx4xlv8gCKBq7V3V8PDeOjEuJBw1MqMJkIkxQYMVZ4TpmF-kg4w-sj7Oc2rM/s712/ant3.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="510" data-original-width="712" height="229" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhRSuaADEwrqroqvGftiNaj-TGzSQYhQIu2v1hM3zqOIVVcpl2THO0rV-dQJvuiJJhYR59cOXVv7WMcYhjHgMNo6woxuB_q1KmzB5UTuk7ZN9imDDVm1aqaxUlIN31pJcVqxx4xlv8gCKBq7V3V8PDeOjEuJBw1MqMJkIkxQYMVZ4TpmF-kg4w-sj7Oc2rM/s320/ant3.jpg" width="320" /></a></div><p style="text-align: justify;">I’ll start this post by stressing that I’m certainly not anti-AI. I actually believe that drug design tools that are being described as AI-based are potentially very useful in drug discovery. For example, I’d expect natural language processing capability to enable drug discovery scientists to access relevant information without even having to ask questions. I actually have a long-standing interest in automated molecular structure editing (see <a href="https://doi.org/10.1002/3527603743.ch11" target="_blank">KS2005</a>) and see the ability to build chemical structures in an automated manner using Generative AI as a potentially useful addition to the drug designer’s arsenal. Physical chemistry is very important in drug design and there are likely benefits to be had from building physicochemical awareness into the AI tools (one approach would be to use atom-based measures of interaction potential and I’ll direct you to some relevant articles: <a href="https://doi.org/10.1039/P29890001355" target="_blank">A1989</a> | <a href="https://doi.org/10.1039/P29940000199" target="_blank">K1994</a> | <a href="https://doi.org/10.1023/A:1008743229409" target="_blank">LB2000</a> | <a href="https://doi.org/10.1002/anie.200301739" target="_blank">H2004</a> | <a href="https://doi.org/10.1021/jm801331y" target="_blank">L2009</a> | <a href="https://doi.org/10.1021/ci9000234" target="_blank">K2009</a> | <a href=" https://doi.org/10.1002/chem.201101071" target="_blank">L2011</a> | <a href="https://doi.org/10.1021/acs.jmedchem.5b01946" target="_blank">K2016</a> | <a href="https://doi.org/10.1021/acs.jmedchem.2c01147" target="_blank">K2022</a> ) </p><p style="text-align: justify;">All that said, the AI field does appear to be associated with a degree of hype and number of senior people in the drug discovery field seem to have voluntarily switched off their critical thinking skills (it might be a trifle harsh to invoke terms like “herding instinct” although doing so will give you a better idea of what I’m getting at). Trying to deal with the diverse hype of AI-based drug design in a single blog post is likely to send any blogger on a one-way trip to the funny farm so I’ll narrow the focus a bit. Specifically, I’ll be trying to understand the meaning of the term “AI-designed drug”.</p><p style="text-align: justify;">The prompt for this post came from the publication of “Inside the nascent industry of AI-designed drugs” <a href="https://www.nature.com/articles/s41591-023-02361-0" target="_blank">DOI</a> in Nature Medicine and I don’t get the impression that the author of the article is too clued up on drug design: </p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;"><i><span style="color: red;">Despite this challenge, the use of artificial intelligence (AI) and machine learning to understand drug targets better and synthesize chemical compounds to interact with them has not been easy to sell.</span></i></p></blockquote><p style="text-align: justify;">Apparently, AI is going to produce the drugs as well as design them:</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;"><i><span style="color: red;">“We expect this year to see some major advances in the number of molecules and approved drugs produced by generative AI methods that are moving forward”, Hopkins says.</span></i></p></blockquote><p style="text-align: justify;">I’d have enjoyed being a fly on the wall at this meeting although perhaps they should have been asking “why” rather than “how”:</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px;"><p style="text-align: justify;"><i><span style="color: red;">“They said to me: Alex, these molecules look weird. Tell us how you did it”, Zhavaoronkov [sic] says. "We did something in chemistry that humans could not do.”</span></i></p></blockquote><p style="text-align: justify;">So what I think it means to claim that a drug has been “AI-designed” is that the chemical structure of the drug has been initially generated by a computer rather than a human (I’ll be very happy to be corrected on this point). Using computers to generate chemical structures is not exactly new and people were enumerating combinatorial libraries from synthetic building blocks over two decades ago (that’s not to deny that there has been considerable progress in the field of generating chemical structures). Merely conceiving a structure does not, however, constitute design and I’d question how accurate it would be to use the term “AI-designed” if structures generated by AI had been subsequently been evaluated using non-AI methods such as free energy perturbation.</p><p style="text-align: justify;">One piece of advice that I routinely offer to anybody seeking to transform or revolutionize drug discovery is to make sure that you understand what a drug needs to do. First, the drug needs to interact to a significant extent with one or more therapeutic targets (while not interacting with anti-targets such as hERG and CYPs) and this is why molecular interactions (see <a href="https://doi.org/10.1021/jm100112j" target="_blank">B2010</a> | <a href="https://doi.org/10.1002/anie.201408487" target="_blank">P2015</a> ) are of great interest in medicinal chemistry. Second, the drug needs to get to its target(s) at a sufficiently high concentration (the term exposure is commonly used in drug discovery) in order to have therapeutically useful effects on the target(s). This means that achieving controllability of exposure should be seen as a key objective of drug design. One of the challenges facing drug designers is that it’s not generally possible to measure intracellular concentration for drugs in vivo and I recommend that AI/ML leaders and visionaries take a look at the <a href="https://doi.org/10.1124/dmd.118.085951" target="_blank">SR2019</a> study. </p><p style="text-align: justify;">Given that this post is focused on how AI generates chemical structures, I thought it might be an idea to look at how human chemists currently decide which compounds are to be synthesized. Drug design is incremental which reflects the (current) impossibility of accurately predicting the effects that a drug will have on a human body directly from its molecular structure. Once a target has been selected, compounds are screened for having a desired effect on the target and the compounds identified in the screening phase are usually referred to as hits. </p><p style="text-align: justify;">The screening phase is followed by the hit-to-lead phase and it can be helpful to draw an analogy between drug discovery and what is called football outside the USA. It’s not generally possible to design a drug from screening output alone and to attempt to do so would be the equivalent of taking a shot at goal from the centre spot. Just as the midfielders try move the ball closer to the opposition goal, the hit-to-lead team use the screening hits as starting points for design of higher affinity compounds. The main objective in the hit-to-lead phase to generate information that can be used for design and mapping structure-activity relationships for the more interesting hits is a common activity in hit-to-lead work. </p><p style="text-align: justify;">The most attractive lead series are optimized in the lead optimization phase. In addition to designing compounds with increased affinity, the lead optimization team will generally need to address specific issues such as inadequate oral absorption, metabolic liability and off-target activity. Each compound synthesized during the course of a lead optimization campaign is almost invariably a structural analog of a compound that had already been synthesized. Lead optimization tends to be less ‘generic’ than lead identification because the optimization path is shaped by these specific issues which implies that ML modelling is likely to be less applicable to lead optimization than to lead identification.</p><p style="text-align: justify;">This post is all about how medicinal chemists decide which compounds get synthesized and these decisions are not made in a vacuum. The decisions made by lead optimization chemists are constrained by the leads identified by the hit-to-lead team just as the decisions made by lead identification chemists are constrained by the screening output. While AI methods can easily generate chemical structures, it's currently far from clear that AI methods can eliminate the need for humans to make decisions as to which compounds actually get synthesized.</p><p style="text-align: justify;">This is a good point at which to wrap up. One error commonly made by people with an AI/ML focus is to consider drug design purely as an exercise in prediction while, in reality, drug design should be seen more in a Design of Experiments framework. </p>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-62117276430543080762023-06-08T19:37:00.007+01:002023-08-10T06:45:08.207+01:00Archbishop Ussher's guide to efficient selection of development candidates<p><span style="text-align: justify;">One piece of advice I gave in </span><a href="https://doi.org/10.1186/s13321-019-0330-2" style="text-align: justify;" target="_blank">NoLE</a><span style="text-align: justify;"> is that </span><i style="text-align: justify;">“drug designers should not automatically assume that conclusions drawn from analysis of large, structurally-diverse data sets are necessarily relevant to the specific drug design projects on which they are working”</i><span style="text-align: justify;"> and the </span><a href="https://doi.org/10.1021/acs.jmedchem.1c00416" style="text-align: justify;" target="_blank">L2021</a><span style="text-align: justify;"> study that I’m reviewing in this post will give you a good idea of what I was getting at when I wrote that. I see a fair amount of relatively harmless “stamp collecting” in </span><a href="https://doi.org/10.1021/acs.jmedchem.1c00416" style="text-align: justify;" target="_blank">L2021</a><span style="text-align: justify;"> but there are also some rather less harmless errors of the type that you really shouldn’t be making if cheminformatics is your day job. </span></p><p style="text-align: justify;">I’ll start the review of <a href="https://doi.org/10.1021/acs.jmedchem.1c00416" target="_blank">L2021</a> with annotation of the abstract:</p><p style="text-align: justify;"><i>"Physicochemical descriptors commonly used to define ‘drug-likeness’ and ligand efficiency measures are assessed for their ability to differentiate marketed drugs from compounds reported to bind to their efficacious target or targets.</i> <span style="color: red;">[I would argue that differentiating an existing drug from existing compounds that bind to the same target is not something that medicinal chemists need to be able to do. It is also incorrect to describe efficiency metrics such as LE and LLE as physicochemical descriptors because they are derived from biological activity measurements such as binding affinity or potency.]</span> <i>Using ChEMBL version 26, a data set of 643 drugs acting on 271 targets was assembled, comprising 1104 drug−target pairs having ≥100 published compounds per target. Taking into account changes in their physicochemical properties over time, drugs are analyzed according to their target class, therapy area, and route of administration. Recent drugs, approved in 2010−2020, display no overall differences in molecular weight, lipophilicity, hydrogen bonding, or polar surface area from their target comparator compounds. Drugs are differentiated from target comparators by higher potency, ligand efficiency (LE), lipophilic ligand efficiency (LLE), and lower carboaromaticity.</i><span style="color: red;"> [I may be missing something but stating that drugs tend to differ in potency from non-drugs that hit the same targets does rather seem to be stating the obvious. The same point can also be made about efficiency metrics such as LE and LLE since these are derived, respectively, by scaling potency with respect to molecular size and offsetting potency with respect to lipophicity (LLE).]</span> <i>Overall, 96% of drugs have LE or LLE values, or both, greater than the median values of their target comparator compounds.”</i> <span style="color: red;">[What is the corresponding figure for potency?]</span></p><p style="text-align: justify;">I must admit to never having been a fan of drug-likeness studies such as <a href="https://doi.org/10.1021/acs.jmedchem.1c00416" target="_blank">L2021</a> (when I first encountered analyses of time dependency of drug properties about 20 years ago I was left with an impression that some senior medicinal chemists had a bit too much time on their hands) and it is now ten years since the term <i>"Ro5 envy"</i> was introduced in a notorious JCAMD <a href="https://doi.org/10.1007/s10822-012-9631-5" target="_blank">article</a>. My view is that the data analysis presented in <a href="https://doi.org/10.1021/acs.jmedchem.1c00416" target="_blank">L2021</a> has minimal relevance to drug discovery so I’ll be saying rather less about the data analysis than I’d have done had J Med Chem asked me to review the study.</p><p style="text-align: justify;">The <a href="https://doi.org/10.1021/acs.jmedchem.1c00416" target="_blank">L2021</a> study examines property differences between marketed drugs and compounds reported to bind to efficacious target(s) of each drug. Specifically, the property differences are quantified by difference between the value of the property for the drug and the median of the values of property for the target comparator compounds. If doing this then you really do need to account for the spread in the distribution if you’re going to interpret property differences like these (a large difference in values of a property for the drug and the median property for the target may simply reflect a wide spread in the property distribution for the target). However, I would argue that a more sensible starting point for analysis like this would be to locate (e.g., as a percentile) the value of each drug property within the corresponding property distribution for the target comparator compounds.</p><p style="text-align: justify;">Let’s take a look now at how the authors of <a href="https://doi.org/10.1021/acs.jmedchem.1c00416" target="_blank">L2021</a> suggest their study be used. </p><p style="text-align: justify;"><i>“This study, like all those looking at marketed drug properties, is necessarily retrospective. Nevertheless, those small molecule drug properties that show consistent differentiation from their target compounds over time, namely, potency, ligand efficiencies (LE and LLE), and the aromatic ring count and lipophilicity of carboaromatic drugs, are those that are most likely to remain future-proof. Candidate drugs emerging from target-based discovery programs should ideally have one, or preferably both, of their LE and LLE values greater than the median value for all other compounds known to be acting at the target.”</i></p><p style="text-align: justify;">I would argue that the <a href="https://doi.org/10.1021/acs.jmedchem.1c00416" target="_blank">L2021</a> study has absolutely no relevance whatsoever to the selection of compounds for development since the team will have data available that enables them to rule out the vast majority of the project compounds for nomination. A discovery team nominating a compound for development will have achieved a number of challenging objectives (including potency against target and in one or more cell-based assays) and the likely response of team members to a suggestion that they calculate medians for LE and LLE for comparison with nomination candidate(s) is likely to be bemused eye-rolling. In general, a discovery team nominating a development candidate has access to a lot of unpublished potency measurements (which won’t be in ChEMBL) and it’s usually a safe assumption that the development candidate will be selected from the most potent compounds (LE and LLE values for these compounds are also likely to be above average). In the extremely unlikely event that the discovery team nominates a compound with LE or LLE values below the magic median values then you can be confident that the decision has been based on examination of measured data (consider the likelihood of the discovery team members acting on a suggestion that they should pick another compound with LE or LLE value above the magic median values because doing so will increase the probability of success in clinical development). </p><p style="text-align: justify;">As the start of the post, I did mention some errors that you don’t want to be making if cheminformatics is your day job and regular readers of this blog will have already guessed that I’m talking about ligand efficiency (LE). I should point out l that the problem is with the ligand efficiency metric and not the ligand efficiency concept which is both scientifically sound and useful, especially in fragment-based design where molecular size often increases significantly in the hit-to-lead phase. </p><p style="text-align: justify;">The problem with the LE metric is that perception of efficiency changes when you express affinity (or potency) using a different unit and this is shown clearly in <a href="https://jcheminf.biomedcentral.com/articles/10.1186/s13321-019-0330-2/tables/1" target="_blank">Table 1</a> in <a href="https://doi.org/10.1186/s13321-019-0330-2" target="_blank">NoLE</a>. Expressing a quantity using a different unit doesn’t change the quantity so any change in perception is clearly physical nonsense. That’s why I appropriate a criticism (it’s not even wrong) usually attributed to Pauli when taking gratuitous pot shots at the LE metric. The change in perception is also cheminformatic nonsense and that’s why it’s rather unwise to use the LE metric if cheminformatics is your day job. <a href="https://doi.org/10.1021/acs.jmedchem.1c00416" target="_blank">L2021</a> does cite <a href="https://doi.org/10.1186/s13321-019-0330-2" target="_blank">NoLE</a> but simply notes the LE metric’s <i>“scientific basis and application have provoked a literature debate”</i>.</p><p style="text-align: justify;">The <a href="https://doi.org/10.1021/acs.jmedchem.1c00416" target="_blank">L2021</a> study asserts that <i>“the absolute LE value of a drug candidate is less important”</i> but the problem is that even differences in LE change when you express affinity (or potency) using a different concentration unit. This is shown in <a href="https://jcheminf.biomedcentral.com/articles/10.1186/s13321-019-0330-2/tables/2" target="_blank">Table 2</a> in <a href="https://doi.org/10.1186/s13321-019-0330-2" target="_blank">NoLE</a> and the problem is that there is no objective way to select a particular concentration unit as ‘better’ than all the other concentration units. To conclude, can we say that a medicinal chemistry leader’s choice of concentration unit (1 M) is any better (or any worse) than that of Archbishop Ussher (4.004 μM)? </p>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-46833915588802416772023-04-01T06:43:00.007+01:002023-08-05T18:11:24.711+01:00A clear demonstration of the benefits of long residence time<p style="text-align: justify;">Residence time is a well-established concept in drug discovery and the belief that off-rate is more important than affinity has many adherents in both academia and industry. The concept has been articulated as follows in a <i>Nature Reviews in Drug Discovery</i> <a href="https://doi.org/10.1038/nrd.2015.18" target="_blank">article</a>:</p><p style="text-align: justify;"><i>“Biochemical and cellular assays of drug interactions with their target macromolecules have traditionally been based on measures of drug–target binding affinity under thermodynamic equilibrium conditions. Equilibrium binding metrics such as the half-maximal inhibitory concentration (IC50), the effector concentration for half-maximal response (EC50), the equilibrium dissociation constant (Kd) and the inhibition constant (Ki), all pertain to in vitro assays run under closed system conditions, in which the drug molecule and target are present at invariant concentrations throughout the time course of the experiment [<a href="https://doi.org/10.1038/nrd2082" target="_blank">1</a> | <a href="https://doi.org/10.1021/bi8002023" target="_blank">2</a> | <a href="https://doi.org/10.1517/17460441003677725" target="_blank">3</a> | <a href="https://doi.org/10.4155/fmc.11.112" target="_blank">4 </a>| <a href="https://pubmed.ncbi.nlm.nih.gov/16350889/" target="_blank">5</a></i>]<i>. However, in living organisms, the concentration of drug available for interaction with a localized target macromolecule is in constant flux because of various physiological processes.”</i></p><p style="text-align: justify;">I used to be highly skeptical about the argument that equilibrium binding metrics relevant are not relevant in open systems in which the drug concentration varies with time. The key question for me was always how the rate of change in the drug concentration compares with the rate of binding/unbinding (if the former is slower than the latter then the openness of the in vivo system would seem to be irrelevant). I also used to wonder why an equilibrium binding measurement made in an open system (e.g., Kd from isothermal titration calorimetry) should necessarily be more relevant to the in vivo system than an equilibrium binding measurement made in a series of closed systems (e.g., Ki from an enzyme inhibition assay). Nevertheless, I always needed to balance my concerns against the stark reality that the journal impact factor of <i>Nature Reviews of Drug Discovery</i> is a multiple of my underwhelming h-index. </p><p style="text-align: justify;">Any residual doubts about the relevance of residence time completely vanished recently after I examined a manuscript by Prof Maxime de Monne of the Port-au-Prince Institute of Biogerontology who is currently on secondment to the Budapest Enthalpomics Group (BEG). The manuscript has not yet been made publicly available although, with the help of my associate ‘Anastasia Nikolaeva’ in Tel Aviv, I was able to access it and there is no doubt that this genuinely disruptive study will forever change how we use AI to discover new medicines.</p><p style="text-align: justify;">Prof de Monne’s study clearly demonstrates that it is possible to manipulate off-rate independently of on-rate and dissociation constant, provided that binding is enthalpically-driven to a sufficient degree. The underlying mechanism is back-propagation of the binding entropy deficit along the reaction coordinate to the transition state region where the resulting unidirectional conformational changes serve to suppress dissociation of the ligand. The math is truly formidable (my rudimentary understanding of Haitian patois didn’t help either) and involves first projecting the atomic isothermal compressibility matrix into the polarizability tensor before applying the Barone-Samedi transformation for hepatic eigenvalue extraction. ‘Anastasia Nikolaeva’ was also able to ‘liberate’ a prepared press release in which a beaming BEG director Prof Kígyó Olaj explains, “Possibilities are limitless now that we have consigned the tedious and needlessly restrictive Principle of Microscopic Reversibility to the dustbin of history".</p>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com2tag:blogger.com,1999:blog-2909827059962062852.post-5422218019840964982023-02-22T11:07:00.006+00:002023-02-24T11:02:50.578+00:00Structural alerts and assessment of chemical probes<p style="text-align: center;"> << <a href="https://fbdd-lit.blogspot.com/2023/02/chemical-probes-and-permeability.html">previous</a> |</p><p style="text-align: justify;">I’ll wrap up (at least for now) the series of posts on chemical probes by returning to the use of cheminformatic models for assessment of the suitability of compounds for use as chemical probes. My view is that there is currently no cheminformatic model, at least in the public domain, that is usefully predictive of the suitability (or unsuitability) of compounds for use as chemical probes and that assessments should therefore be based exclusively on experimental measurements of affinity, selectivity etc. Put another way, acceptable chemical probes will need to satisfy the same criteria regardless of the extent to which they offend the tastes of PAINS filter evangelists (and if PAINS really are as bad as the evangelists would have us believe then they’re hardly going to satisfy these acceptability criteria). My main criticism of PAINS filters (summarized in this <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">comment</a> on the ACS assay interference <a href="https://doi.org/10.1021/acs.jmedchem.7b00229" target="_blank">editorial</a>) is that there is a significant disconnect between dogma and data. </p><p style="text-align: justify;">I’ll start by saying something about cheminformatics since, taken together, the PAINS substructures can be considered as a cheminformatic predictive model. If you’re using a cheminformatic predictive model then you also need to be aware that it will have an applicability domain which is limited by the data used to train and validate the model. Consider, for example, that you have access to a QSAR model for hERG blockade that has been trained and validated using only data for compounds that are protonated at the assay pH. If you base decisions on predictions for compounds that are neutral under assay conditions then you’d be using the model outside its applicability domain (and therefore in a very weak position to blame the modelers if the shit hits the fan). While cheminformatic predictive models might (or might not) help you get to a desired endpoint more quickly you’ll still need experimental measurements in order to know that you have indeed got the desired end point.</p><p style="text-align: justify;">But let’s get back to PAINS filters which were introduced in this 2010<a href="https://doi.org/10.1021/jm901137j" target="_blank"> study</a>. PAINS is an acronym for pan-assay interference compounds and you could be forgiven for thinking that PAINS filters were derived by examining chemical structures of compounds that had been shown to exhibit pan-assay interference. However, the original PAINS study doesn’t appear to present even a single example of a compound that is shown experimentally to exhibit pan-assay interference and the medicinal chemistry literature isn’t exactly bursting at the seams with examples of such compounds.</p><p style="text-align: justify;">The data set on which the PAINS filters were trained consisted of the hits (assay results in which the response was greater than a threshold when the compound was tested at a single concentration) from six high-throughput screens, each of which used AlphaScreen read-out. Although PAINS filters are touted as predictors of pan-assay interference it would be more accurate to describe them as predictors of frequent-hitter behavior in this particular assay panel (as noted in a <a href="https://fbdd-lit.blogspot.com/2023/02/promiscuity.html" target="_blank">previous post</a> promiscuity generally increases as the activity threshold is made more permissive). From a cheminformatic perspective the choice of this assay panel appears to represent a suboptimal design of an experiment to detect and characterize pan-assay interference (especially given that data from <i>“more than 40 primary screening campaigns against enzymes, ion channels, protein-protein interactions, and whole cells”</i> were available for analysis). Those who advocate the use of PAINS filters for the assessment of the suitability of compounds for use as chemical probes (and the Editors-in-Chief of more than one ACS journal) may wish to think carefully about why they are ignoring a similar <a href="https://doi.org/10.1021/ci050504m" target="_blank">study</a> based on a larger, more diverse (in terms of targets and read-outs) data set that had been published four years before the PAINS <a href="https://doi.org/10.1021/jm901137j" target="_blank">study</a>. </p><p style="text-align: justify;">Although a number of ways in which potential nuisance compounds can reveal their dark sides are discussed in the original PAINS study the nuisance behavior is not actually linked to the frequent-hitter behavior reported for compounds in the assay panel. Also, it can be safely assumed that none of the six protein-protein interaction targets of the PAINS assay panel feature a catalytic cysteine and my view is that any frequent-hitter behavior that is observed in the assay panel for ‘cysteine killers’ is more likely to be due to reaction with (or quenching of) singlet oxygen. It’s also worth pointing out that when compounds are described as exhibiting pan-assay interference (or as frequent hitters) that the relevant nuisance behavior has often been predicted (or assumed) as opposed to being demonstrated with measured data. I would argue that even a ‘maximal PAINS response’ (the compounds is actually observed as a hit in each of the six assays of the PAINS assay panel) would not rule out the use of a compound as a chemical probe.</p><p style="text-align: justify;">I have argued on cheminformatic grounds that it’s not appropriate to use PAINS filters for assessment of potential probes but there’s another reason that those seeking to set standards for chemical probes shouldn’t really be endorsing the use of PAINS filters for this purpose. “A conversation on using chemical probes to study protein function in cells and organisms” that was recently <a href="https://doi.org/10.1038/s41467-022-31271-x" target="_blank">published</a> in Nature Communications stresses the importance of Open Science. However, the PAINS structural alerts were trained on proprietary data and using PAINS filters to assess potential chemical probes will ultimately raise questions about the level of commitment to Open Science. I made a very similar point in my <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">comment</a> on the ACS assay interference <a href="https://doi.org/10.1021/acs.jmedchem.7b00229" target="_blank">editorial</a> (Journal of Medicinal Chemistry considers the publication of analyses of proprietary data to be generally unacceptable).</p><p style="text-align: justify;">Let’s take a look at “The promise and peril of chemical probes” that was <a href="https://doi.org/10.1038/nchembio.1867" target="_blank">published</a> in Nature Chemical Biology in 2015. The authors state:</p><p style="text-align: justify;"><i>“We learned that many of the chemical probes in use today had initially been characterized inadequately and have since been proven to be nonselective or associated with poor characteristics such as the presence of reactive functionality that can interfere with common assay features [<a href="https://doi.org/10.1038/513481a" target="_blank">3</a>] (<a href="https://www.nature.com/articles/nchembio.1867#Tab2" target="_blank">Table 2</a>). The continued use of these probes poses a major problem: tens of thousands of publications each year use them to generate research of suspect conclusions, at great cost to the taxpayer and other funders, to scientific careers and to the reliability of the scientific literature.”</i></p><p style="text-align: justify;">Let’s take a look at <a href="https://www.nature.com/articles/nchembio.1867#Tab2" target="_blank">Table 2</a> (Examples of widely used low-quality probes) from "The promise and peril of chemical probes". You’ll see “PAINS” in the problems column of <a href="https://www.nature.com/articles/nchembio.1867#Tab2" target="_blank">Table 2</a> for two of the six low-quality probes in and this rings a number of alarm bells for me. Specifically, it is asserted that flavones are “often promiscuous and can be pan-assay interfering (PAINS) compounds” and Epigallocatechin-3-gallate is a “promiscuous PAINS compound” which raises a number of questions. Were the (unspecified) flavones and Epigallocatechin-3-gallate actually observed to be promiscuous and if so what activity threshold was used for quantifying promiscuity? Were any of the (unspecified) flavones or Epigallocatechin-3-gallate actually observed to exhibit pan-assay interference? Were affinity and selectivity measurements actually available for the (unspecified) flavones or Epigallocatechin-3-gallate?</p><p style="text-align: justify;">I’ll conclude the post by saying something about cheminformatic predictive models. First, to use a cheminformatic predictive model outside its applicability domain is a serious error (and will cast doubts on the expertise of anybody doing so). Second, predictions might (or might not) help you get to a desired end point but you’ll still need measured data to establish that you’ve got to the desired end point or that a compound is unfit for a particular purpose. </p>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com1tag:blogger.com,1999:blog-2909827059962062852.post-7401603444119734002023-02-15T01:29:00.018+00:002023-02-20T14:16:27.628+00:00Frequent-hitter behavior and promiscuity <p style="text-align: justify;">I’ll be discussing promiscuity in this post and, if there’s one thing that religious leaders and drug discovery scientists agree on, it’s that promiscuity is a Bad Thing. In the drug discovery context compounds that bind to many targets or exhibit ‘activity’ in many assays are described as promiscuous. I first became aware that promiscuity was a practical (as opposed to a moral) problem when we started to use high-throughput screening (HTS) at Zeneca in the mid-1990s and we soon learned that not all screening output smells of roses (the precursor company ICI had been a manufacturer of dyestuffs which are selected/designed to be brightly colored and for their ability to stick to stuff).</p><p style="text-align: justify;">You’ll often encounter assertions in the scientific literature that compounds are promiscuous and my advice is to carefully check the supporting evidence if you plan to base decisions on the information. In many cases, you’ll find out that the ‘promiscuity’ is actually predicted and the problem with many cheminformatic models is that you often (usually?) don’t know how predictive the model is going to be for the compounds that you’re interested in. You have to be careful basing decisions on predictions because it is not unknown for predictivity of models and strengths of trends in data to be overstated. As detailed in this <a href="https://doi.org/10.1007/s10822-012-9631-5" target="_blank">article</a>, relationships between promiscuity (defined as number of assays for which ‘activity’ exceeds a specified threshold) and physicochemical descriptors such as <a href="https://doi.org/10.1038/nrd2445" target="_blank">lipophilicity</a> or <a href="https://doi.org/10.1016/j.sbi.2006.01.013" target="_blank">molecular weight</a> are made to appear rather stronger than they actually are. Scope of models may also be overstated and claims that compounds exhibit pan-assay interference have been made on the basis that the compounds share structural features with other compounds (the structures were not disclosed) that were identified as frequent-hitters in a panel of six assays that all use the AlphaScreen read-out.</p><p style="text-align: justify;">The other reason that you need to be wary of statements that compounds are promiscuous is that the number of assays for which ‘activity’ exceeds a threshold increases as you make the threshold more permissive (I was actually taught about the relationship between permissiveness and promiscuity by the Holy Ghost Fathers at high school in Port of Spain). I’ve ranked some different activity thresholds by permissiveness in Figure 1 that will hopefully give you a clearer idea of what I’m getting at. In general, it is prudent to be skeptical of any claim that promiscuity using a highly permissive activity threshold (e.g., ≥ 50% response at 10 μM) is necessarily relevant in situations where the level of activity against the target of interest is much greater (e.g., IC50 = 20 nM with well-behaved concentration response and confirmed by affinity measurement in SPR assay). My own view is that compounds should only be described as promiscuous when concentration responses have been measured for the relevant ‘activities’ and I prefer to use the term ‘frequent-hitter’ when ‘activity’ is defined in terms of response in the assay read-out that exceeds a particular cut off value.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjNC_38yZXqOZTWJJpTIgc97OvnxBk98fKSVBQ6q7Wb--5KwsFdcn47ehqm6B8CNswaLFY3y6fbaNQ2ScxhnRr3AT-NHQcRJfuSp52w7L8auw57iih-W0pK6-8d2Rm9EMMu5SDxOT5ES1CKYhzYPHtXkh6A0kSHZe4so7ZKfKqez70PygxbxDYG4fDNHg/s1280/promiscuity.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="656" data-original-width="1280" height="164" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjNC_38yZXqOZTWJJpTIgc97OvnxBk98fKSVBQ6q7Wb--5KwsFdcn47ehqm6B8CNswaLFY3y6fbaNQ2ScxhnRr3AT-NHQcRJfuSp52w7L8auw57iih-W0pK6-8d2Rm9EMMu5SDxOT5ES1CKYhzYPHtXkh6A0kSHZe4so7ZKfKqez70PygxbxDYG4fDNHg/s320/promiscuity.jpg" width="320" /></a></div><p style="text-align: justify;">Frequent-hitter behavior is a particular concern in analysis of HTS output and an observation that a hit compound in the assay of interest also hits in a number of other assays raises questions about whether further work on the compound is justified. In a <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">comment</a> on the ACS assay interference editorial, I make the point that the observation that a compound is a frequent hitter may reflect interference with read-out (which I classified as Type 1 behavior) or an undesirable mechanism of action (which I classified as Type 2 behavior). It is important to make a distinction between these two types of behavior because they are very different problems that require very different solutions. One criticism that I would make of the original <a href="https://doi.org/10.1021/jm901137j" target="_blank">PAINS study</a>, the chemical con artists <a href="https://doi.org/10.1038/513481a" target="_blank">perspective</a> in Nature and the ACS assay interference <a href="https://doi.org/10.1021/acs.jmedchem.7b00229" target="_blank">editorial</a> is that none of these articles make a distinction between these two types of nuisance behavior.</p><p style="text-align: justify;">I’ll first address interference with assay read-out and the problem for the drug discovery scientist is that the ‘activity’ is not real. One tactic for dealing with this problem is to test the hit compounds in an assay that uses a different read-out although, as described in this <a href="https://doi.org/10.1177/1087057106286653 " target="_blank">article</a> by some ex-AstraZeneca colleagues, it may be possible to assess and even correct for the interference using a single assay read-out. Interference with read-out should generally be expected to increase as the activity threshold is made more permissive (this is why biophysical methods are often preferred for detection and quantitation of fragment binding) and you may find that a compound that interferes with a particular assay read-out at 10 μM does not exhibit significant interference at 100 nM. Interference with read-out should be seen as a problem with the assay rather than a problem with the compound. </p><p style="text-align: justify;">An undesirable mechanism of action is a much more serious problem than interference with read-out and testing hit compounds in an assay that uses a different read-out doesn’t really help because the effects on the target are real. Some undesirable mechanisms of action such as colloidal aggregate formation are relatively easy to detect (see Aggregation Advisor <a href="http://advisor.docking.org" target="_blank">website</a>) but determining the mechanism of action typically requires significant effort and is more challenging when potency is low. An undesirable mechanism of action should be seen as a problem with the compound rather than a problem with the assay and my view is that this scenario should not be labelled as assay interference.</p><p style="text-align: justify;">I’ll wrap up with a personal perspective on frequent-hitters and analysis of HTS output although I believe my experiences were similar to those of others working in industry at the time. From the early days of HTS at Zeneca where I worked it was clear that many compounds with ‘ugly’ molecular structures were getting picked up as hits but it was often difficult to demonstrate objectively that ugly hits were genuinely unsuitable for follow-up. We certainly examined frequent-hitter behavior although some ‘ugly’ hits were not frequent-hitters. We did use <a href="https://en.wikipedia.org/wiki/SMILES_arbitrary_target_specification" target="_blank">SMARTS</a>-based substructural flags (referred to as the ‘de-crapper’ by some where I worked) for processing HTS output and we also looked at structural neighborhoods for hit structures using <a href="https://doi.org/10.1007/s10822-009-9264-5" target="_blank">Flush</a> (the lavatorial name of the software should provide some insight into how we viewed analysis of HTS output). The tactics we used at Zeneca (and later at AstraZeneca) were developed using real HTS data and I don’t think anybody would have denied that there was a subjective element to the approaches that we used. </p>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com7tag:blogger.com,1999:blog-2909827059962062852.post-86440498539611230252023-02-08T00:10:00.005+00:002023-02-22T11:08:23.345+00:00Chemical probes and permeability<p style="text-align: center;"><< <a href="https://fbdd-lit.blogspot.com/2023/01/chemical-probes-response-to-practical.html">previous</a> || <a href="https://fbdd-lit.blogspot.com/2023/02/structural-alerts-and-assessment-of.html" target="_blank">next</a> >></p><p style="text-align: justify;">I’ll start this post by with reference to a disease that some of you many never have heard of. Chagas disease is caused by the very nasty T. cruzi parasite (not to be confused with the even nastier American politician) and is of particular interest in Latin America where the disease is endemic. T. cruzi parasites have an essential requirement for ergosterol and, as discussed in <a href="https://doi.org/10.1371/journal.pntd.0000651" target="_blank">C2010</a>, are potentially vulnerable to inhibition of sterol 14α-demethylase (CYP51), which catalyzes the conversion of lanosterol to ergosterol. However, the CYP51 inhibitor posaconazole (an antifungal medication) <a href="https://doi.org/10.1056/NEJMoa1313122" target="_blank">showed poor efficacy</a> in a clinical trials for chronic Chagas disease. Does this mean that CYP51 is a bad target? The quick answer is “maybe but maybe not” because we can’t really tell whether the lack of efficacy is due to irrelevance of the target or inadequate exposure.</p><p style="text-align: justify;">We commonly invoke the free drug hypothesis (FDH) in drug design which means that we assume that the free concentration at the site of action is the same as the free plasma concentration (the term ‘free drug theory’ is also commonly used although I prefer FDH). The FDH is covered in the <a href="https://doi.org/10.1038/nrd3287 " target="_blank">S2010</a> (see Box 1 and 2) and <a href="https://doi.org/10.1002/jps.23614 " target="_blank">B2013</a> articles and, given that the targets of small molecule drugs tend to be intracellular, I’ll direct you to the excellent Smith & Rowland <a href="https://doi.org/10.1124/dmd.118.085951" target="_blank">perspective</a> on intracellular and intraorgan concentrations of drugs. When we invoke the FDH we’re implicitly assuming that the drug can easily pass through barriers, such as the lipid bilayers that enclose cells, to get to the site of action. In the absence of active transport, the free concentration at the site of action of a drug will tend to lag behind the free plasma concentration with the magnitude of the lag generally decreasing with permeability. Active transport (which typically manifests itself as efflux) is a more serious problem from the design perspective because it leads to even greater uncertainty in the free drug concentration at the site of action and it’s also worth remembering that transporter expression may vary with cell type. It’s worth mentioning that uncertainty in the free concentration at the site of action is even greater when targeting intracellular pathogens, as is the case for Chagas disease, malaria and tuberculosis.</p><p style="text-align: justify;">Some may see chemical probes as consolation prizes in the drug discovery game and, while this may sometimes be the case, we really need to be thinking of chemical probes as things that need to be designed. As is well put in <i>“A conversation on using chemical probes to study protein function in cells and organisms”</i> that was recently<a href="https://doi.org/10.1038/s41467-022-31271-x" target="_blank"> published</a> in Nature Communications: </p><p style="text-align: justify;"><i>“But drugs are different from chemical probes. Drugs don’t necessarily need to be as selective as high-quality chemical probes. They just need to get the job done on the disease and be safe to use. In fact, many drugs act on multiple targets as part of their therapeutic mechanism.”</i></p><p style="text-align: justify;">High selectivity and affinity are clear design objectives and, to some extent, optimization of affinity will tend to lead to higher selectivity. High quality chemical probes for intracellular targets need to be adequately permeable and should is should not be subject to active transport. The problems caused by active efflux are obvious because chemical probes need to get into cells in order to engage intracellular targets but there’s another reason that adequate permeability and minimal active transport are especially important for chemical probes. In order to interpret results, you need to know the free concentration of the probe at the site of action and active transport, whether it manifests itself as efflux or influx, leads to uncertainty the intracellular free concentration. Although it may be possible to measure intracellular free concentration (see <a href="https://doi.org/10.1021/mp4000822" target="_blank">M2013</a>) it’s fiddly to do so if you’re trying to measure target engagement at the same time and it’s not generally possible to do so in vivo. It's much better to be in a position to invoke the FDH with confidence and this point is well made in the Smith and Rowland <a href="https://doi.org/10.1124/dmd.118.085951" target="_blank">perspective</a>:</p><p style="text-align: justify;"><i>“Many misleading assumptions about drug concentrations and access to drug targets are based on total drug. Correction, if made, is usually by measuring tissue binding, but this is limited by the lack of homogenicity of the organ or compartment. Rather than looking for technology to measure the unbound concentration it may be better to focus on designing high lipoidal permeable molecules with a high chance of achieving a uniform unbound drug concentration.”</i></p><p style="text-align: justify;">If the intention is to use a chemical probe for in vivo studies then you’ll need to be confident that adequate exposure at the site of action can be achieved. My view is that it would be difficult to perform a meaningful assessment of the suitability of a chemical probe for in vivo studies without relevant experimental in vivo measurements. You might, however, be able to perform informative in vivo experiments with a chemical probe in the absence of existing pharmacokinetic measurements (provided that you monitor plasma levels and know how tightly the probe is bound by plasma proteins) although you’ll still need to invoke the FDH for intracellular targets. </p><p style="text-align: justify;">If you’re only going to use a chemical probe in cell-based experiments then you really don’t need to worry about achieving oral exposure and this has implications for probe design. The requirement for a chemical probe to have acceptable pharmacokinetic characteristics imposes constraints on design (which may make it more difficult to achieve the desired degree of selectivity) while pharmacokinetic optimization is likely to consume significant resources. As is the case for chemical probes intended for in vivo use, you’ll want to be in a position to invoke the FDH.</p><p style="text-align: justify;">In this post, I’ve argued that you need to be thinking very carefully about passive permeability and active transport (whether it leads to efflux or influx) when designing, using or assessing chemical probes. In particular, having experimental measurements available that show that a chemical probe exhibits acceptable passive permeability and is not actively transported will greatly increase confidence that the chemical probe is indeed fit for purpose. It’s not my intention to review methods for measuring passive permeability or active transport in this post although I’ll point you to the <a href="https://doi.org/10.1016/j.ejps.2018.04.016" target="_blank">B2018</a>,<a href="https://doi.org/10.1021/acs.molpharmaceut.1c00009 " target="_blank"> S2021</a>, <a href="https://doi.org/10.4155/fmc.11.149" target="_blank">V2011</a> and <a href="https://doi.org/10.1016/j.addr.2021.05.005 " target="_blank">X2021</a> articles in case any of these are helpful.</p>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-25876122563000748452023-01-28T20:31:00.005+00:002023-01-30T01:20:29.630+00:00More approaches to design of covalent inhibitors of SARS-CoV-2 main protease<p style="text-align: center;"><< <a href="https://fbdd-lit.blogspot.com/2023/01/some-approaches-to-design-of-covalent.html" target="_blank">previous</a> |</p><div style="text-align: justify;">I’ll pick up from the <a href="https://fbdd-lit.blogspot.com/2023/01/some-approaches-to-design-of-covalent.html">previous post</a> on design covalent inhibitors of SARS-CoV-2 main protease (structure and chart numbering follows from there). As noted previously, I really think that you need to exploit conserved structural features, such as the catalytic residues and the oxyanion hole, if you’re genuinely concerned about resistance and I do consider it a serious error to make a virtue out of non-covalency. As in the previous post, I've linked designs to the original <a href="https://covid.postera.ai/covid" target="_blank">Covid Moonshot</a> submissions whenever possible. </div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">I’ll kick the post off with <b><a href="https://covid.postera.ai/covid/submissions/5ddd54c7-5e98-43f2-9f08-dfa996b1293a/1" target="_blank">14</a></b> (Chart 5) which replaces a methylene in the lactam ring of <b><a href="https://covid.postera.ai/covid/submissions/3e354a91-7037-4566-808f-5c7f0f34a3a7/1" target="_blank">10</a></b> (Chart 4 in <a href="https://fbdd-lit.blogspot.com/2023/01/some-approaches-to-design-of-covalent.html">previous post</a>) with oxygen. This structural transformation results in 0.8 log unit reduction in lipophilicity (at least according to the algorithm used for the Covid Moonshot) and might also simplify the synthesis.</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjyBlNY8zwLUPjK8nvEjfWCcIc-H2Xc4GOzCdiqfv9o2G22KkLeipYqoPNXNeeWnZlZZQ46qtK2Om9rz3iH35gr8qJr3VVA5-RutBvO0l7NJvhFeb2vr3KfKgaGioEaI3NBILxAuOjWKmkZtAa30UvnmQcVhc17Gv8q6p1NvLC9ncwgdP1YJtfqGHuyWQ/s1280/chart5b.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="339" data-original-width="1280" height="85" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjyBlNY8zwLUPjK8nvEjfWCcIc-H2Xc4GOzCdiqfv9o2G22KkLeipYqoPNXNeeWnZlZZQ46qtK2Om9rz3iH35gr8qJr3VVA5-RutBvO0l7NJvhFeb2vr3KfKgaGioEaI3NBILxAuOjWKmkZtAa30UvnmQcVhc17Gv8q6p1NvLC9ncwgdP1YJtfqGHuyWQ/s320/chart5b.jpg" width="320" /></a></div><div class="separator" style="clear: both; text-align: justify;">Designs <b><a href="https://covid.postera.ai/covid/submissions/7b413b46-c80c-4617-8678-b75b287b118c/3" target="_blank">15</a></b> and <b><a href="https://covid.postera.ai/covid/submissions/7b413b46-c80c-4617-8678-b75b287b118c/4" target="_blank">16</a></b> (also in Chart 5) link the nitrile warhead from nitrogen rather than carbon and this structural transformation eliminates a chiral centre in each of <b><a href="https://covid.postera.ai/covid/submissions/3e354a91-7037-4566-808f-5c7f0f34a3a7/1" target="_blank">10</a></b> and <b><a href="https://covid.postera.ai/covid/submissions/5ddd54c7-5e98-43f2-9f08-dfa996b1293a/2" target="_blank">11</a></b> (Chart 4 in <a href="https://fbdd-lit.blogspot.com/2023/01/some-approaches-to-design-of-covalent.html">previous post</a>) and may be beneficial for affinity (see discussion around <b><a href="https://covid.postera.ai/covid/submissions/a692de38-5589-44a0-be0f-17a8ef0c9d1a/1" target="_blank">8</a></b> and <b>9</b> in Chart 3 of the <a href="https://fbdd-lit.blogspot.com/2023/01/some-approaches-to-design-of-covalent.html">previous post</a>). In substituted hydrazine derivatives, the nitrogen lone pairs (or the π-systems which the nitrogens are in) tend to avoid each other and so I’d expect nitrile warheads of <b><a href="https://covid.postera.ai/covid/submissions/7b413b46-c80c-4617-8678-b75b287b118c/3" target="_blank">15</a></b> and <b><a href="https://covid.postera.ai/covid/submissions/7b413b46-c80c-4617-8678-b75b287b118c/4" target="_blank">16</a></b> to adopt axial orientations. I’d anticipate that the nitrile warhead will be directed toward the catalytic cysteine for <b><a href="https://covid.postera.ai/covid/submissions/7b413b46-c80c-4617-8678-b75b287b118c/3" target="_blank">15</a></b> but away from the catalytic cysteine for <b><a href="https://covid.postera.ai/covid/submissions/7b413b46-c80c-4617-8678-b75b287b118c/4" target="_blank">16</a></b> and I favor the former for this for this reason. It's also worth mentioning that even if the nitrile is directed away from the catalytic cysteine it may occupy the oxyanion hole.</div><div><br /><div style="text-align: justify;">I’ll finish with couple of designs based on aromatic sulfur that are shown in Chart 6. Design <a href="https://covid.postera.ai/covid/submissions/4cf5aa07-f379-4799-a5da-8ce7630f56c0/1" target="_blank"><b>17</b></a> was originally submitted by <a href="https://www.linkedin.com/in/voleinikovas/" target="_blank">Vladas Oleinikovas</a> although I’ll also link my <a href="https://covid.postera.ai/covid/submissions/158bee2a-97e9-4215-962d-09788404ab3b">resubmission</a> of this design because the notes include a detailed discussion of a design rationale along with a proposed binding mode. My view is that the catalytic cysteine could get within striking distance of the ring sulfur (which can function as a <a href="https://doi.org/10.1021/jacs.7b08511" target="_blank">chalcogen-bond</a> donor and potentially even an electrophile). Although 2,1-benzothiazole is not obviously electrophilic, it’s worth noting that acetylene linked by saturated carbon can replace the nitrile as an electrophilic warhead (this isosteric replacement leads to irreversible inhibition as discussed in this <a href="https://doi.org/10.1021/jacs.8b11027" target="_blank">article</a>). I’ve also included <a href="https://covid.postera.ai/covid/submissions/158bee2a-97e9-4215-962d-09788404ab3b/4" target="_blank"><b>18</b></a> which replaces 2,1-benzothiazole with (what I’d assume is) a more electrophilic heterocycle. I would anticipate that any covalent inhibition by these compounds will be irreversible.</div></div><div><br /></div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOpCJUmksJp12wopi32uJWb8r-gTrq9HzuVpmSU_qUa44sWZcsAAk6PlpwBXUD_CpLDQ-ZKCiOCqA-HHjXzT8tHmg1Z2DOhRSXaivZnkWSf0WvM8_t7Fguxg2VCFYBbzh3s3CmbPJedzYfbDKCCicCmix78PkUo0fSebIt56mKmMn5VShZ-8L4HpMLjA/s1280/chart6b.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="303" data-original-width="1280" height="76" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOpCJUmksJp12wopi32uJWb8r-gTrq9HzuVpmSU_qUa44sWZcsAAk6PlpwBXUD_CpLDQ-ZKCiOCqA-HHjXzT8tHmg1Z2DOhRSXaivZnkWSf0WvM8_t7Fguxg2VCFYBbzh3s3CmbPJedzYfbDKCCicCmix78PkUo0fSebIt56mKmMn5VShZ-8L4HpMLjA/s320/chart6b.jpg" width="320" /></a></div><br /><div class="separator" style="clear: both; text-align: center;"><br /></div><div><br /></div></div>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-45694946587617320452023-01-25T00:59:00.003+00:002023-02-08T00:14:10.412+00:00Assessment of chemical probes: response to Practical Fragments<p style="text-align: center;"><< <a href="https://fbdd-lit.blogspot.com/2023/01/assessment-of-chemical-probes.html">previous</a> | <a href="https://fbdd-lit.blogspot.com/2023/02/chemical-probes-and-permeability.html">next</a> >></p><p style="text-align: justify;">I had originally intended to look at permeability in this post but I do need to respond to Dan Erlanson’s <a href="http://practicalfragments.blogspot.com/2023/01/the-chemical-probes-portal-at-eight.html" target="_blank">post</a> at Practical Fragments. I see Dan’s position (<i>“everything is an artifact until proven otherwise”</i>) as actually very similar to my position (<i>“chemical probes will have to satisfy the same set of acceptability criteria whether or not they trigger structural alerts”</i>) and we’re both saying that you need to perform the necessary measurements if you’re going to claim that a compound is acceptable for use as a chemical probe. Where Dan’s and my respective positions appear to diverge is that I consider structural alerts based on primary screening output (i.e., % response when assayed at a single concentration) to be of minimal value for assessment of optimized chemical probes. My <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">comment</a> on the “The Ecstasy and Agony of Assay Interference Compounds” <a href="https://doi.org/10.1021/acs.jmedchem.7b00229" target="_blank">editorial</a> should make this position clear. </p>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-56261283963976342012023-01-19T20:21:00.010+00:002023-02-07T22:37:25.085+00:00Some approaches to design of covalent inhibitors of SARS-CoV-2 main protease<p style="text-align: center;"><< <a href="https://fbdd-lit.blogspot.com/2021/01/tom-lehrers-guide-to-design-of-sars-cov.html" target="">previous</a> | <a href="https://fbdd-lit.blogspot.com/2023/01/more-approaches-to-design-of-covalent.html">next</a> >></p><p style="text-align: justify;">I last <a href="https://fbdd-lit.blogspot.com/2021/01/tom-lehrers-guide-to-design-of-sars-cov.html" target="_blank">posted</a> on Covid-19 early in 2021 and quite a lot has happened since then. Specifically, a number of vaccines are now available (I received my first dose of AstraZeneca CoviShield in May 2021 while still stranded in Trinidad) and <a href="https://www.paxlovid.com/" target="_blank">paxlovid</a> has been approved for use as a Covid-19 treatment (Derek describes his experiences taking paxlovid in this <a href="https://www.science.org/content/blog-post/paxlovid-personally" target="_blank">post</a>). The active ingredient of paxlovid is the SARS-CoV-2 main protease inhibitor <a href="https://en.wikipedia.org/wiki/Nirmatrelvir" target="_blank">nirmatrelvir</a> and the <a href="https://en.wikipedia.org/wiki/Ritonavir" target="_blank">ritonavir</a> with which it is dosed serves only to reduce clearance of nirmatrelvir by inhibiting metabolic enzymes. In the current post, I’ll be looking at covalent inhibition of SARS-CoV-2 main protease with a specific focus on reversibility and here are some <a href="https://doi.org/10.6084/m9.figshare.12060627.v1" target="_blank">notes</a> that I whipped up as a contribution to the <a href="https://covid.postera.ai/covid" target="_blank">Covid Moonshot</a>.</p><p style="text-align: justify;"><a href="https://en.wikipedia.org/wiki/Nirmatrelvir" target="_blank">Nirmatrelvir</a> (<a href="https://doi.org/10.1126/science.abl4784" target="_blank"><b>1</b></a>) is shown in Chart 1 along with SARS-CoV-2 main protease inhibitors from the <a href="https://covid.postera.ai/covid" target="_blank">Covid Moonshot</a> <b>(<a href="https://covid.postera.ai/covid/submissions/e194df51-085e-4f88-81b0-9336c8b58ebe/1" target="_blank">2</a></b>), a group of (mainly) Sweden-based academic researchers (<a href="https://doi.org/10.1021/jacs.1c08402" target="_blank"><b>3</b></a>) and Yale University (<a href="https://doi.org/10.1021/acscentsci.1c00039" target="_blank"><b>4</b></a>). <a href="https://en.wikipedia.org/wiki/Nirmatrelvir" target="_blank">Nirmatrelvir</a> incorporates a nitrile group that forms a covalent bond with the catalytic cysteine and the other inhibitors bind non-covalently to the target. The <a href="https://doi.org/10.1042/bj1240107" target="_blank">first example</a> of a nitrile-based cysteine protease inhibitor that I’m aware of was published over half a century ago and the nitrile warhead has since proved popular with designers of cysteine protease inhibitors (it has a small steric footprint and is not generally associated with metabolic lability or chemical instability). Furthermore, covalent bond formation between the thiol of a catalytic cysteine and the carbon of the nitrile warhead is typically reversible. Here’s a recent <a href="https://doi.org/10.1039/D2MD00204C" target="_blank">review</a> on the nitrile group in covalent inhibitor design and this <a href="https://doi.org/10.1016/j.bmcl.2017.10.002" target="_blank">comparative study</a> of electrophilic warheads may also be of interest.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhRoexSjPHc09Z2cz3hjcf_hF6bp4Iw3gf1anT5xTwIafK2-AuNVQlUgB2BtCJB0mJSxqBlkbCI65x-DoAy-I4HO-o9AJW7u17H-Rg22T51abVQaV154JkqaOUblPu81p1RzceIrrEnm0Fp_hVlsPN3sJIeL5oLgmb3weYB6KNtmMaqPUhJxXTt1d61-Q/s1280/chart1.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="720" data-original-width="1280" height="180" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhRoexSjPHc09Z2cz3hjcf_hF6bp4Iw3gf1anT5xTwIafK2-AuNVQlUgB2BtCJB0mJSxqBlkbCI65x-DoAy-I4HO-o9AJW7u17H-Rg22T51abVQaV154JkqaOUblPu81p1RzceIrrEnm0Fp_hVlsPN3sJIeL5oLgmb3weYB6KNtmMaqPUhJxXTt1d61-Q/s320/chart1.jpg" width="320" /></a></div><p style="text-align: justify;">At this point, we should be thinking about the directions in which design of SARS-CoV-2 main protease inhibitors needs to go. Two directions I see as potentially productive are dose reduction (a <a href="https://www.nhs.uk/medicines/paxlovid/how-and-when-to-take-paxlovid" target="_blank">course of paxlovid treatment</a> consists of two 150 mg nirmatrelvir tablets and one 100 mg ritonavir tablet taken twice daily for five days) and countering resistance (here’s a <a href="https://doi.org/10.1126/science.add7226" target="_blank">relevant article</a>).</p><p style="text-align: justify;">Two tactics for achieving a lower therapeutic dose are to increase affinity and reduce clearance. Dose prediction is not as easy as you might think because the predictions are typically very sensitive to input parameters. For example, a two-fold difference in IC50 would often be regarded as within normal assay variation by medicinal chemists but development scientists and clinicians would view doses of 300 mg and 600 mg very differently. </p><p style="text-align: justify;">Excessive clearance is a problem from the perspective of achieving adequate exposure and I'd also anticipate greater variability in exposure between patients when clearance is high. Clearance is clearly an issue for <a href="https://en.wikipedia.org/wiki/Nirmatrelvir" target="_blank">nirmatrelvir</a> because it needs be co-dosed with <a href="https://en.wikipedia.org/wiki/Ritonavir" target="_blank">ritonavir</a> (to inhibit metabolic enzymes) and this has implications for patients taking other medications. <a href="https://en.wikipedia.org/wiki/Nirmatrelvir" target="_blank">Nirmatrelvir</a> lacks aromatic rings and deuteration is an obvious tactic to reduce metabolic lability (although cost of goods is likely to be more of an issue than for a cancer medicine that you'll need to take out a second mortgage for). I would anticipate that bicyclo[1.1.1]pentanyl will be less prone to metabolism than t-butyl (CH bonds tend to be stronger in strained rings and for bridgehead CHs) and the binding mode suggests that this replacement could be accommodated. </p><p style="text-align: justify;"><span style="text-align: left;">Details of resistance to <a href="https://en.wikipedia.org/wiki/Nirmatrelvir" target="_blank">nirmatrelvir</a> (<a href="https://doi.org/10.1016/j.bbrc.2022.09.010" target="_blank">P2022</a> | <a href="https://doi.org/10.1126/sciadv.add7197" target="_blank">Z2022</a>) are starting to emerge and this information should be certainly be used in design and to assess other structural series. Nevertheless, if you’re genuinely concerned about potential for resistance then you really can’t afford to ignore conserved structural features in the target such as the catalytic residues (cysteine and histidine) and the oxyanion hole. I would also anticipate that the risk of resistance will increase with the spatial extent of the inhibitor.</span></p><p style="text-align: justify;">This post is about covalent inhibitors. Although I’m pleasantly surprised by the potencies achieved for non-covalent SARS-Cov-2 Main Protease inhibitors, I consider making a virtue of non-covalent inhibition to be a serious error. Binding of covalent inhibitors to their targets can be reversible or irreversible and, in the context of design, reversible covalent inhibitors have a lot more in common with non-covalent inhibitors than with irreversible covalent inhibitors (for example, you can't generally use mass spectroscopy to <a href="https://doi.org/10.1016/j.drudis.2020.03.016" target="_blank">screen covalent fragments</a> that bind reversibly). In the context of drug design, covalent bonds have much more stringent geometric requirements than non-covalent interactions such as hydrogen bonds. </p><p style="text-align: justify;">I generally favor reversible binding when targeting catalytic cysteines as discussed in these <a href="https://doi.org/10.6084/m9.figshare.12060627.v1" target="_blank">notes</a> and this <a href="https://doi.org/10.1021/acs.jcim.2c00466" target="_blank">article</a>. It is typically less difficult to design reversible covalent inhibitors to target a catalytic cysteine than it is to design irreversible covalent inhibitors because you can use crystal structures of protein-ligand complexes just as you would for non-covalent inhibitors. In contrast, the crystal of a protein-ligand complex (the reaction ‘product’) is not especially relevant in design of irreversible inhibitors because target engagement is under kinetic rather than thermodynamic control and the more relevant transition state models must therefore be generated computationally. Furthermore, assays for irreversible inhibitors are more complex, and assessment of functional selectivity and safety is more difficult than for reversible inhibitors. All that said, however, I’m certainly not of the view that irreversible inhibitors are inherently inferior to reversible inhibitors for targeting catalytic cysteines. This is also a good point to mention an <a href="https://doi.org/10.1021/jacs.8b11027" target="_blank">article</a> which shows how isosteric replacement (with an alkyne) of the nitrile warhead of the reversible cathepsin K inhibitor <a href="https://doi.org/10.1016/j.bmcl.2007.12.047" target="_blank">odanacatib</a> results in an irreversible inhibitor (the article is particularly relevant if you’re interested in chemical probes for cysteine proteases).</p><p style="text-align: justify;">I contributed some designs for reversible covalent inhibitors to the <a href="https://covid.postera.ai/covid" target="_blank">Covid Moonshot</a> and it may be helpful to discuss some of them. Each design was intended to link the nitrile warhead to the ‘3-aminopyridine-like’ scaffold used in the <a href="https://covid.postera.ai/covid" target="_blank">Covid Moonshot</a> which means that the designs all use a heteroaromatic P1 group (typically isoquinoline linked at C4) rather than the chiral P1 group (pyrrolidinone linked at C3) used for <a href="https://en.wikipedia.org/wiki/Nirmatrelvir" target="_blank">nirmatrelvir</a> and a number of other SARS-CoV-2 main protease inhibitors. The ‘3-aminopyridine-like’ scaffold lacks essential hydrogen bond donors (elimination of hydrogen bond donors is suggested as a tactic for increasing aqueous solubility in this <a href="https://doi.org/10.1021/acs.jmedchem.2c01147" target="_blank">article</a>). One of the cool things about the way the <a href="https://covid.postera.ai/covid" target="_blank">Covid Moonshot</a> was set up is that I can link designs as they were originally submitted (often with a detailed rationale and proposed binding mode).</p><p style="text-align: justify;">The most direct way to link a nitrile to the ‘3-aminopyridine-like’ scaffold is with methylene (<a href="https://covid.postera.ai/covid/submissions/bbe8d7ff-23b7-4848-a830-4c2ce8fa64e9/2" target="_blank"><b>5</b></a>, Chart 2) but there is a problem with this approach because substituting anilides (and their aza-analogs) on nitrogen with sp3 carbon <a href="https://doi.org/10.1021/ol034344g" target="_blank">inverts</a> the cis/trans geometrical preference of the anilides (I discussed the design implications of this in these <a href="https://doi.org/10.6084/m9.figshare.12440486.v1" target="_blank">notes</a>). This implies that binding of <a href="https://covid.postera.ai/covid/submissions/bbe8d7ff-23b7-4848-a830-4c2ce8fa64e9/2" target="_blank">5</a> to the target is expected to incur a conformational energy penalty and it is significant that N-methylation of <a href="https://covid.postera.ai/covid/submissions/6c2cb422-4898-41bd-9409-adfdfb62f5c2/1" target="_blank">6</a> results in a large reduction in potency. Although <a href="https://covid.postera.ai/covid/submissions/bbe8d7ff-23b7-4848-a830-4c2ce8fa64e9/2" target="_blank">5</a> was inactive in the enzyme inhibition assay, I think that it would still be worth seeing if covalent bond formation can be observed by crystallography for this compound.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjkTsioL-AjwdrK3xLbOeaW8abfYvZYY4Z8fiUdit8b8UIp-8Cd9pJlwgCHzOIg8uNCOPsAAeEBy2uGi9JTk9RdNfOeVQsC8NkjN1W-PD6LzPO7au03V74SQsiyP0Ktn72O1QDx0E329hzKxaEcSKCCd0LphzUXUYbmUjKx8R_5XbN4EvgApZTaZvIk5Q/s1280/chart2.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="369" data-original-width="1280" height="92" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjkTsioL-AjwdrK3xLbOeaW8abfYvZYY4Z8fiUdit8b8UIp-8Cd9pJlwgCHzOIg8uNCOPsAAeEBy2uGi9JTk9RdNfOeVQsC8NkjN1W-PD6LzPO7au03V74SQsiyP0Ktn72O1QDx0E329hzKxaEcSKCCd0LphzUXUYbmUjKx8R_5XbN4EvgApZTaZvIk5Q/s320/chart2.jpg" width="320" /></a></div><p style="text-align: justify;">However, you won’t invert cis/trans geometrical preference if you substitute an anilide nitrogen with nitrogen rather than sp3 carbon (Chart 3). This was the basis for submitting <a href="https://covid.postera.ai/covid/submissions/a692de38-5589-44a0-be0f-17a8ef0c9d1a/1" target="_blank"><b>8</b></a>, which is related to azapeptide nitriles, as a design. Azapeptide nitriles [<a href="https://doi.org/10.1002/anie.200705858" target="_blank">L2008</a> | <a href="https://doi.org/10.1002/chem.201103322 " target="_blank">Y2012</a> | <a href="https://doi.org/10.1002/jlcr.3729" target="_blank">L2019</a> | <a href="https://doi.org/10.1080/14756366.2021.2024527" target="_blank">B2022</a>] are typically <a href="https://doi.org/10.1016/j.bmcl.2017.10.002" target="_blank">more potent</a> than the corresponding peptide nitriles and, to be honest, this remains something of a mystery to me (one possibility is that the imine nitrogen of the azapeptide nitrile adduct is more basic than that of the corresponding peptide nitrile adduct and is predominantly protonated under assay conditions). I see cyanohydrazines and cyanamides as functional groups that would be worth representing in fragment libraries if you want to target catalytic cysteine residues and I’ll point you toward a <a href="https://doi.org/10.1016/j.bmc.2020.115743" target="_blank">relevant crystal structure</a>. The acyclic hydrazine and cyanamide substructures in <a href="https://covid.postera.ai/covid/submissions/a692de38-5589-44a0-be0f-17a8ef0c9d1a/1">8</a> trigger structural alerts although there are approved drugs that incorporate acyclic hydrazine (<a href="https://en.wikipedia.org/wiki/Atazanavir" target="_blank">atazanavir</a> | <a href="https://en.wikipedia.org/wiki/Bumadizone" target="_blank">bumadizone</a> | <a href="https://en.wikipedia.org/wiki/Gliclazide" target="_blank">gliclazide</a> | <a href="https://en.wikipedia.org/wiki/Goserelin" target="_blank">goserelin</a> | <a href="https://en.wikipedia.org/wiki/Isocarboxazid" target="_blank">isocarboxazid</a> | <a href="https://en.wikipedia.org/wiki/Isoniazid" target="_blank">isoniazid</a>) and N-cyano (<a href="https://en.wikipedia.org/wiki/Cimetidine" target="_blank">cimetidine</a>) substructures. The basis for these structural alerts is obscure and it’s worth noting that <a href="https://covid.postera.ai/covid/submissions/a692de38-5589-44a0-be0f-17a8ef0c9d1a/1"><b>8</b></a> is incorrectly flagged as an enamine and having a nitrogen-oxygen single bond. As a cautionary tale on structural alerts, I’ll refer you to this <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">comment</a> in which I read the riot act (i.e., the JMC guidelines for authors) to a number of ACS journal EiCs Nevertheless, I’d still worry about the presence of an acyclic hydrazine substructure although these concerns would be eased if each nitrogen atom was bonded to an electron-withdrawing group, as is the case for <a href="https://covid.postera.ai/covid/submissions/a692de38-5589-44a0-be0f-17a8ef0c9d1a/1">8</a>, and all NHs were capped (see<b> 9</b>).</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi7BWUGWH0bApQ3H1Csh-gOblth6IGHOLXLuQNUSBTXkeRwIZoml2-wOxfVHJSpGx_niv0yA0xkdjpOk_9vAKvdvJj-tkcTRLwlPkE-3MfROV7aTGPSxbpusBX6_qraDfghvKDjEdvfmF7TRPKYjqpsJkNdyimxQ-KXEvTztqPC8DIuVHf55yheH2_01w/s1280/chart3.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="317" data-original-width="1280" height="79" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi7BWUGWH0bApQ3H1Csh-gOblth6IGHOLXLuQNUSBTXkeRwIZoml2-wOxfVHJSpGx_niv0yA0xkdjpOk_9vAKvdvJj-tkcTRLwlPkE-3MfROV7aTGPSxbpusBX6_qraDfghvKDjEdvfmF7TRPKYjqpsJkNdyimxQ-KXEvTztqPC8DIuVHf55yheH2_01w/s320/chart3.jpg" width="320" /></a></div><div class="separator" style="clear: both; text-align: center;"><span style="text-align: left;"><br /></span></div><div class="separator" style="clear: both; text-align: justify;"><span style="text-align: left;">An alternative tactic to counter inversion of the cis/trans geometrical preference is to lock the conformation with a ring and designs </span><a href="https://covid.postera.ai/covid/submissions/3e354a91-7037-4566-808f-5c7f0f34a3a7/1" style="text-align: left;" target="_blank"><b>10</b></a><span style="text-align: left;"> and </span><a href="https://covid.postera.ai/covid/submissions/5ddd54c7-5e98-43f2-9f08-dfa996b1293a/2" style="text-align: left;" target="_blank"><b>11</b></a><span style="text-align: left;"> (Chart 4) can be seen as 'hybrids' of </span><a href="https://covid.postera.ai/covid/submissions/bbe8d7ff-23b7-4848-a830-4c2ce8fa64e9/2" style="text-align: left;" target="_blank"><b>5</b></a><span style="text-align: left;"> with </span><a href="https://covid.postera.ai/covid/submissions/c9c1e0d8-0262-485a-801f-3f9784a8fd20/3" style="text-align: left;" target="_blank"><b>12</b></a><span style="text-align: left;"> and </span><a href="https://covid.postera.ai/covid/submissions/c9c1e0d8-0262-485a-801f-3f9784a8fd20/4" style="text-align: left;" target="_blank"><b>13</b></a><span style="text-align: left;"> respectively (in fragment-based design, </span><a href="https://doi.org/10.1021/jm030543u" style="text-align: left;" target="_blank">hybridization</a><span style="text-align: left;"> is usually referred to as fragment merging). The effect of the conformational lock can be clearly seen since </span><a href="https://covid.postera.ai/covid/submissions/c9c1e0d8-0262-485a-801f-3f9784a8fd20/3" style="text-align: left;" target="_blank"><b>12</b></a><span style="text-align: left;"> and </span><a href="https://covid.postera.ai/covid/submissions/c9c1e0d8-0262-485a-801f-3f9784a8fd20/4" style="text-align: left;" target="_blank"><b>13</b></a><span style="text-align: left;"> are essentially equipotent with </span><a href="https://covid.postera.ai/covid/submissions/6c2cb422-4898-41bd-9409-adfdfb62f5c2/1" style="text-align: left;" target="_blank"><b>6</b></a><span style="text-align: left;"> (the primary reason for proposing </span><a href="https://covid.postera.ai/covid/submissions/c9c1e0d8-0262-485a-801f-3f9784a8fd20/3" style="text-align: left;" target="_blank"><b>12</b></a><span style="text-align: left;"> and </span><a href="https://covid.postera.ai/covid/submissions/c9c1e0d8-0262-485a-801f-3f9784a8fd20/4" style="text-align: left;" target="_blank"><b>13</b></a><span style="text-align: left;"> as designs was actually to present the nitrile warhead to the catalytic cysteine). A substituent on carbon next to a lactam nitrogen tends to adopt an axial orientation and I’d anticipate that </span><a href=" https://covid.postera.ai/covid/submissions/3e354a91-7037-4566-808f-5c7f0f34a3a7/1" style="text-align: left;" target="_blank"><b>10</b></a><span style="text-align: left;"> will be less prone to epimerization than </span><a href="https://covid.postera.ai/covid/submissions/5ddd54c7-5e98-43f2-9f08-dfa996b1293a/2" style="text-align: left;" target="_blank"><b>11</b></a><span style="text-align: left;">. Although I'm unaware of nitriles being deployed on cyclic amine substructures for cysteine protease inhibition, the structures of the DPP-4 inhibitors </span><a href="https://en.wikipedia.org/wiki/Saxagliptin" style="text-align: left;" target="_blank">saxagliptin</a><span style="text-align: left;"> and </span><a href="https://en.wikipedia.org/wiki/Vildagliptin" style="text-align: left;" target="_blank">vildagliptin</a><span style="text-align: left;"> are relevant.</span></div><div class="separator" style="clear: both; text-align: justify;"><span style="text-align: left;"><br /></span></div><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhGTZJeHle6g0iYh3sVeC7yMYR6sYFASPwVM09lPW62e4qDfZfuQv2LYr2bYu8gUU_YiBGFS-DhIqreP62nk8jx6hL9yJlPhGd6HOjbblm4HZq8DrWRXElpCmTc4qbfW8ebzODIX-LwD-mgrZ5PYtWkkLwE99qevdMgL0bdUechvUcfzH0vk3P5tEXpCA/s1280/chart4.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="579" data-original-width="1280" height="145" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhGTZJeHle6g0iYh3sVeC7yMYR6sYFASPwVM09lPW62e4qDfZfuQv2LYr2bYu8gUU_YiBGFS-DhIqreP62nk8jx6hL9yJlPhGd6HOjbblm4HZq8DrWRXElpCmTc4qbfW8ebzODIX-LwD-mgrZ5PYtWkkLwE99qevdMgL0bdUechvUcfzH0vk3P5tEXpCA/s320/chart4.jpg" width="320" /></a></div><br /></div><div class="separator" style="clear: both; text-align: justify;">This is a good point at which to wrap up. If cysteine protease inhibition is a key component of pandemic preparedness strategy then you really do need to be thinking about covalent inhibition. I'll be looking at some more design themes for covalent inhibitors of SARS-CoV-2 in the next Covid post.</div>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-9956751119807870322023-01-02T21:12:00.004+00:002023-01-25T01:06:31.267+00:00Assessment of chemical probes<div style="text-align: center;">| <a href="https://fbdd-lit.blogspot.com/2023/01/chemical-probes-response-to-practical.html" target="">next</a> >></div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">I’ll be taking a look at some of the criteria, specifically structural alerts, by which chemical probes are assessed and here’s the <a href="https://www.chemicalprobes.org/" target="_blank">link</a> to the Chemical Probes Portal. Before getting into the post there are a couple of points that I need to stress. First, structural alerts derived from analysis of screening hits (defined as responses that exceed a threshold when assayed at a particular concentration) are not necessarily useful for assessing higher affinity compounds for which concentration responses have been determined. Second, chemical probes will have to satisfy the same set of acceptability criteria whether or not they trigger structural alerts. </div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">I’ll start by commenting on “A conversation on using chemical probes to study protein function in cells and organisms” that was recently <a href="https://doi.org/10.1038/s41467-022-31271-x" target="_blank">published</a> in Nature Communications since it was this article that triggered the blog post. I consider most of the views expressed in the in the article to be sound although I disagree with much of what is stated in the following paragraph:</div><div style="text-align: justify;"><br /></div><div style="text-align: justify;"><i>“The first essential thing that needs to be done is to eliminate the really bad nuisance compounds, which can have problematic behavior—like being non-specifically very reactive with proteins; forming colloidal aggregates that non-specifically adsorb and inactivate proteins; exerting toxicity toward cells, for example through a membrane damaging effect called phospholipidosis; or exhibiting spectral or fluorescence properties that interfere with the biological assay read-out. These undesirable compounds are often referred to as Pan Assay Interference or PAINS compounds, as highlighted by Jonathan Baell [<a href="https://doi.org/10.1038/513481a" target="_blank">4</a>]. There are software filters or algorithms available that should be used routinely to identify any risk of such chemical promiscuity and simple lab assays should be run to check for the various problematic properties we mentioned. Such compounds should never be considered further or used as chemical probes. They should be excluded from compound libraries. Yet many are sold by commercial vendors as chemical probes and widely used.”</i></div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">In 2017 a number of ACS journals simultaneously published “The Ecstasy and Agony of Assay Interference Compounds” <a href="https://doi.org/10.1021/acs.jmedchem.7b00229" target="_blank">editorial</a> and I believe that a number of points raised in a <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">comment</a> on this editorial are still relevant to dealing with nuisance compounds. In the <a href="https://doi.org/10.1021/acs.jcim.7b00313" target="_blank">comment</a>, I classified bad behavior of screening ‘actives’ as Type 1 (compound hits in the assay but does not affect target function) and Type 2 (compound affects target function through an undesirable mechanism of action). These are two very different problems and each requires very different solutions. Type 1 behavior, which can also be described as interference with read-out, is primarily a problem from the perspective of analysis of high-throughput screening (HTS) output because you don’t know whether observed ‘activity’ is real or not. From the perspective of probe promiscuity, Type 1 behavior is much less of a problem than Type 2 behavior because the ‘activity’ is not real. If you’re trying to decide whether a potential chemical probe is acceptable then genuine activity at 50 nM against another protein is going to hurt a whole lot more than responses of >50% in several assays at a test concentration of 10 μM. </div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">It is asserted in the <a href="https://doi.org/10.1038/s41467-022-31271-x" target="_blank">conversation</a> that there are <i>“software filters or algorithms available that should be used routinely to identify any risk of such chemical promiscuity”</i>. When recommending their use of predictive models for assessment of potential probes, it’s important to be aware of their inherent limitations. Specifically, models derived from analysis of data have applicability domains that are imposed by the data used to build the models. For example, <a href="https://doi.org/10.1021/jm901137j" target="_blank">PAINS filters</a> were derived from analysis of the output of six screens that all use the same read-out (AlphaScreen) and this limits the applicability domain of the <a href="https://doi.org/10.1021/jm901137j" target="_blank">PAINS filter model</a> to prediction of frequent-hitter behavior in AlphaScreen assays. It is asserted in the <a href="https://doi.org/10.1038/s41467-022-31271-x" target="_blank">conversation</a> that commercial vendors are selling compounds as chemical probes that are unfit for purpose and I strongly recommend that anybody making such assertions should carefully examine the supporting evidence. I would argue that sharing structural features with compounds (for which structures that have not been disclosed) that have been observed to exhibit frequent-hitter behavior when screened at a single concentration (e.g., 10 μM) would not credibly support an assertion that a compound is unsuitable for use as a chemical probe. A specific criticism I would make of the way that structural alerts (especially those derived using proprietary data) are used is that it is sometimes suggested, for example in the ACS assay interference<a href="https://doi.org/10.1021/acs.jmedchem.7b00229" target="_blank"> editorial</a>, that HTS hits that don’t trigger structural alerts can be checked less thoroughly than hits that do trigger structural alerts.</div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">The<a href="https://www.chemicalprobes.org/information-centre" target="_blank"> Information Centre</a> of the Chemical Probes Portal includes a “Toxicophores and PAINS Alerts” <a href="https://www.chemicalprobes.org/information-centre#pains" target="_blank">section</a> in which it is correctly stated that <i>“the presence of the toxicophore or PAINS substructures within the chemical structure of a compound does not necessarily mean that it will be non-specifically active or toxic, or give rise to assay interference”</i>. The “Toxicophores and PAINS Alerts” <a href="https://www.chemicalprobes.org/information-centre#pains" target="_blank">section</a> might work better as a “Structural Alerts” section and the toxicophores citation appears to be incorrect (<a href="https://doi.org/10.1016/j.bmcl.2008.07.071" target="_blank">reference 10</a> actually cites an article on toxicity risks associated with excessive lipophilicity). If doing this, I would recommend saying something about the applicability domains of any structural alerts that are highlighted and considering the inclusion of <a href="http://advisor.bkslab.org/" target="_blank">Aggregator Advisor</a> (link to <a href="https://doi.org/10.1021/acs.jmedchem.5b01105" target="_blank">article</a>) and <a href="https://datascience.unm.edu/badapple/" target="_blank">BadApple</a> (here's link to <a href="https://doi.org/10.1186/s13321-016-0137-3" target="_blank">article</a>) </div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">Alternatively, it might be an idea to create separate “Nuisance Compounds” and “Toxicophores” sections because these are very different problems. I would generally recommend the use of the term “nuisance compounds” since <a href="https://doi.org/10.1021/jm901137j" target="_blank">PAINS</a> and colloidal aggregators are sometimes treated as separate categories of bad actor, as is the case in the ACS assay interference <a href="https://doi.org/10.1021/acs.jmedchem.7b00229" target="_blank">editorial</a>, and the criteria for labelling compounds as <a href="https://doi.org/10.1021/jm901137j" target="_blank">PAINS</a> are ambiguous. It would certainly be useful to include some reviews on assay interference, such as <a href="https://www.ncbi.nlm.nih.gov/sites/books/NBK553584/" target="_blank">this one</a>, in a “Nuisance Compounds” section. I quite like this <a href="https://doi.org/10.1177/1087057106286653" target="_blank">article</a> by former colleagues which shows how interference with read-out can be assessed and even corrected for. As for a “Structural Alerts” section, the applicability domains of any predictive models should be indicated so people don’t end up using models that have been trained using hits from screening at 10 μM to assess probes with 20 nM affinity.</div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">This is a good point at which to wrap up and it’s worth stressing that the essence of the criticism of <a href="https://doi.org/10.1021/jm901137j" target="_blank">PAINS filters</a> is simply that the rhetoric is not supported by the data. Those like me who are critical of the way that <a href="https://doi.org/10.1021/jm901137j" target="_blank">PAINS filters</a> are used are certainly not suggesting that screening hits all smell of roses (back in 1995 I used the Daylight toolkits to build the SMARTS-matching software that was used in the Zeneca ‘de-crapper’ and colleagues also created the <a href="https://doi.org/10.1007/s10822-009-9264-5" target="_blank">Flush software</a>) nor are we denying that assay interference is a serious problem. Although I believe that it is certainly helpful to have scientists who have worked with HTS data share their experiences and opinions with respect to hit quality, I would argue that there are dangers in giving such opinions too much weight (this <a href="https://doi.org/10.1021/jm049740z" target="_blank">article</a> may be of interest) especially when data that might be used to justify the opinions are proprietary. Specifically, I would strongly advise against making statements that a compound is unfit for use as a chemical probe unless the assertion is supported by measured data in the public domain for the compound in question.</div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">I’ll leave it there for now. In the next post on chemical probes, I’ll be taking a look at permeability.</div><div><br /></div>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com2tag:blogger.com,1999:blog-2909827059962062852.post-39803221411968248302022-07-24T17:13:00.005+01:002022-07-25T06:23:49.467+01:00HB Donor Fragment Selection Themes<p style="text-align: justify;">In this post I’ll look at a couple of fragment selection themes with a hydrogen bond donor (HBD) focus. The material has been taken from the recent ‘HBDs in drug design’ <a href="https://doi.org/10.26434/chemrxiv-2022-0mzxq" target="_blank">preprint</a> (HBD3) which introduced the term ‘hydrogen bond donor-acceptor asymmetry’ and suggested that we need to think differently about HBDs and hydrogen bond acceptors (HBAs) in drug design. One example of these hydrogen bond donor-acceptor asymmetries is that HBAs are typically more strongly solvated than HBDs in aqueous media and this is especially relevant to lead optimization (as shown in the graphical abstract for <a href="https://doi.org/10.26434/chemrxiv-2022-0mzxq" target="_blank">HBD3</a> below). </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgPkJcZ90yazKaayQV0ufVSu2RIGfnlpFBiKba7Llu17JqASYuHEfFI5oNZX0CLqb8Qf-56mGO9RQjGdyxzY-I8rY47255VWPoCU1k-UvWcAvyG09Q4B3r2gVdSPM-_1GU2q5PY0Wiv3FE4OI3KKp7Dhc0BZydN-EzQZPHp0y50I6n8dhlxXUQRKP3ucQ/s1280/hbd3_ga.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="720" data-original-width="1280" height="180" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgPkJcZ90yazKaayQV0ufVSu2RIGfnlpFBiKba7Llu17JqASYuHEfFI5oNZX0CLqb8Qf-56mGO9RQjGdyxzY-I8rY47255VWPoCU1k-UvWcAvyG09Q4B3r2gVdSPM-_1GU2q5PY0Wiv3FE4OI3KKp7Dhc0BZydN-EzQZPHp0y50I6n8dhlxXUQRKP3ucQ/s320/hbd3_ga.jpg" width="320" /></a></div><p style="text-align: justify;">However, this post is about fragment selection, rather than fixing ADME, and so I’ll say something about differences between HBDs and HBAs in the context of binding to targets. Let’s suppose that you’d like to exploit an HBD in the binding site of your target. All you need to do is place an HBA at a point in space where it can form a good hydrogen bond (taking care to address issues like steric footprint and conformational energy) and you’ve got it sorted. However, life is not quite so simple if you’re trying to exploit an HBA in the binding site because the HBD (e.g., amide NH) that you present to it will almost invariably be accompanied by an HBA (e.g., amide carbonyl O). In contrast, it is relatively easy to design an HBA (e.g., pyridine N) into a ligand structure that is not accompanied by an HBD. </p><p style="text-align: justify;">In <a href="https://doi.org/10.26434/chemrxiv-2022-0mzxq" target="_blank">HBD3</a>, I describe the HBA that accompanies pretty much every neutral HBD as ‘co-occurring’. The problem for designers is that the co-occurring HBA, which is likely to come with a larger desolvation penalty than that for the HBD, needs to be accommodated and this places constraints on design. It’s also more difficult to achieve ‘line-of-sight’ access with HBDs than is the case for HBAs (you’re likely to need line-of-sight access when targeting a polar atom at the bottom of a relatively narrow binding pocket). The following figure should give you a better idea of what I’m getting at and let’s assume that we’re trying to donate an HB to HBA sitting at the bottom of a narrow and otherwise non-polar binding pocket. Although each of the three structures has appropriate geometry for line-of-sight access, things are not likely to end well if you try to exploit this line-of-sight access in a real-life design situation.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjS6rWUVuh0jbnlV8XeUkNJBDgCVc5jl3SAp2JqYidJeT9KvFtBiv8-UbGMsRYG6PLIKte_lRUlMgM1My_SDC52hBGZ3IQPKWH6RsCxfdfxi3El5UWZEvsnzWafI3kCc_iiMGZd-j2yb8OcV7a4ODzzy8KaXQQQv_EN67vbzgUfeA_WxGxmGMRzsDhvFg/s1280/hbd1b.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="589" data-original-width="1280" height="147" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjS6rWUVuh0jbnlV8XeUkNJBDgCVc5jl3SAp2JqYidJeT9KvFtBiv8-UbGMsRYG6PLIKte_lRUlMgM1My_SDC52hBGZ3IQPKWH6RsCxfdfxi3El5UWZEvsnzWafI3kCc_iiMGZd-j2yb8OcV7a4ODzzy8KaXQQQv_EN67vbzgUfeA_WxGxmGMRzsDhvFg/s320/hbd1b.jpg" width="320" /></a></div><p style="text-align: justify;">Let’s start with the phenol and, although not pertinent to this discussion, it’s worth mentioning that hydroxyl groups are prone to conjugation in phase 2 metabolism (drugs get hydroxylated in phase 1 metabolism in order to facilitate clearance). Donation of an HB by a ligand hydroxyl to a target HBA also brings the hydroxyl oxygen (the co-occurring HBA) into proximity with the molecular surface of the target. This increases the likelihood of an energetic penalty resulting from desolvation of the phenolic oxygen. One subtle point is that donation of an HB by the phenolic hydroxyl increases the HB basicity of the oxygen which effectively increases the energetic cost of desolvating it.</p><p style="text-align: justify;">The co-occuring HBA of the primary amide is an even bigger problem than for phenol because the high polarity of the carbonyl oxygen means that it carries a large desolvation penalty (bad news if you’re trying to hit an HBA at the bottom of a narrow and otherwise non-polar binding pocket). If this is not enough of a problem, you also need to worry about desolvation penalties associated with the second HBD (the primary amide has two HBDs and methyl-capping will take out the one that you need for hitting that HBA at the bottom of the binding pocket). As Lady Bracknell might have observed, “One desolvated polar atom may be regarded as a misfortune; to lose solvation of two polar atoms looks like carelessness”.</p><p style="text-align: justify;">The last of the trio of structures is pyrazole linked at C4 and this avoids problems that might result from biasing the tautomeric preference. Pyrazole is a great warhead if you’re targeting a proximal HBD and HBA (as is the case when trying to <a href="https://doi.org/10.1021/jm070091b" target="_blank">hit a kinase hinge</a>). However, pyrazole’s HBA may become a liability when trying to hit the HBA at the bottom of that otherwise non-polar binding pocket. Why not just take out pyrazole’s HBA, you might ask? The problem is that pyrroles are very electron rich and tend to be quite reactive. One tactic is to move the co-occurring HBA from the ring to the linker (<b>1</b> and <b>2</b>) in a way that makes the linker electron-withdrawing and pray for a less destabilizing contact between the co-occurring HBA and the binding site. Alternatively, you can take out the co-occurring HBA and modify the linker to make it more electron-withdrawing (<b>3</b>). I’ve included Hammett σ values in the graphic and these will give you an idea how the substituents vary in their ability to suck electron density out of the pyrrole ring (beneficial both for making the pyrrole ring more rugged and increasing the HB acidity of its NH HBD). I see these fragments as being of about the <a href="https://doi.org/10.1016/j.drudis.2019.03.009" target="_blank">right size to be screened crystallographically</a> but you might want something a bit larger than methyl if you’re using another detection method.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiH7NtBcMj5bCS3PgrDAM14zTmELYXzsZnT-fVuSL_e3jo9SFZO7ZK4M04HGtFI271ZouwUiWgT-nl0qG8Xfkz4WZc6KWYdXsZP8-9qb1TKnUAPkbeK0ScDiQJIa74bR79QMEOcPVijx4v968SDtrcxB0PNq4ZCDk61Am0e0_KEoUFtd3zHGgBTg3uq9g/s688/hbd2b.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="348" data-original-width="688" height="162" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiH7NtBcMj5bCS3PgrDAM14zTmELYXzsZnT-fVuSL_e3jo9SFZO7ZK4M04HGtFI271ZouwUiWgT-nl0qG8Xfkz4WZc6KWYdXsZP8-9qb1TKnUAPkbeK0ScDiQJIa74bR79QMEOcPVijx4v968SDtrcxB0PNq4ZCDk61Am0e0_KEoUFtd3zHGgBTg3uq9g/s320/hbd2b.jpg" width="320" /></a></div><div style="text-align: justify;">If you’re designing (or trying to improve the coverage of) a fragment library then another selection theme that you might want to think about is fragments that can present a high ‘density’ of HBDs to a target while minimizing the number of co-occurring HBAs. One way to do this is to use the guanidine substructure although this will cause some medicinal chemists to roll their eyes (concerns about permeability) while <a href="https://doi.org/10.1016/S1359-6446(03)02831-9" target="_blank">Ro3’s</a> adherents would be likely to denounce you for heresy (actually not such a bad thing and I think that the late, great <a href="https://en.wikipedia.org/wiki/Denis_Healey" target="_blank">Denis Healey</a> might have likened this to “being savaged by a dead sheep”). Guanidine itself is extremely basic (pKa = 13.6 | <a href="http://doi.org/10.1039/JR9510002492" target="_blank">ref</a>) which means very little of the neutral form for diffusing across membranes. However, the pKa of guanidine is also extremely sensitive to substitution and a number of approved drugs incorporate this substructure. I should also point out that, even in the neutral form of guanidine, the amide-like nitrogen atoms do not function as HBAs (even though they’d be counted as such when applying <a href="https://doi.org/10.1016/S0169-409X(96)00423-1" target="_blank">Ro5</a>).</div><p style="text-align: justify;">I’ve made a small selection of substituted guanidines that I think may of interest for screening as fragments. The pKa values that I quote in this post are from an <a href="https://doi.org/10.1039/P29860001765" target="_blank">article</a> by two former colleagues (Peter Taylor and Alan Wait who are sadly both deceased) and this is an excellent source of measured guanidine pKa values. Two of these (<b>4</b> and <b>5</b>) will be predominantly protonated at neutral pH although there’ll still be a significant amount of the neutral form that you’ll need for permeability. The other two guanidines will be predominantly neutral at neutral pH although <b>6</b> is sufficiently basic to protonate in lysosomes. As for the pyrroles, I see these as about the <a href="https://doi.org/10.1016/j.drudis.2019.03.009" target="_blank">right size to be screened crystallographically</a> but you might want something a bit bigger than methyl if you plan to use a different detection method.</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjHPBgVsEmmJgegquH9A31pLvgnftuoeItGciiuKq7dLrIKZ9tvKx1GqGH316jWLiq6DvzOkgFEBr_1Q8yXExI47A2Obh76-bbPXYak3rY6yVHWIfkLNc8u1qN2gsNKbGsg5E1O0SOx4AiXPRMqdw9zzGWn0_GFvIP-5lKlX2tGmqYL49BYOsMx6St7_Q/s1066/hbd3b.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="315" data-original-width="1066" height="95" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjHPBgVsEmmJgegquH9A31pLvgnftuoeItGciiuKq7dLrIKZ9tvKx1GqGH316jWLiq6DvzOkgFEBr_1Q8yXExI47A2Obh76-bbPXYak3rY6yVHWIfkLNc8u1qN2gsNKbGsg5E1O0SOx4AiXPRMqdw9zzGWn0_GFvIP-5lKlX2tGmqYL49BYOsMx6St7_Q/s320/hbd3b.jpg" width="320" /></a></div><br /> <p></p><div><br /></div>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-44830703687190256862022-04-01T07:50:00.001+01:002022-04-01T11:16:35.101+01:00Enthalpic fragments<p style="text-align: justify;">Enthalpy-driven binding has been presented as a rationale for screening fragments although some have argued that thermodynamic signature is actually a 'red herring' in the context of drug discovery. Binding of a ligand grown from a fragment hit incurs a translational entropy penalty that is similar to that of the original fragment hit and it is therefore it is hardly surprising that synthetic elaboration results in binding that is more driven by entropy.</p><p style="text-align: justify;">A recent collaborative study between researchers in the Budapest Enthalpomics Group (BEG) and Prof Wilhelmina Wiplasch, well known for her seminal study ‘The Ecstasy and Agony of Recreational PAINS’, shows this view to be hopelessly naïve. The mathematical treatment used in the study is formidable and was originally developed by Prof Wiplasch during a sabbatical at the Port-au-Prince Institute of Biogerontology. Briefly, deep learning was used to model the time-dependent covariance and kurtosis of the polarizability tensor for a series of rhodanines, showing that the enthalpic nature of fragment binding is caused by their greater ligand efficiencies. “This model comprehensively outperforms all competitors”, explains Group Leader Prof Kígyó Olaj, “and we have shown for the very first time that the Sackur-Tetrode equation can be safely consigned to the dustbin of History”.</p>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-4769469470622532032021-01-12T10:46:00.007+00:002023-01-21T10:59:55.809+00:00Tom Lehrer's guide to design of SARS-CoV-3 main protease inhibitors for treatment of COVID-32<p style="text-align: center;"><span style="font-family: inherit;"><< <a href="https://fbdd-lit.blogspot.com/2020/05/how-not-to-repurpose-drug.html" target="_blank">previous</a> || <a href="https://fbdd-lit.blogspot.com/2023/01/some-approaches-to-design-of-covalent.html">next</a> >></span></p><p style="text-align: justify;"><span style="font-family: inherit; font-size: medium;">It’s been ages
since my last COVID-19<a href="https://fbdd-lit.blogspot.com/2020/05/how-not-to-repurpose-drug.html" target="_blank"> post</a> (How not to repurpose a 'drug') and I’ll kick
blogging off for 2021 with a follow up to an even older <a href="https://fbdd-lit.blogspot.com/2020/07/sars-cov-2-main-protease-crowdsourcing.html" target="_blank">post</a> (SARS-CoV-2 main
protease. Crowdsourcing, peptidomimetics and fragments). I consider it unlikely that a SARS-CoV-2 main protease inhibitor, designed from scratch, will be
available in time to have real impact on the current pandemic (in saying this, I’m
making the huge assumption that defeat does not get snatched from the jaws of
victory on the vaccination front). While many grinning Lean Six Sigma ‘belts’
(and their synchronously smiling allies in Human Resources) would denounce this
as negative and defeatist, what I’m really getting at is that we need to think
about targeting SARS-CoV-3 main protease when designing inhibitors for SARS-CoV-2 main
protease. As Tom Lehrer advises in the intro to <a href="https://www.youtube.com/watch?v=yrbv40ENU_o" target="_blank">So Long, Mom</a>, “If any songs are
going to come from World War III, we better start writing them now”.</span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEji9neI2WkVaN0-gP23Taw_YQBbcAs2R2xN9V8U5j3EdwqgSUxOeMoyzc6T5OKPvx4UdLoeMn-E1qMW-RFAUXxUeMIkgjCgiWLGdrhKbfqlnFrtH9VjDtmXitcRF3lWHPvQQHU5-o746D8F/s2048/20210101_082314.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1536" data-original-width="2048" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEji9neI2WkVaN0-gP23Taw_YQBbcAs2R2xN9V8U5j3EdwqgSUxOeMoyzc6T5OKPvx4UdLoeMn-E1qMW-RFAUXxUeMIkgjCgiWLGdrhKbfqlnFrtH9VjDtmXitcRF3lWHPvQQHU5-o746D8F/s320/20210101_082314.jpg" width="320" /></a></div><div class="separator" style="clear: both; text-align: center;">Happy New Year
(this orchid opened during night of Dec 31/Jan 1)</div><p style="text-align: justify;"><span style="font-size: medium;"><span style="font-family: inherit;">If we’re
designing a </span><a name="_Hlk60513282" style="font-family: inherit;">SARS-CoV-2 main protease inhibitor</a><span style="font-family: inherit;"> to
also hit SARS-CoV-3 main protease then it’d be a good idea to engineer it to
have greater affinity than necessary for the current target. In the fourth of his <a href="https://fbdd-lit.blogspot.com/2013/08/malans-ten-rules-for-air-fighting.html" target="_blank">rules for air fighting</a>, ‘Sailor’ Malan (readers may also be interested in his insights into <a href="https://fbdd-lit.blogspot.com/2016/05/sailor-malans-guide-to-fragment.html" target="_blank">fragment screening library design</a>) asserts that “height gives you the initiative” which can
be adapted for drug design as “affinity gives you the initiative”. </span><span style="font-family: inherit;"> </span><span style="font-family: inherit;">We should anticipate that inhibitors optimized
against the current target will have lower affinity for the future target(s)
although it’s obviously not a problem if this proves not to be the case. In any
case, high affinity allows you to use a lower dose and that’s an important
consideration if you’re planning for healthy people such as nurses and doctors
to take the drug prophylactically in order to remain healthy. For a SARS-CoV-2
main protease inhibitor, I’d be looking at a target affinity of 1 nM (or better)
which I believe would be achievable without causing too many self-appointed
arbiters of 'compound quality' to spit feathers. Pfizer began a phase I study of
the SARS-CoV-2 main protease inhibitor<a href="https://doi.org/10.1101/2020.09.12.293498" target="_blank"> </a></span><span style="font-family: inherit;"><a href="https://doi.org/10.1101/2020.09.12.293498" target="_blank">PF-00835321</a></span><span style="font-family: inherit;"> (K</span><sub style="font-family: inherit;">i
</sub><span style="font-family: inherit;">= 0.27 nM; dosed intravenously as the phosphate pro-drug PF-07304814) in
September 2020 although this compound had actually come from a discontinued SARS-CoV
projec</span><span style="font-family: inherit;">t. </span></span></p><p style="text-align: justify;"><span style="font-size: medium;">If we want to
maximize the chances that a SARS-CoV-2 main protease inhibitor will exhibit comparative
affinity for SARS-CoV-3 (or even SARS-CoV-4) then we need to exploit protein
structural features that are likely to be conserved between the different main
proteases. This points to milking as much activity as possible out of the
core substructure of the inhibitor as a design strategy. With this in mind, I
suggest that we really do need to exploit the catalytic cysteine if we’re
serious about treating COVID-32 (or worried about SARS-CoV-2 main protease mutations). </span></p><p style="text-align: justify;"><span style="font-size: medium;">In drug design,
we typically exploit a catalytic cysteine by forming a covalent bond between the
thiol sulfur and an electrophilic atom in the molecular structure of the
inhibitor (<a href="https://doi.org/10.1101/2020.09.12.293498" target="_blank">PF-00835321</a> uses the carbon of a carbonyl group to engage the
catalytic cysteine). The functional group containing the electrophilic atom is
commonly referred to as a “warhead” and covalent bond formation between
cysteine can either be reversible or irreversible. Geometric constraints
associated with covalent bond formation are typically a lot more stringent than
for hydrogen bonds and you’ll make life much easier for yourself by getting the
warhead into structures as early as possible in hit-to-lead. I generally recommend using reversible warheads in design of cysteine
protease inhibitors (<a href="https://doi.org/10.1101/2020.09.12.293498" target="_blank">PF-00835321</a> binds reversibly to SARS-CoV-2) and present my
reasoning in this <a href="https://doi.org/10.6084/m9.figshare.12060627.v1" target="_blank">document</a>. In essence, irreversible inhibition adds complexity to design (both K<sub>i</sub> and k<sub>inact</sub> need to be controlled) while
placing greater technical demands on the design team (e.g. for generation of the
structural models for transition states required for structure-based design).</span></p><p style="text-align: justify;"><span style="font-size: medium;">The argument
typically presented in support of irreversible inhibition (and slow binding
kinetics) is that it leads to longer duration of action. This argument emphasizes
benefits of slow (or zero) off-rate during the elimination phase while ignoring
disadvantages of slow on-rate during the distribution phase and I’ll point you to an insightful <a href="http://dx.doi.org/10.1016/j.drudis.2017.07.016" target="_blank">article</a> by my former colleague Rutger Folmer. While there will
be situations in which irreversible inhibition really is the best option, the decision
as to whether to go for reversible or irreversible inhibitors is one that should
be carefully considered at the start of the project. In drug discovery, it
usually ends in tears once the tail starts wagging the dog as would be the case if choice of screening tactics (<a href="https://doi.org/10.1016/j.drudis.2020.03.016" target="_blank">covalent fragment screening</a> typically finds irreversible binders) were to dictate lead optimization strategy. In particular, I wouldn't really recommend the
laissez faire approach to project management (“once
the rockets are up, who cares where they come down”) <a href="https://www.youtube.com/watch?v=TjDEsGZLbio" target="_blank">chronicled</a> by Tom Lehrer.</span></p><p style="text-align: justify;"><span style="font-size: medium;">Here's some information that may be of interest if you're selecting or designing warheads to form covalent bonds with catalytic cysteines. First, a couple of comparative
studies of <a href="https://doi.org/10.1016/j.bmcl.2017.10.002" target="_blank">reversible</a> and <a href="https://doi.org/10.1016/j.bmc.2019.04.002" target="_blank">irreversible</a> warheads. Second, some papain
inhibition data taken from the literature ( <a href="https://doi.org/10.1111/j.1432-1033.1977.tb11798.x" target="_blank">B1977</a> | <a href="https://www.jbc.org/content/247/24/8195.short" target="_blank">W1972</a> | <a href="https://doi.org/10.1042/bj1240107" target="_blank">L1971</a> ), summarized
in the graphic below, that are relevant to fragment library design.</span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgyWxhqnIpLUQGH4h-VwBwYVTRCEjfGeJy00-tjNdztQXBvVTmYtg4Gc1-ybDbegAfEyrc1sgfMp7-rnJWIwq5wumxJQilZrbVScHRIdcLp3AXlB7S4rLL3uAbVyfx43jCptwKL4EXFOpwd/s923/papain_inhibs_2.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="415" data-original-width="923" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgyWxhqnIpLUQGH4h-VwBwYVTRCEjfGeJy00-tjNdztQXBvVTmYtg4Gc1-ybDbegAfEyrc1sgfMp7-rnJWIwq5wumxJQilZrbVScHRIdcLp3AXlB7S4rLL3uAbVyfx43jCptwKL4EXFOpwd/s320/papain_inhibs_2.jpg" width="320" /></a></div><p style="text-align: justify;"><span style="font-size: medium;"><span style="font-family: inherit;">Off-target
activity is always a concern in drug design since this can cause toxicity (it’s
often considered politer to say “adverse drug reaction” rather than use the uncouth
T-word although Tom Lehrer provides a useful </span><a href="https://www.youtube.com/watch?v=yhuMLpdnOjY" style="font-family: inherit;" target="_blank">perspective</a><span style="font-family: inherit;">) and that’s a strong rationale for trying to achieve a low therapeutic dose.
It’s my understanding (still wading through literature) that SARS-CoV-2 main
protease functions in the endoplasmic reticulum which means that the relevant
physiological pH is close to neutral. Many proteases (potential anti-targets
for SARS-CoV-2 main protease inhibitors) function in acidic compartments such
as lysosomes and the presence of a basic center in the molecular structure of
an inhibitor will tend to draw it into these acidic compartments. When
designing SARS-CoV-2 inhibitors, the safest option is simply to avoid basic
centers (see <a href="https://doi.org/10.1021/jm0504961" target="_blank">F2005</a>) . In particular, to link a ‘gratuitous’ basic center and an irreversible
warhead would be to tempt </span><a href="https://youtu.be/uEa_0cEXQow?t=77" style="font-family: inherit;" target="_blank">launchpad misadventure</a><span style="font-family: inherit;">.</span></span></p><p style="text-align: justify;"><span style="font-family: inherit; font-size: medium;">I'll conclude the post with an observation that the COVID-19 pandemic seems to have triggered a parallel pandemic in scholarly publishing which is forcing scientists to be more creative in finding new ways to getting their messages to stand out. I'll let Tom Lehrer have the <a href="https://www.youtube.com/watch?v=pvhYqeGp_Do&t=4s" target="_blank">last word</a>. </span></p><p></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><o:p></o:p></p>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com1tag:blogger.com,1999:blog-2909827059962062852.post-771163416817486802020-08-09T19:44:00.011+01:002021-01-12T10:56:01.630+00:00How not to repurpose a 'drug' <div style="text-align: center;"><<<a href="https://fbdd-lit.blogspot.com/2020/07/sars-cov-2-main-protease-crowdsourcing.html"> previous</a> || <a href="https://fbdd-lit.blogspot.com/2021/01/tom-lehrers-guide-to-design-of-sars-cov.html">next</a> >></div><div style="text-align: center;"><br /></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjf7tBFVN4a24NJr2cBelyYcpzZo8TqHp4z4a9ig5r4SV6AYUziDTASE_LHvfefkGzKZWK0twbrL0dqCkoLHm9MqpOJAPd6I79pNyd_itFATWo3pwK2ODsXbmtlIR6Wp4pBFCnKUDRkgZxL/s1600/desai2.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="611" data-original-width="971" height="201" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjf7tBFVN4a24NJr2cBelyYcpzZo8TqHp4z4a9ig5r4SV6AYUziDTASE_LHvfefkGzKZWK0twbrL0dqCkoLHm9MqpOJAPd6I79pNyd_itFATWo3pwK2ODsXbmtlIR6Wp4pBFCnKUDRkgZxL/s320/desai2.jpg" width="320" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div style="text-align: justify;">I sometimes wonder what percentage of the pharmacopoeia will have been proposed for repurposing for the treatment of COVID19 by the end of 2020. In particular, I worry about the long-term, psychological effects on bloggers such as Derek who is forced to play whack-a-mole with <a href="https://blogs.sciencemag.org/pipeline/archives/2020/05/22/hydroxychloroquine-enough-already" target="_blank">hydroxychloroquine repurposing studies</a>. Those attempting to use text mining and machine learning to prioritize drugs for repurposing should take note of the views expressed in this <a href="https://twitter.com/aledmedwards/status/1253404392642002945" target="_blank">tweet</a>. </div><br /><div style="text-align: justify;">The idea behind drug repurposing is very simple. If an existing drug looks like it might show therapeutic benefits in the disease that you’re trying to treat then you can go directly to assessing efficacy in humans without having to do any of those irksome Phase I studies. However, you need to be aware that the approval of a drug always places restrictions on the dose that you can use and route of administration (for example, you can't administer a drug intravenously if it has only been approved for oral adminstration). One rationale for drug repurposing is that the target(s) for the drug may also have a role in the disease that you’re trying to treat. Even if the target is not directly relevant to the disease, the drug may engage a related target that is relevant with sufficient potency to have a therapeutically exploitable effect. While these rationales are clear, I do get the impression that some who use text-mining and machine learning to prioritize drugs for repurposing may simply be expecting the selected drugs to overwhelm targets with druglikeness. </div><div><br /></div><div style="text-align: justify;">There are three general approaches to directly tackle a virus such as SARS-CoV-2 with a small molecule drug (or chemical agent). First, destroy the virus before it even sees a host cell and this is the objective of hand-washing and disinfection of surfaces. Second, prevent the virus from infecting host cells, for example, by <a href="https://doi.org/10.1038/s41594-020-0469-6" target="_blank">blocking</a> the interaction between the spike protein and ACE2. Third, prevent the virus from functioning in infected cells, for example, by <a href="https://doi.org/10.1126/science.abb4489" target="_blank">inhibiting</a> the SARS-CoV-2 main protease. One can also try to mitigate the effects of viral infection, for example, by <a href="https://www.sciencemag.org/news/2020/06/cheap-steroid-first-drug-shown-reduce-death-covid-19-patients" target="_blank">using anti-inflammatory drugs</a> to counter cytokine storm although I’d not regard this as tackling the virus directly.</div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">In this post, I’ll be reviewing an <a href="https://doi.org/10.1007/s11095-020-02842-8" target="_blank">article</a> which suggests that quaternary ammonium compounds could be repurposed for treatment of COVID-19. The study received NIH funding and this may be of interest to researchers who failed to secure NIH funding. The article was received on 06-May-2020, accepted on 18-May-2020 and published on 25-May-2020. One of the authors of the article is a member of the editorial advisory board of the journal. As of 08-Aug-2020, two of the authors are described as cheminformatics experts in their Wikipedia biographies and one is also described as an expert in computational toxicology. </div><div><br /></div><div style="text-align: justify;">The authors<a href="https://doi.org/10.1007/s11095-020-02842-8" target="_blank"> state</a>: <span style="color: red;"><i>“This analysis identified ammonium chloride, which is commonly used as a treatment option for severe cases of metabolicalkalosis, as a drug of interest. Ammonium chloride is a quaternary ammonium compound that is known to also have antiviral activity (<a href="https://doi.org/10.1016/0042-6822(85)90345-9" target="_blank">13</a>,<a href="https://doi.org/10.1101/2020.01.31.929042" target="_blank">14</a>) against coronavirus (Supplementary Material) and has a mechanism of action such as raising the endocytic and lysosomal pH, which it shares with chloroquine (<a href="https://doi.org/10.1186/1743-422X-8-163" target="_blank">15</a>). Review of the text-mined literature also indicated a high-frequency of quaternary ammonium disinfectants as treatments for many viruses (Supplementary Material) (<a href="https://doi.org/10.1016/j.jhin.2004.12.023" target="_blank">16</a>,<a href="https://doi.org/10.1016/j.ajic.2009.03.012" target="_blank">17</a>), including coronaviruses: these act by deactivating the protective lipid coating that enveloped viruses like SARS-CoV-2 rely on.”</i> </span></div><div><br /><div style="text-align: justify;">Had I described ammonium chloride as a “quaternary ammonium compound” at high school in Trinidad (I was taught by the <a href="https://fbdd-lit.blogspot.com/2017/01/confessions-of-units-nazi.html" target="_blank">Holy Ghost Fathers</a>), I’d have received a correctional package of licks and penance. For cheminformatics ‘experts’ to make such an error should remind us that each and <a href="https://fbdd-lit.blogspot.com/2015/04/expertitis.html" target="_blank">every expert has an applicability domain and a shelf life</a>. However, the errors are not confined to nomenclature since the cationic nitrogen atoms of a quaternary ammonium compound and a protonated amine are very different beasts. While a protonated amine can deprotonate in order to cross a lipid bilayer, the positive charge of a quaternary ammonium compound can be described as ‘permanent’ and this has profound consequences for its physicochemical behavior. First, the protonation state of a quaternary ammonium nitrogen does not change in response to a change in pH. This means that, unlike amines, quaternary ammonium compounds are not drawn into lysosomes and other acidic compartments. Second, the positive charge needs to be balanced by an anion (in some cases, this may be in the same covalent framework as the quaternary ammonium nitrogen). Despite being positively charged, the quaternary ammonium group is not as polar as you might think because it can’t donate hydrogen bonds to water. However, to get out of water it needs to take its counterion (which is typically polar) with it. I like to think about quaternary ammonium compounds (and other permanent cations) as hydrophobic blobs that are held in solution by the solvation of their counterions. A typical quaternary ammonium compound can also be considered as a detergent in which the polar and non-polar parts are not covalently bonded to each other. </div><br /><div style="text-align: justify;">My view is that the antiviral ‘activity’ reported for ammonium chloride and chloroquine is a red herring when considering potential antiviral activity of quaternary ammonium compounds because neither has a quaternary ammonium center in its molecular structure. Nevertheless, I consider <i>“raising the endocytic and lysosomal pH”</i> to be an unconvincing ‘explanation’ for the antiviral ‘activity’ of ammonium chloride and chloroquine since one would anticipate analogous effects for any base of comparable pKa. One should also anticipate considerable collateral damage to result from raising the endocytic and lysosomal pH (assuming that the ‘drug’ is able overwhelm the buffering systems that have evolved to maintain physiological pH in live humans). The pH raising ‘explanation’ for antiviral ‘activity’ reminded me of suggestions that cancer can be cured by drinking aqueous sodium bicarbonate and I’ll direct readers to this relevant <a href="https://blogs.sciencemag.org/pipeline/archives/2017/01/20/be-sure-the-funds-are-deposited" target="_blank">post</a> by Derek. </div><div style="text-align: justify;"><br /></div><div style="text-align: justify;">This brings us to cetylpyridinium chloride and miramistin shown below and I’ve included the structure of paraquat in the graphic. While miramistin does indeed have a quaternary ammonium nitrogen in its molecular structure, cetylpyridinium chloride is not a quaternary ammonium compound (the cationic nitrogen is only connected to three atoms) and would be more correctly referred to as an N-alkylpyridinium compound (or salt). Nevertheless, this is a less serious error than describing ammonium chloride as a quaternary ammonium compound because cetylpyridinium is, at least, a permanent cation. Neither cetylpyridinium chloride nor miramistin are quite as clean as the authors might have you believe (take a look at <a href="https://doi.org/10.1016/0278-6915(91)90113-l" target="_blank">L1991</a> | <a href="https://doi.org/10.1016/0278-6915(95)00099-2" target="_blank">L1996</a> | <a href="https://doi.org/10.1289/ehp1404" target="_blank">D2017 </a>| <a href="https://doi.org/10.1016/j.cbi.2020.108962" target="_blank">K2020</a> | <a href="https://science.sciencemag.org/content/369/6502/403.abstract" target="_blank">P2020</a>). I’d expect an N-alkylpyridinium cation to be more electrophilic than a tetraalkylammonium cation and paraquat, with two N-alkylpyridinium substructures is highly toxic. Would Lady Bracknell's toxicity assessment have been that one N-alkylpyridinium may be regarded as a misfortune while two looks like carelessness?</div></div><div class="separator" style="clear: both;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi_-wBdw535ztlRv-uQJXduVkI1up5hSEWnn1HznujGyCGUNaNjJGKAuQKs0Tmu2xf3ZuGfXfZk4aeiiSzFNuhqTaaBoDmvRzdzTpCV-ThS7NrfBAP2wF1ARIc-n-O5dXE0BWe6ghOKWFvo/s1065/quats1.jpg" style="display: block; padding: 1em 0px; text-align: center;"><img border="0" data-original-height="234" data-original-width="1065" height="113" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi_-wBdw535ztlRv-uQJXduVkI1up5hSEWnn1HznujGyCGUNaNjJGKAuQKs0Tmu2xf3ZuGfXfZk4aeiiSzFNuhqTaaBoDmvRzdzTpCV-ThS7NrfBAP2wF1ARIc-n-O5dXE0BWe6ghOKWFvo/w512-h113/quats1.jpg" width="512" /></a></div><div style="text-align: justify;">I have no problem with hypothesizing that a chemical agent, such as cetypyridinium chloride, which destroys SARS-CoV-2 on surfaces could do the same thing safely when sprayed up your nose, into your mouth or down your throat. If tackling the virus in this manner, you do need to be thinking about the effects of the chemical agent on the mucus (which is believed to protect against viral infection). The authors <a href="https://doi.org/10.1007/s11095-020-02842-8" target="_blank">assert</a> that cetylpyridinium chloride “<i>has been used in multiple clinical trials”</i> although they only cite this <a href="https://doi.org/10.1186/s12879-016-2177-8" target="_blank">study</a> in which it was used in conjunction with glycerin and xanthan gum (claimed by the authors of the clinical study to “form a barrier on the host mucosa, thus preventing viral contact and invasion”).</div> <div style="text-align: justify;"><br /></div><div style="text-align: justify;">The main challenge to a proposal that cetylpyridinium chloride be repurposed for treatment of COVID-19 is that the compound does not appear to have actually been conventionally approved (i.e. shown to be efficacious and safe) as a drug for dosing as a nasal spray, mouth wash or gargle. Another difficulty is that cetylpyridinium chloride does not appear to have a specific molecular target. Something that should worry readers of the article is that the authors make no reference to literature in which potential toxicity of cetylpyridinium chloride and quaternary ammonium compounds is discussed.</div><br /><div style="text-align: justify;">This is a good place to wrap up and, here in Trinidad's Maraval Valley, I'm working a cure for COVID-19. I anticipate a phone call from Stockholm later in the year.</div><div><p class="MsoNormal" style="line-height: normal; margin: 12pt 0cm 0cm;"></p><div class="separator" style="clear: both;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi8_H3uZvQGRMFwx8UZlB_oTSWa6ONb5vPJolzHke8DWL3KGKlPRYXDz1AAIlupXIV1KXb_yOmQh71WIt-3ZMEpGgHF3ykOJuxQKa35UUsxqO_b74B0Sz4cDfeoUvFgGTNAkiw-S26LGLjx/s2048/20200526_102527.jpg" style="display: block; padding: 1em 0px; text-align: center;"><img border="0" data-original-height="1536" data-original-width="2048" height="384" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi8_H3uZvQGRMFwx8UZlB_oTSWa6ONb5vPJolzHke8DWL3KGKlPRYXDz1AAIlupXIV1KXb_yOmQh71WIt-3ZMEpGgHF3ykOJuxQKa35UUsxqO_b74B0Sz4cDfeoUvFgGTNAkiw-S26LGLjx/w512-h384/20200526_102527.jpg" width="512" /></a></div>
<br /></div>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-81545403626359028262020-08-02T14:47:00.022+01:002021-01-06T10:11:12.368+00:00Why fragments?<br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEid_qgPQMKnzBclv6EeA0MhByqOXp9Vc4x_nLcRQ1L2dZyueK61VieeWw6NNs2sbSCXf2j4zHHjo42wSy0NXf4OuhjIpdrYQwb2fGbjsePu7XrmBf6j38k-eCgMSMMFp5rGzUQMmJWGu3Yu/s2591/20200724_171337.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1214" data-original-width="2591" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEid_qgPQMKnzBclv6EeA0MhByqOXp9Vc4x_nLcRQ1L2dZyueK61VieeWw6NNs2sbSCXf2j4zHHjo42wSy0NXf4OuhjIpdrYQwb2fGbjsePu7XrmBf6j38k-eCgMSMMFp5rGzUQMmJWGu3Yu/s320/20200724_171337.jpg" width="320" /></a></div><div class="separator" style="clear: both; text-align: center;"><font face="inherit" size="2">Paramin panorama</font></div><div class="separator" style="clear: both; text-align: center;"><span style="font-family: verdana; text-align: justify;"><br /></span></div><div style="text-align: justify;"><font face="inherit">Crystallographic fragment screens have been run recently against the main protease (at <a href="https://doi.org/10.1101/2020.05.27.118117" target="_blank">Diamond</a>) and the Nsp3 macrodomain (at <a href="https://fraserlab.com/macrodomain/" target="_blank">UCSF</a> and <a href="https://www.diamond.ac.uk/covid-19/for-scientists/NSP3-macrodomain-structure-and-XChem.html" target="_blank">Diamond</a>) of SARS-Cov-2 and I thought that it might be of interest to take a closer look at why we screen fragments. Fragment-based lead discovery (FBLD) actually has origins in both crystallography [<a href="https://doi.org/10.1007/BF00129424" target="_blank">V1992</a> | <a href="https://doi.org/10.1021/jp952516o" target="_blank">A1996</a>] and computational chemistry [<a href="https://doi.org/10.1002/prot.340110104" target="_blank">M1991</a> | <a href="https://doi.org/10.1007/BF00124387" target="_blank">B1992</a> | <a href="https://doi.org/10.1002/prot.340190305" target="_blank">E1994</a>]. Measurement of affinity is important in fragment-to-lead work because it allows fragment-based structure-activity relationships to be established prior to structural elaboration. Affinity measurement is typically challenging when fragment binding has been detected using crystallography although affinity can be estimated by observation of the response of occupancy to concentration (the ∆G° value of −3.1 kcal/mol <a href="https://doi.org/10.1021/jm070091b" target="_blank">reported</a> for binding of pyrazole to protein kinase B was derived in this manner).</font></div><div><font face="inherit"><br /></font></div><div style="text-align: justify;"><font face="inherit">Although fragment-based approaches to lead discovery are widely used,
it is less clear why fragment-based lead discovery works as well as it appears
to. While it has been <a href="https://doi.org/10.1021/acs.jmedchem.6b00197" target="_blank">stated</a> that “fragment hits form high-quality interactions
with the target”, the concept of <a href="https://fbdd-lit.blogspot.com/2018/04/the-maximal-quality-of-molecular.html" target="_blank">interaction quality</a> is not sufficiently well-defined
to be useful in design. I ran a <a href="https://twitter.com/pwk2013/status/1280818951375728641" target="_blank">poll</a> which asked about the strongest rationale
for screening fragments. The 65 votes
were distributed as follows: ‘high ligand efficiency’ (23.1%), ‘enthalpy-driven
binding’ (16.9%), ‘low molecular complexity’ (26.2%) and ‘God loves fragments’
(33.8%). I did not vote.</font></div><div style="text-align: justify;"><font face="inherit"><br /></font></div><div style="text-align: justify;"><font face="inherit">The belief is that fragments are especially ligand-efficient has many
adherents in the drug discovery field and it has been <a href="https://doi.org/10.1016/S1359-6446(05)03511-7" target="_blank">asserted</a> that “fragment
hits typically possess high ‘ligand efficiency’ (binding affinity per heavy
atom) and so are highly suitable for optimization into clinical candidates with
good drug-like properties”. The <a href="https://doi.org/10.1186/s13321-019-0330-2" target="_blank">fundamental problem</a> with ligand efficiency (LE),
as conventionally calculated, is that perception of efficiency varies with the arbitrary
concentration unit in which affinity is expressed (have you ever wondered why
Kd , Ki or IC50 has to be expressed in mole/litre for calculation of
LE?). This would appear to be an rather undesirable characteristic for a design
metric and LE evangelists might consider trying to explain why it’s not a
problem rather than dismissing it as a “limitation” of the metric or trying to shift
the burden of proof is onto the skeptics to show that the evangelists’ choice
of concentration unit for calculation of LE is not useful.</font></div><div style="text-align: justify;"><font face="inherit"><br /></font></div><div style="text-align: justify;"><font face="inherit">The problems associated with the arbitrary nature of the concentration
unit used to express affinity were first <a href="https://doi.org/10.1021/cr800551w" target="_blank">identified </a>in 2009 and further discussed in <a href="https://doi.org/10.1007/s10822-014-9757-8" target="_blank">2014</a> and <a href="https://doi.org/10.1186/s13321-019-0330-2" target="_blank">2019</a>. Specifically, it was
noted that LE has a nontrivial dependency on the concentration, C°, used to define
the standard state. If you want to do solution thermodynamics with
concentrations defined then you do need to specify a standard concentration.
However, it is important to remember that the choice of standard concentration
is necessarily arbitrary if the thermodynamic analysis is to be valid. If your
conclusions change when you use a different definition of the standard state then
you’ll no longer be doing thermodynamics and, as Pauli might have observed,
you’ll not even be wrong. You probably don't know it, but when you use the LE metric, you’re making the sweeping
assumption that all values of Kd, Ki and IC50 tend to a value of 1 M in the
limit of zero molecular size. Recalling the conventional criticism of homeopathy, is
there really a difference between a solute that is infinitely small and a
solute that is infinitely dilute?</font></div><div style="text-align: justify;"><font face="inherit"><br /></font></div><div style="text-align: justify;"><font face="inherit">I think that’s enough flogging of inanimate equines for one blog post
so let’s take a look at enthalpy-driven binding. My view of thermodynamic
signature characterization in drug discovery is that it’s, in essence, a solution that’s desperately
seeking a problem. In particular, there does not appear to be any physical basis for claims
that the thermodynamic signature is a measure of <a href="https://fbdd-lit.blogspot.com/2018/04/the-maximal-quality-of-molecular.html" target="_blank">interaction quality</a>. In case you’re thinking that I’m an
unrepentant Luddite, I will concede that thermodynamic signatures could prove
useful for validating physics-based models of molecular recognition and in, in
specific cases, they may point to differences in binding mode within congeneric series. I should also stress that the modern isothermal calorimeter
is an engineering marvel and I'd always want this option for label-free, affinity measurement in any project.</font></div><div style="text-align: justify;"><span style="font-family: inherit;"><br /></span></div><div style="text-align: justify;"><span style="font-family: inherit;">It is common to see statements in the thermodynamic signature
literature to the effect that binding is ‘enthalpy-driven’ or ‘entropy-driven’
although it was </span><a href="https://doi.org/10.1021/cr800551w" style="font-family: inherit;" target="_blank">noted</a><span style="font-family: inherit;"> in 2009 (coincidentally, in the same article that
highlighted the nontrivial dependence of LE on C</span><span style="font-family: inherit;">°</span><span style="font-family: inherit;">) that
these terms are not particularly meaningful. The problems start when you make
comparisons between the numerical values of ∆H (which is independent of C</span><span style="font-family: inherit;">°</span><span style="font-family: inherit;">) and T∆S</span><span style="font-family: inherit;">°</span><span style="font-family: inherit;"> (which depends on
C</span><span style="font-family: inherit;">°</span><span style="font-family: inherit;">). If I’d presented
such a comparison in physics class at high school (I was taught by the </span><a href="https://fbdd-lit.blogspot.com/2017/01/confessions-of-units-nazi.html" style="font-family: inherit;" target="_blank">Holy Ghost Fathers</a><span style="font-family: inherit;"> in Port of Spain), I would have been caned with a ferocity
reserved for those who’d dozed off in catechism class. I’ll point you toward an </span><a href="https://doi.org/10.1016/j.drudis.2016.11.019" style="font-family: inherit;" target="_blank">article</a><span style="font-family: inherit;"> which
asserts that, “when compared with many traditional druglike compounds,
fragments bind more enthalpically to their protein targets”. I have a number of
issues with this article although this is not the place for a comprehensive
review (although I’ll probably pick it up in ‘The Nature of Lipophilic Efficiency’ when that gets written).</span></div><div style="text-align: justify;"><span style="font-family: inherit;"><br /></span></div><div style="text-align: justify;"><span style="font-family: inherit;">While I don’t believe that the authors have actually demonstrated that
fragments bind more enthalpically than ligands of greater molecular size, I
wouldn’t be surprised to discover that gains in affinity over the course of a
fragment-to-lead (F2L) campaign had come more from entropy than enthalpy. First,
the lost translation entropy (the component of ∆S</span><span style="font-family: inherit;">°</span><span style="font-family: inherit;">
that endows it with its dependence on C</span><span style="font-family: inherit;">°</span><span style="font-family: inherit;">)
is shared over greater number of intermolecular contacts for structurally-elaborated
compounds and this <a href="https://doi.org/10.1073/pnas.78.7.4046" target="_blank">article</a> is relevant to the discussion. Second, I’d expect the
entropy of any water molecule to increase when it is moved to bulk solvent from
contact with molecular surface of ligand or target (regardless of polarity of
the molecular surface at the point of contact). Nevertheless, this is something
that you can test easily by examining the response of (∆H + T∆S</span><span style="font-family: inherit;">°</span><span style="font-family: inherit;">) to ∆G</span><span style="font-family: inherit;">°</span><span style="font-family: inherit;"> (best to not to aggregate
data for different targets and/or temperatures when analyzing isothermal
titration calorimetry data in this manner). But even if F2L affinity gains were
shown generally to come more from entropy than enthalpy, would that be a strong
rationale for screening fragments?</span></div><div style="text-align: justify;"><span style="font-family: inherit;"><br /></span></div><div style="text-align: justify;"><span style="font-family: inherit;">This gets us onto molecular complexity and this </span><a href="https://doi.org/10.1021/ci000403i" style="font-family: inherit;" target="_blank">article</a><span style="font-family: inherit;"> by Mike Hann
and GSK colleagues should be considered essential reading for anybody thinking
about selecting of compounds for screening. The </span><a href="https://doi.org/10.1021/ci000403i" style="font-family: inherit;" target="_blank">Hann model</a><span style="font-family: inherit;"> is a conceptual
framework for molecular complexity but it doesn’t provide much practical
guidance as to how to measure complexity (this is not a criticism since the
thought process should be more about frameworks and less about metrics). I
don’t believe that it will prove possible to quantify molecular complexity in an objective manner that is useful for designing compound libraries (I will be
delighted to be proven wrong on this point). The approach to handling molecular
complexity that I’ve used in screening library design is to restrict extent of
substitution (and other substructural features that can be considered to be
associated with molecular complexity) and this is closer to ‘needle screening’
as </span><a href="https://doi.org/10.1021/jm000017s" style="font-family: inherit;" target="_blank">described</a><span style="font-family: inherit;"> by Roche scientists in 2000 than to the </span><a href="https://doi.org/10.1021/ci000403i" style="font-family: inherit;" target="_blank">Hann model</a>.</div><div style="text-align: justify;"><span style="font-family: inherit;"><br /></span></div><div style="text-align: justify;"><span style="font-family: inherit;">Had I voted in the poll, ‘low molecular
complexity’ would have got my vote. Here’s
what I said in </span><a href="https://doi.org/10.1186/s13321-019-0330-2" style="font-family: inherit;" target="_blank">NoLE</a><span style="font-family: inherit;"> (it’s got an entire section on
fragment-based design and a practical suggestion for redefining ligand efficiency so that perception does not change with C</span><span style="font-family: inherit;">°):</span></div><div style="text-align: justify;"><i style="color: red; font-family: inherit;"><br /></i></div><div style="text-align: justify;"><i style="color: red; font-family: inherit;">"I would argue that the rationale for screening fragments against
targets of interest is actually based on two conjectures. First, chemical space
can be covered most effectively by fragments because compounds of low molecular
complexity [<a href="https://doi.org/10.1021/ci000403i" target="_blank">18</a>, <a href="https://doi.org/10.1021/jm000017s" target="_blank">21</a>, <a href="https://doi.org/10.1007/s10822-009-9264-5" target="_blank">22</a>] allow TIP [target interaction potential] to be explored
[<a href="https://doi.org/10.1002/prot.340110104" target="_blank">70</a>,<a href="https://doi.org/10.1007/BF00124387" target="_blank">71</a>,<a href="https://doi.org/10.1021/jp952516o" target="_blank">72</a>,<a href="https://doi.org/10.1126/science.274.5292.1531" target="_blank">73</a>,<a href="https://doi.org/10.1021/ja026166c" target="_blank">74</a>] more efficiently and accurately. Second, a fragment that has
been observed to bind to a target may be a better starting point for design
than a higher affinity ligand whose greater molecular complexity prevents it
from presenting molecular recognition elements to the target in an optimal
manner."</i></div><div style="text-align: justify;"><span style="font-family: inherit;"><br /></span></div><div style="text-align: justify;"><span style="font-family: inherit;">To be fair, those who advocate the use of LE and thermodynamic signatures
in fragment-based design do not deny the importance of molecular complexity.
Let’s assume for the sake of argument that <a href="https://fbdd-lit.blogspot.com/2018/04/the-maximal-quality-of-molecular.html" target="_blank">interaction quality</a> can actually be defined and
is quantified by the LE value and/or the thermodynamic signature for binding of
compound to target. While these are massive assumptions, LE values and
thermodynamic signatures are still effects rather than causes.</span></div><div style="text-align: justify;"><span style="font-family: inherit;"><br /></span></div><div style="text-align: justify;"><span style="font-family: inherit;">The last option for poll was ‘God loves fragments’ and more respondents
(33.8%) voted for this than any of the first three options. I would interpret a
vote for ‘God loves fragments’ in three ways. First, the respondent doesn’t consider
any one of the first three options to be a stronger rationale for screening fragments
than the other two. Second, the respondent doesn’t consider any of the first three
options to be a valid rationale for screening fragments. Third, the respondent
considers fragment-based approaches to have been over-sold.</span></div><div style="text-align: justify;"><font face="inherit" style="font-family: inherit;"><br /></font></div><div style="text-align: justify;"><font face="inherit" style="font-family: inherit;">This is a good place to wrap up. While I remain an enthusiast for fragment-based approaches to lead discovery, I do also believe that they have been somewhat oversold. The sensitivity of LE evangelists to criticism of their metric may stem from the use of LE to sell fragment-based methods to venture capitalists and, internally, to skeptical management. A shared (and serious) deficiency in the conventional ways in which LE and thermodynamic signature are quantified is that perception changes when the arbitrary concentration, C°, that defines the standard state is changed. While there are ways in which this deficiency can be addressed for analysis, it is important that the deficiency be acknowledged if we are to move forward. Drug design is difficult and if we, as drug designers, embrace shaky science and flawed data analysis then those who fund our activities may conclude that the difficulties that we face are of our own making. </font><span style="font-family: inherit;"> </span><span style="font-family: inherit; text-align: left;"> </span></div>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com4tag:blogger.com,1999:blog-2909827059962062852.post-74362799816592228072020-07-18T23:04:00.008+01:002020-07-20T10:30:25.671+01:00SARS-CoV-2 main protease. Crowdsourcing, peptidomimetics and fragments<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-bidi-font-size: 11.0pt; mso-hansi-font-family: Calibri;"><< <a href="https://fbdd-lit.blogspot.com/2020/05/covid-19-stuff.html" target="_blank">previous</a> || next >></span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-bidi-font-family: "Times New Roman"; mso-hansi-font-family: Calibri;"><i>“Just take the ball and throw
it where you want to. Throw strikes. Home plate don’t move.”</i><o:p></o:p></span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-bidi-font-family: "Times New Roman"; mso-hansi-font-family: Calibri;">Satchel Paige (1906-1982) <o:p></o:p></span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 12pt;">The <a href="https://postera.ai/covid" target="_blank">COVID Moonshot</a> and <a href="https://opensourcecovid19data.wordpress.com/" target="_blank">OSC19</a> are
examples of what are sometimes called crowdsourced or open source approaches to
drug discovery. While I’m not particularly keen on the use of the term ‘open
source’ in this context, I have absolutely no quibble with the goal of seeking
cures and treatments for diseases that are ignored by commercial drug discovery
organizations. Open source drug discovery originated with <a href="http://www.osdd.net/" target="_blank">OSDD</a> in India and it should be noted that the approach has also been pioneered for malaria by <a href="http://opensourcemalaria.org/" target="_blank">OSM</a>. </span><span style="font-size: 12pt;"> </span><span style="font-size: 12pt;">I see
crowdsourcing primarily as a different way to organize and resource drug
discovery rather than as a radically different way to do drug discovery.</span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 12pt;">One point that’s not always appreciated by cheminformaticians,
computational chemists and drug discovery scientists in academia
is that there’s a bit <a href="https://fbdd-lit.blogspot.com/2015/02/theres-more-to-molecular-design-than.html" target="_blank">more to drug discovery than making predictions</a>. In particular, I
<a href="https://fbdd-lit.blogspot.com/2019/05/transforming-computational-drug.html" target="_blank">advise</a> those seeking to transform drug discovery to ensure that they actually
know what a drug needs to do and understand the constraints under which drug
discovery scientists work. Currently, it does not appear to be possible to
predict the effects of compounds in live humans from molecular structure with
the accuracy needed for prediction-driven design and this is the primary reason
that drug discovery is incremental in nature. A big part of drug discovery is
generation of the information needed in order to maintain progress and there
are gains to be had by doing this as efficiently as possible. Efficient
generation of information, in turn, requires a degree of coordination that may
prove difficult to achieve in a crowdsourced project.</span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 12pt;">The SARS-CoV-2 main protease (Mpro) is one of a number of
potential targets of interest in the search for COVID-19 therapies. Like the
cathepsins that are (or, at least, have been) of interest to the pharma/biotech
industry as potential targets for therapeutic intervention, Mpro is a cysteine
protease. If I’d been charged with quickly delivering an inhibitor of Mpro as a
candidate drug then I’d be taking a very close look at how the pharma/biotech
industry has pursued cysteine protease targets. <a href="https://www.drugbank.ca/drugs/DB12239" target="_blank">Balacatib</a>, <a href="https://www.drugbank.ca/drugs/DB06670" target="_blank">odanacatib</a>
(cathepsin K inhibitors) and <a href="https://www.drugbank.ca/drugs/DB15297" target="_blank">petesicatib</a> (cathepsin S inhibitor) can each be described as
a peptidomimetic with a warhead (nitrile) that forms a covalent bond reversibly
with the catalytic cysteine.</span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 12pt;">A number of peptidomimetic Mpro inhibitors have been described in the
literature and this <a href="https://cdsouthan.blogspot.com/2020/02/opening-2019-ncov-hamburger.html" target="_blank">blog post</a> by <a href="https://twitter.com/cdsouthan" target="_blank">Chris Southan</a> may be of interest. I’ve been
looking at the <a href="https://doi.org/10.1126/science.abb4489" target="_blank">published</a> inhibitors shown below in Chart 1 (which exhibit antiviral activity and have been subjected to pharmacokinetic and toxicological evaluation) and have written some <a href="https://doi.org/10.6084/m9.figshare.12611318.v1" target="_blank">notes on mapping the structure-activity relationship</a> for compounds like these. I should
stress that compounds discussed in these notes are not expected to be
dramatically more potent than the two shown in Chart 1 (in fact, I expect at least
one to be significantly less potent). Nevertheless, I would argue that assay
results for these proposed synthetic targets would inform design.</span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgl10h_VlKFAHVUWeOlLSJPjL4sC5izh-3MEjFRzg6PtJXGtQkiRxgQrRJ7Pw7aa1gWox2R_Z3-NzMQDjXqbY1fcUq2l2FjoIfIZuSI8RvSrsuCSePpmAQAmgY3-N6Fl9_obk-t4EInLIAE/s1015/covid_pept_mim_1.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="454" data-original-width="1015" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgl10h_VlKFAHVUWeOlLSJPjL4sC5izh-3MEjFRzg6PtJXGtQkiRxgQrRJ7Pw7aa1gWox2R_Z3-NzMQDjXqbY1fcUq2l2FjoIfIZuSI8RvSrsuCSePpmAQAmgY3-N6Fl9_obk-t4EInLIAE/s320/covid_pept_mim_1.jpg" width="320" /></a></div><p></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 12pt;">My assessment of these compounds is that there is significant room
for improvement and I think that it would be relatively easy to achieve a pIC50
of 8 (corresponding to an IC50 of 10 nM) using the aldehyde warhead. I’d
consider taking an aldehyde forward (there are <a href="https://doi.org/10.1016/j.bmcl.2013.09.070" target="_blank">options</a> for dosing as a prodrug)
although it really would be much better if there was also the option to exchange this warhead for the <a href="https://doi.org/10.1042/bj1240107" target="_blank">nitrile</a> (a warhead that is much-loved by industrial medicinal chemists since
it’s rugged, polar and contributes minimally to molecular size). While I’d
<a href="https://doi.org/10.1016/j.bmcl.2017.10.002" target="_blank">anticipate</a> that replacement of aldehyde with nitrile will lead to a reduction
in potency, it’s necessary to quantify the potency loss to enable the potential
of nitriles to be properly assessed. The binding mode observed for <b>1</b> is shown below in Figure 1 and it’s likely that the groove region will need to be more fully
exploited (this </span><a href="https://doi.org/10.1016/j.bmcl.2009.06.090" style="font-size: 12pt;" target="_blank">article</a><span style="font-size: 12pt;"> will give you an idea of the sort of thing I have in
mind) in order to achieve acceptable potency if the aldehyde warhead is
replaced by nitrile.</span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEii9Tpu4AV-lCDZ9nLDBl9lVhLxYhNzw0Ef4uRq7ylV7MaJ4Jzt5W42rlqwoodeQGDRPIhyMUcFiMos9BYE7U3gnySEU6vjgJOucTMFTZyHNd5RUVVrHC9hkylSsqrJE8JhizLkT6-Q6IjY/s1280/inhib_1_bind_mode.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="720" data-original-width="1280" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEii9Tpu4AV-lCDZ9nLDBl9lVhLxYhNzw0Ef4uRq7ylV7MaJ4Jzt5W42rlqwoodeQGDRPIhyMUcFiMos9BYE7U3gnySEU6vjgJOucTMFTZyHNd5RUVVrHC9hkylSsqrJE8JhizLkT6-Q6IjY/s320/inhib_1_bind_mode.jpg" width="320" /></a></div><p></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 12pt;">The </span><a href="https://postera.ai/covid" style="font-size: 12pt;" target="_blank">COVID Moonshot</a><span style="font-size: 12pt;"> project currently
appears to be in what many industrial drug discovery scientists would call the
hit-to-lead phase.</span><span style="font-size: 12pt; mso-spacerun: yes;"> </span><span style="font-size: 12pt;">In my view the principal
objective of hit-to-lead work is to create options since having options will
give the lead optimization team room to manoeuvre (you can think of hit-to-lead
work as being a bit like playing in midfield). The </span><a href="https://postera.ai/covid" style="font-size: 12pt;" target="_blank">COVID Moonshot</a><span style="font-size: 12pt;"> project is
currently focused on exploitation of hits from a </span><a href="https://doi.org/10.1101/2020.05.27.118117 " style="font-size: 12pt;" target="_blank">fragment screen</a><span style="font-size: 12pt;"> against MPro
and, while I’d question whether this approach is likely to get to a candidate
drug more quickly than the conventional structure-based design used in industry
to pursue cathepsins, it’s certainly an interesting project that I’m happy to
contribute to. It’s also worth mentioning that fragment screens have been run
against SARS-CoV-2 Nsp3 macrodomain at </span><a href="https://fraserlab.com/macrodomain/" style="font-size: 12pt;" target="_blank">UCSF</a><span style="font-size: 12pt;"> and </span><a href="https://www.diamond.ac.uk/covid-19/for-scientists/NSP3-macrodomain-structure-and-XChem.html" style="font-size: 12pt;" target="_blank">Diamond</a><span style="font-size: 12pt;"> since there are no known inhibitors for this target.</span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 12pt;">Here’s a <a href="http://practicalcheminformatics.blogspot.com/2020/05/using-structure-activity-landscape.html" target="_blank">blog post</a> </span><span style="font-size: 12pt;">by </span><a href="https://twitter.com/wpwalters" style="font-size: 12pt;" target="_blank">Pat Walters</a><span style="font-size: 12pt;"> in which he examines the
structure-activity relationships emerging for the fragment-derived inhibitors.
Specifically, he uses a metric known as the </span><a href="https://doi.org/10.1021/ci7004093" style="font-size: 12pt;" target="_blank">Structure-Activity Landscape Index</a><span style="font-size: 12pt;">
(SALI) to quantify the sensitivity of activity to structural changes. Medicinal
chemists apply the term ‘</span><a href="https://doi.org/10.1021/ci060117s" style="font-size: 12pt;" target="_blank">activity cliff</a><span style="font-size: 12pt;">’ to situations where a small change in
structure results in a large change in activity and I’ve </span><a href="https://doi.org/10.1186/s13321-019-0330-2" style="font-size: 12pt;" target="_blank">argued</a><span style="font-size: 12pt;"> that the idea
of quantifying the sensitivity of a physicochemical effect to structural modifications
goes all the way back to </span><a href="https://doi.org/10.1039/TF9383400156" style="font-size: 12pt;" target="_blank">Hammett</a><span style="font-size: 12pt;">. </span><span style="font-size: 12pt; mso-spacerun: yes;"> </span><span style="font-size: 12pt;">One point
that comes out of Pat’s post is that it’s difficult to establish structure-activity relationships for low affinity ligands with a conventional
biochemical assay. When applying fragment-based approaches in lead discovery,
there are distinct advantages to being able to measure low binding affinity (~
1 mM) since this allows fragment-based structure-activity relationships to be
explored prior to synthetic elaboration of fragment hits. As Pat notes, inadequate
solubility in assay buffer clearly places limits on the affinity that can be
reliably measured in any assay although <a href="https://www.ncbi.nlm.nih.gov/books/NBK553584/" target="_blank">interference with the readout</a> of a
biochemical assay can also lead to misleading results. This is one reason that
biophysical detection of binding using methods such as <a href="https://doi.org/10.1021/ml900002k" target="_blank">surface plasmon resonance</a> (SPR) are favored in fragment-based lead discovery. Here’s an
<a href="https://doi.org/10.1177%2F1087057109341768" target="_blank">article</a> by some of my former colleagues which shows how you can assess the impact of interference with the readout of a biochemical assay</span><span style="font-size: 12pt;"> (and even correct for it if
the effect isn’t too great).</span><span style="font-size: 12pt; mso-spacerun: yes;"> </span><span style="font-size: 12pt; mso-spacerun: yes;"> </span><span style="font-size: 12pt; mso-spacerun: yes;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 12pt;">My first </span><a href="https://doi.org/10.6084/m9.figshare.12440486.v1" style="font-size: 12pt;" target="_blank">contribution</a><span style="font-size: 12pt;"> to the </span><a href="https://postera.ai/covid" style="font-size: 12pt;" target="_blank">COVID Moonshot</a><span style="font-size: 12pt;"> project is illustrated
in Chart 2 and the fragment-derived inhibitor </span><b style="font-size: 12pt;">3</b><span style="font-size: 12pt;"> from which I started is also
featured in Pat’s post. From inspection of the crystal structure, I noticed
that the catalytic cysteine might be targeted by linking a ‘reversible’ warhead
from the amide nitrogen (</span><b style="font-size: 12pt;">4</b><span style="font-size: 12pt;"> and </span><b style="font-size: 12pt;">5</b><span style="font-size: 12pt;">). Although this might look fine on paper, the experimental data in this </span><a href="https://doi.org/10.1021/ol034344g" style="font-size: 12pt;" target="_blank">article</a><span style="font-size: 12pt;"> suggest that linking any saturated carbon to the amide nitrogen
will bias the preferred amide geometry away from </span><i style="font-size: 12pt;">trans</i><span style="font-size: 12pt;"> to </span><i style="font-size: 12pt;">cis</i><span style="font-size: 12pt;">. Provided that the
intrinsic gain in affinity resulting from linking the warhead is greater than
the cost of adopting the bound conformation, the structural modification will
lead to a net increase in affinity and the structures could be locked (here's an </span><a href="https://doi.org/10.1016/j.bmcl.2005.03.068" style="font-size: 12pt;" target="_blank">article</a><span style="font-size: 12pt;"> that shows how this can work) into the bound conformation (e.g. by forming a
ring).</span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhd0aKvrsellIyc49EvT7IA-1MlgzuRcOgMaG6XTqZUahOBdhOi-TeaXIqNVom7FFpDjZPuFbmzfKW5gMpXzuBEY99PjXOfWgYaHfe4zaaDoETrrwMpfSqr4zY46E5e7x1-LmCHwBhNjkYL/s1280/frag_derived_inhibs_1.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="720" data-original-width="1280" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhd0aKvrsellIyc49EvT7IA-1MlgzuRcOgMaG6XTqZUahOBdhOi-TeaXIqNVom7FFpDjZPuFbmzfKW5gMpXzuBEY99PjXOfWgYaHfe4zaaDoETrrwMpfSqr4zY46E5e7x1-LmCHwBhNjkYL/s320/frag_derived_inhibs_1.jpg" width="320" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div><p></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 12pt;">In addition to being accessible to a warhead linked from the amide
nitrogen of </span><b style="font-size: 12pt;">3</b><span style="font-size: 12pt;">, the catalytic cysteine is also within striking distance of the carbonyl
carbon and it would be prudent to consider the possibility that </span><b style="font-size: 12pt;">3</b><span style="font-size: 12pt;"> and its analogs can function as substrates for Mpro. There is precedent for this type of behavior
and I’ll point you toward an </span><a href="https://doi.org/10.1021/jm100488w" style="font-size: 12pt;" target="_blank">article</a><span style="font-size: 12pt;"> that notes that a series of esters identified
as cruzain inhibitors can function as substrates and more recent </span><a href="https://doi.org/10.1021/acs.jcim.9b00802" style="font-size: 12pt;" target="_blank">article</a><span style="font-size: 12pt;"> that
presents cruzain inhibitors that I’d consider to be potential substrates. A crystal
structure of the protein-ligand complex is potentially misleading in this context since the enzyme might not be catalytically active. I believe that </span><b style="font-size: 12pt;">6</b><span style="font-size: 12pt;"> could be
used to explore this possibility since the carbonyl carbon would be expected to
be more electrophilic and 3-hydroxy, 4-methylpyridine would be expected to be a
better leaving group than its 3-amino analog.</span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 12pt;">This is a good point to wrap things up. I think that <a href="https://en.wikipedia.org/wiki/Satchel_Paige" target="_blank">Satchel Paige</a> gave us some pretty good advice on how to approach drug discovery and that's yet another reason that Black Lives Matter.</span></p>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com2tag:blogger.com,1999:blog-2909827059962062852.post-65197338337666924912020-05-27T04:20:00.005+01:002020-07-20T02:15:45.148+01:00COVID-19 stuff|| <a href="https://fbdd-lit.blogspot.com/2020/07/sars-cov-2-main-protease-crowdsourcing.html" target="_blank">next</a> >><br />
<div class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: justify;"><span style="font-size: large;"><br /></span></div><font face="verdana"><div style="text-align: justify;">It’s been ages since the last blog post. I’d been thinking of marking my return with an April Fools post but this didn’t seem right given the seriousness of the COVID-19 pandemic. However, I do realize that many people only follow the blog for the April Fools posts so I’ll link them here for easy reference [<a href="https://www.blogger.com/#">2013</a> | <a href="https://www.blogger.com/#">2015</a> | <a href="https://www.blogger.com/#">2016</a> | <a href="https://www.blogger.com/#">2017</a> | <a href="https://www.blogger.com/#">2018</a> | <a href="https://www.blogger.com/#">2019</a>]. I’m currently in Trinidad so I’ll share a photo from Berwick-on-Sea, on Trinidad's north coast (and the correspondence address for a two [ <a href="https://www.blogger.com/#">K2017</a> | <a href="https://www.blogger.com/#">K2019</a> ] of my more controversial articles). </div></font>
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<font face="verdana"><div style="text-align: justify;">I should say at the outset that I’ve never previously worked in antiviral area nor tried to help fight a global pandemic. X-ray crystal structures had been <a href="https://www.blogger.com/#">published</a> for the main protease of SARS-Cov-2 back in March and these generated some <a href="https://www.blogger.com/#">discussion on twitter</a> with <a href="https://www.blogger.com/#">Martin Stoermer</a> and <a href="https://www.blogger.com/#">Ash Jogalekar</a> (who actually triggered it). The upshot of the discussion was that the discussion was that a hydrogen bond between protein and ligand appeared to be of suboptimal geometry. Martin and I wrote a short<a href="https://www.blogger.com/#"> article</a> which we uploaded to <a href="https://www.blogger.com/#">figshare</a> and Martin also did a <a href="https://www.blogger.com/#">blog post</a>. I’ve decided to post my contributions to the COVID-19 response on <a href="https://www.blogger.com/#">figshare</a> rather than cluttering <a href="https://www.blogger.com/#">ChemRxiv</a> and <a href="https://www.blogger.com/#">bioRxiv</a> with preprints that I have no intention of ever submitting to a journal. I should point out the main protease is just one of a number of SARS-CoV-2 targets that one might exploit and I’ll direct you to this helpful <a href="https://www.blogger.com/#">review</a>.</div></font><div style="text-align: justify;"><br /></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><div style="text-align: justify;"><span><font face="verdana">The two inhibitors that Martin and I wrote about are both <a href="https://www.blogger.com/#">peptidomimetics</a> and each inhibitor structure incorporates a warhead which can form a covalent bond with the catalytic cysteine sulfur. I was particularly interested in the inhibitor with the 𝞪-ketoamide warhead because the inhibition would be expected to be reversible (always a good idea to check though) and I’ll get on to why that’s significant a bit later in the post. When I examine a crystal structure, I first look for what, out of laziness, I’ll call ‘weaknesses’ in the binding mode. These ‘weaknesses’ can be local as is the case for contact between polar and non-polar regions of molecular surface or a hydrogen bond with less than ideal geometry. However, ‘weaknesses’ can also be non-local when a ligand binds in a form (protonation state, tautomer, conformer) that is relatively high in energy. Generally, ‘weaknesses’ in binding modes should always be seen as design opportunities, especially when they are non-local, and here’s an <a href="https://www.blogger.com/#">example</a> of how recognition of instability of the bound conformation was used in fragment-based design of PTP1B inhibitors</font><font size="4">.</font></span></div>
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<div style="text-align: justify;"><br /></div><font face="verdana"><div style="text-align: justify;">It can be helpful to think in terms of design themes when optimizing both hits and leads. Typically, there is insufficient data for building useful predictive models at the start of a project and the optimization process involves efficient generation of the information required for making decisions. As such optimization of both hits and leads should be seen in a Design of Experiments framework. After seeking insights from BB (my mother's dog), I wrote up some <a href="https://www.blogger.com/#">design themes</a>.</div></font><div style="text-align: justify;"><br /></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjbuoTOI3ZwKs_8IUN3yxruU0HKaG2nkcfYT01WB4HsAdV0zne4kE-R_XXGp2lgrZpBwybYyRmic7U77yh6LaL7cj6_kqhAciIhYp34KNVZy9JeX_hAdqpVli7tuZwSG_fZwvKK1c_q1wZu/s1600/bb.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="704" data-original-width="950" height="237" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjbuoTOI3ZwKs_8IUN3yxruU0HKaG2nkcfYT01WB4HsAdV0zne4kE-R_XXGp2lgrZpBwybYyRmic7U77yh6LaL7cj6_kqhAciIhYp34KNVZy9JeX_hAdqpVli7tuZwSG_fZwvKK1c_q1wZu/s320/bb.jpg" width="320" /></a></div><div style="text-align: justify;"><br /></div><font face="verdana"><div style="text-align: justify;">A crystallographic fragment screen has been run against SARS-CoV-2 and a number of electrophilic fragments were screened using mass spectroscopy. These <a href="https://www.blogger.com/#">two screens</a> serve as a launch pad for the <a href="https://www.blogger.com/#">COVID Moonshot</a> which looks interesting (although I’d suggest easing off a bit on the propaganda). One limitation of crystallographic fragment screening is that it is very difficult to measure the affinity of fragments which means that it is not generally feasible to explore the structure-activity relationships of fragments prior to structural elaboration. That said, it’s not impossible and I’ll point you to this <a href="https://www.blogger.com/#">article</a> which reports a value of -3.1 kcal/mol for the free energy of binding of pyrazole to protein kinase B that was derived from the concentration response of occupancy. The results of the crystallographic screen also have implications for the design of peptidomimetic inhibitors (in particular, the results point to pyridine as a bioisostere for the pyrrolidinone that is commonly used as a P1 substituent) and these some <a href="https://www.blogger.com/#">notes</a> may be helpful. </div></font><div style="text-align: justify;"><br /></div>
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<font face="verdana"><span>Reversibility is an issue that you definitely need to be aware of when designing compounds to inhibit cysteine proteases and these </span><a href="https://doi.org/10.6084/m9.figshare.12060627.v1"><span>notes</span></a><span> may be helpful. The issue arises because formation of a covalent bond between an electrophilic center (commonly referred to as a ‘warhead’) and the thiol of the catalytic cysteine is a commonly used tactic in inhibitor design. I'll direct you to a </span><a href="https://dx.doi.org/10.1038/nrd3410"><span>review of covalent drugs</span></a><span>, an </span><a href="http://doi.org/10.1016/j.bmcl.2013.10.003"><span>article</span></a><span> that discusses some of the things that you need consider when working with covalent inhibitors and a </span><a href="https://drug-hunter.com/2020/03/22/covalents1/" target="_blank"><span>blog post</span></a><span> on approved covalent drug mechanisms. There does appear to be a degree of prejudice [</span><a href="https://doi.org/10.1016/S1359-6446(97)01083-0"><span>R1997</span></a><span> | </span><a href="http://dx.doi.org/10.1021/jm901137j"><span>BH2010</span></a><span> | </span><a href="http://dx.doi.org/10.1038/513481a"><span>BW2014</span></a><span>] against covalent inhibition and some even appear to be unaware that covalent inhibition can be reversible.</span></font></div>
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<font face="verdana"><div style="text-align: justify;">If designing covalent cysteine protease inhibitors, I would generally favor reversible inhibition over irreversible inhibition. My primary reason for taking this view is that design of reversible inhibitors is less complex because IC50 can be interpreted in terms of affinity and you can use pretty much the same structure-based approaches as you would for non-covalent inhibitors. You can't really interpret IC50 for an irreversible inhibitor and the enzyme will be 100% inhibited if it's in contract with an irreversible inhibitor for long enough. The inhibitory activity of irreversible inhibitors is <a href="https://www.blogger.com/#">typically quantified</a> by the ratio of the inactivation rate constant (kinact) to the inhibition constant (Ki) which makes the enzyme inhibition assay more complex for irreversible inhibitors. Furthermore, you'll need to build transition state models in order to do structure-based design.</div></font><div><div style="text-align: justify;"><span style="font-size: large;"><br /></span></div>
<font face="verdana"><div style="text-align: justify;">It is possible that irreversible inhibition could lead longer duration of action although you also need to consider the consequences of slow inactivation of the enzyme. If thinking along these lines, you should look at this <a href="https://www.blogger.com/#">article</a> by Rutger Folmer. Generally, the decision to go for reversible or irreversible inhibitors is one that drug discovery teams should think through carefully and the decision should determine screening tactics (rather than vice versa). </div></font></div></div>Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com0tag:blogger.com,1999:blog-2909827059962062852.post-25239969312960316312019-05-29T03:59:00.000+01:002019-06-09T15:01:12.085+01:00Transforming computational drug discovery (but maybe not)<br />
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<i><span style="font-size: large;">"A theory has only the alternative of being right or wrong. A model has a third possibility: it may be right, but irrelevant."</span></i></div>
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<i><span style="font-size: large;">Manfred Eigen (1927 - 2019)</span></i></div>
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<span style="font-size: large;">I'll start this blog post with some unsolicited advice to those who seek to transform drug discovery. First, try to understand what a drug needs to do (as opposed to what compound quality 'experts' tell us a drug molecule should look like). Second, try to understand the problems that drug discovery scientists face and the constraints under which they have to solve them. Third, remember that many others have walked this path before and difficulties that you face in gaining acceptance for your ideas may be more a consequence of extravagant claims made previously by others than of a fundamentally Luddite nature of those whom you seek to influence. As has become a habit, I'll include some photos to break the text up a bit and the ones in this post are from Armenia.</span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEikEgPfcZcqyAC738XQ-fBBBRX-bOMFU4Ipx61pxDa-NksQ_NBpCqocLbogLCkym5DU1M7AapHxA9qoF9hTELsrxK9QEXVVGzexaM-GRQCBPrF5_OmuPyeMOyZs-zJ1ANcZIvvguBH18CjO/s1600/IMGP5274.JPG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1072" data-original-width="1600" height="214" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEikEgPfcZcqyAC738XQ-fBBBRX-bOMFU4Ipx61pxDa-NksQ_NBpCqocLbogLCkym5DU1M7AapHxA9qoF9hTELsrxK9QEXVVGzexaM-GRQCBPrF5_OmuPyeMOyZs-zJ1ANcZIvvguBH18CjO/s320/IMGP5274.JPG" width="320" /></a></div>
Mount Ararat taken from the Cascade in Yerevan. I stayed at the excellent <a href="http://cascadehotel.am/" target="_blank">Cascade Hotel</a> which is a two minute walk from the bottom of the Cascade.<br />
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<span style="font-size: large;">Here are a couple of slides from my recent <a href="https://doi.org/10.6084/m9.figshare.7963682.v1" target="_blank">talk</a> at Maynooth University that may be helpful to machine learning evangelists, AI visionaries and computational chemists who may lack familiarity with drug design. The introductions to articles on <a href="https://doi.org/10.1186/s13321-019-0330-2" target="_blank">ligand efficiency</a> and <a href="https://doi.org/10.1007/s10822-012-9631-5" target="_blank">correlation inflation</a> might also be relevant.</span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjGh9khxH2Xc-hs_ZmeXbfC7ixYMqiwm9b1PQyuUVPyKVh6UcYBHa7u2STpYCI47Ubop44cbRjy90-K5k8V2vEEqsz2bf3OUYVzN2ijzfQiApKI9pXtNWcjUA0OrVhkCoRfjeEdYhl11m5G/s1600/design_objectives.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="681" data-original-width="926" height="235" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjGh9khxH2Xc-hs_ZmeXbfC7ixYMqiwm9b1PQyuUVPyKVh6UcYBHa7u2STpYCI47Ubop44cbRjy90-K5k8V2vEEqsz2bf3OUYVzN2ijzfQiApKI9pXtNWcjUA0OrVhkCoRfjeEdYhl11m5G/s320/design_objectives.jpg" width="320" /></a></div>
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<span style="text-align: center;">Defining controllability of exposure (drug concentration) as a design objective is extremely difficult while unbound intracellular drug concentration is not generally measurable in vivo.</span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhwZG6ID7Q7Coh_I9JpiL2xwG8l4cdP22dQQNSWG3hoy4I1NTAIVql4rxKGvxSjmA4X9CGNDoY3VOWPqdOqBqdHamQ8OWS_Fn0oaJJgazVcaZ6Yw99U3-fn63KHQtne7dwAsGG6NBFa98Ot/s1600/characteristics_of_design.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="591" data-original-width="923" height="204" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhwZG6ID7Q7Coh_I9JpiL2xwG8l4cdP22dQQNSWG3hoy4I1NTAIVql4rxKGvxSjmA4X9CGNDoY3VOWPqdOqBqdHamQ8OWS_Fn0oaJJgazVcaZ6Yw99U3-fn63KHQtne7dwAsGG6NBFa98Ot/s320/characteristics_of_design.jpg" width="320" /></a></div>
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Computational chemists and machine learning evangelists commonly make (at least) one of two mistakes when seeking to make impact on drug design. First, they see design purely as an exercise in prediction. Second, they are unaware of the importance of exposure as the driver of drug action. I believe that we'll need to change (at least) one of these characteristics of drug design if we are to achieve genuine transformation.</div>
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<span style="font-size: large;"><span style="text-align: center;">In this post, I'm going to take a look at an <a href="http://dx.doi.org/10.1021/acsmedchemlett.8b00437" target="_blank">article</a> in ACS Medchem Letters entitled 'Transforming Computational Drug Discovery with Machine Learning and AI'. The article opens with a Pablo Picasso quote although I'd argue that the observation made by Manfred Eigen at the beginning of the blog post would be way more appropriate. The World Economic Forum (WEF) is quoted as referring to<i> "</i></span><i>to the combination of big data and AI as both the fourth
paradigm of science and the fourth industrial revolution"</i>. The WEF reference reminded me of an <a href="https://doi.org/10.1021/acsmedchemlett.5b00157" target="_blank">article</a> (published in the same journal and reviewed in this <a href="https://fbdd-lit.blogspot.com/2016/02/the-boys-who-cried-wolf.html" target="_blank">post</a>) that invoked <i>"views obtained from senior medicinal chemistry leaders"</i>. However, I shouldn't knock the WEF reference too much since we observed in the <a href="https://doi.org/10.1007/s10822-012-9631-5" target="_blank">correlation inflation article</a> that <i>"lipophilicity is to medicinal chemists what interest rates are to central bankers"</i>.</span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjxgXQdZjL4u3JxYVi6YKJXnd9R4Gssexgnm_abq5-0clJoogKW5wkO6Ny69HVm0K0duihA2BO9Zn__IR4F8N22O5sIIBUUZRb_YOxgw8VW1GMnhJlx6oXVGFHJA_UbF5u4aM0fKwFPPgGZ/s1600/IMGP5417.JPG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1072" data-original-width="1600" height="214" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjxgXQdZjL4u3JxYVi6YKJXnd9R4Gssexgnm_abq5-0clJoogKW5wkO6Ny69HVm0K0duihA2BO9Zn__IR4F8N22O5sIIBUUZRb_YOxgw8VW1GMnhJlx6oXVGFHJA_UbF5u4aM0fKwFPPgGZ/s320/IMGP5417.JPG" width="320" /></a></div>
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The Temple of Garni is the only Pagan temple in Armenia and is sited next to a deep gorge (about 20 metres behind me). I took a keen interest in the potential photo opportunities presented by two Russian ladies who had climbed the safety barrier and were enthusiastically shooting selfies...</div>
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<span style="font-size: large;">Much of the focus of the <a href="http://dx.doi.org/10.1021/acsmedchemlett.8b00437" target="_blank">article</a> is on the ANI-1x potential (and related potentials), <a href="https://doi.org/10.1063/1.5023802" target="_blank">developed</a> by the authors for calculation of molecular energies. These potentials were derived by using a deep neural network to fit calculated (<a href="https://en.wikipedia.org/wiki/Density_functional_theory" target="_blank">DFT</a>) molecular energies to calculated molecular geometry descriptors. This certainly looks like an interesting and innovative approach to calculating energies of molecular structures. It's also worth mentioning the <a href="https://openforcefield.org/" target="_blank">Open Force Field Initiative</a> since they too are doing some cool stuff. I'll certainly be watching to see how it all turns out.</span></div>
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<span style="font-size: large;">One key question concerns accuracy of <a href="https://en.wikipedia.org/wiki/Density_functional_theory" target="_blank">DFT</a> energies. The authors talk about a "zoo" of force fields but I'm guessing the diversity of <a href="https://en.wikipedia.org/wiki/Density_functional_theory" target="_blank">DFT</a> protocols used by computational chemists may be even greater than the diversity of force fields (here's a useful <a href="https://doi.org/10.1080/00268976.2017.1333644" target="_blank">review</a>). Viewing the <a href="https://en.wikipedia.org/wiki/Density_functional_theory" target="_blank">DFT</a> field as an outsider, I don't see a clear consensus as to the most appropriate <a href="https://en.wikipedia.org/wiki/Density_functional_theory" target="_blank">DFT</a> protocol for calculating molecular energy and the lack of consensus appears to be even more marked when considering interactions between molecules. It's also worth remembering that the <a href="https://en.wikipedia.org/wiki/Density_functional_theory" target="_blank">DFT</a> methods are themselves parameterized. </span></div>
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<span style="font-size: large;">Potentials such as those described by the authors are examples of what drug discovery scientists would call a quantitative structure-property relationship (QSPR). When assessing whether or not a model constitutes AI in the context of drug discovery, I would suggest consideration of the nature of the model rather than the nature of the algorithm used to build the model. The fitting of <a href="https://en.wikipedia.org/wiki/Density_functional_theory" target="_blank">DFT</a> energies to molecular descriptors that the authors describe is considerably more sophisticated than would be the case for a traditional QSPR. However, there are a number of things that you need to keep in mind when fitting measured or calculated properties to descriptors regardless of the sophistication of the fitting procedure. This <a href="http://fbdd-lit.blogspot.com/2018/09/on-nature-of-qsar.html" target="_blank">post</a> on QSAR as well as the recent exchange ( <a href="http://fbdd-lit.blogspot.com/2019/01/thoughts-on-ai-in-drug-discovery.html" target="_blank">1</a> | <a href="http://practicalcheminformatics.blogspot.com/2019/01/my-response-to-peter-kennys-comments-on.html" target="_blank">2</a> | <a href="https://fbdd-lit.blogspot.com/2019/01/response-to-pat-walters-on-ml-in-drug.html" target="_blank">3</a> ) between <a href="http://practicalcheminformatics.blogspot.com/" target="_blank">Pat Walters</a> and me may be informative. First, over-fitting is always a concern and validation procedures may make an optimistic assessment of model quality when the space spanned by descriptors is unevenly covered. Second, it is difficult to build stable and transferable models if there are relationships between descriptors (the traditional way to address this problem is to first perform principal component analysis which assumes that the relationships between descriptors is linear). Third, it is necessary to account for numbers of adjustable parameters in models in an appropriate manner if claiming that one model has outperformed another.</span></div>
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Armenia appeared to be awash with cherry blossoms when I visited in April. This photo was taken at Tatev Monastery which can be accessed by cable car.</div>
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<span style="font-size: large;">The authors have described what looks to be a promising approach to calculation of molecular energies. Is it AI in the context of drug discovery? I would say, "no, or at least no more so than the QSPR and QSAR models that have been around for decades". Will it transform computational drug discovery? I would say, "probably not". Now I realize that you're thinking that I'm a complete Luddite (especially given my <a href="https://doi.org/10.1186/s13321-019-0330-2" target="_blank">blinkered skepticism</a> of the drug design metrics introduced by Pharma's Finest Minds) but I can legitimately claim to have <a href="https://doi.org/10.1016/j.bmcl.2005.03.068" target="_blank">exploited</a> knowledge of ligand conformational energy in a real discovery project. I say "probably not" simply because drug designers have been able to calculate molecular energy for many years although I concede that the SOSOF (same old shit only faster) label would be unfair. That said, I would expect faster, more accurate and more widely applicable methods to calculate molecular energy to prove very useful in computational drug discovery. However, utility is a necessary, but not sufficient, condition for transformation.</span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEik-tXHsrD30eA3CbEnnnSfTX56Pbf5cym-LtftJz028xJ1qNFFNx3CzHrUgETiKAlUHEhTWFoZRqEPHbC3lQld-GowhWkuA3cKeVkRcS66iIzz9cscdUirwOAFiySDv03LRI5UVIh_nq51/s1600/IMGP5457.JPG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1600" data-original-width="1072" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEik-tXHsrD30eA3CbEnnnSfTX56Pbf5cym-LtftJz028xJ1qNFFNx3CzHrUgETiKAlUHEhTWFoZRqEPHbC3lQld-GowhWkuA3cKeVkRcS66iIzz9cscdUirwOAFiySDv03LRI5UVIh_nq51/s320/IMGP5457.JPG" width="214" /></a></div>
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Geghard Monastery was carved from the rock</div>
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<span style="font-size: large;">So I'll finish with some advice for those who manage (or, if you prefer, lead) drug discovery. Suppose that you've got some folk trying to sell you an AI-based system for drug design. Start by getting them to articulate their understanding of the problems that you face. If they don't understand your problems then why should you believe their solutions? Look them in the eye when you say "unbound intracellular concentration" to see if you can detect signs of glazing over. In particular, be wary of crude scare tactics such as the suggestion that those medicinal chemists that don't use AI will lose their jobs to medicinal chemists who do use AI. If the terrors of being left behind by the Fourth Industrial Revolution are invoked then consider deploying the conference room furniture that you bought on eBay from Ernst Stavro Blofeld Associates.</span></div>
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Selfie with MiG-21 (apparently Artem's favorite) at the Mikoyan Brothers <a href="http://www.comtourist.com/history/mikoyan-brothers-museum/" target="_blank">Museum</a> in Sanahin where the brothers grew up. Anastas was even more famous than his brother and played a key role in defusing the Cuban Missile Crisis.</div>
Peter Kennyhttp://www.blogger.com/profile/12180360326821860667noreply@blogger.com2