Leadeth me unto Truth and delivereth me from those who have already found it

A theory has only the alternative of being true or false.

A model has a third possibility: it may be true, but irrelevant.

With apologies to Manfred Eigen (1927 - 2019)

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[This post was updated on 25-Jun-2024]

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.

Encountering words like “truth” and “beauty” (here's a good example) 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 FM2024 (Structure is beauty, but not always truth) that was recently published in Cell (and has already been reviewed 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 FM2024:

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.

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 NoLE, 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.

The remainder of the post follows the FM2024 section headings.    

A structure is a model, not experimental reality

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 article, cited by FM2024, on why we still need experimentally-determined structures). 

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. 

Representing wiggling and jiggling is hard

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.

The authors state:

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 [7] and residence times [8] of bound ligands.

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 F2018 study and this blog post).

In vitro can be deceiving

The authors identify “addressing the misleading nature of in vitro structures” as a fundamental challenge and they state:

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.

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 HC2017) 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".  

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.  

Drugs mingle with many different receptors

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 article that I recommend to everybody working drug discovery). I argue in NoLE 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.  

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).

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).

I've inserted some comments (italicised in red) into the following quoted text: 

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. [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 (B2012 | J2020) and a link to some information about in vitro safety pharmacology profiling panels from Eurofins. Update 25-Jun-2024: recent review on secondary pharmacology.] 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 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.] 

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; [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.] they can block or be substrates for all manner of pumps and transporters, changing their distribution through the body; [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 H2012 and R2015). 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 T2021 and H2020) in drug discovery. Update 25-Jun-2024: I thank Mohamed Diwan M. AbdulHameed (google scholar profile) for making me aware that inhibition of  bile salt export pump (BESP) is considered a risk factor for drug-induced liver injury (DILI). Here's a relevant article.] 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. [Inhibition of CYPs is generally considered undesirable from the safety perspective because of the potential for drug-drug interactions (see H2020). That said, the CYP3A inhibitor ritonavir (see CG2003) is used in the COVID-19 treatment Paxlovid to slow metabolism of SARS-CoV-2 main protease nirmatrelvir.]  They are themselves substrates for P450s and other metabolizing enzymes and, once altered, can no longer carry out their assigned, life-saving function. [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.] 

Taken together, we refer to these DMPK-related proteins, somewhat tongue-in-cheek, as the “avoidome” (Figure 2). [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.]  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. [It is not clear whether this assertion is based on experimental observations.] 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.  

We believe the structural biology community should “embrace the avoidome” with the same enthusiasm that structure-based design has been applied to intended targets. [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 Target 2035 initiative, 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.] 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. [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 link 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.]  Crucially, a detailed understanding of the ways that drugs engage with avoidome targets would significantly expedite drug discovery.  [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.] This information holds the potential to achieve a profound impact on the discovery of new and enhanced medicines.

Conclusion

The authors assert: 

In drug discovery, truth is a molecule that transforms the practice of medicine. [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.]

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 FM2024 will be useful for readers of the article while providing helpful feedback for the authors and for the Editors of Cell. 

On quality criteria for covalent and degrader probes

I’ll be taking a look at H2023 (Expanding Chemical Probe Space: Quality Criteria for Covalent and Degrader Probes) in this post and this article has also been discussed In The Pipeline. 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 H2023 Perspective is intended to jumpstart:

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.

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 H2023 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.

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.

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 SR2019 (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 review of S2023 (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.

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.

The proposed quality criteria for covalently acting small-molecule probes are given in Figure 2 of H2023 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.  

I’ll start with Section 2.1 (Criteria for Assessing Potency of Covalent Probes) and my comments are italicised in red. 

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. (21) Best practice is to use k.inact (the rate of inactivation) over K.i (the affinity for the target) values instead. (22) [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 review of S2023) and I recommend this tweetorial from Keith Hornberger.]

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). [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.]  Carefully designed biochemical assays used in determining IC50 values can be well-suited as surrogates for k.inact/K.i measurements. (24) [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.]

2.2. Criteria for Assessing Covalent Probe Selectivity

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. [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 THZ1 probe in my review of the S2023 study may be relevant.]

2.3. Chemical Matter Criteria for Covalent Probes

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. [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).] 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. [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.]

3.3. Chemical Matter Criteria for Degrader Probes

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). (78) [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 ChEMBL 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 K2017 the PAINS substructure model introduced in BH2010 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.] 

This is a good point to wrap up my contribution to the important scientific discussion that H2023 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.

On the misuse of chemical probes

It’s now time to get back to chemical probes and I’ll be taking a look at S2023 (Systematic literature review reveals suboptimal use of chemical probes in cell-based biomedical research) which has already been reviewed in blog posts from Practical Fragments, In The Pipeline and the Institute of Cancer Research. Readers of this blog are aware that PAINS filters usually crop up in posts on chemical probes but there are other things that I want to discuss and, in any case, references to PAINS in S2023 are minimal. Nevertheless, I’ll still stress that a substructural match of a chemical probe with a PAINS filter 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.

The authors of S2023 claim to have revealed “suboptimal use of chemical probes in cell-based biomedical research” and I’ll start by taking a look at the abstract (my annotations are italicised in red):

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. [A study such as S2023 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.] 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 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.] These findings indicate that the best practice with chemical probes is yet to be implemented in biomedical research. [My view is that the best practice with chemical probes is yet to be defined.] 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. [The authors of S2023 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 criteria 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".]

The following sentence does set off a few warning bells for me:

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.

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 S2023 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 S2023 given that uncertainty in intracellular concentration is an issue when working with chemical probes (as well as in PK-PD modelling).  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 “findings indicate that the best practice with chemical probes is yet to be implemented in biomedical research”.

Let’s take a look at the criteria by which the authors of S2023 have assessed the use of chemical probes. They assert that “Even the most selective chemical probe will become non-selective if used at a high concentration” 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 review 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.

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 Chemical Probes Portal. It is significant that the recommendations from different groups of chemical probe experts with respect to the maximum concentration at which UNC1999 should be used differ by almost an order of magnitude:

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.

One of the eight chemical probes featured in S2023 is THZ1 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 article). 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 THZ1 was included as one of the eight chemical probes covered by the S2023 study so I checked to see what the Chemical Probes Portal had to say about THZ1 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 “time-dependent binding established supporting covalent mechanism”.  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 THZ1 is summarized as “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”. The results from the assays used to measure on-target and off-target potency for THZ1 do not appear to be directly comparable.

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 S2023 authors assert that an additional orthogonal target-engaging probe can be substituted for a matched target-inactive control compound but this appears to contradict criteria for classical modulators given by the Chemical Probes Portal.