Saturday, 28 January 2023

More approaches to design of covalent inhibitors of SARS-CoV-2 main protease

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I’ll pick up from the previous post 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 Covid Moonshot submissions whenever possible. 

I’ll kick the post off with 14 (Chart 5) which replaces a methylene in the lactam ring of 10 (Chart 4 in previous post) 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.
Designs 15 and 16 (also in Chart 5) link the nitrile warhead from nitrogen rather than carbon and this structural transformation eliminates a chiral centre in each of 10 and 11 (Chart 4 in previous post) and may be beneficial for affinity (see discussion around 8 and 9 in Chart 3 of the previous post). 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 15 and 16 to adopt axial orientations. I’d anticipate that the nitrile warhead will be directed toward the catalytic cysteine for 15 but away from the catalytic cysteine for 16 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.

I’ll finish with couple of designs based on aromatic sulfur that are shown in Chart 6. Design 17 was originally submitted by Vladas Oleinikovas although I’ll also link my resubmission 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 chalcogen-bond 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 article). I’ve also included 18 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.

Wednesday, 25 January 2023

Assessment of chemical probes: response to Practical Fragments

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I had originally intended to look at permeability in this post but I do need to respond to Dan Erlanson’s post at Practical Fragments. I see Dan’s position (“everything is an artifact until proven otherwise”) as actually very similar to my position (“chemical probes will have to satisfy the same set of acceptability criteria whether or not they trigger structural alerts”) 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 comment on the “The Ecstasy and Agony of Assay Interference Compounds” editorial should make this position clear. 

Thursday, 19 January 2023

Some approaches to design of covalent inhibitors of SARS-CoV-2 main protease

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I last posted 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 paxlovid has been approved for use as a Covid-19 treatment (Derek describes his experiences taking paxlovid in this post).  The active ingredient of paxlovid is the SARS-CoV-2 main protease inhibitor nirmatrelvir and the ritonavir 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 notes that I whipped up as a contribution to the Covid Moonshot.

Nirmatrelvir (1) is shown in Chart 1 along with SARS-CoV-2 main protease inhibitors from the Covid Moonshot (2), a group of (mainly) Sweden-based academic researchers (3) and Yale University (4).  Nirmatrelvir incorporates a nitrile group that forms a covalent bond with the catalytic cysteine and the other inhibitors bind non-covalently to the target. The first example 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 review on the nitrile group in covalent inhibitor design and this comparative study of electrophilic warheads may also be of interest.

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 course of paxlovid treatment 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 relevant article).

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. 

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 nirmatrelvir because it needs be co-dosed with ritonavir (to inhibit metabolic enzymes) and this has implications for patients taking other medications. Nirmatrelvir 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. 

Details of resistance to nirmatrelvir (P2022 | Z2022) 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.

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 screen covalent fragments 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.   

I generally favor reversible binding when targeting catalytic cysteines as discussed in these notes and this article. 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 article which shows how isosteric replacement (with an alkyne) of the nitrile warhead of the reversible cathepsin K inhibitor odanacatib results in an irreversible inhibitor (the article is particularly relevant if you’re interested in chemical probes for cysteine proteases).

I contributed some designs for reversible covalent inhibitors to the Covid Moonshot 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 Covid Moonshot 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 nirmatrelvir 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 article). One of the cool things about the way the Covid Moonshot was set up is that I can link designs as they were originally submitted (often with a detailed rationale and proposed binding mode).

The most direct way to link a nitrile to the ‘3-aminopyridine-like’ scaffold is with methylene (5, Chart 2) but there is a problem with this approach because substituting anilides (and their aza-analogs) on nitrogen with sp3 carbon inverts the cis/trans geometrical preference of the anilides (I discussed the design implications of this in these notes).  This implies that binding of 5 to the target is expected to incur a conformational energy penalty and it is significant that N-methylation of 6 results in a large reduction in potency. Although 5 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.

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 8, which is related to azapeptide nitriles, as a design.  Azapeptide nitriles [L2008 | Y2012 | L2019 | B2022] are typically more potent 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 relevant crystal structure. The acyclic hydrazine and cyanamide substructures in 8 trigger structural alerts although there are approved drugs that incorporate acyclic hydrazine (atazanavir | bumadizone | gliclazidegoserelin | isocarboxazid | isoniazid) and N-cyano (cimetidine) substructures. The basis for these structural alerts is obscure and it’s worth noting that 8 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 comment 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 8, and all NHs were capped (see 9).

An alternative tactic to counter inversion of the cis/trans geometrical preference is to lock the conformation with a ring and designs 10 and 11 (Chart 4) can be seen as 'hybrids' of 5 with 12 and 13 respectively (in fragment-based design, hybridization is usually referred to as fragment merging). The effect of the conformational lock can be clearly seen since 12 and 13 are essentially equipotent with 6 (the primary reason for proposing 12 and 13 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 10 will be less prone to epimerization than 11. Although I'm unaware of nitriles being deployed on cyclic amine substructures for cysteine protease inhibition, the structures of the DPP-4 inhibitors saxagliptin and vildagliptin are relevant.

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.

Monday, 2 January 2023

Assessment of chemical probes

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I’ll be taking a look at some of the criteria, specifically structural alerts, by which chemical probes are assessed and here’s the link 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.      

I’ll start by commenting on “A conversation on using chemical probes to study protein function in cells and organisms” that was recently published 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:

“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 [4]. 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.”

In 2017 a number of ACS journals simultaneously published “The Ecstasy and Agony of Assay Interference Compounds” editorial and I believe that a number of points raised in a comment on this editorial are still relevant to dealing with nuisance compounds. In the comment, 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. 

It is asserted in the conversation that there are “software filters or algorithms available that should be used routinely to identify any risk of such chemical promiscuity”. 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, PAINS filters 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 PAINS filter model to prediction of frequent-hitter behavior in AlphaScreen assays. It is asserted in the conversation 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 editorial, that HTS hits that don’t trigger structural alerts can be checked less thoroughly than hits that do trigger structural alerts.

The Information Centre of the Chemical Probes Portal includes a “Toxicophores and PAINS Alerts” section in which it is correctly stated that “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”. The “Toxicophores and PAINS Alerts” section might work better as a “Structural Alerts” section and the toxicophores citation appears to be incorrect (reference 10 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 Aggregator Advisor (link to article)  and BadApple (here's link to article)  

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 PAINS and colloidal aggregators are sometimes treated as separate categories of bad actor, as is the case in the ACS assay interference editorial, and the criteria for labelling compounds as PAINS are ambiguous.  It would certainly be useful to include some reviews on assay interference, such as this one, in a “Nuisance Compounds” section. I quite like this article 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.

This is a good point at which to wrap up and it’s worth stressing that the essence of the criticism of PAINS filters is simply that the rhetoric is not supported by the data. Those like me who are critical of the way that PAINS filters 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 Flush software) 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 article 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.

I’ll leave it there for now. In the next post on chemical probes, I’ll be taking a look at permeability.