19 February 2024

Hot spots real and imagined

Practical Fragments has written several times about “hot spots”: regions on proteins where small molecules and fragments readily bind. Knowing whether your target protein has a hot spot can help you decide whether to pursue the target in the first place. A variety of computational approaches have been developed for finding hot spots, most of which start with a crystallographically determined structure. In a new J. Chem. Inf. Mod. paper, Sandor Vajda and collaborators at Boston University and Stony Brook University ask whether computational models of proteins can also be used for one of the more popular methods, FTMap.
 
The researchers started with a set of 62 proteins, each of which had a published crystal structure bound to a fragment (MW < 200 Da) as well as to a larger molecule. The predicted structures of these proteins were then downloaded from the AlphaFold2 (AF2) site, and these models were truncated to correspond to the residues seen in the crystal structures to facilitate comparisons. The computational models were quite similar to the experimental models, particularly when comparing the positions of the peptide backbone atoms which define the overall shape of the proteins.
 
Next, the researchers applied the program FTMap, which computationally explores the surface of proteins using a set of 16 very small probes such as ethanol. Hot spots are regions where lots of probes bind, and the “hotness” of these spots correlates with the number of bound probes. FTMap assessed hotness on the AF2 structures and the crystallographicaly determined structures. (Before running FTMap, the bound ligands in the crystal structures were computationally removed.) Additionally, the researchers ran FTMap on unliganded crystal structures for the 47 proteins where these had been reported.
 
FTMap was broadly successful at finding the hotspots defined by bound fragments, succeeding 77% of the time starting with either the fragment-bound or unliganded structures and 71% starting with the AF2 models. Implementing stricter criteria (demanding the experimental fragment binding site be the top hot spot, for example) reduced the success to 56% for the crystallographic starting points and 47% for the AF2 models.
 
The paper discusses several examples in detail, in particular the two where the AF2 models were most different from the experimental models. Both of these were large, multidomain proteins. When AF2 models of just the ligand-binding domains were used, the models were significantly improved. This seems to be a generally useful hack: generating truncated AF2 models for other proteins also improved the performance of FTMap.
 
The utility of AF2 models for docking has been the subject of some debate, with some arguing that even though the overall protein folds may be accurate, local side chain conformations may be wrong, and a single side chain rotation may make the difference between ligand binding or not. This paper suggests that hot spots are not too sensitive to these subtleties, and that AF2 models can be used for finding hot spots.

12 February 2024

Fragment screening across the proteome, noncovalently

Last week we discussed methodological improvements to industrialize covalent fragment screening across the proteome. While I’m a huge fan of covalent binders, their noncovalent counterparts are the vanilla ice cream of FBLD: also tasty and much more common. Back in 2017 we described how “fully functionalized fragments,” or FFFs, could be used to screen noncovalent fragments in cells. A new paper in Nat. Chem. Biol. by Christopher Parker and collaborators at Scripps and BMS further optimizes the approach.
 
FFFs contain, in addition to the variable fragment, a photoreactive group (often a diazirine) and an alkyne tag. When exposed to light the photoreactive group can react with nearby proteins and the alkyne tag can be used to fish out the proteins. In the new paper the researchers started with a dozen FFFs.
 
One challenge, which we discussed in 2021, is that the FFFs may react with many sites on a given protein. During analysis, a protein is typically digested into peptides for mass spectrometry. If a FFF reacts at several sites on a peptide the resulting spectra will be “chimeric” and more difficult to characterize.
 
The researchers developed methods to take these chimeric spectra into account when searching for sites of modification. The approach, called Dizco (for diazirine probe-labeled peptide discoverer) can identify three times as many peptides as standard approaches, as well as more detailed information on sites of modifications. 
 
Two pairs of FFF probes consisted of enantiomers, and these showed differential labeling across the proteome, consistent with specific molecular recognition. The researchers also confirmed binding of a few FFF probes to several proteins using a cellular thermal shift assay (CETSA).
 
In all, the probes modified 3603 peptides on 1669 proteins. The sites of modification were then mapped onto predicted or modeled three dimensional structures of the proteins. Importantly, and consistent with the 2017 work, most of the labeled sites were near predicted pockets. The researchers confirmed this for four proteins by showing that FFF probe binding could be competed by adding ligands known to bind to the pockets.
 
Next, the researchers docked (using AutoDock) their FFF probes onto 175 proteins (108 from structures in the Protein Data Bank and 67 from AlphaFold structures). They found that the docking experiments recapitulated the experimental data, and in fact often placed the diazirine tag near the protein residues found to react. Strikingly, and in another step forward for in silico approaches, docking against structures from AlphaFold was nearly as effective as those from the protein data bank.
 
As the researchers conclude, “we identified many binding pockets that have no reported ligands… these probes may serve as leads for further optimization.” It will be fun to see how far they go.

05 February 2024

Fragment screening across the proteome, industrialized

Last week we discussed covalent fragment screens against isolated enzymes, which can be very effective. But screening in cells or cell lysates preserves proteins in a more physiological environment and allows many proteins across the proteome to be screened simultaneously. In 2016 we wrote about covalent screens in human cell lysates which identified fragment hits for 758 cysteine residues in 637 proteins. Mass spectrometry techniques have improved since then in terms of both speed and sensitivity, as illustrated in a new Cell Chem. Biol. paper from Steve Gygi, Qing Yu, and collaborators at Harvard Medical School and Biogen. (Disclosure: Steve Gygi is on the Scientific Advisory Board of my current company, Frontier Medicines.)
 
The approach is called TMT-ABPP, or tandem mass tag activity-based protein profiling, and it involves multiple improvements to previous methods, some of which Steve discussed at the Discovery on Target meeting last year. Covalent fragments are added separately to cell lysate aliquots, after which a desthiobiotin iodacetamide (DBIA) probe is introduced. If a given site on a protein has reacted with a fragment, it will not be available to react with the DBIA probe.
 
Next, proteins are digested to peptides and labeled with TMT (tandem mass tag) reagents, which allow multiple samples (18 in this case, either individual fragments or DMSO-only controls) to be combined for simultaneous analysis. Peptides functionalized with the DBIA probe are captured on streptavidin resin while those that had previously reacted with a covalent fragment will not stick to the resin and be lost. Peptides eluted from the resin are then analyzed by mass spectrometry. The “competition ratio” between treated and untreated lysate gives a measure of how strongly a given site on a given protein is labeled by a fragment.
 
Multiple other tweaks, such as capturing proteins using magnetic beads and using a special type of mass-spectrometry (high-field asymmetric waveform ion mobility spectrometry, or FAIMS), further streamline the process to a 96-well plate format, with each well containing a mere 10-20 µg of cell lysate, as much as 100-fold less than earlier approaches.
 
The researchers benchmarked TMT-ABPP using three reactive “scout fragments,” including compound 1 from last week’s post. Collectively they identified 6813 cysteine residues hit by one or more of the scouts.
 
To demonstrate throughput, the researchers next screened 192 fragments, a third of which were acrylamides while the rest were chloroacetamides. Even with two controls for every 16 samples, this only required 12 injections on a mass spectrometer and resulted in hits against 38,450 cysteines, about 50-fold more than the 2016 paper. Proteins that were more highly expressed were better represented, as were proteins with known reactive cysteine residues, such as thioredoxins. Surprisingly though, surface-exposed cysteine residues were only slightly enriched over more buried cysteines.
 
The researchers also applied TMT-ABPP to five well-characterized covalent molecules, including the mutant KRASG12C inhibitor ARS-1620, which we wrote about here. In addition to the G12C site of KRAS, several other proteins were also liganded, including adenosine kinase (ADK). The researchers confirmed that ARS-1620 inhibited ADK in an enzymatic assay.
 
As the researchers note, “proteome-wide profiling of thousands of compounds remains a formidable challenge, both technically and financially.” This paper reveals how to significantly reduce the costs. By using such approaches, it is possible to build a catalog of fragment ligands for thousands of proteins. Doing so with a well-curated library could enable rapid fragment-to-lead campaigns.

29 January 2024

Covalent fragments vs a SARS-CoV-2 helicase

Last week we wrote about the difficulties of trying to understand even well-characterized covalent inhibitors of well-characterized targets. Most projects have far less information, as illustrated in a recent paper in J. Am. Chem. Soc. by Ekaterina Vinogradova, Tarun Kapoor, and collaborators at Rockefeller University and Sanders Tri-Institutional Therapeutics Discovery Institute, who report the first inhibitors of a particular SARS-CoV-2 enzyme.
 
The researchers were interested in helicases, enzymes that unwind DNA, RNA, or both. To do so, helicases cycle between “open” and “closed” forms, with conformational changes of as much as 15 Å. That dynamism complicates structure-based drug design, and many screens have yielded false positives. An irreversible covalent inhibitor that remained bound to the enzyme through its gyrations would potentially be easier to optimize.
 
The protein nsp13 from SARS-CoV-2 is essential for viral replication and thus an attractive drug target. The researchers started by testing previously reported and reactive “scout fragments” in a functional assay. Compound 1 inhibited the enzyme, and mass-spectrometry (MS) assays revealed that it modified three sites on the protein. Although multiple modifications are not desirable, the enzyme does contain 26 cysteine residues, so it could be worse. Peptide mapping and mutagenesis experiments revealed that modification of cysteine 556 (C556) is responsible for the inhibitory activity of compound 1.
 
A series of analogs culminated in compound 3b, which had low micromolar activity after a four hour incubation and also seemed more selective than compound 1, with less modification of other cysteine residues. The enantiomer of compound 3b was at least 6-fold less potent, suggesting molecular recognition rather than simple reactivity. In addition to nsp13, the researchers examined two mammalian helicases with disease relevance, WRN and BLM, and found that compound 3b was modestly selective for nsp13. (The researchers find different inhibitors for these two enzymes, though these are weaker and not as extensively characterized as those for nsp13.)

Cysteine 556 is not in the ATP-binding site and does not seem to be involved with RNA binding, and the researchers suggest that compound 3b may act allosterically. It seems to be highly conserved too, which might mean mutational resistance is less likely to evolve.
 
As the researchers acknowledge, compound 3b contains a chloroacetamide warhead, which is likely too reactive and unstable to move forward into in vivo studies, let alone the clinic. Also, had I reviewed the manuscript I would have requested the researchers to provide kinact/KI values rather than merely IC50 values; a rough calculation using the methodology in this paper suggests a modest 10 M-1s-1 for compound 3b. That said, the discovery that liganding C556 inhibits nsp13 means that working to develop more potent and selective molecules may be worth the effort.

22 January 2024

Covalent complexities for kinase inhibitors

Covalent drugs are becoming increasingly popular. But as more researchers search for them, they may encounter pitfalls. A new paper in J. Med. Chem. by  David Heppner and collaborators at the State University of New York Buffalo, AssayQuant Technologies, and Eberhard Karls Universität Tübingen provides a nice roadmap for avoiding them.
 
The researchers focus on covalent inhibitors of epidermal growth factor receptor (EGFR), a kinase that is frequently mutated in cancer. The first drugs against this target, such as erlotinib, were non-covalent, and these have been largely displaced by more effective covalent molecules such as afatinib. Unfortunately, these earlier drugs are not effective against a common mutant (T790M), spurring the development of third generation molecules such as osimertinib, which was approved by the FDA in 2015. Osimertinib has been extensively studied, with more than 2800 references in PubMed. Yet it is not as well understood as you might expect.
 
The team uses this system to demonstrate how characterizing irreversible inhibitors is not simple. For reversible enzyme inhibitors, researchers frequently discuss IC50 values or, when they are being more precise, inhibition constants (Ki). The latter are in theory absolute values that do not depend on concentrations of cofactors such as ATP. But for irreversible inhibitors, the IC50 values change depending on how long (and at what concentration) incubation occurs. The proper assessment of an irreversible inhibitor is kinact/KI, which takes into account both the irreversible inactivation step (kinact) as well as the inhibition constant (KI). Note that Ki is not the same as KI ; the former describes only the initial reversible association between protein and inhibitor, while KI incorporates the irreversible step. Told you it was complicated!
 
And it gets worse. The researchers examined three irreversible covalent inhibitors under various conditions. In one condition, the inhibitors were pre-dissolved in 10% DMSO before being added to the assay mixture to give a final DMSO concentration of 1%. In another condition, the inhibitors were dissolved in pure DMSO before being added to the assay. Despite the final concentration of DMSO being the same (1%), the second condition gave kinact/KI values up to 11-times greater (more potent).
 
If subtle experimental variations in one lab can change values by more than an order of magnitude, you might expect the literature to vary even more, and you’d be right. In the case of osimertinib, the reported values of kinact/KI vary by nearly 500-fold. Some of the experimental parameters the researchers consider are concentrations of reducing agents such as DTT, which can react with covalent inhibitors, and serum albumin, which also contains a free cysteine residue. Although these did not seem to be problematic for osimertinib itself, they could affect other molecules.
 
Another consideration for kinases in particular is the concentration of the cofactor ATP. The value of kinact/KI itself will vary depending on [ATP], and the researchers describe how to calculate a “true” kinact/KI which could be used to compare the potency of a given inhibitor against the wild-type vs mutant forms of the enzyme. But while this is more theoretically rigorous, it may be less biologically relevant, since physiological ATP concentrations are less variable than differences in the Michaelis constant (KM) for ATP for different kinases and mutants.
 
There is lots more to digest in this paper, including analyses of structure-kinetic relationships (SKR, akin to structure-activity relationships, or SAR) for different inhibitors and thorough experimental descriptions. The take-home message is that, due in part to different and often incomplete details, “potency measurements are generally difficult to compare among literature studies,” and “any potency assessments should include appropriate controls under the same conditions as the experimental inhibitors.”

15 January 2024

What makes molecules aggregate?

The propensity for some small molecules to form aggregates in water has bedeviled fragment-finding efforts for decades. Indeed, the phenomenon was not fully recognized until early this century. Although plenty of tools are available for detecting aggregates, I still see too many papers that omit these crucial quality controls. As annoying as aggregation can be in activity assays, in certain cases it could actually be useful for formulating drugs. There has been speculation that the good oral bioavailability of venetoclax is due to aggregation. But despite computational methods to predict aggregation, the structural features of molecules that cause them to aggregate are still not well understood. In a new open-access Nature Comm. paper, Daniel Heller and collaborators at Memorial Sloan Kettering Cancer Center and elsewhere provide some answers.
 
The researchers had previously published an article describing how indocyanine green (ICG) could be used to stabilize and visualize aggregates, and they applied the same technique to examine the aggregation potential of a small set of fragments. Benzoic acid and 2-napthoic acid did not aggregate, while 4-phenylbenzoic acid did. Intrigued, the researchers tested a set of 14 4-substituted biphenyl fragments and found that those containing both a hydrogen bond donor and acceptor, such as acids, sulfonamides, amides, and ureas, could aggregate, while those containing only donors (aniline) or acceptors (nitrile) did not.
 
Fourier transform infrared spectroscopy was used to examine the stretching region of the carbonyl of 4-phenylbenzoic acid in various states: in an aqueous aggregate, in solution in either t-butanol or DMSO, or in the solid state. Interestingly, the aggregate most resembled the solid state, consistent with close-packed self-assembly as opposed to free in solution.
 
From all this, the researchers hypothesized that a combination of aromatic groups and hydrogen bond donors and acceptors was necessary for aggregation. However, having these features does not mean aggregation is inevitable. Neither 3-phenylbenzoic acid nor 2-phenylbenzoic acid formed aggregates, with the former precipitating while the latter remained completely soluble. These three phenylbenzoic acid isomers behave very differently despite the fact that they have the same calculated logP values, and the suggestion is that the latter two molecules are less able to form pi-pi stacking interactions that lead to stable aggregation.
 
Next the researchers examined the approved drug sorafenib, which had previously been shown to aggregate. This was confirmed, and the aggregates were characterized with a battery of biophysical methods including dynamic light scattering, transmission electron microscopy, and X-ray scattering, along with molecular dynamics simulations. The conclusion is that sorafenib forms amorphous aggregates whose assembly is driven by a combination of pi-pi stacking and hydrogen-bonding. A series of sorafenib analogs was synthesized, and those that could not form strong intermolecular hydrogen bonds were less prone to aggregation.
 
All of this is fascinating from a molecular assembly viewpoint and will help to explain and predict which compounds are likely to aggregate, for better or for worse. But as of now, experimental assessment is still best practice for any new compound.

08 January 2024

Electrophilic MiniFrags vs HDAC8

In fragment-based lead discovery, small is good – at least down to a certain point. While most fragments consist of between 7 and 20 non-hydrogen atoms, some investigators have built libraries of much smaller fragments with at most 7 or 8 heavy atoms. We’ve written about MiniFrags and MicroFrags, which are typically screened crystallographically at high concentrations to find hot spots. In a new open-access J. Med. Chem. paper, Franz-Josef Meyer-Almes, György Keserű, and collaborators at the Budapest University of Technology and Economics, the University of Applied Sciences Darmstadt, and the University of Veterinary Medicine Vienna have applied the concept to covalent fragments.
 
The researchers started with a set of 84 fragments, all heterocycles functionalized with one of six warheads, which we wrote about here. They systematically methylated nitrogen atoms on some of these to generate 58 more fragments containing obligate positive charges, such as compound B6+ below. The intrinsic reactivity of the fragments was assessed by reacting them with the biologically relevant thiol glutathione (GSH).
 
Methylating the heterocycles made them more electrophilic and thus more reactive. For example, only 16 of the 84 non-methylated fragments had a half-life (t1/2) < 48 hours against GSH, in contrast with 30 of the 58 methylated fragments. In fact, 17 of the methylated fragments had t1/2 < 10 minutes.
 
Next, all 142 fragments were screened at 250 µM for 2 hours at 30 ºC in a biochemical assay against histone deacetylase 8 (HDAC8), an enzyme important for cell cycle progression. Hits were confirmed in dose-response experiments after 1 hour pre-incubation. Consistent with the glutathione data, only 12 of the non-methylated compounds showed IC50 < 50 µM, while 54 of the 58 methylated compounds were active. One of the fragments, B6+, had a kinact/KI value of 4006 M-1s-1, not far from that found in approved covalent drugs.
 
HDAC8 contains ten cysteine residues, and sites of modification were determined using both site-directed mutagenesis as well as tryptic digestion followed by mass spectrometry. In total, seven residues could be labeled by one or more fragments. The most reactive cysteine, C153, is close to the binding site of a previously reported inhibitor (compound 1), and the researchers tried merging reactive fragments such as B6+ onto this molecule. The best molecule, compound 3, had a kinact/KI value of 1566 M-1s-1. However, the drop from B6+ alone suggests that the non-covalent affinity component of compound 1 may have been lost.
 

This is an interesting approach, and as the researchers note, activity assays available for covalent fragments are higher-throughput than the crystallographic screens required for MiniFrags and MicroFrags. On the other hand, there are limitations. For one thing, the obligate positive charge on the methylated fragments could overwhelm other properties, and could even lead to denaturation of proteins at high concentrations, rendering screens uninformative. These fragments are also less likely to be cell permeable.
 
Finally, as we wrote ten years ago, characterizing irreversible covalent fragments presents a challenge in deconvoluting intrinsic reactivity from specific binding. Computational mapping of hot spots on HDAC8 using FTMap revealed that some correlate with modified cysteine residues. But other modified cysteine residues are in surface-exposed flexible loops with no nearby pockets, and hits against these are likely not advanceable. The fact that some of the fragments modify as many as five cysteine residues on HDAC8 suggests they may be too reactive.
 
Still, the systematic characterization of this library is useful experimentally and for training models. It will be interesting to see it deployed against additional protein targets.

02 January 2024

Fragment events in 2024

We don't know for sure what 2024 has in store for us, but barring pandemics or other disasters, the year is shaping up to be an annus mirabilis for fragments. For the first time ever, all four of the recurring fragment meetings are scheduled for the same year, and other conferences also look exciting. I hope to see you at one.

March 3-5: RSC-BMCS Ninth Fragment-based Drug Discovery Meeting will be held in Cambridge, UK. This venerable biannual event will be particularly focused on case studies "that have delivered compounds to late stage medicinal chemistry, preclinical, or clinical programmes." You can read my impressions of the 2013 meeting here and the 2009 event here.
 
April 1-4: CHI’s Nineteenth Annual Fragment-Based Drug Discovery, the longest-running fragment event, returns as always to San Diego. This is part of the larger Drug Discovery Chemistry meeting. You can read impressions of the 2023 meeting here, the 2022 event here, the 2021 virtual meeting here, the 2020 virtual meeting here, the 2019 meeting here, the 2018 meeting here, the 2017 meeting here, the 2016 meeting here; the 2015 meeting herehere, and here; the 2014 meeting here and here; the 2013 meeting here and here; the 2012 meeting here; the 2011 meeting here; and 2010 here
 
June 2-4:  The theme of the Tenth NovAliX Conference, to be held in the Swiss resort town of Brunnen, is "reinventing drug discovery." You can read my impressions of the 2018 Boston event here, the 2017 Strasbourg event here, and Teddy's impressions of the 2013 event herehere, and here.
 
June 25-27: FBDD Down Under 2024 will take place in beautiful Brisbane. I believe this is the fifth FBDD DU event and the first to be held outside Melbourne. You can read my impressions of FBDD DU 2019 and FBDD DU 2012.
 
September 22-25: After a six year hiatus, FBLD 2024 will be held in Boston. This will mark the eighth in an illustrious series of conferences organized by scientists for scientists. You can read impressions of FBLD 2018FBLD 2016FBLD 2014, FBLD 2012FBLD 2010, and FBLD 2009.
 
September 30 to Oct 3: Autumn is usually a nice time of year in Boston, so why not stick around to attend CHI’s Twenty-Second Annual Discovery on Target. As the name implies this event is more target-focused than chemistry-focused, but there are always plenty of FBDD-related talks. You can read my impressions of the 2023 meeting here, the 2022 meeting here, the 2021 event here, the 2020 virtual event here, the 2019 event here, and the 2018 event here.
 
Know of anything else? Please leave a comment or drop me a note.

18 December 2023

Review of 2023 reviews

The annual Practical Fragments look-back on the preceding year may not be the most highly anticipated year-end tradition, but I hope you find something of interest in this twelfth edition.
 
I was fortunate to attend several conferences and wrote about CHI’s Discovery on Target in Boston and Drug Discovery Chemistry in San Diego. As for reviews, Louise Walsh and collaborators at Astex, Vrije Universiteit Amsterdam, Novartis, and Frontier Medicines (me!) published our annual analysis of fragment-to-lead success stories in J. Med. Chem., this one covering the year 2021. Some twenty other reviews of interest to this readership were also published. I’ll cover them thematically below.
 
Methods
Crystallography is the most popular fragment-finding technique, and in Expert Opin. Drug Disc. Wladek Minor and collaborators at University of Virginia and Jagiellonian University examine “the current role and evolution of X-ray crystallography in drug discovery and development.” At the start of 2023 the Protein Data Bank (PDB) contained more than 200,000 structures, which sounds impressive until you learn that the AlphaFold database contains more than 200 million predicted protein structures. But this is not experimentalist vs machine: the researchers note how machine learning approaches can be used to more rapidly refine and improve experimental data with resources such as CheckMyBlob and PDB-REDO.
 
For those wishing to dig deeper, two papers in Methods Enzymol. go into experimental detail. In the first, Natalie Tatum and colleagues at Newcastle University describe “crystallographic fragment screening in academic drug discovery.” May Sharpe and collaborators at the Swiss Light Source and University of Hohenheim describe their fast fragment-screening pipeline in a comprehensive (49 page) guide. The focus is on reproducibility, and there is plenty of practical advice. For example, “the authors have even been successful in flying with crystal plates,” though getting these through airport security may be easier in some countries than others.
 
Protein-detected NMR was the first truly practical fragment-based approach, and another paper in Methods Enzymol. by Brian Volkman, Brian Smith, and colleagues at Medical College of Wisconsin describes “fragment-screening by protein-detected NMR.” This distills eight years of effort building their internal protein-detected NMR fragment screening platform that has been applied to 16 proteins thus far. The chapter is particularly detailed on protein and library preparation and screening.
 
Compared with crystallography and NMR, virtual screening can be dramatically faster; we’ve highlighted multibillion-compound screens. In WIREs Comput. Mol. Sci., Artem Cherkasov, Francesco Gentile, and colleagues at University of British Columbia and University of Ottawa discuss (open access) how computational methods are “keeping pace with the explosive growth of chemical libraries.” They cover brute force methods, fragment-based virtual screening, and machine-learning based methods, all while avoiding hype, and conclude that it will take time for these methods to “have a real impact on practical drug discovery.”
 
Finally, Marianne Fillet and collaborators at University of Liege and University of Namur provide a general review in Trends Anal. Chem. covering multiple methods to detect non-covalent fragments. These include established techniques such as biochemical assays, ligand-observed NMR, crystallography, thermal shifts, and SPR, as well as less common ones such as WAC, microscale thermophoresis, ACE, and DEL. The paper includes several nice tables and even a decision tree to help choose among the various approaches.
 
Covalent fragments
Many techniques to detect noncovalent interactions also apply to reversible covalent inhibitors, the subject of a review in Med. Chem. Res. by Faridoon and collaborators at Genhouse Bio and Olema Oncology. The researchers focus on various warheads including cyanoacrylamides, nitriles, ketones and aldehydes, boronic acids, and others, and provide multiple examples for each.
 
In contrast, an open-access review in Pharmaceuticals by Monique Multeder and collaborators at Leiden University Medical Center discusses methods to detect both reversible as well as irreversible covalent protein-drug adducts. Crystallography is the most informative, but the researchers also delve into various mass-spectrometry techniques including top-down (with intact proteins) and bottom-up (after digestion of modified proteins). Also covered are activity-based protein profiling (ABPP) methods, NMR, and fluorescence-based approaches. The nearly 300 references make a useful compendium.
 
One of the most exciting recent developments is “proteome-wide fragment-based ligand and target discovery,” the subject of an open-access review in Isr. J. Chem. by Ines Forrest and Christopher Parker, both at Scripps. This concise, highly readable account covers a lot of ground, from ABPP to fully functionalized fragments (FFFs) to phenotypic screening.
 
If you’re doing covalent FBLD you’ll need a library of covalent fragments, and if you’re building one, I’d recommend a review in Prog. Med. Chem. by David Mann and colleagues at Imperial College London. The paper nicely summarizes design principles such as choice of warhead and the fact that reactivity can vary considerably even among compounds with the same warhead. Synthetic methods and screening approaches are also well covered, along with methods to distinguish specific binding from nonspecific reactivity.
 
Most covalent fragments target cysteine residues, but there at least nine other potentially reactive amino acids, and these are the subject of an open-access review by György Keserű and colleagues at Budapest University of Technology and Economics in Trends Pharm. Sci. Lysine, serine, threonine, tyrosine, and histidine are the most common targets, though some of the warheads are so reactive that specificity will be challenging, let alone reasonable pharmacokinetic properties. This is especially true for aspartic and glutamic acids, methionine, and tryptophan.
 
Finally, another article in Trends Pharm. Sci. by Carlo Ballatore and colleagues at University of California San Diego describes using covalent strategies to develop stabilizers and inhibitors of protein-protein interactions (PPIs). Site-directed fragment tethering with disulfide and imine chemistry is a focus, particularly in the context of 14-3-3 proteins. Proximity-enabled covalent strategies, in which warheads are grafted onto non-covalent molecules, are also covered. There is also a short section on covalent PROTACs – more on that topic below.
 
Targets
Keeping with the theme of protein-protein interactions, Ge-Fei Hao, Guang-Fu Yang, and collaborators at Central China Normal University and Guizhou University discuss fragment-based approaches against “undruggable” PPIs in Trends Biochem. Sci. After describing why protein-protein interactions can be difficult, the paper presents several successful case studies, including venetoclax, sotorasib, and targeting 14-3-3 proteins.
 
Targeted protein degradation continues to be a major focus for drug discovery, and this is commonly achieved by hijacking E3 ligases to cause them to ubiquitinate a target of interest. Iacovos Michaelides and Gavin Collie (AstraZeneca) describe how FBLD has been used to find ligands against E3s in an open-access J. Med. Chem. paper. There are more than 600 E3s, and because their biology relies on protein-protein interactions they are often tough targets. Fragment hits can be weak and difficult to advance, though the researchers do describe several success stories including against KEAP1 and XIAP/cIAP. Covalent fragments have the potential to permanently reprogram E3 ligases, and these are covered well too.
 
Another difficult type of target is RNA, the topic of two reviews. In an open-access Curr. Opin. Struct. Biol. paper Kevin Weeks and colleagues at University of North Carolina Chapel Hill provide a concise and beautifully illustrated overview of the field. They note that “RNA-targeted FBLD is in its infancy,” but given that the first report dates to 2002 it is a long childhood, and the paper does a good job of describing the challenges.
 
A more extensive treatment of “fragment-based approaches to identify RNA binders” is provided by Matthew Disney and colleagues at UF Scripps in J. Med. Chem. The paper describes many case studies, some of which we’ve covered, and also contains a handy table comparing the pros and cons of a dozen different methods for finding RNA-binding fragments.
 
Tuberculosis kills more than 1.5 million people each year, and fragment-based approaches have been applied against multiple targets within the pathogen, as reviewed by Baptiste Villemagne and colleagues at University Lille in Eur. J. Med. Chem. We’ve covered many of these studies on Practical Fragments, but as the paper notes none have advanced to the clinic. This is attributed in part to cell permeability, and the researchers suggest turning to phenotypic screens (see below).
 
Other
Fragment linking can be difficult but highly effective, especially for difficult targets. An overview of published linkers is provided by Isabelle Krimm and collaborators at Université Claude Bernard Lyon and Université Montpellier in Expert Opin. Drug Disc. The paper includes a table summarizing 40 fragment linking stories, noting that most linkers are short and flexible. Another table summarizes 19 examples of target-guided synthesis, including dynamic combinatorial chemistry. As the paper notes, all of these are small model studies based on known compounds. In silico approaches, the last topic covered, will probably prove more practical.
 
And on the subject of practical, Dean Brown (Jnana Therapeutics) provides an “analysis of successful hit-to-clinical candidate pairs” in J. Med. Chem. This is an update to his 2018 article and captures 156 clinical candidates reported in the journal between 2018 and 2021. Of these, 14 had fragments in their lineage. Most of these drugs appear in our list of fragment-derived clinical candidates (though berotralstat does not – I’ll need to look closer). The paper contains lots of interesting analyses. For example, of the 138 oral drugs, 39 had a molecular weight > 500 Da, 24 had Clog > 5, and 17 had more than 10 hydrogen bond acceptors (HBA). On the other hand, none had more than 5 HBD, emphasizing that you should be parsimonious with hydrogen bond donors.
 
Finally, veteran drug hunter Nicholas Meanwell provides “reflections on a 40-year career in drug design and discovery” (open access) in Med. Chem. Rev. Those of you who saw his talk earlier this year at the CHI DDC meeting will know what to expect, and those of you who didn’t will be in for a treat. A personal and entertaining romp through pharma starting in the early 1980s, the paper is full of surprises, such as the pursuit of minor impurities in a phenotypic screen that ultimately led to the hepatitis C drug daclatasvir. Nicholas notes that “you discover what you screen for, so screen design is of paramount importance.”
 
The paper also reveals a passion for medicinal chemistry: “In a search for inspiration for design concepts, I sat down one Saturday afternoon in early October of 1987 and perused every molecule in the United States Adopted Names (USAN) dictionary.” And, as he notes near the end, “Decision making in drug discovery and development is a delicate balancing act, inherently flawed based on absence of predictive accuracy, and knowing when to conclude a discovery program with grace is also an important trait.” That said, he provides examples of successful programs that were almost killed multiple times – and others that were killed at Bristol Myers Squibb but subsequently succeeded elsewhere. While this is frustrating on one level, Nicholas takes satisfaction in the fact that “the science that we conducted and the molecules and pharmacophores that we defined have been of benefit to mankind.”
 
There are still a couple weeks left in the year, but that’s it for Practical Fragments for 2023. Thanks for reading, and special thanks for commenting. And if you live in one of the 70+ countries with elections in 2024, please vote.

11 December 2023

Fragments vs IP6K1 without using structural information

The three members of the inositol hexakisphophate kinase family are potential targets for a wide range of diseases, from Alzheimer’s to cancer to metabolic disease. However, current inhibitors are not specific for individual isoforms. Also, the most potent compounds contain a carboxylic acid moiety, which is usually at odds with brain penetration. In a new ACS Med. Chem. Lett. paper, James Barrow and collaborators at Johns Hopkins School of Medicine, the Lieber Institute for Brain Development, and AstraZeneca describe neutral, selective inhibitors.
 
The researchers started with a high-throughput biochemical screen of 17,000 fragments, each at 100 µM, against IP6K1. The library itself is available here. After dose-response follow-up studies, 90 hits confirmed, with IC50 values as good as 2 µM. Most of the hits contained carboxylic acids, but compound 5 did not, and also had good ligand efficiency.
 
No crystal structures of IP6K1 have been reported, so the researchers used an AlphaFold model for docking compound 5. This suggested a fragment growing approach. A variety of replacements for the pyrrolidine were attempted, and while some of these had improved activity many also proved to be chemically unstable. Removing the nitrogen and growing led to compound 24, which was both chemically stable and had sub-micromolar activity.
 
The quinazolinone core itself was associated with poor solubility, and the researchers made multiple attempts to modify it, such as introducing additional nitrogen atoms or methylating to remove a hydrogen-bond donor. Unfortunately, all these modifications led to significant losses in potency.
 
Compound 24 is highly selective for IP6K1 over IP6K2 and somewhat selective over IP6K3. Unfortunately, it showed no cellular activity, possibly due to modest biochemical potency and solubility. Nonetheless, this brief paper illustrates that starting with a larger than normal fragment library can lead to new chemotypes. Screening the larger library gave the researchers more chances to find fragments that did not contain carboxylic acids. Indeed, the difficulty of modifying the quinazolinone moiety demonstrates the utility of screening more molecules. Had the closely related molecules been in the library, they might not have turned up as hits, but their presence would suggest that relevant chemical space had been interrogated.
 
The paper is also a nice example of optimizing hits in the absence of structural information. Although much needs to be done to turn compound 24 into a chemical probe, the fact that it is still so small (almost rule-of-three compliant) provides hope that this can be done.

04 December 2023

Screening tough proteins by SPR

Surface-plasmon resonance (SPR) is among the most popular methods for finding fragments. However, as we have noted, SPR can be very prone to operator error and misinterpretation. In a recent (open access) SLAS Discovery paper, U. Helena Danielson (Uppsala University) and a who’s-who team of biophysicists from across Europe provide experimental strategies for screening difficult proteins.
 
The researchers chose five different proteins, some of which were screened in two or three different forms for a total of nine protein constructs. Six of these were screened against their FL1056 library, a custom-built 1056-member library which include molecules from the FragNet program. The library includes a number of “three dimensional” molecules as assessed by principal moment of inertia (PMI). The other library, FL90, is a small set of commercially available fragments we highlighted here.
 
Before screening compounds against proteins, the researchers conducted a “clean screen.” This involved injecting fragments (at 500 µM each) over the sensor surface using the same buffer that would be used in the actual screen to pre-identify fragments that stick to the surface. This typically disqualified about 1% of fragments, though for one set of conditions the number was closer to 3%.
 
That work done, the researchers turned to the actual screens. After proteins were immobilized on the sensor chips, the fragments were typically screened at a single concentration of 250 µM each. The threshold for the initial hit cutoff was set low, often around 10% of the library, to minimize false negatives. Subsequent follow-up studies at varying concentrations were used for confirmation. This led to a significant winnowing, with the final number of confirmed hits between 0.5 and 7% of the library.
 
The proteins themselves were intentionally chosen to present various difficulties. Acetylcholine binding protein (AChBP, which we wrote about here) forms a large (125 kDa) pentameric complex with multiple binding sites. Lysine demethylase 1 (LSD1) is a multidomain, cofactor-dependent protein that requires a partner protein, CoREST, for activity. LSD1 was screened in the presence or absence of CoREST. Farnesyl pyrophosphate synthase (FPPS, which we wrote about here) is a target for cancer and osteoporosis, and the microbial forms are targets for trypanosomiasis drugs. Human as well as Trypanosoma cruzi and Trypanosoma brucei proteins were screened. Protein tyrosine phosphatase 1B (PTP1B, which we recently wrote about here) is a difficult enzyme with a couple allosteric sites. The C-terminal region is intrinsically disordered, and the protein was screened with or without this region. Finally, human tau is both intrinsically disordered and prone to aggregation. As we noted earlier this year it is of interest due to its potential role in Alzheimer’s disease.
 
Happily, hits were identified against all the proteins, some with ligand efficiency values above 0.5 kcal/mol per heavy atom. The chemical structures for selected hits are shown, and the researchers do appropriately caution that validating them using orthogonal (non-SPR) methods is essential before further studies.
 
I do wish the researchers had noted whether shapely hits were enriched or depleted among the confirmed hits. To my eye most seemed fairly flat, and some seemed dubiously PAINS-like, including an eyebrow-raising dinitro-catechol. Nonetheless, the paper is a nice summary of multiple SPR campaigns. If you’re about to embark on one yourself, it is worth carefully perusing.

27 November 2023

Beware of fused tetrahydroquinolines

Practical Fragments has written frequently about pan-assay interference compounds, or PAINS. These molecules contain substructures that frequently show up in hits that tend not to be advanceable, often wasting considerable effort. One criticism of the PAINS concept is that the original definitions were based on a limited number of screens in one assay format. In a new (open-access) J. Med. Chem. paper, Alison Axtman and collaborators at University of North Carolina Chapel Hill, Emory University, and Oxford University characterize one class of PAINS in more detail.
 
The researchers focused on fused tetrahydroquinolines, or THQs. Of the 51 molecules containing this substructure in the original 2010 PAINS paper, 34 hit in at least one of the assays, and one hit all six. At the time Jonathan Baell and Georgina Holloway noted that “it is not clear for some PAINS, such as the fused tetrahydroquinolines, what the relevant mechanisms of interference may be.” 

 
 
The new paper notes that fused THQs are common in screening libraries, with more than 15,000 commercially available. They also frequently show up as hits: the researchers summarize more than two dozen examples against a wide variety of targets including phosphatases, kinases, protein-protein-interactions, and more. In most cases the hits are modestly active, with low to mid micromolar IC50 values, though a few are high nanomolar. 
 
Promiscuity per se is not necessarily bad. Just last week we noted that the 7-azaindole fragment was the starting point for three approved drugs. However, despite showing up as hits in so many screens, only one peer-reviewed paper reports a crystal structure of a fused THQ bound to a protein, and the researchers note that “no optimized chemical probes or approved drugs contain the chemotype.”
 
Importantly, fused THQs hit in a variety of different assay formats, including spectrophotometric, chemiluminescent, SPR, and radiochemical. Thus, these are not merely problematic in the AlphaScreen format studied in the original PAINS paper.
 
So what’s going on? The researchers found that, while molecules containing the fused THQ core were initially colorless, they darkened when dissolved in DMSO or chloroform, turning purple within 72 hours. Interestingly, the reaction seems to be light-dependent: solutions stored in the dark remained colorless. Thin layer chromatography and NMR showed new species forming, and mass spectrometry revealed oxidation with loss of two or four hydrogen atoms. The isolated double bond in the cyclopentene ring seemed to be the culprit, as the saturated analog was stable. Indeed, all of the hits shown in the paper contain the double bond, so fused THQs that lack this feature may be fine – if they ever show up in your assay.
 
It is still not clear exactly how the decomposition products light up so many assays, but in general it’s a good idea to steer clear of molecules that fall apart in ambient light, unless you’re trying to make a photosensitizer.
 
The researchers conclude that “it is tragic to continue to watch groups invest time and resources in dead-end hit-to-lead campaigns, and the medicinal chemistry community will benefit everyone if the cycle stops.”
 
This concludes our public service announcement.

20 November 2023

Capivasertib: the seventh approved fragment-derived drug

On Thursday last week the FDA approved capivasertib for certain breast cancer patients. This marks the seventh fragment-derived drug to be approved. It is also the first approved drug targeting the kinase AKT.
 
Practical Fragments first wrote about capivasertib, then called AZD5363, way back in 2013, where we described the decade-long odyssey from fragment to drug. Interestingly that fragment, 7-azaindole, was also the starting point for two other approved drugs, pexidartinib and vemurafenib. As we noted at the time, “high-affinity molecules were obtained relatively quickly, but these still required a huge amount of effort to achieve selectivity, oral bioavailability, and other properties.”
 
What happened next is a poster child to counter one of the false beliefs Christopher Austin noted as being widespread outside industry: “Once an investigational therapy gets into humans for the first time, regulatory approval and marketing are all but assured.”
 
Capivasertib entered the clinic in 2010 in the first of more than 30 studies listed on ClinicalTrials.gov to date. One challenge was finding patients that would benefit sufficiently to offset a long list of side effects, including diarrhea and glucose fluctuations. In the end, the current approval is in combination with fulvestrant for “adult patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative locally advanced or metastatic breast cancer with one or more PIK3CA/AKT1/PTEN-alterations, as detected by an FDA-approved test, following progression on at least one endocrine-based regimen in the metastatic setting or recurrence on or within 12 months of completing adjuvant therapy.”
 
Needless to say, these were not the first patients tested. Use of genetic testing to match patients with a drug likely to help them is not routine even today, let alone in 2010. Managing side effects also required figuring out how much of the drug to dose and how often. But additional combination trials are ongoing. Perhaps, as with venetoclax, capivasertib will eventually prove to be useful for a wider range of patients.
 
The first marketed fragment-derived drug, vemurafenib, sprinted from program initiation to approval in just six years. Capivasertib took twenty. As we previously noted, success in drug discovery is not necessarily fast or inevitable. Every year more than 40,000 people die of breast cancer in the US alone, but the death rate has slowly been declining. Hopefully the introduction of capivasertib will continue to reduce this.
 
Congratulations to all the researchers at AstraZeneca, Astex, and the Institute for Cancer Research for participating and persisting in this 20-year marathon to bring a new treatment to people with cancer.

13 November 2023

An update on the COVID Moonshot

On March 18, 2020, a group called the COVID Moonshot released crystal structures of 71 fragments bound to the SARS-CoV2 Mpro protein. The same day, they launched an online crowdsourcing initiative seeking ideas for how to advance these fragments, none of which had activity in an enzymatic assay. The results of this experiment in open science have just been published in Science, appropriately open-access.
 
Within the first week, the group received more than 2000 submissions. Ultimately more than 20,000 molecules were submitted, and all of these were evaluated in “alchemical free-energy calculations.” These are computationally intensive, requiring ~80 GPU hours per compound, so the consortium used the volunteer-based distributed computing network Folding@home. Compounds were evaluated not just for potency but also synthetic accessibility, and those that passed were synthesized at Enamine and tested in various functional assays.
 
In addition to accepting submissions for how to advance fragments, a core group of researchers proposed their own ideas. Interestingly, at least in the early stages of the project, this elite group did no better at coming up with more potent or synthetically accessible molecules, despite being intimately involved with the project. This finding validates the open-sourcing of ideas from the larger scientific community.
 
Ultimately more than 2400 compounds were synthesized, and more than 500 crystal structures were determined. All experimental results were posted online to help guide the synthesis of additional compounds. Speed was consistently prioritized, not just with high-throughput crystallography but also high-throughput chemistry and "direct-to-biology" screening of crude reaction mixtures.
 
The paper highlights one lead series, which originated from a community submission (TRY-UNI-714a760b-6, itself fragment-sized) inspired by merging overlapping fragments. This mid micromolar inhibitor was ultimately optimized to MAT-POS-e194df51-1, with mid-nanomolar activity in both biochemical and cell assays. (Despite a chloroacetamide in one of the original fragments and a nitrile in the final molecule, which is the warhead found in the approved covalent Mpro inhibitor nirmatrelvir, MAT-POS-e194df51-1 is non-covalent.) 
 

The molecule is potent against known SARS-CoV-2 variants, including recent ones such as Omicron. A crystal structure of the final molecule also overlays remarkably well onto the initial fragments.
 
The paper notes that there is still considerable work to do, particularly optimizing the pharmacokinetics to lower clearance and improve bioavailability. These efforts can take vast sums of time and money, and the lead series has been adopted by the Drugs for Neglected Diseases initiative for further development. Although a handful of drugs are already approved against SARS-CoV-2, there is room for improvement: Derek Lowe posted a vivid personal account of his experience on nirmatrelvir here.
 
When we wrote about the COVID Moonshot in March of 2020, we correctly predicted that vaccines would be approved before drugs from this effort emerged. Fortunately, our warning that “there will be a SARS-CoV-3” has not proven correct – yet. But open science endeavors such as the COVID Moonshot will help us prepare for this eventuality. We may not have made it to the moon yet, but perhaps we’ve learned how to leave Earth’s orbit.

06 November 2023

Finding weak fragments for membrane proteins with WAC

Last week we wrote about NMR, one of the most popular fragment-finding methods. This week we turn to a less common technique: weak affinity chromatography, or WAC. As we’ve written previously, WAC involves immobilizing a protein of interest in a chromatography column and flowing a ligand-containing solution through the column. If the ligand interacts with the protein, its elution time will be delayed in proportion to its affinity. In a new (open-access) Molecules paper, Claire Demesmay and collaborators at Universite Claude Bernard Lyon and Ecole Supérieure de Biotechnologie de Strasbourg extend the technique to membrane proteins.
 
Membrane proteins are themselves tricky to study, since removing them from their membranes often denatures them. One trick is to use nanodiscs, which are tiny lipid bilayer islands surrounded by proteins that keep them soluble in water. These scaffolding proteins can also be biotinylated so that the nanondiscs can be attached to streptavidin, which itself can be linked to a surface or matrix. Each nanodisc holds one or at most a few membrane proteins.
 
When we first wrote about WAC in 2011 the technique used standard HPLC columns, which required non-negligible amounts of protein. Here, the technique has been miniaturized to use glass capillaries with volumes of less than 1 microliter, requiring only a few tens of picomoles of protein. The researchers fill the capillaries with a bio-compatible polymer, functionalize it with streptavidin, and then capture biotinylated nanodiscs containing the membrane protein of interest.
 
A long-recognized challenge with WAC is nonspecific binding of the fragments to the column or matrix. Here, the researchers chose a filling (or monolith) that is more hydrophilic (for aficionados, they picked poly(DHPMA-co-MBA)) and found it superior to the previous polymer both with regards to capacity and non-specific binding.
 
Another challenge with WAC is detecting low-affinity binders: because interactions with the protein are weak, the shift in retention time is harder to detect. One solution is to pack more protein in the column, and the researchers develop a clever way of doing this with a “multilayer grafting” approach in which successive injections of streptavidin and nanodiscs more effectively fill the capillary. The combination of a more hydrophilic filling and multilayer grafting increased the column capacity for nanodiscs by three-fold.
 
The researchers tested their approach on the adenosine-A2A receptor (AA2AR), which has frequently been used as a model GPCR. Two previously reported weak ligands, both with affinities around 0.2 mM, could be detected, and competition with an orthosteric binder revealed that they were binding specifically.
 
This is a nice, how-to guide for performing WAC on membrane proteins, and the paper includes detailed equations for calculating affinities from differences in retention times. I look forward to seeing the technique used in de novo screens.

30 October 2023

NMR for SAR: All about the ligand

In last week’s post we described a free online tool for predicting bad behavior of compounds in various assays. But as we noted, you often get what you pay for, and computational methods can’t (yet) take the place of experimentation. In a new (open-access) J. Med. Chem. paper, Steven LaPlante and collaborators at NMX and INRS describe a roadmap for discovering, validating, and advancing weak fragments. They call it NMR by SAR
 
Unlike SAR by NMR, the grand-daddy of fragment-finding techniques which involves protein-detected NMR, NMR for SAR focuses heavily on the ligand. The researchers illustrate the process by finding ligands for the protein HRAS, for which drug discovery has lagged in comparison to its sibling KRAS.
 
The researchers started by screening the G12V mutant form of HRAS in its inactive (GDP-bound) state. They screened their internal library of 461 fluorinated fragments in pools of 11-15 compounds (each at ~0.24 mM) using 19F NMR. An initial screen at 15 µM protein produced a very low hit rate, so the protein concentration was increased to 50 µM. After deconvolution, two hits confirmed, one of which was NMX-10001.
 
The affinity of the compound was found to be so low that 1H NMR experiments could not detect binding. Thus, the researchers kept to fluorine NMR to screen for commercial analogs. They used 19F-detected versions of differential line width (DLW) and CPMG experiments to rank affinities, and the latter technique was also used to test for compound aggregation using methodology we highlighted in 2019. Indeed, the researchers have developed multiple tools for detecting aggregators, such as those we wrote about in 2022.
 
Ligand concentrations were measured by NMR, which sometimes differed from the assumed concentrations. As the researchers note, these differences, which are normally not measured experimentally, can lead to errors in ranking the affinities of compounds. The researchers also examined the 1D spectra of the proteins to assess whether compounds caused dramatic changes via pathological mechanisms, such as precipitation.
 
The researchers turned to protein-detected 2D NMR for orthogonal validation and to determine the binding sites of their ligands. These experiments revealed that the compounds bind in a shallow pocket that has previously been targeted by several groups (see here for example). Optimization of their initial hit ultimately led to NMX-10095, which binds to the protein with low double digit micromolar affinity. This compound also blocked SOS-mediated nucleotide exchange and was cytotoxic, albeit at high concentrations.

I do wish the researchers had measured the affinity of their molecules towards other RAS isoforms as this binding pocket is conserved, and inhibiting all RAS activity in cells is generally toxic. Moreover, the best compound is reminiscent of a series reported by Steve Fesik back in 2012.
 
But this specific example is less important than the clear description of an NMR-heavy assay cascade that weeds out artifacts in the quest for true binders. The strategy is reminiscent of the “validation cross” we mentioned back in 2016. Perhaps someday computational methods will advance to the point where “wet” experiments become an afterthought. But in the meantime, this paper provides a nice set of tools to find and rigorously validate even weak binders.