Filtering fluorescent frauds from fragments for PTP1B

Protein tyrosine phosphatase 1B (PTP1B) is a long-standing target for diabetes and other diseases. But as we’ve written previously, it is challenging to drug due to its small and highly charged active site, which is conserved among phosphatases. In a new open-access Drug Design Dev. Ther. paper, Frank Boeckler and colleagues at Eberhard Karls Universität Tübingen screen fragments against PTP1B and demonstrate the importance of distinct, rigorous controls.

 

The Boeckler group previously developed a fragment library enriched in halogens (HEFLib, which we discussed here) and a separate covalent fragment library (CovLib). These, as well as two other small libraries, were screened against PTP1B using a fluorescence activity assay. Compounds were incubated with the enzyme for an hour at 2 mM in the presence of a substrate (DiFMUP, 6,8-difluoro-4-methylumbelliferyl phosphate) that releases a fluorescent reporter (DiFMU) when hydrolyzed by the enzyme. A molecule that inhibits the enzyme should lead to less fluorescence. Samples were run in triplicate, and detergent was included to avoid aggregation artifacts. A total of 515 fragments were tested, leading to 56 preliminary hits, defined as molecules that decreased fluorescence by more than 50%.

 

A major challenge with fluorescence assays is that the compounds themselves can interfere with the readout. To assess this, the researchers retested their hits both before and after incubation of PTP1B with substrate. A fluorescence artifact should give similar activity in both conditions, while a true inhibitor should only cause a decrease in fluorescence when added at the beginning of the assay, before the protein has had a chance to process substrate. Unfortunately, only 11 hits passed this test.

 

To further confirm mechanism, the researchers developed an HPLC-based assay that separates the substrate DiFMUP from the product DiFMU. Because this assay detects PTP1B activity using separation rather than fluorescence, it should be immune from fluorescence artifacts. Only three of the 56 preliminary hits confirmed in this assay. Dose-response experiments revealed them to be quite weak, with IC₅₀ values above 5 mM. All three are potentially covalent inhibitors, and two showed reactivity with glutathione. PTP1B has a reactive active-site cysteine so covalent inhibitors are not surprising, though it remains to be seen how specific they are.

 

This paper is a useful reminder of the importance of validation: an initial 11% hit rate dropped to just 0.6%. Full experimental details for all the assays are provided, as are the structures of the hits, while SMILES strings of all 515 compounds screened are listed in the supplementary material. If you’re running a fluorescent fragment screen for the first time, this paper is worth consulting.

Malicious metals muddy fragment-to-lead optimization

Despite the effectiveness of vaccines against SARS-CoV-2, COVID-19 continues to plague us. The handful of approved small molecule drugs target only two proteins and have much room for improvement. One interesting but underexplored target is the nonstructural protein 14 (NSP14), a 3’ to 5’ RNA exonuclease, which is important both for viral replication as well as immune escape. In a new open-access ACS Chem. Biol. paper, Jae Jung, Shaun Stauffer, and colleagues at the Cleveland Clinic describe how their efforts against NSP14 were thwarted.

 

The researchers started with the crystal structures of two fragments that had been identified in a high-throughput crystallographic screen at XChem. They reproduced these in-house, confirming the published structures, and also made and characterized a few analogs. Crystallography demonstrated these bind in a similar manner. Encouragingly, they also showed activity in a biochemical assay.

 

The two published fragments bind next to one another, presenting a good opportunity for fragment merging or linking. The researchers used the computational tool Fragmentstein, which we wrote about here, to design new molecules. Some of these molecules were active in the biochemical assay, and a crystal structure of a merged compound revealed that it bound as expected. Importantly, none of the molecules inhibited an unrelated endonuclease.

 

So far, so good, but the researchers were suspicious about the SAR. For example, changing an isopropyl to a cyclopropyl group weakened the activity  from 2.4 to 150 µM, despite the fact that the moiety is largely solvent exposed. After resynthesizing and more carefully purifying the molecules, the researchers found them to be completely inactive in the biochemical assay.

 

NSP14 contains two catalytic magnesium ions and three structural zinc ions, and the researchers considered the possibility that metal contaminants might have been responsible for the activity. Sure enough, when they screened the Metal Ion Interferences Set (MIIS), which we wrote about here, they found that half a dozen metal ions potently inhibited the assay. They tested whether any of the spuriously active compounds contained palladium and ruled this out, but did not test for other metals. Indeed, metals may not even be to blame: the active molecules all contain thiazoles, and as we discussed in 2022 these can sometimes interfere with assays. What is clear is that the exciting initial activity results were artifacts, and the researchers were sufficiently diligent to figure it out for themselves.

 

One of the most disturbing findings is that the crystal structures looked fine, despite the compounds having no measurable activity. As we’ve written previously, the lack of affinity information is the biggest drawback of fragment screening by crystallography. Perhaps NMR would have been able to invalidate the false-positives, though as we have written both protein-detected and ligand-detected methods can be fooled. As our 2024 poll emphasized, using multiple methods to validate fragment binding is important. And resynthesizing and carefully purifying compounds helps too.

 

These sorts of cautionary tales are not published as often as they should be. Kudos to this team for both warranted skepticism and providing a warning for others.

Ivermectin postmortem: PAINful experiences with a good drug

Ivermectin is a miracle drug. It cures infections caused by several types of parasitic roundworms, including those that cause river blindness and elephantiasis, two highly unpleasant diseases. Half of the 2015 Nobel Price for Physiology or Medicine was awarded to researchers who discovered the drug.

 

More recently, ivermectin was touted as a treatment for SARS-CoV-2. Unfortunately, after more than 90 human clinical trials, the preponderance of evidence shows that it is ineffective against COVID-19. A new (open-access) Perspective in J. Med. Chem. by Olaf Andersen, Jayme Dahlin, and collaborators at Weill Cornell Medicine, the National Institutes of Health, University of North Carolina Chapel Hill, and University of California San Francisco explores why it proved so misleading, with lessons for other drug repurposing efforts.

 

In 2011, ivermectin was one of 480 compounds tested in a biochemical assay at 50 µM and appeared to disrupt the interaction between HIV integrase and a mammalian protein involved in viral trafficking. In April 2020, low micromolar concentrations of ivermectin were reported to have anti-SARS-CoV-2 activity in a cellular assay. Given a terrifying new disease with no treatments, an approved drug that showed even tenuous activity looked like a lifeline. Researchers around the world began studying ivermectin. PubMed citations jumped from 459 in 2019 to 734 in 2021.

 

The new paper examines some of this past work and dives deeply into the biological effects of ivermectin. The molecule is poorly soluble in water (around 1-2 µM) and partitions into cellular membranes, where it activates a chloride channel receptor in worms, paralyzing them. At this it is quite potent: when taken as directed, human plasma concentrations of ivermectin are around 60 nM, but because the drug is highly protein bound the free concentration is only around 6 nM.

 

The astute reader may notice that the apparent antiviral activity was observed at low micromolar concentrations of ivermectin, three orders of magnitude higher than physiologically relevant, and the initial biochemical assay was conducted at an even higher concentration. In fact, that work was done using an AlphaScreen, which is notoriously sensitive to artifacts. The new paper demonstrates that ivermectin forms aggregates at low micromolar concentrations, and that these aggregates interfere with the AlphaScreen. (We previously wrote about how many of the early reports of compounds active against SARS-CoV-2 proteins were in fact aggregators.)

 

If ivermectin perturbs membrane proteins in worms, what does it do in human cells? The researchers tested the drug against panels of G protein-coupled receptors (GPCRs) and found that in one assay format it inhibited more than a quarter of 168 GPCRs at 10 µM, much higher than physiologically relevant but comparable to the in vitro experiments with SARS-CoV-2. Further studies revealed that ivermectin changes the properties of membranes at low micromolar concentrations, as assessed by multiple methods including electrophysiological assays. Several ivermectin analogs were also tested and found to have similar activity, consistent with nonspecific effects. Impressively, these experiments were done blinded to the experimenter.

 

You might think that messing with membranes would not be good for cells, and you would be right. The researchers found that ivermectin decreased cell viability at low micromolar concentrations in a variety of assay formats through multiple mechanisms, both cytotoxic and apoptotic. Importantly, the concentrations at which ivermectin was active against cells were similar to the concentrations where it showed activity against SARS-CoV-2. The researchers also analyzed 766 PubChem assays and found that ivermectin is active in nearly a third of those assessing cellular toxicity. Killing a host cell is certainly one way of killing a virus, but likely not a useful one.

 

In summary, the original data suggesting that ivermectin is a developable antiviral agent was flawed. The researchers describe this as “a saga of the damage that can be done by assay interference compounds” and a “cautionary tale for the dangers of ‘pandemic exceptionalism.’” They continue:

 

The fact that a repurposed drug is well-characterized clinically, or that there is an ongoing pandemic, may justify performing clinical and mechanistic experiments in parallel, but not skipping mechanistic studies, where the key experiments could have been done in a matter of a few weeks/months.

 

This J. Med. Chem. paper is a meticulous, comprehensive study; with 71 pages of supporting information there is far more to cover than I can do justice to in a blog post. The paper also includes a useful flowchart for derisking nonspecific membrane perturbation. It is well worth reading, particularly for those new to drug discovery. As Richard Feynman warned, “the first principle is that you must not fool yourself -- and you are the easiest person to fool.”

Hundreds of crystallographic ligands for FABP4 – many not as expected

The ten human fatty-acid binding proteins (FABPs) shuttle lipids around cells. As we noted several years ago, FABP4 and FABP5 are potential drug targets for diabetes and atherosclerosis, but selectivity over FABP3 is needed to avoid cardiotoxicity. Markus Rudolph and colleagues at Hoffmann-La Roche describe progress towards selective molecules in three consecutive open-access Acta. Cryst. D papers. Perhaps more importantly, they gift a massive high quality data set to the scientific community – along with some important caveats about data for protein-ligand structures.

 

The first paper focuses on purification and NMR characterization of FABP4. Recombinant FABPs are normally expressed in E. coli, and they always contain natural fatty acids that copurify with the protein. This can complicate ligand binding studies, since the endogenous fatty acids act as competitors. Indeed, the researchers highlight two structures in the protein data bank (PDB) whose supposed ligands are probably fatty acids.

 

To solve this problem, the researchers denature FABP4, separate the fatty acid, and then refold the protein. This truly apo form of the protein was studied by NMR, revealing that the protein becomes more rigid upon ligand-binding.

 

The second paper is of more general interest. It reports a set of 229 crystal structures of various FABPs, of which 216 have a bound ligand. Of these, 75 have associated IC₅₀ values for at least one FABP, and 50 compounds have IC₅₀ values reported for FABP3, FABP4, and FABP5. Importantly, the structures are solved to high resolution, with a median of 1.12 Å. Two crystal forms are particularly suitable for soaking, and compounds were typically soaked at 60 mM in 30% DMSO overnight.

 

All the crystal structures are deposited in the PDB, and all the binding data are provided in the supporting information. Given FABPs’ predilection for carboxylic acids, the ligands contain a variety of carboxylic acid mimetics. This wealth of high-quality data should be valuable for constructing machine-learning binding models, and the researchers conclude by calling “on other industrial organizations to also make their legacy data available such that prediction models with broader applicability may be developed more quickly.”

 

But it was the third paper that really caught my attention: the researchers summarized it as “what is written on the bottle is not what is in the crystal.” In fact, of the 216 ligands reported, a whopping 33 (15%) do not match the compound registered. These are grouped into several categories and described in detail.

 

Human error is the simplest to explain: the researchers show an example where a 1,2-benzoxazole was registered as a 1,3-benzoxazole. Because the molecules have the same molecular weight, mass spectrometry could not distinguish them. Similarly, the researchers find several cases where the wrong enantiomer or diastereomer was registered. In another case, a racemic mixture led to a single enantiomer bound to FABP4, with the protein acting as a “chiral sponge.”

 

Other cases are more unusual, and include ring closing, ring opening, acyl shifts, hydrolysis, and instances of ligand decomposition or incomplete reactions. The researchers note that small amounts of impurities could be particularly problematic at the high ligand concentrations used for soaking; they calculate that just 0.06% impurity would be equivalent to the total amount of FABP in a crystal. Some fragment screens are done at even higher concentrations, further increasing the risk of enriching impurities.

 

A 15% rate of unexpected ligands is comparable to the numbers we blogged about here, but those were commercial libraries, whereas this set is from Roche, which likely has better internal quality control. One factor that led to the recognition of the problem is the high resolution, where a single atom change could be readily seen. Another is the buried nature of the ligands; ligands bound on the surface of a protein may have more dynamically disordered bits, which would be difficult to distinguish from missing moieties caused by decomposition.

 

Indeed, the researchers examine two other proteins, PDE10 and ATX, for which they have also released ~200 ligand-bound structures but at lower average resolutions. There are some unexpected ligands for these proteins too, but many fewer than for the FABPs – or perhaps we just can’t observe some of them.

 

As we noted back in 2014, up to a quarter of ligand-containing crystal structures in the PDB may contain serious errors, and the researchers cite a study suggesting that 12% are “just bad.” These could have obvious negative consequences for training computational models, and the researchers call on the community to set standards to create a rigorously chosen training set. Perhaps this discussion could be held in parallel with the discussion on how to house fragment screening data, which we wrote about last month.

The Chemical Probes Portal turns ten. Use it!

Last week we highlighted a new tool to computationally predict whether a molecule might aggregate, thereby causing false positives. This doesn’t necessarily mean the molecules are bad (after all, some approved drugs aggregate), but it’s all too easy to screen molecules under inappropriate conditions. This brings up the topic of chemical probes, and as it happens the Chemical Probes Portal turns ten years old this year, as celebrated in a Cancer Cell Commentary by Susanne Müller, Domenico Sanfelice, and Paul Workman and a blog post by Ben Kolbington at the Institute of Cancer Research.

 

We first wrote about the Chemical Probes Portal in July 2015, when it contained just 7 compounds. When we returned in 2023 it contained more than 500 compounds, and by the end of last year the number was up to 803. As of today it lists 1174 probes for 622 targets. Nearly a third of the probes also have chemically related inactive controls. These seem like large numbers, but the the human genome conservatively encodes for some 20,000 proteins, and the ambitious Target 2035 initiative seeks chemical probes for all of them.

 

The new paper emphasizes that the standards are in some ways higher for chemical probes than for approved drugs: “whereas probes principally require a high degree of selectivity, drugs need ‘only’ to be safe and effective and may often hit several targets.” Dimethyl fumarate comes to mind as a highly promiscuous covalent modifier that is nonetheless a useful drug for multiple sclerosis and psoriasis.

 

Even when a compound hits a target of interest, that doesn’t mean any biological effects observed are due to the target, particularly when the readout is cell death. The researchers note that TH588 was originally reported as a potent inhibitor of MTH1, but it actually kills cancer cells by binding to tubulin, a fact not always mentioned by chemical suppliers. Another study found that ten clinical compounds were still active in cells even when their putative target was knocked out using CRISPR.

 

The tone of the Commentary is pragmatic, emphasizing that for new or difficult targets, it may be difficult to find good chemical probes. For example, LY294002 is mentioned as a “pathfinder tool” that was useful to explore the biology around the PI3 kinase family but has now been superseded by more selective molecules.

 

Unfortunately, not everyone seems to have gotten the message. Curcumin, which as we noted can aggregate, form nonselective covalent adducts, fluoresce, and generate reactive oxygen species, appears in >2600 PubMed publications – just in the past year. What a waste.

 

If you’re exploring the biology of a target, please check the Portal to see whether there are good probes. If you’re reading (or reviewing!) a paper that reports small molecule studies, please check to see whether the probe has been assessed - especially to see if it shows up as one of more than 250 Unsuitables. And if you’re interested in participating, please consider reviewing or even hosting a Probe Hackathon.

Can machine learning help you avoid SCAMs?

Among the many types of artifacts that can fool screens and derail efforts to find leads, small colloidally aggregating molecules (SCAMs) are particularly pernicious. As we discussed way back in 2009, these molecules can form aggregates in aqueous buffer that interfere with a variety of assays, leading to wasted resources and embarrassing publications.

 

The problem is that there isn’t necessarily anything wrong with the molecules per se, and even many approved drugs can form aggregates. Thus, it is difficult to predict whether any given molecule will be a troublemaker. In a new (open-access) Angew. Chem. Int. Ed. paper, Pascal Friederich, Rebecca Davis, and collaborators at Karlsruhe Institute of Technology and University of Manitoba Winnipeg explore whether machine learning can help.

 

The researchers built a Multi-Explanation Graph Attention Network, or MEGAN, which is accessible through a simple web interface. Rather than a homicidal doll, this MEGAN represents atoms as nodes and bonds as edges in a graph, similar to the Fragment Network we wrote about here. MEGAN was trained on a set of 12,338 aggregators and 177,048 non-aggregating molecules. Importantly, the researchers used explainable AI (xAI), which colors portions of the molecule according to their importance for (non)aggregation.

 

Testing MEGAN on a set of 1500 aggregators and 1500 non-aggregators, none of which were included in the training set, yielded an accuracy of 82%. Given that most molecules don’t aggregate, a model biased towards non-aggregators would be expected to have a high accuracy, and to account for this the researchers assessed the “F1” score, which was similarly impressive.

 

 

The researchers provide several examples in which subtle variations transform a molecule from a non-aggregator to an aggregator, and show that MEGAN correctly predicts these. Furthermore, it “shows its work,” highlighting the chemical features underlying the prediction. For example, 9H-pyrido[3,4-b]indole is predicted with 92% confidence not to be an aggregator.

 

 

Just adding a methyl group flips the odds in favor of aggregation to 92%.

 

Exploring the molecular features that lead to aggregation can reveal general trends, such as rigid, “flat” molecules with moieties that can serve either as hydrogen bond donors or acceptors. This is consistent with a paper we discussed last year, though unfortunately the researchers do not cite it.

 

To further assess the tool, it was tested against a set of drugs that had been characterized as aggregators or non-aggregators. MEGAN correctly classified 15 of 30 aggregators and 24 of 28 non-aggregators. In contrast, a different program caught only 2 of the aggregators. The researchers note that most of the training data for MEGAN came from a single screen in phosphate buffer at pH 7, and aggregation can be very dependent on buffer components and pH.

 

Practical Fragments has previously highlighted other aggregation predictors, most notably Aggregator Advisor and Liability Predictor. As for any computational model, the old chestnut “trust but verify” applies. MEGAN appears to be a useful tool, but please run physical experiments if the molecule is important.

They may be cons, but they’re our CONS

Practical Fragments has written repeatedly about various assay artifacts (vide infra). Different technologies are susceptible to different interference mechanisms, making general rules difficult. Earlier this year we wrote about the Metal Ion Interference Set, or MIIS: a collection of a dozen salts that could be used to assess the sensitivity of assays to metal contaminants. In a recent open-access JACS Au paper, Huabin Hu (Uppsala University), Jonathan Baell (Monash University), and collaborators extend the concept to small molecules.

 

The researchers have compiled a Collection Of useful Nuisance compounds, or CONS, perhaps with a nod to “Chemical con artists foil drug discovery” published a decade ago, which we highlighted here. The 103 members of the CONS are divided into three categories.

 

The first set contains five aggregators: molecules that have been shown to form colloidal clusters that non-specifically interfere with biological assays, as discussed here.

 

The largest set, at 67 members, consists of PAINS, or pan-assay interference compounds, which we first wrote about in 2010. These are themselves divided into various subcategories: non-specific electrophiles such as curcumin and an isothiazolone, redox cyclers such as quinones, contaminants such as the decomposition products of certain fused tetrahydroquinolines, miscellaneous, metal chelators, and additional mechanisms including optical interference and singlet oxygen quenchers, which are particularly problematic in AlphaScreen assays.  

 

The last set consists of 31 compounds that can cause problems in phenotypic assays. Some of these non-specifically disrupt cell membranes. Others have well-defined but toxic effects, such as interfering with tubulin or intercalating into DNA. Such bioactivity is not always a bad thing: some of these molecules, such as topotecan and colchicine, are approved drugs, but it’s useful to be aware of whether these types of activities will affect your assay.

 

One criticism of the PAINS concept is that it lumps together multiple mechanisms. (Pete Kenny wrote about this recently.) Another criticism is that, by focusing on chemical substructures, true hits may be unfairly deprioritized based on structure alone. What’s nice about the CONS list is that the potentially interfering mechanisms of each molecule are documented and categorized so they can be considered when establishing an assay. For example, you may not care whether a compound interferes in a phenotypic assay if you are performing a screen on an isolated enzyme.

 

The entire set of compounds is available from Enamine, and additional vendors are provided in a supplementary table. If you’re doing a lot of assays, particularly on new targets and mechanisms, it may be worth testing the CONS to understand what kinds of false positives might occur.

Catching virtual cheaters

As experienced practitioners of fragment-based lead discovery will know, the best way to avoid being misled by artifacts is to combine multiple methods. (Please vote on which methods you use if you haven’t already done so.) Normally this advice is for physical methods, but what’s true in real life also applies to virtual reality, as demonstrated in a recent J. Med. Chem. paper by Brian Shoichet and collaborators at University of California San Francisco, Schrödinger, and University of Michigan Ann Arbor.

 

The Shoichet group has been pushing the limits of computational screening using ever larger libraries. Five years ago they reported screens of more than 100 million molecules, and today multi-billion compound libraries are becoming routine. But as more compounds are screened, an unusual type of artifact is emerging: molecules that seem to “cheat” the scoring function and appear to be virtual winners but are completely inactive when actually tested. Although rare, as screens increase in size these artifacts can make up an increasingly large fraction of hits.

 

Reasoning that these types of artifacts may be peculiar to a given scoring function, the researchers decided to rescore the top hits using a different approach to see whether the cheaters could be caught. They started with a previous screen in which 1.71 billion molecules had been docked against the antibacterial target AmpC β-lactamase using DOCK3.8, and more than 1400 hits were synthesized and tested. These were rescreened using a different scoring approach called FACTS (fast analytical continuum treatment of solvation). Plotting the scores against each other revealed a bimodal distribution, with most of the true hits clustering together. Of the 268 molecules that lay outside of this cluster, 262 showed no activity against AmpC even at 200 µM.

 

Thus encouraged, the researchers turned to other studies in which between 32 and 537 compounds had been experimentally tested. The top 165,000 to 500,000 scoring hits were tested using FACTS, and 7-19% of the initial DOCK hits showed up as outliers and thus likely cheaters. For six of the targets, none of these outliers were strong hits. For each of the other three, a single potent ligand had been flagged as a potential cheater.

 

To evaluate whether this “cross-filtering” approach would work prospectively as well as retrospectively, the researchers focused on 128 very high scoring hits from their previous AmpC virtual screen that had not already been experimentally tested. These were categorized as outliers (possible cheaters) or not and then synthesized and tested. Of the 39 outliers, none were active at 200 µM. But of the other 89, more than half (51) showed inhibition at 200 µM, and 19 of these gave K_i values < 50 µM. As we noted back in 2009, AmpC is particularly susceptible to aggregation artifacts, so the researchers tested the ten most potent inhibitors and found that only one formed detectable aggregates.

 

In addition to FACTS, the researchers also used two other computational methods to look for cheaters: AB-FEP (absolute binding free energy perturbation) and GBMV (generalized Born using molecular volume), both of which are more computationally intensive than either FACTS or DOCK. Interestingly, GBMV performed worse than FACTS, finding at best only 24 cheaters but also falsely flagging 9 true binders. AB-FEP was better, finding 37 cheaters while not flagging any of the experimentally validated hits.

 

This is an important paper, particularly as virtual screens of multi-billion compound libraries become increasingly common. Indeed, the researchers note that “as our libraries grow toward trillions of molecules… there may be hundreds of thousands of cheating artifacts.”

 

And although the researchers acknowledge that their cross-filtering aproach has only been tested for DOCK, it seems likely to apply to other computational methods too. I look forward to seeing the results of these studies.

How to avoid metal artifacts

Back in 2017 we observed with characteristic subtlety that “heavy metals suck.” That post described a hit-finding campaign which foundered when the apparent activity of the fragments turned out to be due to contaminating zinc. A new paper in J. Med. Chem. by Thomas Gerstberger, Peter Ettmayer, and colleagues at Boehringer Ingelheim (BI) describes a similar story, along with suggestions of how to avoid being misled.

 

BI had a collaboration with FORMA Therapeutics that entailed screening roughly 1.7 million compounds against ten targets using biochemical and cell-based assays. The effort resulted in chemical probes against BCL6 and SOS1 and a clinical compound against the latter. Another target was the activated (GTP-loaded) form of KRAS^G12D. Of the 6917 hits from the primary AlphaScreen assay, 1535 gave dose-response curves and passed various counter screens. Of these, 87 representative compounds were tested in STD NMR and thermal shift assays. Only seven confirmed by STD NMR, but these did not confirm by SPR or crystallography.

 

In parallel, the researchers were successfully using FBLD to develop inhibitors of KRAS^G12D, which we wrote about here. Some of the fragment hits were structurally similar to those from the HTS screen, and further searching of the FORMA library led to fairly potent (high nanomolar or low micromolar) hits in the AlphaScreen assay. Two of these even yielded crystal structures, though despite their chemical similarity to one another they bound to the protein in completely different orientations.

 

Unfortunately, follow-up work “revealed erratic structure-activity relationships,” and upon resynthesis the compounds were much less active. At this point the researchers became suspicious, and analyses of the original samples showed they contained >20,000 ppm of palladium contamination. Furthermore, PdCl₂ itself turned out to be a low micromolar inhibitor in the assay.

 

Metals are frequently used as catalysts or reagents in organic synthesis and can be difficult to completely remove during purification. Worse, their presence is often not detectable using standard purity assessments such as HPLC and NMR. Particularly in the case of fragments, which are expected to have low affinities, a small amount of metal contaminant could give a reasonable-looking but misleading signal in an assay.

 

To avoid this problem in the future the researchers developed a Metal Ion Interference Set, or MIIS, consisting of a dozen different metal ions and other salts, all soluble in DMSO so as to be compatible with typical screens. The MIIS is now routinely screened before initiating HTS campaigns, and the results of 74 assays are summarized in the paper. Pd²⁺, Au³⁺, and Ag¹⁺ are particularly nasty, often giving IC₅₀ values < 1 µM, but every metal gave IC₅₀ values < 10 µM in at least two assays. Biochemical assays such as AlphaScreen or TR-FRET were more susceptible to artifacts, with 20.9% showing IC₅₀ < 10 µM, while biophysics assays such as mass spectrometry were better behaved, with only 2.3% showing IC₅₀ < 10 µM. Cellular assays were also surprisingly robust, with 6.3% showing IC₅₀ < 10 µM.

 

This is a nice paper showing that even a massive screen may produce no useful chemical matter. Soberingly, the fact that some of the fragments gave reasonable-looking crystal structures even though the functional activity came from metal contaminants is a salutary reminder that just because you have a crystal structure of a bound ligand doesn’t mean you have a viable starting point.

 

Forewarned is forearmed, and the MIIS appears to be a valuable tool for assessing assay sensitivity to metal ions, which are all too often lurking invisibly in compound samples.

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.

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.

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.

A Liability Predictor for avoiding artifacts?

False positives and artifacts are a constant source of irritation – and worse – in compound screening. We’ve written frequently about small molecule aggregation as well as generically reactive molecules that repeatedly come up as screening hits. It is possible to weed these out experimentally, but this can entail considerable effort, and for particularly difficult targets, false positives may dominate. Indeed, there may be no true hits at all, as we noted in this account of a five-year and ultimately fruitless hunt for prion protein binders.

 

A computational screen to rapidly assess small molecule hits as possible artifacts would be nice, and in fact several have been developed. Among the most popular are computational filters for pan-assay interference compounds, or PAINs. However, as Pete Kenny and others have pointed out, these were developed using data from a limited number of screens in one particular assay format. Now Alexander Tropsha and collaborators at University of North Carolina Chapel Hill and the National Center for Advancing Translational Science (NCATS) at the NIH have provided a broader resource in a new J. Med. Chem. paper.

 

The researchers experimentally screened around 5000 compounds, taken from the NCATS Pharmacologically Active Chemical Toolbox, in four different assays: a fluorescence-based thiol reactivity assay, an assay for redox activity, a firefly luciferase (FLuc) assay, and a nanoluciferase (NLux) assay. The latter two assays are commonly used in cell-based screens to measure gene transcription. The thiol reactivity assay yielded around 1000 interfering compounds, while the other three assays each produced from 97 to 142. Interestingly, there was little overlap among the problematic compounds.

 

These data were used to develop quantitative structure-interference relationship (QSIR) models. The NCATS library of nearly 64,000 compounds was virtually screened, and around 200 compounds were tested experimentally for interference in the four assays, with around half predicted to interfere and the other half predicted not to interfere. The researchers had also previously built a computational model to predict aggregation, and this – along with the four models discussed here – have been combined into a free web-based “Liability Predictor.”

 

So how well does it work? The researchers calculated the sensitivity, specificity, and balanced accuracy for each of the models and state that “they can detect around 55%-80% of interfering compounds.”

 

This sounded encouraging, so naturally I took it for a spin. Unfortunately, my mileage varied. Or, to pile on the metaphors, lots of wolves successfully passed themselves off as sheep. Iniparib was recognized correctly as a possible thiol interference compound. On the other hand, the known redox cycler toxoflavin was predicted not to be a redox cycler – with 97.12% confidence. Similarly, curcumin, which can form adducts with thiols as well as aggregate and redox cycle, was pronounced innocent. Quercetin was recognized as possibly thiol-reactive, but its known propensity to aggregate was not. Weirdly, Walrycin B, which the researchers note interferes with all the assays, got a clean bill of health. Perhaps the online tool is still being optimized.

 

At this point, perhaps the Liability Predictor is best treated as a cautionary tool: molecules that come up with a warning should be singled out for particular interrogation, but passing does not mean the molecule is innocent. Laudably, the researchers have made all the underlying data and models publicly available for others to build on, and I hope this happens. But for now, it seems that no computational tool can substitute for experimental (in)validation of hits.

A rule of two for using chemical probes?

Earlier this year we highlighted the growth of the Chemical Probes Portal, a free website that profiles more than 500 small molecules targeting more than 400 proteins. Each chemical probe is evaluated by experts based on published literature and then scored for use in cells or in vivo. More than 300 chemical probes have received three or four stars and are thus recommended. But even a good probe can be misused, and this is the subject of a recent (open-access) Nat. Comm. paper from Adam McCluskey, Lenka Munoz, and colleagues at the University of Sydney and the University of Newcastle. (The paper has also been discussed by Paul Workman and Derek Lowe.)

 

The researchers chose eight probes targeting histone methyltransferases, a histone demethylase, a histone acetyltransferase, and several kinases. All but one of these probes had first been disclosed before 2015. A literature search revealed 662 papers that used these probes in cellular studies, ranging from 21 to 134 publications per probe.

 

Centuries ago the alchemist Paracelsus noted that everything is poisonous at high enough doses, and indeed even the best probes might hit dozens or hundreds of protein targets. For this reason the Chemical Probes Portal recommends maximum concentrations for cellular assays. The researchers examined whether papers exceeded these concentrations. The overall results were encouraging, with just 22% of papers exceeding recommended limits. However, there was considerable variation: for one chemical probe, 70% of papers exceeded the limit. (For this particular case, the maximum recommended cellular concentration was just 250 nM.)

 

Because chemical probes can have off-target activity even at recommended concentrations, best practices are to include a related but inactive control compound plus a second chemically differentiated probe. All but one of the eight probes chosen for analysis had orthogonal probes available, and five had inactive controls. So how frequently were these used? Unfortunately, 58% of papers did not use an orthogonal probe, and a whopping 92% of papers did not use available inactive control compounds. In fact, just 4% of the papers “used chemical probes within the recommended concentration range and included inactive compounds as well as orthogonal chemical probes.”

 

A wider analysis of nearly 15,000 papers that cited the 662 publications produced similar results, with 17% exceeding recommended concentrations, 59% not using differentiated chemical probes, and 83% not using inactive controls.

 

The researchers propose a “'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.” To encourage best practices, the paper provides a simple “Researchers’ Flowchart” to help investigators select probes and controls. And because science is self-regulated, they provide a five-item “Reviewers’ Checklist.” The paper also includes a nice list of links to other resources, including webinars and slide decks.

 

Overall I think following these guidelines would be beneficial, and the Reviewers’ Checklist in particular could be usefully incorporated into journal publication requirements.

 

Of course, the vast majority of protein targets don’t have even a single good chemical probe, let alone two or more. Which means that there are plenty of opportunities to identify new probes and make better use of those that already exist.

The Chemical Probes Portal at Eight

Back in 2015, Practical Fragments highlighted a new resource calling itself “The Chemical Probes Portal.” At the time it included just seven probes, and my post concluded, “I hope this takes off. Understanding the natural world is hard enough even with well-behaved reagents and carefully controlled experiments.”

 

Well, take off it has, as illustrated by a new (open access) paper in Nucleic Acids Res. by Susanne Müller (Goethe University Frankfurt), Bissan Al-Lazikani (MD Anderson Cancer Center), Paul Workman (Institute of Cancer Research), and collaborators.

 

The paper notes that “the widespread use of small molecule compounds that are claimed as chemical probes but are lacking sufficient quality, especially being inadequately selective for the desired target or even broadly promiscuous in behavior, has resulted in many erroneous conclusions in the biomedical literature.” As an antidote, the Portal is an “expert review-based public resource to empower chemical probe assessment, selection, and use.”

 

Any scientist can suggest a potential probe, and these are then internally reviewed and curated. Assuming enough public information is available about the molecule, probes are then sent to three members of a Scientific Expert Review Panel for further vetting. Reviewers rate probes from one to four stars for use in cellular and/or animal models and recommend relevant concentration ranges. Importantly, reviewers can also include comments to highlight off-targets, lack of certain data, oral bioavailability, or anything else.

 

From a mere seven probes in 2015 the Portal has grown to include more than 500 molecules covering more than 400 protein targets in about 100 protein families. About two thirds of the probes have three or more stars, meaning they are recommended. The Portal is very easy to use and can be searched by probe or protein. Laudably, all the data can also be easily downloaded in bulk.

 

In addition to the chemical probes, the Portal also contains around 250 “Historical Compounds” that have been described in the literature but “are not recommended to be used to study the function of specific proteins as they are seriously flawed.” These include molecules such as gossypol, a known aggregator that has been reported as an inhibitor of multiple proteins, and curcumin. If you see a molecule used as a probe in the literature, it’s worth checking to see whether it shows up in the Portal.

 

The Chemical Probes Portal features heavily in a Conversation between Cheryl Arrowsmith (Structural Genomics Consortium) and Paul Workman published (open access) last year in Nat. Commun. The researchers concisely define chemical probes as “small-molecule modulators to interrogate the functions of their target proteins, as opposed to protein location, or other physical properties.” Importantly, they differentiate chemical probes from drugs. “Drugs don’t necessarily need to be as selective as high-quality chemical probes. They just need to get the job done on the disease and be safe to use. In fact, many drugs act on multiple targets as part of their therapeutic mechanism.” I have frequently heard people make comments such as, “this is just a probe, not a drug,” but a good probe should actually be more selective than many drugs.

 

That said, you do want a drug to actually hit the target of interest. The researchers highlight iniparib, a putative PARP inhibitor that made it all the way to phase 3 clinical trials for breast cancer and was tested in >2500 cancer patients. It failed. Moreover, that failure cast a pall over the field which likely delayed the development of actual PARP inhibitor drugs.

 

The researchers also discuss aggregators, which are still being reported uncritically in the literature, along with PAINS. “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.”

 

This statement raised the hackles of Pete Kenny. In a recently published critique, he states: “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.”

 

Dear reader, please try the following experiment. Enter “iniparib supplier” in your favorite search engine and see what comes up. For me, the first 10 results include several that describe it as a PARP inhibitor. I won’t link to them here because I don’t want to encourage traffic to their sites. (This is also part of the reason Practical Fragments has discontinued PAINS shaming, as it only increases the profile of sloppy or harmful papers.)

 

Pete goes on to write: “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.”

 

Frankly, I don’t understand Pete’s position, which I parodied here. Life is short and biology is complicated, so why waste time with dirty or inadequately characterized reagents? For me, everything is an artifact until proven otherwise. And the Chemical Probes Portal goes a long way towards demonstrating whether a particular probe is fit for purpose.

The agony and ecstasy of thiazoles

Earlier this year we highlighted an analysis of rings found in drugs. Thiazoles are tied for thirteenth place, occurring in at least 30 drugs. (A substructure search in DrugBank pulls up 49.) They pack a lot of diversity into just 5 heavy atoms, with a nitrogen atom capable of acting as a hydrogen bond acceptor as well as a sulfur atom. But they can also be tricksy: an analysis several years ago found that 2-aminothiazoles are over-represented as hits in fragment screens but are often not advanceable. A new open-access paper in ACS Med. Chem. Lett. by Rok Frlan and collaborators at the University of Ljubljana confirms and broadens these conclusions.

 

The researchers assembled a library of 44 fragment-sized 1,3-thiazoles and five 1,3,4-thiadizaozles. These were then screened at 0.5 or 0.625 mM against four unrelated enzymes in biochemical assays. Two of the enzymes contain catalytic cysteine residues, and these had high hit rats: 14 hits for the SARS-CoV-2 3CL^pro enzyme and 26 for the E. coli MurA enzyme. In contrast, MetAP1a had only 3 hits, while DdlB had none. Are any of these hits real?

 

None of the compounds had been classified as PAINS, and aggregation was deemed unlikely for all but one compound based on chemical searches and the presence of detergent in the assays for MurA, DdlB, and 3CL^pro. One compound also seemed to interfere with the fluorescent assays and was ruled a false positive. So far, so good.

 

However, 8 of the compounds turned out to be unstable in aqueous buffer. Moreover, four compounds turned out to be redox active in at least one of three different assays. Redox cycling can generate reactive oxygen species, which inhibit cysteine-dependent proteins nonspecifically.

 

Next the researchers tested to see whether their fragments reacted with a small test thiol, 5-mercapto-2-nitrobenzoic acid. Shockingly, 19 of them did, and most of these inhibited at least one of the enzymes. Many of these contain potential leaving groups such as halogen atoms, but some didn’t, leaving the nature of the reaction unclear. Still, the results suggest that the fragments are more thiol-specific than protein-specific, and so another potential source of false leads.

 

When the researchers retested the ability of the fragments to inhibit the enzymes in the presence of the reducing agent DTT, only one of the CL^pro hits reproduced – and that was the compound that showed fluorescence interference. The results were not quite so bad for MurA, though many hits fell out.

 

Finally, the researchers tried to correlate reactivity with quantum-mechanical calculations using several different methods. Unfortunately, as they note, “no meaningful relationships were observed.” Laudably, data for all the compounds are provided, so interested readers are free to try their own analyses.

 

In the end it is not clear whether any of the hits will be useful, but the high correlation between pathological mechanisms and activity does not make one optimistic. As the first paragraph above makes clear, this does not mean that thiazoles should be avoided. Indeed, the researchers explicitly state that “we do not want to establish a general knockout criterion to exclude thiazole or thiadizaole screening hits from further development, but it is essential to evaluate their reactivity if they prove to be hits.” This is where orthogonal biophysical methods, such as crystallography, can distinguish true hits from artifacts.

An epidemic of aggregators, and suggestions for cures

COVID-19 has been with us for over two years now. While the human effects have been unquestionably negative, for science it has been the best of times and the worst of times. The development of remarkably effective vaccines in less than a year stands as a triumph of twenty-first century medicine, as does the discovery of nirmatrelvir, a covalent inhibitor of the SARS-CoV-2 main protease M^pro (also called 3CL-Pro). But there is a lot of junk-science out there too, as illuminated in a recent J. Med. Chem. paper by Brian Shoichet and colleagues at University of California San Francisco.

 

Before vaccines and custom-built drugs were developed, labs everywhere started screening all the compounds they could get against targets relevant for COVID-19. The most popular molecules to test were approved drugs, the idea being that if any of these turned out to be effective they could immediately be put to use.

 

One of the most common artifacts in screening is caused by aggregation: small molecules can form colloids that non-specifically inhibit a variety of different assays. This phenomenon has been understood for more than two decades; Practical Fragments wrote about it back in 2009. Unfortunately, many labs ignore it.

 

The UCSF lab investigated 56 drugs that had been reported in 12 papers as inhibitors against two targets relevant for SARS-CoV-2, including 3CL-Pro. The molecules were characterized in multiple assays: particle formation and clean autocorrelation curves in dynamic light scattering (DLS), inhibition of an aggregation-sensitive enzyme in the absence of detergent but no inhibition in the presence of detergent, and a high Hill slope in the dose-response curve. Nineteen molecules, four of them fragment-sized, were positive in most of these assays, clearly indicating aggregation. (Interestingly, several of these gave reasonable Hill slopes (<1.4), and the researchers suggest this be a “soft criterion.”) Another 14 molecules gave more ambiguous results, such as forming particles by DLS but not inhibiting the sentinel enzyme.

 

OK, so maybe the molecules are aggregators, but perhaps they also act legitimately? Unfortunately, of the 12 drugs reported in the literature to inhibit 3CL-Pro, only two inhibited the enzyme in the presence of detergent, and one of these was five-fold less potent than reported. And as the researchers point out, detergent is not a magic elixir, and sometimes only right-shifts the onset of aggregation. Moreover, of the 19 molecules conclusively found to be aggregators, detergent was not included for 15 of them in the original publications. Brian may be too polite to write this, but channeling my inner Teddy, I would argue that the authors are negligent for failing to test for aggregation, as are the editors and reviewers who allowed these papers to be published.

 

And the problem is not confined to the COVID-19 literature. The researchers examined a commercial library of 2336 FDA-approved drugs, 73 of which are known aggregators. Another 356 were flagged in the very useful Aggregation Advisor tool (see here), and 6 of 15 experimentally evaluated tested positive in all the aggregation assays.

 

How do you avoid being misled by these artifacts? An extensive suite of tools for assessing aggregation is provided in a recent Nat. Protoc. paper by Steven LaPlante and colleagues at Université du Québec and NMX. The procedures are described in sufficient detail that they “can be easily performed by graduate students and even undergraduate students.”

 

Most of the focus is on various NMR techniques, such as one we wrote about here. The easiest is an NMR dilution assay, in which a 20 mM solution of a compound in DMSO is serially diluted into aqueous buffer at concentrations from 200 to 12 µM. If the number, shape, shift, or intensities of the NMR resonances changes, aggregation is likely.

 

Another assay involves testing compounds in the absence and presence of various detergents, including NP40, Triton, SDS, CHAPS, Tween 20, and Tween 80. Again, changes in the NMR spectra suggest aggregation.

 

The researchers note that “no one technique can detect all the types of aggregates that exist; thus, a combination of strategies is necessary.” Indeed, the various techniques can distinguish different types of aggregates which can vary in size and polydispersity. On a lemons-to-lemonade note, these “nano-entities” might even be useful for “drug delivery, anti-aggregates, cell penetrators and bioavailability enhancers.”

 

We live in the age of wisdom and the age of foolishness. As scientists – and as people – it is our responsibility to aspire to the former by being aware of “unknown knowns,” such as aggregation. And perhaps, by even taking advantage of the weird phenomena that can occur with small molecules in water.

STD NMR on putative SARS-CoV-2 main protease ligands

Over the past sixteen months SARS-CoV-2 has infected more than 146 million people worldwide and killed over 3 million. Highly effective vaccines are now available, but not everywhere, and how long the vaccinations will last as new variants arise remains unknown. COVID-19 will likely be with us indefinitely, necessitating drugs as well as vaccines.

 

More than a year ago we highlighted the COVID Moonshot effort, which began with a crystallographic screen against the essential main protease (M^pro) to find starting points for drug discovery. In a new open-access J. Biomol. NMR paper, Ioannis Vakonakis and collaborators at University of Oxford and University of Patras have attempted to characterize some of the hits using saturation transfer difference NMR (STD NMR, see here for a brief description).

 

The researchers had access to a 950 MHz NMR (jealous much?). Samples were screened at 10 µM protein using irradiation of a M^pro methyl group with a chemical shift at 0.5 ppm. To try to minimize differences in relaxation parameters among different ligands, only the strongest STD signals in aromatic moieties were examined.

 

Of 39 non-covalent ligands discovered in the crystallographic screen, five either did not produce an NMR signal or the spectra were inconsistent with the expected structures, suggesting the ligands may be insoluble or unstable in aqueous buffer. The remaining 34 compounds were nominally screened at 0.8 mM each, but the reference spectra differed in intensity by as much as 15-fold, suggesting dramatic differences in concentration. Since the strength of an STD signal is related to both affinity and concentration, this could obviously complicate interpretation of results. As we’ve written previously, careful curation of your library is essential.

 

Of thirteen active site ligands, only four showed strong STD signals. Dose-response titrations between 0.05 and 4 mM revealed dissociation constants of 1.6-1.7 mM for two of them, with the other two being too weak to accurately measure. Molecular dynamics simulations starting with the known structures were consistent with these results, with the tighter binders tending to maintain their positions more than the weaker binders.

 

The researchers also characterized 650 elaborated molecules from the COVID Moonshot, some of which had been reported to be nanomolar inhibitors. Disturbingly, 35 gave no NMR signal and another 86 yielded weak signals. Among those remaining there was a weak correlation (R²=30%) between IC₅₀ in an enzymatic assay and the STD_ratio (integrated signal intensity of peaks in the STD spectrum over reference spectrum). STD NMR is not appropriate for molecules with K_d < 10 µM, so the researchers also used a competition experiment in which four putative high-affinity molecules would compete a weaker “spy” fragment. This exercise confirmed two ligands but not two others, calling into question their mechanism.

 

STD NMR is often used as part of an assay cascade prior to attempting crystallography, but as crystallography throughput increases there is a case for starting with crystallography, as we argued five years ago. Results from the 39 crystallographic hits perhaps gives pause to that notion, or at least emphasize the need for confirmatory assays. It is easy to be seduced by a high-resolution structure, but because of its sensitivity crystallography may identify ligands so weak as to be unadvanceable. As for the 650 elaborated molecules, it’s too early to draw conclusions, though it’s good to always be on the lookout for false positives.

 

Hopefully the COVID Moonshot will ultimately lead to drugs against SARS-CoV-2. But even if it doesn’t, the intensive focus of multiple techniques on a few proteins is providing useful guidance and best practices that will be applicable to other targets.