Poll results: fragment finding methods and structural information needed for fragment-to-lead efforts

Our most recent poll asked about fragment finding methods. The poll ran from September 21 through November 8 and received 135 responses from 20 countries. Two thirds of these were from the US, about 12% were from the UK, 4% from Germany, 3% from the Netherlands, and 2% from Australia.

 

The first question asked how much structural information you need to begin optimizing a fragment. In contrast to 2017, when we first asked this question, crystallography has significantly increased at the expense of the other choices. 

 

 

I confess to being surprised, as I expected that by now people would be more comfortable beginning optimization in the absence of structural information, an approach that has been quite successful as discussed in a 2019 open-access Cell Chemical Biology review by Ben Davis, Wolfgang Jahnke, and me. Perhaps the increasing speed and accessibility of new methods has so lowered the bar to getting crystal structures that people have the luxury of waiting. Of course, with an online poll there is always the risk that many respondents from the same organization may skew the results.

 

The second question asked which methods you use to find and validate fragments. This is the fifth time we’ve run this poll, starting in 2011. As with our first question, X-ray crystallography came out on top, with nearly 80% of respondents choosing it. This was followed by SPR, at 67%, and thermal shift and ligand-detected NMR, each around 55%. 

 

 

Functional screening was used by nearly half of respondents, with computational methods, protein-detected NMR, and literature starting points used by around a third. Mass spectrometry and ITC were each used by slightly more than a quarter of respondents.

 

For the first time we asked about cryo-EM, and nearly 20% of respondents reported using this technique.

 

MST and affinity-based methods each came in at 13%, with just 4% of respondents using BLI, and 5 individual respondents using other methods. I’d be curious to know what these are.

 

The average respondent reported using just over 5 different techniques, which is down slightly from 6 in 2019 but up from 4 in 2016. Using multiple orthogonal methods is clearly well established as best practice, even if the precise number varies.

 

How do these results compare with your own practices?

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.

Which cryptic sites are ligandable, and why?

Many interesting proteins have flat, featureless surfaces, lacking the deep pockets in which small molecules usually bind. But structures can be deceptive: crevasses can open unexpectedly, revealing “cryptic sites” for ligands. Or not – just because a site is available does not mean it is ligandable (able to bind to ligands with high affinity). A new (open access) paper in Drug Disc. Today by Sandor Vajda and collaborators at Boston University and Stony Brook University asks “which cryptic sites are feasible for drug targets?” (Sandor presented some of this at FBLD 2024 last month.)

 

To get started, the researchers turned to the aptly named CryptoSite, a previously published list of 93 proteins where unexpected pockets had been found. Each protein has at least two published crystal structures, one in the apo form and one with a ligand bound to the (no longer) cryptic pocket. Cryptic sites form primarily through two mechanisms. In the first, amino acid side chains move aside, opening a pocket. In the second, larger motions occur in protein loops or secondary structural elements, such as alpha helices, creating pockets.

 

Of the 18 cases for which cryptic sites formed primarily through the movement of side chains, ten had published affinities for the ligands, and all of these were weak, with the best being low micromolar. In contrast, of the 27 cryptic sites created by loop movements for which affinity information was available, all but two were nanomolar binders. From this evidence, the researchers suggest that cryptic sites formed only by the motion of side chains are not sufficient to support high affinity ligands. Why?

 

The researchers note that side chain motions occur very rapidly, on a timescale of 10^-11 to 10^-10 seconds, much faster than ligand binding, which at its fastest is 10^-8 seconds. Thus, “a fast-moving side chain that spends a substantial fraction of time in the pocket interacting with the other residues competes with ligands for binding and, hence, acts as a competitive inhibitor.” This intuitive picture is supported in the paper by mathematical simulations.

 

In contrast, loop movements occur on 10^-9 to 10^-6 second timescales, while the movements of secondary structure elements are even slower. Thus, a ligand could bind while the cryptic site is open, and, like a wrench in a machine, keep it open.

 

This finding is important. As the researchers point out, the molecular dynamics calculations frequently used to find cryptic pockets are typically run at short timescales likely to miss loop movements. Other computational methods used to assess ligandability may also suffer; the researchers note that their program FTMap, which we’ve written about here and here, overestimates the ligandability of cryptic sites created by side chain movements.

 

Of course, just because a cryptic site is created by loop movements does not mean it is ligandable, as we discussed for interleukin-1β. And the researchers acknowledge that covalent inhibitors might be able to take advantage of less traditionally ligandable sites, cryptic or otherwise. Certainly this has been the case for KRAS. I’m confident that many more examples will be forthcoming.

Fragments vs LpxC revisited

Back in 2020 we described fragment-derived inhibitors of the highly conserved bacterial enzyme LpxC, which is essential for biosynthesis of the outer membrane in Gram-negative bacteria. In a recent (open access) paper in J. Med. Chem., a different group consisting of Ralph Holl and collaborators at Universität Hamburg and several other academic centers describe a new series.

 

The researchers started with compound 9, a molecule they had previously discovered. The substrate for LpxC is a rather large small molecule called (UDP)-3-O-[(R)-3-hydroxymyristoyl]-N-acetylglucosamine. Compound 9 does not occupy the UDP-binding site, so the researchers initially tried building towards it with a series of simple linkers connected to a phenyl group. The (S) enantiomers tended to be more active than the (R)-enantiomers, and the most potent was compound (S)-13a, which showed sub-micromolar activity against LpxC from E. coli as well as P. aeruginosa in an enzymatic assay. (For simplicity only the E. coli data are shown here.)

 

Seeking to improve affinity, the researchers screened 650 fragments in pools of five against LpxC in the presence of compound 9 using STD NMR and WaterLOGSY. After deconvolution, this led to 97 hits. STD-based epitope mapping, which we wrote about here, was used to prioritize fragments likely to have a single, well-defined binding mode, culling the number to 19. Finally, NMR-ILOE experiments (see here) suggested that nine of this set bound in close proximity to compound 9, while the other ten did not. Four of these fragments, including the simple indole F3, were then linked to compound 9 at various positions. This is akin to SAR by NMR, but with less information about the relative binding modes so more trial and error is necessary.

 

Among the roughly two dozen molecules made, compound (S)-13j was the most potent against LpxC, with low nanomolar activity. This compound (and several others) also showed antibacterial activity against E. coli and several other strains of Gram-negative bacteria. In vitro stability studies of compound (S)-13j were promising, though the researchers noted the need for improvement. And, since the molecule contains a hydroxamic acid moiety potentially capable of binding to multiple metalloproteins, it was tested against a handful of mammalian zinc-dependent enzymes and shown to be nearly inactive.

 

Compound (S)-13j is 15-fold more potent than the simple phenyl analog (S)-13a, and molecular modeling suggested this may be due to a hydrogen bond from the protein to the indole NH. Although one could argue that it would have been possible to arrive at compound (S)-13j using standard medicinal chemistry starting from (S)-13a, this may have taken longer without knowledge of the indole fragment. Whether or not the molecules advance further, this is a nice example of using fragment screening to find a second-site binder to improve affinity of an existing lead.

Fragments vs KAT6A: ligand efficiency in action

Epigenetic writers such as histone acetyltransferases (HATs) control gene expression, which often goes awry in cancer. The gene encoding the lysine acetyltransferase KAT6A, for example, is amplified in multiple types of cancer, and its overexpression is associated with poor clinical outcomes for patients with ER⁺ breast cancer. A recent paper in Bioorg. Med. Chem. Lett. from Andrew Buesking and colleagues at Prelude Therapeutics reports a new series of inhibitors.

 

The researchers started by considering previously reported molecules such as PF-9363. Recognizing the importance of the central sulfonamide, they generated a library of 150 fragments containing this core and screened them in a biochemical assay. Taking a similar approach as other researchers reporting on a different epigenetic target we discussed last year, the Prelude team explicitly used ligand efficiency to call hits, setting the cutoff at > 0.3 kcal per mol per heavy atom.

 

A direct deconstruction of PF-9363 led to compound 6, but, seeking something novel, the researchers were drawn to compound 8, with similar ligand efficiency. Modifications were made to both of the terminal aromatic groups, leading to compound 13, which was co-crystallized with KAT6A. This led to further structure-guided modifications, with compound 25 being the most potent.

 

Unfortunately, further improvements in potency were not forthcoming, and the ligand efficiency had dropped steadily from both the initial fragment as well as PF-9363. The researchers conclude by stating that they “decided to pursue alternative approaches.” Still, this paper is a nice, concise example of using metrics both for pursuing a chemical series and, ultimately, discontinuing it.

Discovery on Target 2024

Last week Boston hosted CHI's 22^nd Annual Discovery on Target. With dozens of talks spread across seven or eight concurrent tracks over three days, and an additional day of pre-conference symposia, I’ll just touch on a few themes.

 

Computational Approaches

Artificial intelligence and machine learning were well represented. Brandon White described an ML model built at Axiom to predict liver toxicity, responsible for a quarter of clinical trial failures. As we noted last week, good ML models require lots of data, and Axiom has tested 50,000 small molecules in primary human hepatocytes from multiple donors using assays including high-content imaging. Just input a chemical structure and the model will predict toxicity. When run against the FDA’s database of drug-induced liver injury, the model performed with 74% sensitivity and 97% specificity, and even gave good dose predictions.

 

Woody Sherman (Psivant) laid out a series of “grand challenges for computers in drug discovery.” This is the working title for a publication he is spearheading to focus attention on key problems. They fall into five categories: chemistry (including synthesis, stability, and covalency), structure predictions (including protein-ligand structures, dynamics, and cryptic pockets), energetics (including affinity, selectivity, and kinetics), ADME (including everything from solubility and aggregation to bioavailability), and pharmacology (including toxicity). A sixth category, human considerations (including intellectual property and interpreting experimental data), is also being considered.

 

The success of AlphaFold to predict protein structures shows what computers can achieve, but in that case the effort was enabled by massive amounts of high-quality public data in the Protein Data Bank. Few of these challenges can draw on anything approaching the PDB. Indeed, even parameters as seemingly simple as solubility can change dramatically depending on crystal form and subtle changes to pH.

 

Because these computational challenges are so daunting, collecting them into one forum may prove salutary. And other categories may be worth including, such as target discovery. Woody is looking for co-authors, so reach out to him if you’re interested.

 

Covalent approaches

Covalent approaches to drug discovery have gone mainstream, at least if this conference is any indication. But they are not without risk: Doug Johnson (Biogen) described research implicating the piperidine acrylamide pharmacophore in approved BTK inhibitors with inhibition of ALDH1A1 and possible liver injury.

 

Several talks focused on methodologies. Alexander Federation (Talus) described data-independent acquisition (DIA) mass spectrometry methods, which can be more comprehensive than the more commonly used data-dependent acquisition (DDA) methods in identifying peptides in chemoproteomic studies, which we first discussed here. Talus is focused specifically on transcription factors.

 

As we noted earlier this year, Steve Gygi (Harvard) has been at the forefront of increasing the throughput of mass spectrometry methods, and he described how to increase the number of samples that can be analyzed simultaneously from 18 to 35. He also described two approaches, GoDig and CysDig, to look for up to 200 pre-specified proteins in a sample, ensuring identification of even low-abundance targets.

 

Turning to specific targets, Wai Cheung Adrian Chan described work done at Harvard to find covalent inhibitors against deubiquitinating enzymes (DUBs), reporting that screens of a small library of 178 covalent fragments in cell lysates found hits against several dozen DUBs. (We previously wrote about non-covalent USP7 inhibitors.)

 

Brooke Brauer described the optimization of a covalent inhibitor of Bfl-1 at AstraZeneca, an interesting oncology target. AZ has published some nice papers on this project which I’ll write about soon.

 

Last week we mentioned work Michelle Arkin and collaborators had done on 14-3-3 proteins, and Lynn McGregor described work done at Novartis on the same system. A screen of 6000 covalent compounds identified hits that modified a specific cysteine in 14-3-3 more rapidly in the presence of a peptide derived from the estrogen receptor. Stabilizing this interaction could be useful for treating certain cancers.

 

Not everyone is focused on cysteine: Andrea Zuhl described work done at Hyku Biosciences, which as the name suggests is targeting histidine, tyrosine, and lysine. This has necessitated building a fragment library of more than 6000 compounds, more than 70% of which are stable in buffer. Andrea presented one example targeting the catalytic lysine residue of the oncogenic ALK fusion protein, though the selectivity against other kinases was not disclosed.

 

All of these examples focused on covalent molecules in which the warhead is maintained during optimization. But as we first wrote about here, fully functionalized fragments (FFFs) contain a photoreactive moiety that reacts covalently with nearby proteins but is subsequently discarded. Sherry Niessen described how Belharra has industrialized this process by creating a library of about 11,000 FFF probes. Because of the low efficiency of protein crosslinking (typically <5%), most of the library consists of enantiomeric pairs to facilitate hit identification. Also, the average molecular weight of the library is around 350 Da, and these super-sized fragments tend to perform better than the strictly rule-of-three compliant molecules.

 

Covalent success stories

At least two presentations covered covalent fragment-based drug candidates. Shota Kikuchi (Vividion) described the discovery of VVD-214/RO7589831, a WRN inhibitor we wrote about earlier this year. As I speculated at the time, the cyclopropyl group was introduced to lower the reactivity of the vinyl sulfone warhead. Interestingly though, even early molecules were quite selective for WRN. Like sotorasib, binding is largely driven by the k_inact term of k_inact/K_i, again demonstrating that high reactivity for the target does not necessarily mean high chemical reactivity.

 

Finally, in his plenary keynote Steve Fesik (Vanderbilt University) covered multiple success stories, including the discovery of the KRAS^G12C inhibitor BI 1823911, which we wrote about here. Boehringer Ingelheim has since published molecules that hit multiple KRAS mutants as well as KRAS degraders, and Steve noted that all of these contain the same “squirrely-looking” fragment identified from SAR by NMR, an illustration of the power of fragment-based methods to explore new regions of chemical space.

 

I’ll close there, but please add your thoughts. There are is still at least one good conference coming up this year, and 2025 is quickly approaching.

FBLD 2024

The FBLD meetings have always been calendar highlights. Starting in 2008, before Practical Fragments even existed, they have graced cities around the world in 2009, 2010, 2012, 2014, 2016, and 2018. The plan was for 2020 to be held in Cambridge, UK, but for obvious reasons that didn’t happen. Last week, Boston hosted a triumphant return of the event. With more than 30 talks and dozens of posters I’ll just touch on a few major themes.

 

Crystallography

High-throughput crystallography was prevalent, as befits its growing role in fragment finding. (If you haven’t yet voted in our methods poll on the right side of the page please do so!) Debanu Das (XPose Therapeutics) described how crystallographic screens of just a few hundred fragments identified hits against DNA-damage response proteins such as APE1; these have been advanced to high-nanomolar inhibitors with cell activity. And Andreas Pica described the ALPX platform that enabled screening >4000 hits from an HTS screen against PDEδ resulting in >500 structures.

 

The Diamond Light Source was a pioneer in developing high-throughput crystallography methods, and several speakers described continued progress. Blake Balcomb noted that since 2015 they have collected >240,000 datasets and identified >30,000 ligands. Of these, some 3750 have been deposited into the Protein Data Bank.

 

A crystallographic fragment hit is just the start, and Frank von Delft emphasized that “fragment progression is neither fast nor cheap.” His goal is to take a 100 µM binder to a 10 nM lead in less than a week for less than £1000. Toward this end he and his team are using rapid chemical synthesis and crude reaction screening along with various computational approaches and crowd-sourced science. The COVID Moonshot, which we wrote about here, is one model, and Diamond is trying to create a “Moonshot factory” to pursue other viral targets.

 

Computational Approaches

Computational methods are potentially the least expensive fragment-to-lead method, and these were well represented. One challenge is screening the massive chemical space represented by make-on-demand libraries, and Pat Walters (Relay) described how this can be done using Thompson Sampling, an active-learning method that traces its origins to 1933. Applied to lead discovery, the method involves breaking larger molecules into component fragments and iteratively searching for better binders. Pat showed that searching just 0.1% of a library of 335 million molecules consistently found 90% of the best hits.

 

Most computational methods rely on experimental data, and over the past 25 years Astex has generated >100 crystal structures on each of more than 40 targets, with >6600 bound fragments in total. Paul Mortenson described how these are being used to develop generative models, with chemists providing feedback on suggested molecules.

 

Artificial intelligence is the centerpiece of Isomorphic Labs, which has unfettered access to AlphaFold 3. Rebecca Paul described an example starting from a literature fragment in which the predicted affinities matched well with experiment – and the molecules were considerably more potent than those suggested by an experienced medicinal chemist.

 

Recognizing the need for experimental affinity data for fragments, Isomorphic worked with Arctoris to screen 5420 fragments against 65 kinases covering the diversity of the kinome. After carefully curating the data, including rescreening the actives at a different CRO, they found 485 fragments with an IC₅₀ of 300 µM or better. Interestingly, only about half of these fragments are known kinase binders.

 

Sandor Vajda (Boston University) suggested there may be limitations to machine learning models. He found that using AlphaFold 2 to find cryptic pockets was dependent on their representation in the PDB, with rare experimental states not being predicted. Sandor also proposed an interesting hypothesis that cryptic pockets created only by the movement of side chains are not very ligandable because the side chains move on such a rapid time scale that they effectively act as competitive inhibitors to ligands.

 

Success Stories

No FBLD meeting would be complete without success stories, and FBLD 2024 was no exception. Chaohong Sun noted that nearly 80% of the targets at AbbVie taken into fragment-based screening are novel. Of these, more than 80% yield actionable hits, though 44% are not pursued for a variety of reasons, including finding hits from other sources, hits at novel sites with no obvious function, and changes to the portfolio. Chaohong described a series of STING agonists that was taken forward to low nanomolar leads with in vivo activity.

 

Michelle Arkin (UCSF) described progress on creating molecular glues to link 14-3-3 proteins to the estrogen receptor, which we last wrote about here. Covalent binders to the 14-3-3 protein stabilize the interaction with ERα by more than 100-fold and show activity in cancer cell models.

 

Multiple talks focused on SARS-CoV-2 targets. Ashley Taylor (Vanderbilt) described fragment screens against the papain-like protease PL^Pro that led to both covalent and non-covalent inhibitors. James Fraser (UCSF) described how a massive crystallographic screen against the Nsp3 macrodomain Mac1 led to high nanomolar compounds, which we wrote about here. And Adam Renslo (UCSF) discussed the further optimization of Mac1 inhibitors to yield molecules that could protect mice from a fatal challenge of the virus.

 

A drawback of pursuing novel targets is that sometimes the biology proves uncooperative. Andrew Woodhead described a successful fragment screen at Astex against the oncology target elF4E that led to mid-nanomolar binders that could disrupt the protein-protein interaction with eIF4G in cells. Surprisingly, these molecules had no effect on cell viability, and a series of mutational and targeted-protein degradation experiments suggested that blocking a larger region of the protein-protein binding site might be necessary.

 

Drugs are the ultimate success stories, as David Rees reminded participants in “25 years of thinking small.” In addition to providing an overview of FBLD at Astex, David added up the sales of all seven FDA-approved fragment-derived drugs, which totals more than $3 billion. Harder to quantify—though infinitely more valuable—are the added years of life for patients with once-untreatable cancers. These numbers will only grow as the dozens of fragment-derived molecules in the clinic continue to advance.

 

I’ll close on that note. If you missed FBLD 2024, you’ll have another chance next year: FBLD 2025 is planned for Cambridge (UK) September 21-24 next year. Barring global pandemics.

New poll: structural information needed for F2L and fragment-finding methods

With elections taking place around the world, Practical Fragments is getting into the action. Our new poll revisits two questions from past years to see how things have changed.

 

Our first question asks, “how much structural information do you need to begin optimizing a fragment?” When we ran this poll back in 2017 a third of respondents needed crystallography to begin a fragment-to-lead campaign, while only a quarter would move forward with SAR only. But when Wolfgang Jahnke, Ben Davis, and I published a review in 2018 about advancing fragments in the absence of crystal structures, we found an abundance of approaches. It will be interesting to see whether these numbers have shifted.

 

Our second question asks what method(s) you use to find and validate fragments. For consistency with previous polls please click every method you use, whether as a primary screening technique or for validation. Please note too that we’ve added cryo-electron microscopy. You can read about these methods below, and if you select “other” please describe in the comments.

 

• Affinity chromatography, AS-MS, capillary electrophoresis, or ultrafiltration
• BLI (biolayer interferometry)
• Computational screening
• Cryo-EM (cryogenic electron microscopy)
  
• Functional screening (high concentration biochemical, FRET, cell-based, etc.)
• ITC (isothermal titration calorimetry)
• Literature or known fragments
• MS (mass spectrometry, native or covalent)
• MST (microscale thermophoresis)
• NMR – ligand detected
• NMR – protein detected
• SPR (surface plasmon resonance)
• Thermal shift (or DSF)
• X-ray crystallography
• Other

 

Please vote on the right hand side of the page; click the vote button for each question. (If you don’t see the poll you may need to (1) turn off private browsing, since the free Crowdsignal version we use for the blog cannot support surveys in this mode or (2) view web version on your phone.)

Casting light on target-guided synthesis

Target-guided synthesis, in which a protein templates the formation of its own inhibitor, is a concept first proposed decades ago. There are roughly two flavors. Dynamic combinatorial chemistry (DCC) involves reversible formation of the product, and we wrote in 2017 about some of the challenges. Kinetic target-guided synthesis (KTGS) involves irreversible chemistry, for which the options are limited. The classical click chemistry azide-alkyne cycloaddition is so slow that reactions usually take days, which can be a problem for delicate proteins. A recent (open-access) paper in Angew. Chem. Int. Ed. by Cyrille Sabot et al. describes a bright way to accelerate things.

 

The researchers turned to photochemistry, specifically diazirine chemistry. Illuminating 3-trifluoromethyl-3-phenyldiazirines leads to loss of nitrogen and formation of highly reactive carbenes. The carbenes are so hot that they can react indiscriminately with proteins, as we described here. However, the reaction with thiols is faster than the reaction with other functional groups on proteins, so the researchers reasoned that a library of thiols could out-compete the protein.

 

The carbonic anhydrase bCA-II was chosen as a model protein. Sulfonamide-containing molecules such as compound 5 are known to be good inhibitors. This “anchor” molecule was incubated at 60 µM with seven different diazirines, each at 400 µM, in the presence or absence of 30 µM bCA-II and then irradiated with 365 nM light for a few minutes. Most of the reactions produced similar amounts of product in the presence or absence of bCA-II, but compound 1b yielded about threefold more of compound 2d in the presence of bCA-II, suggesting the reaction was being templated by the protein. 

 

Control experiments lend credence to this hypothesis. First, adding a known competitive bCA-II inhibitor reduced the formation of compound 2d to background levels. Second, other proteins did not cause a similar enhancement in the formation of compound 2d. Finally, conducting the experiment with phenylmethanethiol (ie, a variant of compound 5 lacking the sulfonamide moiety essential for interaction with bCA-II) did not cause an enrichment of the photochemical product in the presence of the enzyme.

 

Chiral HPLC was used to show that compound 2d was slightly enriched for the (R)-enantiomer, with an enantiomeric excess of around 10%, when the reaction was conducted in the presence of bCA-II but not in the absence. The two enantiomers were synthesized and tested, and the (R) form did indeed have slightly better activity (300 nM vs 330 nM).

 

This is a thoughtful, well-conducted investigation. But it makes me even less sanguine about the practicality of KTGS for finding new chemical matter, for several reasons. First, the efficiency of the reaction is poor: the researchers calculate the yield of compound 2d at around 1% of the enzyme concentration, so low that they used single-ion monitoring (SIM) mass spectrometry to detect it. Because of this low efficiency, the concentration of enzyme used needs to be quite high.

 

The most serious strike against KTGS is the fact that all of the diazirines generated potent (sub-micromolar) inhibitors. One of them was even slightly better than compound 2d but did not show enrichment in the presence of bCA-II. False negatives seem to be a major problem, as we’ve written previously.

 

One caveat to my caveats is that compound 2d is only marginally more potent than the starting compound 5. NMR experiments conducted with diazirine 1b suggest binding to the protein, though the affinity was not quantified. Perhaps a different fragment linking system, in which both fragments have measurable affinity for the target, would be better suited to demonstrate the utility of KTGS. For now, this paper does a nice job highlighting its drawbacks.

Fragments vs herpesviridae

The name herpes makes most people think of painful ulcers in the mouth, or worse. But herpesviruses are actually a family of viruses that can also cause chicken pox, mononucleosis, and other diseases. Some 95% of adults are infected by at least one type of herpesvirus, and these can become deadly if people become immunocompromised, such as during an organ transplant. A drug that would inhibit all forms of herpesviruses would be useful, and the first steps are described in a recent ACS Med. Chem. Lett. paper by Michael Plotkin and colleagues at Merck.

 

The details of the primary screen are sparse, though the researchers did say they physically screened more than 100,000 compounds to identify molecules such as compound 5, a modest inhibitor of the DNA polymerases from both cytomegalovirus (CMV) and varicella zoster virus (VZV). (For most compounds the paper reports biochemical activity towards both of these polymerases as well as antiviral activity for CMV, VZV, herpes simplex virus 1 (HSV-1), and HSV-2, but for simplicity I’ll only show data for CMV here. The compounds generally have comparable activity towards different viruses.)

 

Hydrogen bond acceptors such as the ketone in compound 5 were found to be essential for activity, and exploring a variety of analogs led to compound 12, which in addition to submicromolar biochemical activity against the DNA polymerases also showed antiviral activity against CMV and other herpesviruses.

 

The paper goes into considerable detail on the lead optimization. The (S) enantiomer of compound 12 was an order of magnitude more potent than the (R) enantiomer. Modifications made to both of the phenyl rings ultimately led to compound 44, with low nanomolar biochemical activity against the polymerases and sub-micromolar antiviral activity against CMV, VZV, HSV-1, and HSV-2. Importantly, the researchers note that they did not have crystal structures during optimization, a useful reminder that structural information is not always necessary.

 

Compound 44 had modest oral bioavailability in rodents, but closely related compound 42 containing a trifluoromethyl group in place of the bromine was better, albeit with slightly lower biochemical potency. This molecule led to high survival rates in mice when dosed either before or after being exposed to HSV-1. In separate studies, the compound reduced CMV viral load. For both HSV-1 and CMV compound 42 compared favorably to acyclovir and ganciclovir, two commonly used drugs.

 

Although there is still some way to go to a drug, the researchers end by promising to describe “further progress of this series.” I look forward to reading about this.

Fragments in Brazil

Most of the fragment events we’ve highlighted are in the US, Europe, and Australia, but that does not fully reflect where all the good science is happening. In a recent ACS Med. Chem. Lett. paper, Carolina Horta Andrade, Maria Cristina Nonato, and Flavio da Silva Emery introduce CRAFT: the Center for Research and Advancement in Fragments and molecular Targets.

 

Established in 2021, CRAFT is a collaboration between the University of Saõ Paulo and the Federal University of Goiás. The center is focused on endemic diseases of Brazil. As the researchers note, only one of the 60 or so fragment-derived drugs that have entered the clinic is an anti-infective, so there is clearly significant need. CRAFT also has an educational and training component reminiscent of the European FragNet and the Australian Centre for Fragment-Based Design.

 

One focus of CRAFT is fragment library design, including underexplored heterocyclic systems. Importantly, the researchers are investigating new synthetic methodologies to be able to functionalize different regions of the fragments. They are also exploring fragments similar to or derived from natural products.

 

Targets are of course essential, and CRAFT is investing in protein production and characterization, such as the enzyme DHODH from Leishmania; we’ve written recently about a fragment approach to the mammalian counterpart.

 

Finally, CRAFT is investing in structure-based design, ligand-based design, and phenotypic screening. And in 2024 no venture would be complete without use of machine learning.

 

Academic laboratories often struggle with downstream drug discovery efforts such as drug metabolism and pharmacokinetics. CRAFT recognizes this and has partnered with the Welcome Centre for Anti-Infectives Research to train participants in DMPK.

 

The researchers “invite the global scientific community to collaborate with us in addressing neglected diseases.” I hope they succeed. Five years ago we highlighted the consortium Open Source Antibiotics, but that site seems to be updated infrequently. The COVID Moonshot has been more successful but is arguably less urgent given the billions of dollars of industry money that poured into research on SARS-CoV-2. From an ethical perspective society should invest more on combating tropical diseases. And as the planet warms, these diseases will increasingly move out of the tropics.