A fragment prodrug discovered in a phenotypic screen

Glioblastoma multiforme (GBM) is a particularly nasty type of brain tumor with few drug options aside from the DNA alkylating agent temozolomide (TMZ), which is toxic and not particularly effective. Drugs fail for multiple reasons, among them the difficulty many small molecules have crossing the blood-brain barrier. A recent Nature paper by Luis Parada and a large group of collaborators at Memorial Sloan Kettering Cancer Center and elsewhere describes a promising new approach.

 

The researchers screened >200,000 molecules (not necessarily fragments) against primary GBM cells to look for compounds that reduced viability. Generically toxic molecules are so common that they (literally) grow on trees, so hits were counter-screened against mouse embryonic fibroblasts to looks for molecules that selectively killed GBM cells. This led to a rule-of-three compliant compound the researchers dubbed gliocidin.

 

Figuring out how gliocidin works turned out to be a complicated quest, starting with a genome-wide CRISPR-Cas9 screen to look for genes that either protected or sensitized cells to gliocidin. Subsequent work, including knocking out specific genes of interest and LC-MS/MS studies of metabolites, revealed that gliocidin leads to inhibition of a protein called inosine monophosphate dehydrogenase 2 (IMPDH2), which is necessary for guanine synthesis.

 

However, gliocidin is not a direct inhibitor of IMPDH2. Rather, it is is essentially a "pro-prodrug". Gliocidin is first converted into gliocidin-monocucleotide by the enzyme NAMPT (a target we wrote about back in 2014), and subsequently converted to gliocidin-adenine dinucleotide (GAD) by the enzyme NMNAT1. Cryo-EM showed that GAD binds at the NAD⁺ cofactor binding site of IMPDH2, blocking enzyme activity.

In addition to being a DNA-alkylating agent, TMZ induces NMNAT1 expression, thereby increasing conversion of gliocidin to GAD. Consistent with this, the combination of gliocidin and TMZ was more effective than either agent alone in mouse xenograft models. This is a lovely paper that reads like a detective story, and I’m only able to scratch the surface in a brief blog post. It also has multiple lessons for FBDD.

 

First, as expected given its molecular properties, gliocidin has excellent brain penetration. Vicki Nienaber argued in 2009 that FBDD may be ideally suited for finding molecules that can cross the blood-brain barrier, and gliocidin is a case in point.

 

Second, this paper answers emphatically in the affirmative the question we posed in 2022: “Is phenotypic fragment screening worthwhile?”

 

Third, this is another example of in situ inhibitor assembly to generate an analog of NAD⁺; we wrote about a small fragment targeting a different protein here. Given that fragments are the size of many metabolites, fragments as prodrugs could be a productive area of research.

 

But such a prodrug approach is not without risks. In that 2014 post about NAMPT inhibitors, I noted that some molecules had poorly characterized off-target activities, which could perhaps now be explained through this type of in situ activation. The new paper found that GAD does not inhibit two different NAD⁺ or NADPH-dependent enzymes, but hitting off-target enzymes will be something to watch for during optimization. I look forward to following this story.

Flatland: still a nice place to be

In 2009 we highlighted a paper reporting that approved drugs have a higher fraction of sp³-hybridized carbon atoms than discovery-phase compounds. Perhaps focusing on molecules with a high Fsp³, the ratio of sp³-hybridzed carbons to total carbons, would lead to greater success. Or not: a new analysis in Nat. Rev. Chem. by Ian Churcher (Janus Drug Discovery), Stuart Newbold, and Christopher Murray (both at Astex) finds that the relationship has not held up.

 

The 2009 “Escape from Flatland” paper was widely discussed at conferences and has been cited more than 3000 times. But according to the authors of the new study, most of these citations are from papers describing new synthetic methodologies rather than from papers discussing medicinal chemistry.

 

And not all the attention has been positive. As we noted in 2013, Pete Kenny and Carlos Montanari reanalyzed the data and found that an apparent correlation between Fsp³ and solubility disappeared when plotting all discrete data points instead of binned data.

 

More recently, we highlighted a paper that found no significant difference between the shapeliness of drugs, as assessed by their principal moment of inertia (PMI), and the shapeliness of small molecules in the ZINC database.

 

The new paper looks at Fsp³ values for drugs approved during various time periods. Among 980 drugs approved up to 2009, the average Fsp³ was 0.458. However, of the 431 drugs approved after 2009, the average Fsp³ has dropped to 0.392. The researchers speculate that this (statistically significant) decrease may be due to an increase in the number of kinase inhibitors, which are usually highly aromatic, as well as an increase in the use of metal-catalyzed cross coupling reactions.

 

In my analysis of the 2009 paper, I asked whether higher Fsp³ ratios would lead to lower hit rates, and indeed this seems to be the case, as shown in a paper we discussed in 2020. Thus, if you pursue difficult targets, you may increase your chances of finding hits by screening molecules with lower Fsp³ ratios. Also, multiple studies, including one published just last month, have found no correlation between the shapeliness of a fragment (as defined by deviation from planarity, or DFP) and the shapeliness of the resulting lead, so there appears to be no penalty to starting with a flattish fragment. 

 

The researchers conclude that their “analysis of drug development trends over the last 15 years suggests that Fsp³ may not have been a useful metric to optimize.” Importantly, the supplementary information includes a list of >1400 approved drugs and >1500 investigational drugs along with associated properties, so you can do your own analyses.

 

In the end, generalizations will only get you so far, and may even lead you astray. At least for now, there are few shortcuts in the long slog of experimental studies necessary to discover a drug.

Stitching together fragments with Fragmenstein

As we noted just last week, crystallography has unleashed a torrent of protein-ligand complexes, especially fragments. Historically a single structure might be used for fragment growing, but so many structures present an embarrassment of riches, with sometimes dozens of fragments that bind in the same region. Merging or linking these fragments can be done manually, as seen here and here, but how to do so when the binding modes are partially overlapping is not always intuitive. In a new open-access J. Cheminform. paper, Matteo Ferla and colleagues at University of Oxford and elsewhere describe an open-source solution called Fragmenstein.

 

We briefly described Fragmenstein in 2023, where it was used to combine pairs of low-affinity fragments bound to the Nsp3 macrodomain of SARS-CoV-2 to generate sub-micromolar inhibitors. The current paper describes the platform in detail.

 

Fragmenstein starts by taking two (or more) structures of fragments bound to a protein and virtually combining them. This is done by collapsing rings to their individual centroids, stitching these together along with their substituents, and then re-expanding the ring(s) so the substituents will be close to where they were in the initial fragments. This process produces a surprising array of molecules beyond the obvious. For example, if one fragment contains a phenyl ring and the other fragment contains a furan ring, the stitched molecule might just contain the phenyl (if the two rings bind in nearly the same position), a benzofuran (merging the rings), a phenyl ring linked to a furan by one or more atoms, or even a spiro compound if the rings are perpendicular to one another.

 

In silico approaches sometimes suggest molecules that are synthetically challenging to make, but Fragmenstein can also be used to find purchasable analogs.

 

Next, the new molecules are energetically minimized, first by themselves and then while docked into the protein. In contrast to other docking programs, which might allow molecules to sample thousands of different conformations and sites in a protein, Fragmenstein maintains the new molecule in a similar position and orientation to the initial fragments, with the assumption that these have already identified energetically favorable interactions.

 

The researchers successfully applied Fragmenstein retrospectively to several targets. The COVID Moonshot (which we discussed here) crowd-sourced molecule ideas for the SARS-CoV-2 main protease based on structures of bound fragments. Of 87 ligands that had been crystallographically characterized and were designed based on two fragments, Fragmenstein successfully (RMSD < 2 Å) predicted the binding mode for 69%.

 

Fragmenstein can even be used for covalent ligands, as shown for the target NUDT7, which we wrote about here. Merging two fragments led to compound NUDT7-COV-1, and the RMSD between the Fragmenstein model and the crystal structure was an impressive 0.28 Å.

 

Of course, as the researchers acknowledge, the number of possible analogs might be daunting, and deciding which to make or buy is not necessarily straightforward. Also, Fragmenstein assumes that the fragments themselves are making productive interactions with the protein, which may not be the case, as we suggested here. Still, the tool is open-source and worth trying, especially if you are swimming in crystal structures.

The thousandth Practical Fragments post!

As the title states, this is the 1000^th post at Practical Fragments. This blog was conceived in 2008 over drinks at the Third Annual CHI Fragment-based Drug Discovery Conference. (Don’t miss the twentieth in April!) Teddy Zartler said he was planning on starting a blog and asked if I wanted to join. In July of 2008 Teddy wrote the first post, and every month since then has seen at least a couple new ones. I thought it would be fun to look back briefly on the past 16+ years.

 

Methods

The very first Practical Fragments post asked what screening methods people use, and this eventually led to five polls on the topic, the latest of which published just a few months ago. In our first formal poll, in 2011, the average respondent used 2.4 techniques. Today that number has grown to 5 due to the increased recognition that different methods have different strengths and weaknesses.

 

By far the biggest winner among methods has been X-ray crystallography; it jumped from sixth place in 2011 to first place in both 2019 and 2024. Crystal structures have long been prized in drug discovery, but the dramatic increases in throughput and automation over the last decade mean more structures are more available to more users.

 

Computational methods too have improved spectacularly. In 2009 we highlighted an in silico screen of around 67,000 fragments which yielded ten micromolar inhibitors. Today, screening multibillion compound libraries is becoming routine, and artificial intelligence is likely to enable even more opportunities.

 

Pitfalls

One of the reasons that it took so long for FBLD to develop was the myriad artifacts that can haunt screens run at high concentrations. For example, compound aggregation was not widely recognized until the first decade of this century, and even today too many papers are published without checking for this pathological phenomenon.

 

Similarly, pan-assay interference compounds, or PAINS, were not defined until 2010. Scientists at large companies have long known to steer clear of certain chemotypes. Now academics and folks in startups are more aware of problematic substructures, even if Dr. Saysno objects.

 

Long-time readers may recall a series of posts on “PAINS-shaming,” where we highlighted (lowlighted?) papers that lacked appropriate selectivity or mechanistic studies. Occasionally this led to productive discussions, as in this example where an author and journal editor contributed to the comments. But with the increasing use of metrics measuring social media engagement to rank articles I’ve decided that blogging about them may inadvertently reward shoddy science. If you’re looking for most of the things that can go wrong in a screen, check out this open-access review by Ben Davis and me.

 

Covalent craze

One prominent mechanism of PAINS is indiscriminate covalent modification of proteins. For many years drug hunters actively avoided covalent modifiers for fear of off-target modifications and their potentially toxic effects. Indeed, the first several mentions of covalent compounds at Practical Fragments were in the “things to avoid” category. We discussed reversible covalent modifiers in 2012 and 2013, but it wasn’t until 2014 that we wrote about intentionally irreversible fragments.

 

How times have changed! The success and safety of targeted covalent kinase inhibitors has fueled enthusiasm for covalent drugs in general, creating opportunities for fragment-based approaches. Indeed, as we discussed here, both reversible and irreversible fragment-based screens were used in the discovery of the first approved drug targeting the previously intractable target KRAS, and these learnings have been applied at multiple companies to produce an impressive armamentarium against what Darryl McConnell has called “the beating heart of cancer.”

 

To find KRAS inhibitors, researchers screened pure proteins against libraries of covalent fragments. One of the most exciting recent developments in chemoproteomics has been screening covalent fragment libraries in intact cells or cell lysates to find hits against thousands of proteins in their native environment. We first wrote about this approach in 2016, and last year we highlighted the first drug to enter the clinic from covalent screening in cells.

 

And all this is just the beginning: each of our past four annual “review of reviews” posts has featured between three and six papers focused on covalent fragment-based drug discovery.

 

Clinical compounds

My first blog post in 2008 was a brief mention of a C&EN story on FBLD, in which I noted that “an FBLD drug that reaches the market by 2011 would be a ‘psychological’ victory for the whole FBLD community.” Although I claim no prescience, I was happy to see vemurafenib approved in August of 2011.

 

Indeed, I would argue that FBLD-derived drugs are the most meaningful output and validation of the field. Our first systematic tabulation in 2009 counted just 17 that had entered clinical trials, and today there are more than 60. Like investigational drugs in general, the majority of these have stumbled, but at least eight have been approved by the US FDA, and more are working their way through clinical trials. While eight might seem like a modest number, the number of patients they’ve helped is orders of magnitude greater.

 

Closing thoughts

There are far more themes in a thousand posts than I could summarize in a single one: metrics, induced proximity, and newer methods such as cryo-EM all come to mind. But as this post has already surpassed 1000 words, I’ll wrap it up.

 

One minor frustration has been the sparsity of comments; it sometimes feels as if I’m blogging into the void. That said, I’m pleased that some posts may have led to new research, such as this. And blogging can be its own reward: I sometimes find myself using the “Search This Blog” function on the top right-side of the page when I’m trying to remember a paper from years ago.

 

Since Teddy left the FBLD field several years ago I’ve been writing most of the content, with occasional guest posts (such as this from Glyn Williams). At the current rate it might take a couple decades for Practical Fragments to reach 2000 posts, if we even get there. But for now, I’d like to thank each of you for reading. I hope you enjoy it and that is has, at least occasionally, made your scientific pursuits more practical.

Fragments vs PRC1: toward a chemical probe

E3 ligase proteins conjugate ubiquitin to other proteins, changing their function or targeting them for degradation. Hijacking E3 ligases using PROTACs or molecular glues has become a popular approach for targeted protein degradation, but some E3 ligases can be drug targets themselves. For example, Polycomb Repressive Complex 1 (PRC1) ubiquitylates histone H2A in a process essential for proliferation of acute myeloid leukemia and some other cancer cells. In a new J. Med. Chem. paper, Jolanta Grembecka, Tomasz Cierpicki, and colleagues at University of Michigan Ann Arbor describe their discovery of small molecule inhibitors.

 

As the name implies, PRC1 is a protein complex. The core includes either RING1A or RING1B and one of six other proteins. Thus, the researchers sought to find inhibitors of both RING1A and RING1B. They started with a ¹H-¹⁵N HSQC NMR screen of 1000 fragments in pools of 20, with each compound at 0.25 mM. (This and some other work was described in an earlier Nat. Chem. Biol. paper by the same authors.) Compound RB-1 was found to bind very weakly to RING1B, and chemical shift mapping revealed that it binds in a region important for E3 ligase activity.

 

Scaffold hopping led to the pyrrole-containing compound 1b, which was slightly more potent as well as more soluble than RB-1. Fragment growing on both rings led to compound 1f, and further optimization yielded low micromolar compound RB-2. Crystallography with this compound revealed conformational changes that opened a hydrophobic pocket in the protein. Although no electron density for RB-2 was observed, the researchers used the crystal structure in combination with NMR experiments to develop a binding model. This facilitated further optimization, ultimately yielding RB-4, the most potent compound reported, with low micromolar affinity as assessed by isothermal titration calorimetry.

As noted above, it is important to block both RING1B as well as RING1A, and the researchers tested their compounds against both proteins using an AlphaScreen competition assay. Most compounds were equipotent against both proteins, though some showed around two-fold greater affinity for RING1A.

 

Subsequent experiments demonstrated that some of the compounds could block ubiquitylation of H2A in an in vitro assay. Importantly, compound RB-4 functionally inhibited three different PRC1 complexes having either RING1A or RING1B along with one of two other protein partners. Treatment of leukemia cells with the compound also led to changes in gene expression and lower levels of ubiquitylated H2A consistent with PRC1 inhibition.

 

This is a nice fragment-to-lead story, particularly given the difficult nature of the protein and the absence of co-crystal structures. While compound RB-4 is insufficiently potent to be called a chemical probe, it is nonetheless a well-characterized starting point for further optimization.

Berotralstat: an overlooked fragment-derived drug

At the end of 2023 I mentioned that a paper by Dean Brown listed berotralstat as a fragment-derived drug. Readers will notice this molecule does not appear on our “fragments in the clinic” list. Did we miss it? After reading a (2021!) J. Med. Chem. paper by Pravin Kotian and colleagues at BioCryst, I believe the answer is yes.

 

Hereditary angioedema (HAE) is a rare genetic disease caused primarily by deficiencies in a protein that inhibits a serine protease called plasma kallikrein, or PKal. Drugs had already been developed to replace the inhibitor protein, but these need to be injected or infused. Since PKal is an enzyme, the researchers sought to make a small molecule inhibitor that could be taken as a pill.

 

BioCryst had developed an earlier drug called BCX4161, which is potent but has poor oral bioavailability. To find a better molecule, the researchers turned to the rich literature around serine protease inhibitors, which led them to make compound 2, a fragment of previously reported inhibitors of other serine proteases. The protonated benzylamine was expected to bind in the S1 pocket of the enzyme, and indeed the molecule did show weak but measurable activity.

 

Fragment growing led to compound 4, with double-digit micromolar activity. Building off the new phenyl ring led to more potent molecules such as compound 13, with low micromolar activity. Further structure-based design eventually led to BCX7353, or berotralstat. The paper provides good descriptions of the design rationale. For example, the fluorine was added to improve permeability, and the nitrile was added to improve the ADME profile. Modeling was used both to improve potency as well as to gain selectivity over other serine proteases. This proved to be successful: berotralstat is a subnanomolar inhibitor of PKal and at least several thousand-fold selective over trypsin and other serine proteases such as thrombin and FXa.

 

The pharmacokinetic properties of berotralstat in rats and monkeys were also good, and according to clinicaltrials.gov the molecule first entered the clinic in 2015. In December of 2020 the FDA approved berotralstat for prophylactic treatment of HAE attacks.

 

This is a nice story, and I agree with Dean that the discovery of berotralstat was “based on a legacy clinical candidate and fragment approaches.” The earlier molecule BCX4161 contained a benzamidine moiety, which was in part responsible for the poor oral bioavailability. Replacing this with a benzylamine fragment from the literature is a classic fragment strategy, and compound 2 is fully compliant with the rule of three.

 

So how was it missed? The abstract only states that berotralstat was discovered “using a structure-guided drug design strategy.” Indeed, the word “fragment” appears precisely once in the paper, albeit in a very telling sentence: “We evaluated these fragments in our PKal^pur inhibitor assay…”

 

From a timeline perspective, the approval of berotralstat makes it the fifth approved fragment-derived drug, after pexidartinib and before sotorasib. I’ll include it in the next update of clinical compounds, along with my standard disclosure that “the list is almost certainly incomplete.” What else are we missing?

Fragment events in 2025

After a bumper year for conferences in 2024, this year is also shaping up to be eventful. 

April 14-17: CHI’s Twentieth Annual Fragment-Based Drug Discovery, the longest-running annual fragment event, returns as always to San Diego. This is part of the larger Drug Discovery Chemistry meeting. You can read impressions of the 2024 meeting here, the 2023 meeting here, the 2022 event here, the 2021 virtual meeting here, the 2020 virtual meeting here, the 2019 meeting here, the 2018 meeting here, the 2017 meeting here, the 2016 meeting here; the 2015 meeting here, here, and here; the 2014 meeting here and here; the 2013 meeting here and here; the 2012 meeting here; the 2011 meeting here; and 2010 here. 

 

May 5-6: Returning after a five year hiatus, Industrial Biostructures of America will be held in Cambridge, MA and includes a session on FBLD. 

June 2-4:  The Eleventh NovAliX Conference returns to the stunning city of Strasbourg. You can read my impressions of the 2018 Boston event here, the 2017 Strasbourg event here, and Teddy's impressions of the 2013 event here, here, and here.

September 21-24: FBLD 2025 will be held in the original Cambridge (UK),  where it was supposed to be held in 2020. This will mark the ninth in an illustrious series of conferences organized by scientists for scientists. You can read impressions of FBLD 2024, FBLD 2018, FBLD 2016, FBLD 2014, FBLD 2012, FBLD 2010, and FBLD 2009. 

 

September 22-25: You'll need to make a tough choice: FBLD 2025 or CHI’s Twenty-Third Annual Discovery on Target. As the name implies this event is more target-focused than chemistry-focused, but there are always plenty of FBDD-related talks. You can read my impressions of the 2024 meeting here, the 2023 meeting here, the 2022 meeting here, the 2021 event here, the 2020 virtual event here, the 2019 event here, and the 2018 event here.

 

Finally, from November 11-13 CHI holds its second Drug Discovery Chemistry Europe in beautiful Barcelona. This will include likely tracks on lead generation, protein-protein interactions, degraders, machine learning, and probably several fragment talks. 

   

Know of anything else? Please leave a comment or drop me a note.

Review of 2024 reviews

Long winter is here in the global north, with its dim days and gaping nights. As is tradition, Practical Fragments looks back on the year almost done. 2024 was the best year for conferences since the arrival of COVID. I wrote about CHI’s Drug Discovery Chemistry in San Diego, FBDD-DU in Brisbane, FBLD 2024, and CHI’s Discovery on Target, both in Boston. 

Another tradition is the annual J. Med. Chem. fragment-to-lead success story review; the latest covers the year 2022 and was written by Andrew Woodhead (Astex) and collaborators, including yours truly.

 

For a timely and accessible overview of “how to find a fragment,” look no further than a review of that title in ChemMedChem by Marcio Vinicius Bertacine Dias and collaborators at University of São Paulo and University of Warwick. This covers crystallography, cryo-EM, NMR, SPR, thermal shift, virtual screening, functional screening, ITC, mass spectrometry (including HDX-MS), MST, and BLI, and concludes with a nice comparison table. Some twenty other reviews were also published throughout the year, and these are discussed thematically.

 

Structure-based methods

Three reviews cover NMR. In J. Med. Chem., Janet Caceres-Cortes and colleagues at Bristol-Myers Squibb provide “perspectives on nuclear magnetic resonance spectroscopy in drug discovery.” Ligand- and protein-detected screening are covered thoroughly, with examples such as the discoveries of BI-2852 and venetoclax. Applications beyond hit finding are also discussed, such as the characterization of atropisomers in sotorasib, the identification and characterization of impurities and metabolites, in-cell NMR, and much more.

 

“Perspectives on applications of ¹⁹F-NMR in fragment-based drug discovery” is the title of an open-access review in Molecules by Qingxin Li and CongBao Kang at Guangdong Academy of Sciences and A*STAR, respectively. As we discussed in 2020, fluorine NMR is becoming increasingly common in FBLD, and this paper covers the various methods, including using ¹⁹F-NMR to measure ligand affinity. The authors also include a table summarizing 17 fragment screens that used fluorine NMR.

 

The rise of powerful permanent magnets has enabled low-maintenance benchtop NMR instruments that can be yours for as little as $50,000, compared to upwards of $1 million for a 600 MHz superconducting machine. Although sensitivity is at least 150-fold lower, hyperpolarization techniques such as photo-CIDNP, which we wrote about here, can close the gap. The latest developments are described (open access) in Chemistry–Methods by Felix Torres and collaborators at NexMR and the ETHZ.

 

X-ray crystallography has retained the top position among fragment-finding methods according to our most recent poll. In an open-access Applied Research paper, Daren Fearon, Frank von Delft, and collaborators describe high-throughput crystallographic fragment screening at the Diamond Light Source. As of August 2024 they have collected more than 240,000 academic data sets on hundreds of targets, and the paper distills some of the key lessons, some of which were applied to the COVID Moonshot, which we last wrote about here. The paper also describes future developments and needs, such as how and where to house such massive quantities of data.

 

Another center for high-throughput crystallographic screening is the Helmhotz-Zentrum Berlin (HZB) F2X-Facility at the BESSY II synchrotron, and in an open-access Applied Research paper Manfred Weiss and collaborators provide an overview of workflows and capabilities. One unique offering is the F2X-GO kit, in which F2X fragment libraries (which we wrote about here) as well other supplies are shipped to users to do soaking experiments in their own laboratories prior to shipment to the synchrotron.

 

“Structure-based virtual screening of vast chemical space” is the topic of an open-access review in Curr. Opin. Struct. Biol. by Jens Carlsson (Uppsala University) and Andreas Luttens (MIT). The Enamine REAL collection currently contains 40 billion compounds, and the researchers predict that “make-on-demand chemical libraries will likely reach more than one trillion compounds in the next few years,” which presents both opportunities and challenges, particularly given the existence of the “virtual cheaters” we recently discussed. Machine learning and fragment-based methods such as V-SYNTHES could help.

 

Continuing the virtual theme, Li Wang and collaborators at Nantong University discuss “molecular fragmentation as a crucial step in the AI-based drug development pathway” in an open-access Commun. Chem. paper. This summarizes 15 different computational methods for dissecting larger molecules into fragments, and also includes a list of 11 library vendors.

 

Other methods

Among experimental methods, few can match the throughput of fluorescence techniques, the subject of an open-access Heliyon review by Neelagandan Kamariah and colleagues at inSTEM & NCBS in Bangalore. It has short sections on “fluorescence polarization (FP) and anisotropy (FA), Förster resonance energy transfer (FRET), time-resolved Förster resonance energy transfer (TR-FRET), fluorescence lifetime (FLT), protein-induced fluorescence enhancement (PIFE), fluorescence thermal shift assay (FTSA) and microscale thermophoresis.” It also describes applications to GPCRs, protein-protein interactions, and other biological systems.

 

Native mass spectrometry (nMS), which we wrote about most recently in 2022, is the subject of an RSC Med. Chem. review by Louise Sternicki and Sally-Ann Poulsen at Griffith University. Sally-Ann is a leading expert in nMS, and the paper describes the technique and how it compares to other fragment-finding methods. It also includes a nice table summarizing 17 studies published between 2013-2023 that used nMS for FBLD; a 2013 review of nMS covered earlier examples.

 

Covalent Fragments

Mass spectrometry plays a prominent role in finding covalent fragments, as discussed in an open-access SLAS Discovery review by Simon Lucas and colleagues at AstraZeneca. “Covalent hits and where to find them” also describes other biophysical, biochemical, cellular approaches, and even DEL screening. It also discusses covalent libraries (which we wrote about earlier this month) and successful examples such as the discovery of sotorasib. In my opinion the researchers succeed in their “hope that this review will help serve as a useful roadmap to those seeking to drug the undruggable.”

 

A concise open-access review in Curr. Opin. Struct. Biol. by Katrin Rittinger and collaborators at The Francis Crick Institute and GSK focuses on using covalent fragments to assess target tractability, specifically ligandability and functionality. Target-based, proteome-wide and function-first approaches are summarized, and the researchers also discuss the importance of negative control compounds such as inactive enantiomers.

 

Continuing the theme of “assayability,” Micah Niphakis (Lundbeck) and Ben Cravatt (Scripps) review “ligand discovery by activity-based protein profiling” (ABPP) in a Cell Chem. Biol. paper. Because ABPP is usually conducted in cells or cell lysates, full-length proteins are assayed in their native environment, facilitating the discovery of allosteric ligands as in the case of WRN, which we wrote about earlier this year. The paper summarizes multiple examples of finding covalent ligands for challenging targets, and also highlights future challenges such as increasing throughput and targeting residues beyond cysteine.

 

Reversible covalent inhibitors are the topic of an open-access review by Dustin Duncan and colleagues at Brock University in ACS Chem. Biol. The researchers argue that reversible covalent inhibitors may cause less accumulation of the off-target adducts that could form with irreversible inhibitors. The paper includes a figure showing reversible covalent warheads, details on how to characterize them, a nice summary of general considerations, and success stories for JAK3, BTK, and proteases.

 

Targets

Covalent ligands have been particularly important for cancer targets, as reviewed by Xiaoyu Zhang (Northwestern University) and Ben Cravatt (Scripps) in an open-access Annual Review of Cancer Biology paper. The focus is on use of chemical proteomics “to expand the druggability of cancer proteomes.” The paper presents examples of finding and characterizing covalent ligands for a variety of oncology targets including KRAS^G12C.

 

E3 ligases are briefly mentioned, and this target class is the focus of an Expert Opin. Drug Discov. review by Jongmin Park and colleagues at Kangwon National University. As we’ve discussed recently, the 600 or so human E3 ligases are potentially valuable for targeted protein degradation applications such as PROTACs. The review focuses on “fragment-based approaches to discover ligands for tumor-specific E3 ligases.” In addition to summarizing successes against targets such as BCL6 and XIAP, it includes a list of 113 tumor-specific E3 ligases and another list of 52 E3 ligases that are overexpressed in certain tumors.

 

The “impact of fragment-based drug design on PROTAC degrader discovery” is also the subject of a review in Trends in Analytical Chemistry by Xiaoguang Lei and colleagues at Shenzen Bay Laboratory. Here, the focus is more on using FBLD to discover ligands for target proteins rather than for E3s. For example, the researchers describe how the fragment-derived drug navitoclax was used as a starting point for developing DT2216, a clinical-stage BCL-x_L degrader.

 

As Vicki Nienaber noted more than a decade ago, fragment-based drug discovery is ideally suited for targeting the central nervous system, particularly when combined with a ruthless focus on molecular properties. This is the topic of an open-access review in Front. Chem. by Michael Kassiou and collaborators at University of Sydney, CSIRO, and Vast Bioscience. After a brief summary of FBLD the researchers present case studies published since 2015, the last year this topic was reviewed. We’ve covered quite a few on Practical Fragments, including apoE4, Notum, and PDE10A.

 

Other

We mentioned allostery above, and in an open-access FEBS Open Bio. article Andrea Bellelli and collaborators at Sapienza University of Rome and elsewhere ask “is allostery a fuzzy concept?” Digging into half-century old publications from Jacques Monod, the researchers conclude that the concept was “born with an original sin: two definitions.” Indeed, the first mathematical model did not even apply to monomeric proteins. Most readers of this blog will probably be satisfied with the notion that an allosteric ligand is one that binds outside of an active site, but it is worth remembering that “allostery is an umbrella that covers more than a single reaction mechanism and cannot be defined by a single mathematical expression.”

 

Structure comes up frequently on Practical Fragments, but James Fraser (UCSF) and Mark Murcko (Disruptive Biomedical) remind us in Cell that “structure is beauty, but not always truth.” We’ve written multiple posts about getting misled by crystal structures, and in this brief commentary the authors provide “four harsh truths: a structure is a model, not experimental reality; representing wiggling and jiggling is hard; in vitro can be deceiving; drugs mingle with many different receptors.” They conclude that “truth is a molecule that transforms the practice of medicine.”

 

Stumbling towards truth is a little easier with the help of a good chemical probe, and in Nucleic Acids Res. Paul Workman (Institute of Cancer Research) and collaborators provide an updated description of The Chemical Probes Portal. This free community resource now contains 803 probes against 570 targets, including 28 covalent ligands and 51 degraders. Moreover, 332 of the probes have structurally related negative controls. Importantly, the Portal also includes 258 “Unsuitable” compounds that are insufficiently potent or selective to serve as chemical probes. Checking this list can save you valuable time when reading papers about unfamiliar targets.

 

Finally, a brief open-access interview with Nobel Laureate Katalin Karikó in Issues in Science and Technology is an inspiring reminder that “you learn more from failure,” and that the pleasure of doing science can be its own reward.

 

Thanks for reading. Good luck in 2025, and remember that the sun is always out there, even when you can neither feel nor see it.

Covalent fragments vs BFL1: a selective chemical probe

Last week we highlighted the construction of a covalent fragment library at AstraZeneca. The first fruits of this library have recently been published as a pair of papers.

 

The protein BFL1 (or Bfl-1) is a member of the BCL2 family and blocks apoptosis by binding to pro-apoptotic proteins such as BIM, BID, and Noxa. Blocking these types of protein-protein interactions should increase apoptosis in cancer cells. Indeed, BCL2 itself is the target of the approved fragment-derived drug venetoclax, which took heroic measures to discover.

 

Finding noncovalent inhibitors of BFL1 was also expected to be difficult, but fortunately the protein contains a unique cysteine (C55) in the protein-protein binding site, facilitating both covalent attachment and selectivity. As we mentioned last week, the protein was screened against the emerging AstraZeneca covalent library, resulting in the discovery of several hits, including compound 8. Its optimization is described by Simon Lucas and colleagues in the first J. Med. Chem. paper.

Compound 8 showed promising k_inact/K_I for BFL1 as well as micromolar inhibition in a TR-FRET assay using a BIM-derived peptide. Crystallography was initially unsuccessful, but synthesis of close analogs led to compound 13, which is slightly more potent and could be co-crystallized with the protein. The structure confirmed covalent binding and revealed that one of the phenyl rings binds in a lipophilic pocket created by movement of a phenylalanine side chain.

To explore more regions of the protein-protein binding site, the researchers performed a high-concentration crystallographic screen with 384 non-covalent fragments. This yielded nine hits, four of which made hydrogen bonds with a glutamic acid side chain (E78) that had previously been targeted by others. To try to engage with this residue, the researchers modeled and synthesized a series of amine-containing molecules. Happily, one of the highest priority compounds gave a ten-fold boost in potency. Adding a methyl to the benzylic position and tweaking substituents around one of the phenyl rings ultimately led to compound (R,R,S)-26, the best molecule in this paper.

 

Because C55 is unique to BFL1, the hope was that compounds would be selective against other BCL2 family members, and indeed (R,R,S)-26 showed no activity against BCL-xl, BCL2, or MCL1. In vitro ADME parameters were encouraging, and the molecule also showed moderate bioavailability in mice. (R,R,S)-26 showed some cellular activity, though a mass-spectrometry assay showed only ~50% target engagement in cells after treatment at 10 µM for five hours.

 

The second J. Med. Chem. paper, by Adeline Palisse and colleagues, describes further optimization. Structure-based design was supported by “multiple X-ray cocrystal structures,” and as in the first paper the researchers consistently measured the half-life of new molecules against the cellularly abundant thiol glutathione to ensure they were not simply optimizing non-specific reactivity. The paper is an excellent blow-by-blow account of some of the challenges of medicinal chemistry: improving activity at the expense of stability or permeability, for example. The most potent compound has k_inact/K_I = 120,000 M^-1s^-1, but the hepatocyte stability data suggested it would be rapidly cleared.

 

In the end, compound 20 was chosen as the best overall molecule, with a k_inact/K_I comparable to that of the approved drug sotorasib. As with (R,R,S)-26, it showed no activity against BCL-xl, BCL2, or MCL1, and it was also clean against a panel of 48 kinases and fairly clean against a panel of other potential off-target proteins.

 

Among the several BCL2 family members, the protein MCL1 can also bind to BIM, thereby blunting the effects of inhibiting BFL1. Thus, the researchers performed cell assays in the presence of the MCL1 inhibitor AZD5991, whose discovery we wrote about here. In the presence of 0.5 µM AZD5991, compound 20 had an EC₅₀ = 350 nM in a cell viability assay and also activated caspase 3, as expected in apoptosis. A similar effect is also seen in combination with venetoclax.

 

Pharmacokinetic studies in mice revealed that compound 20 is 55% orally bioavailable, and this combined with the other properties suggest this molecule will be a useful chemical probe for exploring the biology of BIM.

How to build a covalent fragment library

Covalent fragment-based lead discovery is becoming ever more popular, driven by success against difficult targets such as KRAS^G12C. These efforts require the design of new libraries, and in a recent J. Med. Chem. paper Simon Lucas and colleagues at AstraZeneca describe their design philosophy. (Co-author Henry Blackwell presented some of this work at the CHI FBLD meeting earlier this year.)

 

AstraZeneca has taken great care in building their fragment libraries; we discussed the revamp of their general fragment library as well as a “low HBD” (hydrogen bond donor) library here and here. For their covalent library, they considered several design features. First, given that any warhead will add molecular weight (four non-hydrogen atoms and a hydrogen-bond acceptor for an acrylamide), larger molecules are necessary, which requires relaxing the rule of three. Indeed, the researchers refer to their library as “lead-like.”

 

Because larger fragments are more complex, more are needed to explore chemical space. The researchers have built their library to 12,000 compounds, larger than the typical respondent from our poll last year. They have also chosen compounds to be maximally diverse rather than including near neighbors.

 

Attractive covalent hits make specific interactions with a protein target; warheads that are too “hot” can react non-specifically, as is the case with certain PAINS. Thus, the researchers chose molecules having moderate reactivity with the biologically relevant nucleophile glutathione (GSH).

 

The design principles are summarized as:

• Molecular weight 250-400 Da
• cLogD 0-4
• GSH t_1/2 > 100 minutes
• Propensity for molecular interactions (such as hydrogen bond donors and acceptors)
• Diversity
• No diastereomeric mixtures (racemates are OK)
• Synthetically tractable
• Purity > 85% (and stable)
  

 

These criteria were used to select ~700 historical compounds from within AstraZeneca’s collection. Next, the researchers chose amines from their internal collection and capped these with an acrylamide moiety, leading to an additional 1200 molecules. They then turned to custom synthesis of scaffolds that were under-represented, commercial compounds, and covalent warheads besides acrylamides, such as cyclic sulfones. The final library consists of 88% acrylamides. Molecular weights range from 150 to 420 Da, and compounds contain 1-6 HBAs, 0-3 HBDs, and 1-3 rings.

 

The paper briefly describes a screen against Bfl-1 (or BFL1), a difficult oncology target we wrote about earlier this year. The protein contains a cysteine residue in the biologically important BH3 binding site, and previous research by others had identified covalent binders.

 

The AstraZeneca researchers tested Bfl-1 against an early version of the library having just 1400 compounds, which were incubated at 20 or 200 µM for 24 hours at 4 °C before analyzing by intact protein mass spectrometry. Hits were defined as giving >50% single labeling and that could be competed with a peptide derived from the binding partner BIM. Six hits are shown in the paper, with k_inact/K_I values ranging from 0.7 to 9.5 M^-1s^-1, comparable to some of the early KRAS^G12C hits. Further development of these molecules is described in a pair of papers that will be the subject of my next post.

 

Including Bfl-1, the library has been screened against 15 targets using mass spectrometry, typically yielding 1-2% hit rates defined as at least 20% labeling of a single site. Given this record of success, if you’re contemplating building a covalent library, this paper is well worth studying.

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.

Mapping protein conformations with fragments

Proteins can be remarkably dynamic, and, as we noted recently, different conformational states can reveal different pockets for small molecule ligands. But how can one survey and categorize all the possibilities? In a recent J. Chem. Inf. Model. paper, Doeke Hekstra and colleagues at Harvard University present a new tool for doing so.

 

High-throughput crystallographic fragment screens are becoming faster and more widely accessible, and the researchers wondered whether the information from these screens could be used to map protein conformational landscapes. To do so, they built a Python program called COLAV, short for COnformational LAndscape Visualization. This open-source tool can compile data from hundreds of protein coordinate files and then, for each protein, calculate the dihedral angles between backbone atoms, the pairwise distances between the alpha-carbon atoms, and the strain.

 

To a first approximation, dihedral angles capture local movements, while distances between alpha-carbons capture global movements, such as the distance between the N-terminus and C-terminus. Strain measurements are also local but can reveal particularly important features such as hinge movements. Also, while dihedral and pairwise distances can be calculated for single proteins, strain measurements are calculated after first aligning multiple structures.

 

Having calculated these three parameters for individual protein structures, COLAV can compare them across the selected set of structures using principal component analysis (PCA). These comparisons can reveal clusters with similar dihedral angles, pairwise distances, or strain.

 

The researchers provide two case studies. The first is the metabolic disease target PTP1B, which we recently wrote about here. This enzyme has been pursued intensively for decades, so the researchers were able to draw on 163 individual protein structures deposited in the protein data bank (PDB) as well as 187 structures from a high-throughput crystallographic fragment screen. PTP1B contains two flexible loops, each of which adopts one of two conformations, and COLAV successfully segregated all 350 structures into four clusters. Importantly, these four clusters were found whether the structures were pulled from the PDB (representing experiments conducted across multiple labs and years) or from the fragment screen, suggesting that a single crystallographic fragment screen can identify most or all of the conformational states available to a protein. This is particularly impressive given that most of the fragments bound in allosteric sites while most of the ligands found in the PDB bound in the active site.

 

Next, the researchers turned to the main protease (MPro) of SARS-CoV-2, the subject of intense and successful drug discovery efforts. They used 656 structures from the PDB and 631 structures from high-throughput crystallographic screens to perform COLAV analyses. Unlike PTP1B, discrete conformational clusters were not observed; rather a continuous band was seen, suggesting that the protein can assume myriad conformations. Here too though, the fragment screens were able to sample most of the conformations observed in the PDB.

 

The fact that a single high-throughput crystallographic screen can capture the conformations seen in hundreds of hard-won discrete protein-ligand crystal structures is encouraging, though of course the paper only describes two case studies. Also, as the researchers note, any structure that cannot be crystallized is not sampled. Since COLAV is free to use, it will be fun to see it applied to other proteins.

Covalent fragments vs chikungunya nsP2

Perhaps because it sounds like “chicken,” when I first heard of chikungunya I thought it was a joke. But there’s nothing funny about a disease whose name comes from a word meaning “to become contorted,” referring to contortion caused by pain, which can last for months. The mosquito-borne alphavirus was first identified in 1952 in West Africa, introduced to the Americas in 2013, and is now spreading rapidly worldwide. There is no specific treatment. In three recent papers, a large group of researchers mostly from the Structural Genomics Consortium take the first steps towards one.

 

Like many viruses, the chikungunya genome encodes polyproteins that are cleaved by viral proteases, in this case a domain of the nonstructural protein 2 (nsP2). This cysteine protease is essential for viral replication, and the three papers collectively describe finding and exploring selective probes against it.  

 

In Proc. Nat. Acad. Sci. USA, Kenneth Pearce (University of North Carolina at Chapel Hill) and collaborators describe a screen of 6120 covalent fragments from Enamine against this target. Compounds were preincubated in a FRET-based functional assay at 20 µM for 30 minutes, resulting in 153 hits that inhibited activity by at least 50%. 43 of these were repurchased for full-dose response curves, and 20 of these had IC₅₀ values < 20 µM. Of these, compound RA-0002034 was the most potent, with IC₅₀ = 180 nM.

The proper way to assess irreversible covalent inhibitors is not the time-dependent IC₅₀, but rather the (theoretically) invariant k_inact/K_I ratio. The researchers measured this for the best hits and found the value for RA-0002034 to be 6400 M^-1s^-1, which is not far below that for the approved covalent drug sotorasib for its target.

 

Mass spectrometry experiments after tryptic digestion revealed the compound binds to the catalytic cysteine of nsP2, as expected, and not to other cysteines. RA-0002034 contains a potentially reactive vinyl sulfone warhead, but the half-life against the biologically relevant nucleophile glutathione is a respectable 130 minutes. A screen against 13 other cysteine proteases was also quite clean, as was chemoproteomic profiling in human cells.

 

The compound was also tested in cellular viral replication assays and found to be remarkably potent, with a low nanomolar EC₅₀ value. Encouragingly, it was also potent against three other alphaviruses, Ross River virus, Venezuelan Equine Encephalitis virus, and Mayaro virus.

 

RA-0002034 appears to be an attractive chemical probe for exploring the biology of chikungunya. Best practices are to also have an inactive control molecule, and the researchers made a substitution off the central pyrazole ring to produce RA-0003161, which is 500-fold less active.

 

The paper includes some SAR-by-catalog, and the chemistry is more extensively explored in an open-access J. Med. Chem. paper by Timothy Willson (UNC Chapel Hill) and collaborators. Although no crystal structures of the compounds bound to nsP2 were available, the researchers used modeling to guide modification of all portions of the molecule. The most potent molecule was 8d, which is slightly more active than RA-0002034. Also, methyl substitution near the electrophilic center is tolerated, which could improve stability, as seen with the covalent WRN inhibitor from Vividion which we wrote about here.

 

One annoying feature of RA-0002034 is its tendency to cyclize to inactive compound 2, a process explored in an open-access Pharmaceuticals paper by Timothy Willson and collaborators. This occurs even at neutral pH. However, replacing the central pyrazole with an isoxazole (compound 10) fixes this problem.

 

Collectively these three publications provide new insights and tools for investigating chikungunya. RA-0002034 is a far more attractive starting point than a molecule Teddy described on Practical Fragments back in 2015. The pharmacokinetics of RA-0002034 need to be improved before in vivo experiments are warranted, but this seems achievable, and I look forward to watching this story develop.

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?