Fragments vs eIF4E: a chemical probe

Cancer cells are known for growing and multiplying quickly, and to do so they need to produce large amounts of protein. The rate determining step in protein translation happens early, when ribosomes are recruited to the 5’-end of mRNA by the eukaryotic initiation factor 4F (eIF4F) complex. This complex has long been a target for drug discovery, and in a recent open-access Nat. Comm. paper Paul Clarke, Andrew Woodhead, Caroline Richardson, and collaborators at Institute of Cancer Research and Astex describe a chemical probe. (Andrew spoke about this program last year at FBLD 2024.)

 

The eIF4F complex includes three core proteins, confusingly named eIF4E, eIF4G, and eIF4A. eIF4E binds to the 5’cap of mRNA and recruits eIF4G. Blocking the interaction of eIF4E either with mRNA or eIF4G could in principle shut down protein synthesis, but intensive efforts by multiple groups have struggled: the mRNA binding site is very polar, and disrupting protein-protein interactions is tough. Thus, the researchers took a fragment approach.

 

Developing a form of eIF4E suitable for fragment screening was itself a challenge because the protein mostly exists as part of a complex in cells and the native monomer is unstable. After making more than two dozen different constructs, the researchers developed a stable, soluble form that could be crystallized. This construct was screened against a library of 1371 fragments in pools of four, each at 500 µM, using CPMG NMR followed by crystallography, leading to 50 hits. A few bound at the mRNA cap-binding site but most bound to a previously unreported “site 2,” which is near where eIF4G binds.

 

One of these, compound 1, has a reasonable ligand efficiency despite its low affinity as assessed by ITC. The phenol appeared to be making no interactions and so was removed. Adding a fluorine usefully enforced the twisted biaryl conformation and filled a small dimple; fragment growing then led to mid micromolar compound 3. Further growing to pick up additional lipophilic and polar contacts eventually led to compound 4, with low nanomolar affinity. Understanding the importance of negative controls for chemical probes, the researchers also switched the stereochemistry at the benzylic carbon to produce compound 5, which has >30-fold lower affinity for eIF4E. 

 

Crystallography revealed that binding of compound 4 to eIF4E causes conformational changes that should impair binding of the protein to eIF4G. Experiments in cell lysates bore out this hypothesis. Moreover, compound 4 also inhibited protein translation in cell lysates at low micromolar concentrations, while compound 5 did not.

 

Unfortunately, these observations did not extend to intact cells. A cellular thermal shift assay (CETSA) demonstrated that compound 4 did stabilize eIF4E in cells with an EC₅₀ = 2 µM, consistent with binding. But it was much less effective at blocking the interaction with eIF4G in cells, even at high concentrations, and showed no inhibition of protein translation.

 

To understand why, the researchers conducted a series of targeted protein degradation and genetic rescue experiments that are beyond the scope of this blog post. The upshot is that eIF4G binds to several regions of eIF4E, and that while compound 4 disrupts binding to the “non-canonical binding site”, it does not block binding to the “canonical binding site,” and thereby does not block overall complex formation. Why there should be a difference between intact cells and cell lysates is not obvious to me, but perhaps the more dilute conditions of cell lysates play a role, as seen for a paper we discussed last year.

 

One interesting feature of this story is that the initial fragment makes no polar interactions with the protein; all of the polar interactions in compound 4 were added during optimization. This is quite the opposite of ASTX660, where all the polar interactions in the final clinical compound came from the initial fragment. Indeed, a 2021 analysis of fragment to lead successes found that fewer than one in ten retained no polar interaction from the initial fragment.

 

This paper also illustrates the gap that can occur between research and publication; a couple of the authors listed as affiliated with Astex left in 2017. But better late than never, and this study nicely integrates fragment-based lead discovery with elegant biology. Compound 4 should be a useful tool for further exploring the nuances of eIF4E.

Crude success against the SARS-CoV-2 main protease: From covalent fragment to noncovalent lead

With increased throughput and reliability of biophysical and other methods, finding fragments against most targets is now fast and easy. Advancing these fragments to leads, not so much. In a new open-access Angew. Chem. Int. Ed. paper, Jacob Bush and collaborators at GSK, University of Strathclyde, and the Francis Crick Institute provide a case study for how to accelerate the process.

 

Almost exactly five years ago we highlighted early efforts against the main protease (M^pro) from SARS-CoV-2. This target turned out to be a good choice, as demonstrated by the rapid discovery and approval of the drug nirmatrelvir. M^pro is a cysteine protease and thus ideally suited for covalent fragment screening.

 

In the new paper, the researchers screened a library of 219 chloroacetamide-containing fragments (each at 5 µM) individually against 0.5 µM protein for 16 hours at 4 ºC and then analyzed them by intact protein mass spectrometry. Six of these gave at least 75% modification, and further characterization found that the most potent, compound 2, had a k_inact/K_I = 170 M^-1s^-1. This (and the other hits) also inhibited the protein in an enzymatic assay, and additional chemoproteomic experiments revealed that compound 2 could bind to the active site cysteine of M^pro in living cells with surprising selectivity; just 11 targets were more strongly engaged than M^pro.

 

To optimize compound 2, the researchers turned to crude reaction screening, also known as direct-to-biology or D2B. As we described here and here, this entails running reactions at small scale and testing them directly, without purification. To validate the approach, the researchers synthesized a subset of the original 219 chloroacetamides in 384-well plates. HPLC studies confirmed the desired product as the major component for 43 of the 69 attempted syntheses; only four failed. Importantly, there was a good correlation in activity between the crude reaction mixtures and the pure molecules.

 

Next, the researchers synthesized a new D2B library of 193 molecules related to compound 2. HPLC analysis of the crude products showed a 77% success rate, with just nine outright failures. The library was screened against M^pro for 1 hour (as opposed to 16 hours in the first screen), resulting in 14 hits. The best of these, compound 7a, was such a rapid modifier that the a k_inact/K_I could not be easily calculated, but it showed nanomolar activity in the enzymatic assay. It was also more selective than compound 2 in cell-based experiments.

 

Chloroacetamides are not considered advanceable as drugs, so the researchers sought to remove the warhead, initially by replacing it with the simple acetamide in compound 12. Although this molecule showed almost no activity in the enzymatic assay, the researchers coupled a diverse set of 146 carboxylic acids to the amine building block and screened the crude reaction mixtures in a functional assay at 50 µM to identify seven molecules that gave nearly complete inhibition, with compound 13 being the most potent. A second D2B library of analogs around compound 13 was screened at 1 µM, leading to the mid-nanomolar compound 14.

 

This is a nice illustration of the power of crude reaction screening to rapidly identify new chemical matter. It is true that M^pro is quite ligandable; we wrote about other non-covalent fragment success stories here and here. However, as we discussed here, D2B can be applied to more challenging targets. The supporting information in the new paper should be particularly valuable for those hoping to try the approach themselves.

 

At FBLD 2024 Frank von Delft set a goal of taking a “100 µM binder to a 10 nM lead in less than a week for less than £1000.” We’re not there yet, but developments in D2B are moving us forward.

Fishing for pearls more efficiently with a new NMR method

NMR is the most venerable approach for finding fragments, and ligand-detected NMR is still among the more popular methods. But the amount of protein required for a full fragment library screen can be a limitation, particularly for more challenging targets. A new paper in Angew. Chem. Int. Ed. by Alvar Gossert and collaborators at ETH Zürich, Bruker, and Karlsruhe Institute of Technology provides a new, less protein-intensive approach.

 

I’ll preface the next paragraph by admitting that not only am I no spectroscopist, I don’t even play one on TV. So, spectroscopy-savvy readers, please feel free to provide more details in the comments, especially if I get something wrong. For fellow non-spectroscopists, the takeaway is that clever NMR tricks increase sensitivity.

 

PEARLScreen, short for Perfect Echo for Advanced Relaxation-based Ligand Screen, is related to the classic Carr-Purcell-Meiboom-Gill (CPMG, or T_1ρ) method, which we wrote about most recently here. As in that older method, PEARLScreen relies on the decrease in signal intensity of a ligand that binds to a protein. This is due to slower tumbling of the bound ligand, resulting in faster relaxation of excited protons (see here). Lengthening the time between excitation and measurement should in theory boost contrast between bound and free ligands, but various technical challenges impede this in practice. PEARLScreen overcomes these challenges using “a perfect echo pulse train with water suppression by excitation sculpting.” In addition to lengthening the relaxation delay, PEARLScreen also allows exchange broadening to occur between the ligand and protein, further increasing sensitivity.

 

The researchers simulated multiple conditions to optimize various parameters, and then experimentally tested PEARLScreen on four different proteins with three types of NMR instruments, starting with a standard high-end 600 MHz.

 

The first protein-ligand pair was trypsin binding to a known benzamidine fragment. This interaction was detectable using a standard T_1ρ experiment with 200 µM ligand and 20 µM protein. Using PEARLScreen, the researchers could reduce the protein concentration to 1 µM while maintaining similar signal to noise .

 

Next, they screened 94 fragments in pools of 8 against three different proteins: PPAT, Abl, and FKBP. In all cases PEARLScreen was more sensitive than T_1ρ, allowing screening at 2.5 µM rather than 20 µM protein. PEARLScreen was also more sensitive than the two other most common ligand-detected NMR methods, STD and WaterLOGSY.

 

We wrote recently about benchtop NMR, and the researchers found that PEARLScreen was also more sensitive than a T_1ρ experiment on an 80 MHz instrument, though the difference was not as dramatic as on the 600 MHz machine. On the other hand, on a 1.2 GHz instrument PEARLScreen was so sensitive that the researchers could screen mixtures of 16 fragments with just 1 µM protein.

 

This is a neat paper, which confidently concludes that “due to the superior sensitivity of the PEARLScreen compared to all established screening experiments at standard fields, we expect it to become the standard experiment for ¹H-detected ligand screening.” We look forward to hearing how it performs for others.

Fragments beat lead-like compounds in a screen against OGG1

The twin rise of make-on-demand libraries and speedy in silico docking has supercharged fragment screening and optimization: we’ve written previously about V-SYNTHES, Crystal Structure First and a related method. Another advance is described by Jens Carlsson (Uppsala University) and a large group of multinational collaborators in an (open access) Nat. Commun. paper.

 

The researchers were interested in 8-oxoguanine DNA glycosylase (OGG1), a DNA-repair enzyme and potential anti-inflammatory and anticancer target. They started with a crystal structure into which they docked 14 million fragments (MW < 250 Da) or 235 million lead-like molecules (250-350 Da) from ZINC15. Multiple conformations and thousands of orientations were sampled for each molecule. In all, 13 trillion fragment complexes and 149 trillion lead-like complexes were evaluated using DOCK3.7, a process that took just 2 hours and 11 hours on a 3500 core cluster.

 

After removing PAINS and molecules similar to previously reported OGG1 inhibitors, the top-scoring 0.05-0.07% molecules from each screen were clustered and, after manual evaluation, 29 fragments and 36 lead-like compounds were purchased from make-on-demand catalogs. These were tested at 495 µM (for fragments) or 99 µM (for larger molecules) in a DSF screen. None of the lead-like compounds significantly stabilized the protein, while several fragments did. Four of the fragments were successfully crystallized with OGG1, and in all cases the key interactions predicted in the computational screens were confirmed in the actual crystal structures.

 

Compound 1 showed the greatest stabilization of OGG1 (2.8 ºC) and some inhibition in an enzymatic assay, but not enough to calculate an IC₅₀. Searching for analogs that contained compound 1 as a substructure in the Enamine REAL database of 11 billion compounds produced few hits, but, as before, thinking in fragments proved fruitful. Searching for molecules containing just the core heterocycle and amide (colored blue below) yielded nearly 43,000 possibilities. Docking these and making and testing a few dozen led to compound 5, with mid-micromolar inhibition. Further iterations led to low micromolar compound 7.

At this point the researchers turned from make-on-demand libraries to synthetically accessible virtual libraries to fine-tune the molecule. After docking 6720 virtual molecules, they synthesized and tested 16, of which 12 were more potent than compound 7, with five of them being submicromolar. Compound 23 showed low micromolar activity in two different cell assays and was selective against four other DNA repair enzymes.

 

The same high-throughput docking approach was applied to three other protein targets: SMYD3, NUDT5, and PHIP. In each case crystal structures of bound fragments were available to use as starting points. Multiple compounds with improved docking scores compared to the initial fragments were identified, though no compounds were actually synthesized and tested.

 

The success in finding compound 1 demonstrates experimentally the advantage fragments have in efficiently searching chemical space. The researchers note that 97% of the >30 billion currently available make-on-demand compounds have molecular weights >350 Da, while only 50 million are < 250 Da. Screening all of these fragments in silico is possible; screening everything, less so. Although the fragment hits for OGG1 were weak, this isn’t always the case, as noted here. The fact that fragment 1 could be advanced to a sub-micromolar inhibitor after synthesizing just a few dozen molecules also testifies to the efficiency of in silico approaches.

 

The paper contains lots of useful details and suggestions for streamlining the process and is well worth perusing if you are trying to find hits against a structurally-enabled protein.

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.