Fragments vs DHX8: towards a chemical probe

RNA helicases present an intriguing but challenging opportunity for drug discovery. Their importance for cell growth makes them attractive cancer targets. But to function, they must undergo considerable conformational changes, and this dynamism makes it difficult to find high-affinity inhibitors. In a new open-access J. Med. Chem. paper, Gurdip Bhalay, Rob van Montfort, and collaborators at The Institute of Cancer Research (ICR), Merck KGaA, and Proteros describe their efforts against DHX8.

 

DHX8 is one of 14 or so DEAH-box helicases and helps release mRNA from spliceosomes. Previous research had found it to be important for the cellular stress response as well as malignant cell transformation and progression, and siRNA-knockdown experiments showed that cancer cells were particularly dependent on DHX8 activity. Thus, the researchers set out to find a chemical probe.

 

The 2000-fragment ICR library was screened (at 300 µM) against the helicase domain of DHX8 using both surface plasmon resonance (SPR) and thermal shift assay (TSA). In addition, the full-length yeast homolog of DHX8, Prp22, was also screened by SPR to look for fragments that might bind outside the helicase domain. The TSA screen yielded 18 hits, while the SPR screen yielded 91, of which 63 also bound Prp22, while another 17 fragments bound Prp22 more strongly than the helicase domain of DHX8.

 

Eight-point dose response curves were collected for the hits, and 10/18 TSA hits gave a concentration-dependent response on both DHX8 and Prp22, along with 42/108 SPR hits, while another 5 SPR hits only bound to Prp22. Surprisingly, there were only 2 hits in common between the TSA and SPR hits; the researchers note the importance of using multiple orthogonal screens.

 

Next, the confirmed hits were tested in three ligand-observed NMR assays: CPMG, WaterLOGSY, and STD. Reassuringly, 39 were active in all three assays. Competition experiments suggested that the fragments did not bind in the ATP-binding site, but some of them were competitive with RNA.

 

Crystallography was attempted on several of the fragments, and compound 1 yielded a structure revealing that it did in fact bind in the “RNA-binding tunnel” of the helicase domain, with the carboxylic acid making contacts similar to a backbone phosphate. Although the dissociation constant (assessed by SPR) was > 1 mM, compound 1 was active in an ATP-hydrolysis assay and an RNA fluorescence anisotropy (FA) binding assay with triple-digit micromolar activity (values for the FA assays are shown here).

 

Much of the paper is devoted to the optimization of this fragment, which, true to the reputation of the helicase family, turned out to be difficult. Testing related compounds led to compound 6, with low micromolar activity, but despite extensive efforts it was impossible to improve the affinity much further. Thus, the researchers reexamined their larger compound collection for other similar compounds and identified compound 24. Although weaker than compound 6, a crystal structure revealed that the phenyl ring binds deeper into the RNA-binding tunnel, providing new opportunities for fragment growing. Structure-based design led to compound 34, with low micromolar activity, and further optimization ultimately led to compound 53, with submicromolar activity.

 

The mechanism of DHX8 requires the RNA-binding tunnel to open and close right where compound 53 sits, so its inhibitory activity can be attributed to blocking this motion. In addition to biochemical activity, the molecule also showed low micromolar activity in a cellular target engagement assay. However, no other cellular data are reported.

 

This is a textbook example of fragment finding and growing against a difficult target. The fact that the researchers returned to fragments after the initial series reached an affinity plateau is a useful reminder that it is all too easy to get stuck in a local minimum, and you may need to take a step back to make progress. Although compound 53 is just on the edge of what may be considered a chemical probe in terms of potency, it is the first reported nanomolar inhibitor of DHX8. The researchers also mention a second series derived from a different fragment, and it will be interesting to see if this yields even better molecules.

Malicious metals muddy fragment-to-lead optimization

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

 

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

 

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

 

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

 

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

 

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

 

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

Review of 2025 reviews

Turning and turning in the widening gyre

The falcon cannot hear the falconer…

 

In our small, annual counterweight to Yeats’ “mere anarchy," Practical Fragments looks back on 2025.

 

This year marked the thousandth post on Practical Fragments, a milestone neither Teddy nor I imagined when the blog launched back in 2008. In terms of conferences, I wrote about CHI’s Drug Discovery Chemistry in San Diego here and Discovery on Target in Boston here.

 

For the past decade I’ve participated in annual J. Med. Chem. perspectives covering fragment-to-lead success stories, two of which published this year. The first, spearhead by Rhian Holvey at Astex, covers the year 2023, while Astex’s David Twigg took the lead on covering the year 2024. In addition to the tabular summaries for which these reviews are best known, both also include tables of “near misses,” none of which made the main tables because the starting points were (sometimes just slightly) too large. Four out of six of these heavyweights are covalent fragments, suggesting that the rule of three may need to be relaxed for these. The most recent paper also includes a table showcasing the eight approved FBLD-derived drugs.

 

Two more general publications are also of interest. In a brief (open-access) editorial in J. Med. Chem. Weijun Xu and Congbao Kang at A*STAR summarize fragment-finding methods and approved drugs and discuss future applications of FBLD in PROTACs and targeting RNA. And in Curr. Res. Pharm. Drug Discov., Geoffrey Wells, Exequiel Porta, and colleagues at University College London present a “graphical review” which covers library design, screening strategies, hit validation, fragment optimization, and a few case studies of approved drugs, along with current challenges.

 

In Drug Des. Devel. Ther., Bangjiang Fang and colleagues at Shanghai University of Traditional Chinese Medicine present an open-access bibliometrics analysis of 1301 fragment-based drug design papers published between 2015 and the end of 2024, which includes top ten lists of institutions, authors, and papers as well as keyword trends and analyses. Annual growth has averaged 1.4%, and the field is both global and collaborative, with 35% of publications involving more than one country.

 

Targets

Two reviews focus on oncology. The first, in Bioorg. Chem. by Milind Sindkhedkar and collaborators at Manipal College of Pharmaceutical Sciences and Lupin Ltd., briefly covers the history and practice of FBDD before providing short summaries of seven of the eight approved drugs to come from it. The second, published open-access in Chem. Rev. by Vanderbilt’s Steve Fesik, is a concise and highly readable introduction and account of the author’s groundbreaking work on BCL-2 family proteins, KRAS, and WDR5.

 

A much longer open-access review in Chem. Rev. by Paramjit Arora and colleagues at New York University covers protein-protein interactions (PPIs). Much of the focus is on larger molecules such as cyclic peptides, peptide mimetics, and other macrocycles, but there are summaries of fragment-based approaches against KRAS and 14-3-3 proteins.

 

The topic of 14-3-3 proteins is treated more fully in an open-access Acc. Chem. Res. paper by Michelle Arkin and colleagues at UCSF. While the focus of most efforts against PPIs is to find inhibitors, for 14-3-3 the goal is to find stabilizers, or molecular glues. The Arkin lab and others have been succeeding using various approaches, particularly disulfide tethering. We wrote about these efforts most recently in 2023, and the new review provides a nice update.

 

Fragment finding methods and libraries

Sahra St. John-Campbell and Gurdip Bhalay, both at The Institute of Cancer Research, published a massive open-access perspective on “target engagement assays in early drug discovery” in J. Med. Chem., covering a host of biochemical, biophysical, and cell-based assays. A table lists more than 50 different techniques, almost half of which are applicable to FBLD. Each row shows what characteristic(s) are measured as well as critical requirements for  protein, sample, and equipment. The paper is also beautifully illustrated with dozens of figures: one shows which techniques are most useful for different types of targets, and each method gets its own diagram.

 

A more focused open-access review is provided by Stefanie Freitag-Pohl and colleagues at Durham University in Biophys. Rev. After surveying various biophysical techniques, the researchers focus on spectral shift analysis, and in particular the Dianthus instrument from NanoTemper Instruments. This plate-based, high-throughput microfluidics-free instrument can detect changes in fluorescence caused by environment or temperature. Examples demonstrate affinity measurements across several orders of magnitude, up to double-digit millimolar, and a nice scheme shows use of the Dianthus in a fragment-screening workflow.

 

Moving to specific techniques, Jia Gao, Ke Ruan, and colleagues at University of Science and Technology of China Hefei provide an open-access survey of “the rise of NMR-integrated fragment-based drug discovery in China” in Mag. Res. Lett. After a brief overview of NMR approaches, they cover case studies from China, most of which are focused on fragment screening rather than optimization.

 

A less common biophysical method is native mass spectrometry (nMS), the subject of an open-access opinion in RSC Med. Chem. by Louise Sternicki and Sally-Ann Poulsen at Griffith University. This is a good survey of the approach; we highlighted a more fragment-focused review by the same authors last year.

 

The most common fragment-finding approach, X-ray crystallography, is covered in two open-access reviews. The first, in Acta Cryst. F by Sarah Bowman and collaborators at University of Buffalo and Brookhaven National Laboratory, focuses on critical early stages, from protein characterization to sample preparation and various crystallization approaches. The second, in Curr. Opin. Strut. Biol. by Martin Noble and colleagues at Newcastle University, starts by briefly reviewing crystallographic fragment screening before turning to fragment libraries. The paper includes a nice table summarizing publicly available libraries at major synchrotrons, with the text describing these in more detail.

 

The provider of one of these libraries, EU-OPENSCREEN, is the subject of an open-access review in SLAS Discov. by Robert Harmel and collaborators at EU-OPENSCREEN ERIC and Fraunhover ITMP. As we wrote last year, EU-OPENSCREEN is a broad consortium whose mission is to advance early drug discovery by providing access to technology and expertise. The new paper summarizes the four compound collections, including the European Fragment Screening Library (EFSL), and surveys progress to date. It also lays out ambitious plans, including expanding to >30 sites in nine countries.

 

Computational approaches and cryptic sites

Despite the hype about artificial intelligence in the broader world, AI in fragment-based drug discovery has been less common. In Curr. Opin. Struct. Biol., Woong-Hee Shin and colleagues at Korea University College of Medicine summarize applications to fragment growing, merging, and linking. The open-access paper includes a handy table of 13 programs, and includes GitHub links where available.

 

Cryptic binding sites, defined by Ehmke Pohl and collaborators at Durham University and Cambridge Crystallographic Data Centre “as binding pockets that exist in the ligand-bound state of a protein but not in its apo form,” are the focus of an open-access review in Bioinform. Adv. The researchers cover earlier computational approaches for finding these, especially molecular dynamics (MD) and machine learning (ML). They note that a key challenge for ML is the limited quantity and quality of experimental data: undiscovered cryptic sites would be misclassified as non-binding sites.

 

Yowen Dong, Ge-Fei Hao, and colleagues at Guizhou University review “computational methods for identifying cryptic pockets” in Drug Discov. Today. As with the previous review, these are divided between molecular dynamics and AI-based techniques, which are discussed individually and then compared. The researchers apply six approaches to the model bacterial protein TEM-1 β-lactamase and find that, for this highly studied single protein, the AI-based methods are much faster (seconds instead of days) and just as accurate, though MD-based methods provide more insight into formation mechanisms of cryptic pockets.

 

Covalent ligands

Allosteric sites are an important sub-class of cryptic pockets, and in J. Med. Chem. Jianing Li and colleagues at Purdue University discuss covalent allosteric inhibitors. After briefly discussing advantages of covalent molecules, they review examples targeting protein phosphatases, kinases, and GTPases, such as KRAS.

 

Of course, covalent molecules are not limited to allosteric sites. An open-access review in Bioorg. Med. Chem. Lett. by Walaa Bedewy, John Mulawka, and Marc Adler at Toronto Metropolitan University summarizes published covalent protein ligands, grouping them by target site:  active sites, residues adjacent to an active site, protein-protein interfaces, cofactor binding sites, and allosteric sites.

 

Chem. Rev. published two massive reviews on covalent ligands, each with more than 300 references. The first, by Tomonori Tamura, Masaharu Kawano, and Itaru Hamachi at Kyoto University, covers a wide range of topics, from covalent drugs, to peptide- and protein-based covalent inhibitors, to chemical biology labeling and target engagement strategies, to covalent bifunctional molecules such as PROTACs and radionucleotide-based molecules, and even covalent modification of DNA and RNA. The paper includes 68 figures, many reproduced from the original publications.

 

The second (open-access) Chem. Rev. paper, by Ku-Lung Hsu and colleagues at University of Texas at Austin, focuses on covalent ligands targeting protein residues other than cysteine, particularly lysine and tyrosine; we highlighted some of Hsu’s work recently. The paper also discusses naturally occurring molecules that bind to lysine, such as pyridoxal phosphate and aldose sugars.

 

Methods for finding covalent ligands are the focus on an open-access review in JACS Au by Mengke You, Hong Liu, and Chunpu Li at Shanghai Institute of Materia Medica. Specifically, they review disulfide tethering, activity-based protein profiling (ABPP), covalent DEL, phage and mRNA display, and sulfur(IV) fluoride exchange (SuFEx), with examples for each.

 

The last paper on this topic, in J. Med. Chem., offers a brief but important overview of all covalent FDA-approved small molecule drugs through 2023. Samuel Dalton and collaborators at Isomorphic Laboratories and Merck counted 128 covalent drugs, about 7% of all small molecule drugs. More than half are antibiotics, and more than 85% target serine or cysteine. Only 10% are reversible, but this number is rapidly increasing, with 11 of the 13 reversible covalent drugs approved since 2010. Importantly, the names, chemical structures, indication, target and target residue, warhead, and key references for all the drugs are provided in the supporting information.

 

Miscellaneous

Deconstruction of ligands to smaller fragments that are then “reconstructed” into new leads is a venerable approach in FBLD and the subject of an open-access perspective in J. Med. Chem. by J. Henry Blackwell, Iacovos Michaelidies, and Floriane Gibault at AstraZeneca. Multiple examples dating as far back as the late 1990s are provided, along with appropriate caveats about potential changes in fragment binding modes and protein conformations.

 

Finally, an open-access perspective in J. Med. Chem. by Dean Brown (Jnana Therapeutics) examines the 104 oral drugs approved from 2020 through 2024, including structures, dosing, pharmacokinetics, and safety. Roughly a third of these drugs are dosed more than once per day, and almost a quarter have a black box warning, while 42% have at least one contraindication. Dean warns that “overly prescriptive [development candidate] criteria may inadvertently stifle the development of innovative drugs,” and that it is difficult but important “to be the champion for a compound that others perceive as ‘un-drug like.’” The growing success of covalent drugs illustrates that some organizations are taking this to heart.

 

And that’s it for 2025. Thanks for reading and special thanks for commenting. And in 2026, may the best of us be filled with passionate intensity.

FTO revisited: fragment linking this time

Four years ago we highlighted a fragment-merging approach targeting fat mass and obesity-associated protein (FTO), an RNA demethylase implicated in acute myeloid leukemia (AML). In a new J. Med. Chem. paper, Ze Dong, Cai-Guang Yang, and collaborators at University of Chinese Academy of Sciences Beijing describe a fragment linking approach that arrives at a similar outcome.

 

As we noted in 2021, previous research had revealed that meclofenamic acid (MA) binds in the substrate-binding site, near the binding site for the 2-oxoglutarate (2-OG) cofactor. In the new paper, the researchers synthesized an analog of MA which they then linked to analogs of 2-OG through a variety of linkers. Among the best of these was compound 8a, with low micromolar activity in an assay using PAGE (polyacrylamide gel electrophoresis) as well as mid-nanomolar activity in a different type of assay. This is an improvement over the MA analog itself (which was only tested in the PAGE-based assay, results shown in the figure). Compound 8a was also selective for FTO over two related proteins.

 

 

As is often seen in fragment linking, the SAR is quite sharp. The two-carbon linker was critical; lengthening the linker by one methylene or adding a methyl group abolished activity. The double bond in the 2-OG mimetic was also important; the saturated version of this molecule was inactive.

 

Surprisingly, a crystal structure of FTO bound to a molecule closely related to 8a revealed that while the MA moiety bound as expected, the 2-OG analog adopted a different conformation. But inexplicably, according to the experimental section, 2-OG was added to the crystallization solution, which would compete with compound 8a. Indeed, the structure deposited in the protein data bank shows the 2-OG analog N-oxalylglycine bound to the catalytic metal ion.

 

With two carboxylic acid moieties, it is no surprise that compound 8a showed no antiproliferative activity in AML cell lines. However, ester prodrugs did show low micromolar activity. Further characterization of one of these showed changes in protein levels consistent with FTO inhibition. This molecule also caused tumor growth inhibition after intraperitoneal dosing in a mouse xenograft model.

 

Superficially, compound 8a resembles compound 11b from the 2021 paper. Like that molecule it is probably too weak to serve as an ideal chemical probe. That said, with one fewer aromatic ring, compound 8a may be better suited for further optimization.

Twentieth-Third Annual Discovery on Target Meeting

The CHI Discovery on Target (DoT) meeting was held last week in Boston. More than 850 people from 24 countries attended, 75% from industry. As usual I’ll just touch on some broad themes.

 

Covalent approaches

Covalent approaches were prominent throughout the conference. One of the very first talks was by Stefan Harry (Harvard/MGH), who described screening 416 cancer cell lines with three reactive “scout probes,” identifying some 6000 cysteine residues that could be covalently liganded. There are some interesting cell and context-dependent differences, and all the data are publicly available and easily searchable through a free online portal called DrugMap. He is now profiling a library of dual-electrophile-containing compounds to identify molecular glues.

 

Knowing which cysteines can be targeted is the first step for covalent drug discovery, and Sherry Ke Li described how she and colleagues at Genentech go about finding ligands. They’ve experimentally determined the reactivity of more than 6400 compounds against free cysteine and used this to train a machine-learning model to predict chemical (as opposed to specific) reactivity. Mass spectrometry (MS) using isolated proteins is the workhorse screening approach, but Sherry also described using variable temperature surface plasmon resonance (SPR) to dissect the individual components of k_inact/K_I.

 

AstraZeneca has also been doing considerable covalent screening, and Hua Xu briefly described the BFL1 story we wrote about here. In addition to pure proteins, they are now also starting to screen their covalent library in cells. Hua also presented earlier work from Pfizer on the discovery of the covalent kinase inhibitor ritlecitinib, which started with a noncovalent binder. Proteomic studies revealed that in addition to the intended target JAK3, it hits other TEC-family kinases too.

 

Adding a covalent warhead to a reversible binder is also the approach taken by MOMA Therapeutics in the discovery of their clinical WRN inhibitor MOMA-341, as presented by Momar Toure. They ended up targeting the same cysteine as Vividion (see here), though the binding mode is somewhat different. MOMA is also pursuing covalent fragment screening using intact protein MS, and Brian Sosa-Alvarado described how they were able to identify nanomolar inhibitors of RAD54L within six months of starting the program, aided by DNA-encoded libraries (DEL).

 

Not everyone is pursuing cysteine: Ken Hsu described how he and his team at University of Texas Austin are using sulfur-triazole exchange chemistry (SuTEx) to target tyrosine residues across the proteome. He noted that although cysteine could react with this warhead, the resulting thiosulfonate would be unstable. This is true in general, but I wonder if, just like the reversible cyanoacrylamide warheads we wrote about more than a decade ago, they could be stabilized within folded proteins.

 

Noncovalent approaches

Covalency is not the only game in town, as exemplified in a talk by Emma Rivers on “integrated hit discovery” at AstraZeneca. I was tickled that she grouped FBLD with HTS as “traditional” approaches, onto which they’ve added DEL and peptide libraries. Importantly, they’re focused on generating and capturing as much high-quality data as possible to enable machine learning – a topic we’ll touch on more below.

 

Nor are proteins the only target; there was a whole track on RNA- and DNA-targeting small molecules, where Benjamin Brigham described the plate-based equilibrium dialysis-based approach taken at Atavistik to screen metabolites and metabolite-like molecules. This led to two fragment-sized hits against RNA encoding SERPINA1, and although the affinities are modest, they do inhibit translation in a cell-free system.

 

At FBLD 2018 Astex presented the first cryo-EM structure of a fragment-protein complex, noting that throughput was an issue. The company has leaned into that challenge and now has three microscopes, including a top-of-the-line Krios, with another on the way. Miguel Zamora-Porras described how they have now solved hundreds of structures. Their Krios can collect data on two compounds per day, and the full cycle time from protein-ligand preparation to structure is about a week. Miguel described how structures of ligands bound to the ion channel protein TRPML1 helped reveal why some were agonists and others antagonists.

 

Data and its discontents

On the subject of structures, Steve Burley (Rutgers) gave an eloquent history and defense of the “RCSB Protein Data Bank: an open access research resource that benefits all humanity.” From its humble beginnings with just seven structures in 1971, the PDB now contains more than 240,000. And these are not just of scientific interest: all 88 of the new molecular entities the FDA approved for oncology between 2010 and 2023 had PDB structures that informed the biology or druggability, and 75% of the efforts involved structure-based design. Steve also mentioned that the question of where and how to store large-scale crystallographic data will be discussed in a meeting sometime in the spring of next year. Finally, Steve is hoping to retire from his position as Director of the RCSB PDB, so if you’re looking to make an impact, please apply.

 

The dramatic advances in protein structure prediction exemplified by AlphaFold would not have been possible without the PDB, but unfortunately the same high-quality information on protein-ligand binding modes and affinities is not available, as noted by Woody Sherman of Psivant. To illustrate the importance of training data, Woody asked ChatGPT to produce a picture of an analog clock set to 6:32. The result? A clock with three hands, one at 10, one at 2, and one at 6, because most images of clocks are set to 10:10.

 

Woody asked whether machine-learning-based docking can extrapolate or just interpolate. Although impressive results have been reported for some protein-ligand complexes, it turns out that there are often similar ligands in the training data. For truly novel ligands, the predictions tend to fall flat. Similarly, allosteric ligands are often (mis)placed into an orthosteric site – just because the model has been trained that that’s where ligands should go. Indeed, although Psivant is heavily invested in computational approaches, Woody mentioned that they often use “wet” approaches for finding initial chemical matter.

 

On the subject of dubious data, Al Edwards (University of Toronto) noted that a third of all immunofluorescence images in the literature use antibodies that give signals in knockout cells. And as we wrote just last month, many reported small molecule “probes” are just as bad. Al is CEO of the Structural Genomics Consortium, whose ambitious Target 2035 aims to find a pharmacological probe for every target in the human genome. As a starter, they’re aiming for 2000 probes in the next five years. They’re using affinity-selected mass spectrometry (ASMS), screening pools of 500 compounds and 8 proteins at a time, and are getting micromolar hits against about 30% of targets. They’re accepting protein submissions, so if you’re looking for starting points against your favorite protein contact them.

 

I’ll end here, but please leave comments. And mark your calendar for Sep. 28 to Oct. 1 next year, when DoT returns to Boston.

Fragments vs KEAP1: Fragment growing this time

Kelch-like ECH-associated protein 1 (KEAP1) binds to nuclear factor erythroid 2-related factor 2 (NRF2), targeting it for degradation. Blocking this interaction has anti-inflammatory effects, and indeed the approved drugs dimethyl fumarate and omaveloxolone are believed to act in part through this mechanism. But those drugs hit a lot of other targets, and more specific molecules have long been sought; we wrote about one in 2016 and another in 2021. In an open-access paper just published in Angew. Chem. Int. Ed., Anders Bach and an international team of collaborators at University of Copenhagen and elsewhere describe a new chemical series.

 

As in the 2016 paper, the researchers started with a crystallographic screen, in this case using the 768-member DSI-poised library, which we wrote about here. This resulted in 80 hits, all binding in the so-called Kelch pocket, which has previously been targeted. Thirteen of these bound in the central region, and compound 1 showed modest but measurable affinity by SPR.

 

All previously reported non-covalent high-affinity KEAP1 ligands contain at least one acidic moiety to interact with arginine residues in the protein, so the researchers used structure-based design to add carboxylic acids, resulting in compound 4, with low micromolar affinity. This molecule, unlike the initial fragment, could also block the KEAP1-NRF2 interaction in a fluorescence polarization assay.

 

Building into a hydrophobic sub-pocket yielded compound 12, and adding strategically placed hydrogen-bond acceptors led to further improvements in affinity, ultimately leading to compound 28, with low nanomolar activity. Crystallography revealed that these molecules bound in a similar fashion as the initial fragment.

 

Compound 28 and related molecules were tested in a variety of assays. They were selective for KEAP1 over 15 other human Kelch domains in a thermal shift assay. Compound 28 activated NRF-2 regulated cytoprotective genes and decreased inflammatory markers in multiple cell lines. It also displayed RNA expression profiles similar to those of other reported non-covalent KEAP1 inhibitors. Cellular potency in some of these assays was as good as 60 nM.

 

This is a nice fragment-to-lead story, though no ADME or DMPK data are reported, and the combination of relatively high molecular weight, negative charge, and lipophilicity suggest that permeability and oral bioavailability may be challenging. Indeed, the researchers note that no non-covalent KEAP1-NRF2 inhibitors have entered the clinic. Perhaps this target is better suited for covalent inhibitors, preferably ones more selective than dimethyl fumarate. More on those later.

Playing fast and loose with electrostatic anchors on RNA

Two weeks ago we discussed how to find ligand-binding sites in RNA. Last week we wrote about how difficult it is to find good ligands even for good binding sites in RNA. A recent open-access paper in J. Med. Chem. by Christian Kersten and colleagues at Johannes Gutenberg-University explores why targeting RNA is so tough.

 

The researchers were interested in two well-characterized riboswitches, naturally occurring RNA elements that bind to small molecules such as metabolites. Specifically, they chose to study a riboswitch that binds to S-adenosyl methionine (SAM, structure here) and a riboswitch that binds to prequeuosine-1 (PreQ₁) and prequeuosine-0 (PreQ₀). 

Due to the phosphate backbone, RNA is highly negatively charged. The researchers asked whether positively charged moieties on ligands can serve as “electrostatic anchors” to generally improve affinity, and if so whether this can lead to any design principles. Multiple biophysical techniques were used to study the interactions of the two riboswitches with various natural and synthetic ligands: surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), and microscale thermophoresis (MST).

 

In the case of the SAM-VI riboswitch, the researchers compared the binding of SAM with closely related molecules having either one fewer positive charge (S-adenosyl homocysteine, or SAH) or synthetic ligands with the same or one more positive charge than SAM. Not surprisingly, SAM has the highest affinity, binding 20-50 fold more tightly than SAH. Further analysis suggested this is largely driven by an increased association rate, in which the positive charge accelerates the kinetics of binding. The driving energy for binding the ligands is enthalpic, but the favorable electrostatic interactions for more positively charged ligands are largely countered by an entropic penalty.

 

Similarly, the affinity of positively charged PreQ₁ for the PreQ₁ riboswitch is higher than the affinity of neutral PreQ₀, though not dramatically. As in the case of the SAM-VI riboswitch, the association rate of the positively charged ligand is more rapid than that of the neutral ligand. Binding for both ligands is highly enthalpic, with unfavorable entropy.

 

Previous reports had described other synthetic ligands for the PreQ₁ riboswitch, each with between one and three cationic centers. However these ligands showed no binding by ITC, questionable binding by MTC, and non-saturable, non-specific “loose binding” by SPR. Positive charges alone are not sufficient for high affinity, specific binding.

 

So what does it all mean? While adding positive charges can improve affinity of ligands for RNA, the increased affinity is usually not dramatic due to enthalpy-entropy compensation. The researchers note that, even for good ligands, the “thermodynamic binding profiles differ from typical protein-ligand interactions, where enthalpic and entropic contributions are usually more balanced.” Moreover, as we’ve noted, protein ligands often gain significant affinity with entropic gains by displacing "high energy water" molecules, but such opportunities are likely less common on the polar surface of RNA.

 

The affinity and ligand efficiency of PreQ₁ for its riboswitch are impressive, so clearly it is possible for small drug-like ligands to bind tightly to RNA. But this interaction is the product of countless eons of evolution. This careful paper suggests why building similarly effective synthetic ligands for most RNA will be difficult.

Targeting SARS-CoV-2 RNA – but not specifically

Last week we highlighted work suggesting that small molecule binding sites in RNA are most likely to be found in complex structures. A new open-access paper in Angew. Chem. Int. Ed. by Harald Schwalbe and collaborators at Goethe University Frankfurt and elsewhere provides both a case in point and an illustration of how difficult it is to target RNA.

 

The researchers had previously screened 15 RNAs from the SARS-CoV-2 virus, an effort we highlighted in 2021. In the new paper, the researchers focus on a portion of the frameshift element, which is important for directing viral replication from either of two partially overlapping open reading frames. The core of this RNA element is a roughly 69-nucleotide-long structure called a pseudoknot. Like most RNA sequences, this one can form multiple structures, including dimers, and the researchers used NMR, small-angle X-ray scattering (SAXS), and native gel electrophoresis to confirm that the construct was behaving as a homogenous monomer, consistent with three previously determined structures.

 

Based on some of the initial fragment hits, the researchers selected 50 similar molecules, of which only 14 were sufficiently soluble for screening. One of the more potent compounds, D05, initially showed promising activity in a ligand-detected NMR assay but turned out to be completely inactive when retested from a fresh stock. It turns out that D05 decomposes to compound 2, which was confirmed as active. Further modification led to compound 4, the most potent compound described. (Dissociation constants were determined by NMR, fluorescence, or both, and the two methods were in good agreement.)

Two-dimensional NMR with isotopically labeled RNA was used to try to determine the location of the binding site(s). Even with access to a 1.2 GHz magnet, the NMR peaks were severely overlapped, so the researchers used segmental isotopic labeling, in which just half of the RNA was labeled at a time. This exercise revealed potentially three different binding sites for compound 2.

 

The researchers also used two different computational approaches, Vina and RLdock, to predict binding sites, each of which could find one or two of the binding sites identified by NMR.

 

Several compounds were tested to see if they could block frameshifting in cell-lysates, and compound 2 showed 40% inhibition at 145 µM.

 

So far so good. But consistent with best practices, the researchers tested compounds 2 and 4 against phenylalanine tRNA. Unfortunately, the two ligands exhibited similar affinities to this control RNA as they did to the SARS-CoV-2 pseudoknot, despite the lack of sequence similarity. This suggests that these ligands bind to RNA nonspecifically. Perhaps this is not surprising given the three binding sites observed in a single 69-mer.

 

In the end, this is a thorough but sobering paper. Despite an impressive screening campaign with multiple biophysical methods, the best ligands seem to have modest affinity and low specificity. Drugging RNA still appears much more difficult than drugging proteins. But for either sort of target, this sort of careful work will be essential to find promising leads.

Identifying ligand-binding pockets in RNA, computationally and experimentally

Most drugs bind to proteins, but RNA provides many interesting targets. Unfortunately, finding drug-like small molecules that bind to RNA is difficult. A new paper in Proc. Nat. Acad. Sci. USA from Kevin Weeks and colleagues at University of North Carolina Chapel Hill provides tools to do so.

 

RNA presents several challenges for drug discovery. First, there are far fewer high-resolution structures than there are for proteins. This is in part due to the second challenge: RNA strands are often wriggly, able to form multiple conformations. And finally, RNA is highly charged and more polar than most proteins, so there are fewer opportunities for the hydrophobic interactions that often provide significant affinity in protein-ligand complexes.

 

These challenges have not deterred intrepid investigators: Practical Fragments first wrote about targeting RNA with fragments way back in 2009. However, examples of high-affinity ligands remain elusive, and in 2023 I wondered whether “most RNA is truly undruggable.”

 

The latest paper leaves me more optimistic. It describes a computational approach to find small-molecule binding sites in RNA. The researchers started with an open-source tool called fpocket, which was built for proteins. The fpocket program places virtual spheres all around a biomolecule, where each sphere contacts the center of four atoms. The size of each sphere depends on local curvature, and clusters of spheres define pockets.

 

To benchmark fpocket on RNA, the researchers first constructed a curated database of drug-like ligands bound to RNA. Of 538 RNA-ligand structures solved at the fairly low bar of ˂ 3.5 Å resolution, only 48 ligands were deemed drug-like by the quantitative estimate of drug-likeness (QED) score. (Although the QED score may be overly restrictive, and many approved drugs have low QED scores, setting a strict threshold means that any pockets identified are likely to be particularly attractive.)

 

Using default (protein-appropriate) parameters, fpocket identified just 63% of known ligand-binding sites in RNA, vs 83% for proteins. Worse, many predicted RNA pockets probably aren’t actually ligandable because they are too exposed to solvent. By tweaking parameters, the researchers improved performance of the program for RNA to 92%, and they also identified several attractive pockets that had previously been missed.

 

When the researchers applied the reparametrized program, redubbed fpocketR, to two bacterial ribosomes, they found several dozen pockets in each, including known antibiotic-binding sites. To assess whether the new pockets could bind fragments, they used an experimental approach called Frag-MaP, which uses fully functionalized fragment (FFF) probes containing a variable fragment, a photoreactive diazirine, and an alkyne. Treating bacterial cells with these FFF probes in the presence of UV light crosslinks them to nearby RNA. Crosslinked probes can then be isolated using click chemistry with the alkyne, and RNA sequencing reveals the sites of modification. Impressively, 89% of ligand binding sites found in the Frag-MaP experiments were predicted by fpocketR.

 

In another validation experiment, fpocketR identified pockets where 7 out of 17 antibiotics bind to bacterial ribosomes. Notably, all but one of the undetected pockets bind antibiotics such as aminoglycosides that don’t appear conventionally drug-like and indeed are not orally bioavailable.

 

Continuing to apply fpocketR to more RNAs led to the identification of dozens of new pockets. Interestingly, most of these pockets occur in complex RNA structures, such as multi-helix junctions or pseudoknots, rather than simpler structures such as bulges and consecutive loops. This could explain the paucity of fragment hits in a study we highlighted in 2023, which focused on simple loops.

 

Now that we know where to find attractive ligand-binding pockets in RNA, hopefully we will be more successful finding high-affinity ligands.

Fragment-based Drug Discovery Down Under (FBDD-DU) 2024

The end of June brought me to Brisbane for the fifth FBDD-DU Conference, which was meeting for the first time outside Melbourne. This was also my first FBDD-DU conference since 2019, and it was nice to see a wide range of talks from around Australia and beyond. As always, I won’t attempt to be comprehensive, so if you attended, please feel free to add your observations.

 

Techniques

Experimental techniques received considerable attention. Félix Torres (NexMR) described using an inexpensive benchtop NMR that doesn’t require liquid helium. Fragments were screened using photochemically induced dynamic nuclear hyperpolarization (photo-CIDNP). The method is so rapid that it is limited more by sample handling than data collection, and the Torres team is speeding things up using flow technology. Right now photo-CIDNP is still very much DIY, but rumor has it that Bruker may soon launch a photochemical module for their benchtop instrument.

 

We’ve written about high-throughput crystallographic screening at the Diamond Light Source, and synchrotrons around the world are building similar platforms. Kate Smith described integrated systems at the Swiss Light Source which automate crystallization, fragment screening, data collection, and data processing. She also described increasing automation of fragment screening using the free-electron laser (FEL), which we wrote about here. Current throughput is around 40 compounds per day and requires large amounts of protein, but these are still early days.

 

Australia is building their own high-throughput crystallography platform, and various components were described by Roxanne Smith (University of Melbourne), Gautham Balaji (Monash Univesrity), and Yogesh Khandokar (ANSTO-Australian Synchrotron). Watch this space!

 

Speaking of Australia, Nyssa Drinkwater described Compounds Australia, a national repository of more than 2.5 million molecules, including several fragment collections. Members, who can be from outside Australia, can store their own libraries within the facility to ease collaborations with other groups, and they can also access public libraries of compounds, including unusual Antipodean natural product extracts. I was fortunate to be able to visit the facility at Griffith University and can attest that it is easily the equal of those in large pharma.

 

Turning to mass spectrometry, Sally-Ann Poulsen (Griffith University) described covalent library screening against PRMT5, a target we’ve written about here. Sally-Ann is also a pioneer of (conventionally non-covalent) native mass spectrometry, and she described applying this methodology to screen small molecules against RNA.

 

But the star of the conference was SPR, appearing in multiple talks. Long-time readers may recall an instrument made by SensiQ, with its gradient injection capability to accelerate data collection. This is now marketed by Sartorius, and Lauren Hartley-Tassell (Griffith University) described using it to screen a glycoprotein. The larger plumbing in the instrument is less prone to clogging, and Lauren said it can even accommodate screening of whole cells.

 

Anything to accelerate the (sometimes painful) process of advancing fragments is always welcome. As Jason Pun (Monash University) noted, eight of nine targets screened in Martin Scanlon’s group started with fragments having affinities worse than 100 µM. Off-rate screening, an SPR technique we wrote about here, can rapidly identify more potent molecules from crude reaction mixtures, but data processing can be tedious. Jason described new software tools to automate this process, and hopefully he will publish the methodology and code. (An aside: over coffee Yun Shi of Griffith University noted that off-rate screening, or ORS, should really be called off-rate constant screening, which would give the more amusing acronym ORCS.)

 

Targets

Turning to targets, Ben Davis (Vernalis) described a collaboration with Servier to advance oncology target USP7 inhibitors from a literature fragment to a preclinical candidate. Crude reaction mixture screening was used extensively, not just by SPR but even in microsome stability studies. Unfortunately the project ended when on-target toxicology effects emerged, which were perversely more severe in higher animal species than they were in mice.

 

Yun Shi described finding tiny heterocyclic fragments that react with the NAD⁺ cofactor of neurodegenerative target SARM1 in situ to generate a potent inhibitor, as we wrote about here. Yun is using ¹⁹F NMR to follow the base-exchange reaction to identify inhibitors to other glycohydrolases too.

 

Deaths due to E. coli are – somewhat surprisingly – more common than those caused by any other pathogen, and Christina Spry described her work at the National Australian University to discover inhibitors of the essential dephosphocoenzyme A kinase (GPCK) enzyme, which catalyzes the final step in the synthesis of Coenzyme A (CoA). Fragment screening by DSF and NMR identified a weak (K_D=380 µM) binder, and fragment growing has led to a low nanomolar inhibitor that is selective against the human form of the enzyme.

 

Continuing the E. coli theme, several talks discussed efforts against the challenging bacterial virulence target DsbA, a twenty-year campaign in Martin Scanlon’s group at Monash as noted by Yildiz Tasdan. The enzyme has a shallow, hydrophobic active site, but the discovery of fragments binding to a cryptic site and crude-reaction screening by ORS (ORCS?) and affinity-selected mass spectrometry (ASMS) has finally led to molecules with dissociation constants around 1 µM.

 

Finally, in his closing keynote address Alvin Hung, who recently founded NeuroVanda, described a wide range of fragment success stories, many of them covered on Practical Fragments, against targets including pantothenate synthetase, GSK3β, PKC-ι, and MNK1/2. Although structural enablement helped in many cases, Alvin was not rigid about the need for atomic-level details: in response to the question whether he would advance a fragment in the absence of structure, he answered simply, “of course.” Perhaps it's time to redo my poll on this subject.

 

I’ll wrap up here, but if you missed this or earlier events this year there are still a couple more conferences in Boston, and 2025 is already starting to take shape.

Fragments in cells, writ large

Earlier this year we highlighted work in which a dozen fragments were screened against cells to look for noncovalent binders across the proteome. A new paper in Science by Georg Winter and collaborators at the Austrian Academy of Sciences, Pfizer, and several other organizations ups the game by more than an order of magnitude, and uses machine learning to make predictions about fragments’ cellular destinations and binding partners. (See also Derek Lowe’s post here.)

 

The researchers started with 407 diverse fully functionalized fragments (FFFs), which as we previously discussed consist of a variable fragment coupled to a photoreactive group and an alkyne moiety that can be used to pull down any bound proteins using click chemistry. These were selected from a larger set of ~6000 FFFs available from Enamine. The FFFs were incubated at 50 µM with intact HEK293T cells, followed by ultraviolet crosslinking.

 

Next, cells were lysed and treated with a biotin-azide probe that reacts with the alkyne on the FFFs. Covalently modified proteins were captured on streptavidin resin and proteolytically digested. Tandem mass tag (TMT) proteomics, which we wrote about here, was used to identify captured proteins. Unlike earlier methods, the researchers did not pinpoint the specific fragment binding sites on proteins.

 

In total the researchers found 2667 proteins bound to one or more fragments, of which ~86% had no reported ligands. Both proteins and ligands varied considerably in promiscuity: some proteins bound to more than half of the FFFs, and some fragments bound to hundreds of proteins, while others bound only a few, or none. To look for specific interactions, the researchers focused on proteins bound by fewer than 10 different ligands.

 

Three protein-ligand interactions were analyzed in some detail: the kinase CDK2 (and other CDK family members), the adapter protein DDB1, and the solute carrier protein SLC29A1. In each case the researchers confirmed the results from their chemoproteomic screens. Follow-up studies with related molecules led to more potent derivatives, with a CDK2 inhibitor showing low micromolar activity in a biochemical assay and an SLC29A1 inhibitor showing micromolar activity in a cell-based assay.

 

The researchers also found patterns in their larger data set. Armed with 47,658 protein-ligand interactions, the researchers were able to use machine learning to start to predict which molecular features were associated with binding. They ranked fragments as promiscuous or nonpromiscuous and built a promiscuity model. Molecules with higher lipophilicity and a greater fraction of aromatic carbon atoms tended to be more promiscuous, but the model could correctly categorize compounds as promiscuous even if they had lower ClogP values, or nonpromiscuous even if they had higher ClogP values.

 

Beyond promiscuity, the researchers used machine learning to predict other behavior, such as subcellular localization. A relatively easy case was to predict which molecules would accumulate in lysosomes; these tended to be hydrophobic basic amines. More impressively, the researchers could predict fragments likely to bind to transmembrane transporters, RNA binding proteins, and even intrinsically disordered proteins. And this is just the start: they hope one day to predict “target proteins from an input chemical structure alone.”

 

Perhaps most exciting, all of the data and models are available for free at Ligand Discovery. You can explore the proteins bound across all 407 fragments, input one or more proteins and find ligands, predict whether any given FFF is likely to be promiscuous or not, and even “build a machine learning model on the fly to predict potential interactions.” 

 

Check it out and let us know your experience.

Covalent fragments vs a SARS-CoV-2 helicase

Last week we wrote about the difficulties of trying to understand even well-characterized covalent inhibitors of well-characterized targets. Most projects have far less information, as illustrated in a recent paper in J. Am. Chem. Soc. by Ekaterina Vinogradova, Tarun Kapoor, and collaborators at Rockefeller University and Sanders Tri-Institutional Therapeutics Discovery Institute, who report the first inhibitors of a particular SARS-CoV-2 enzyme.

 

The researchers were interested in helicases, enzymes that unwind DNA, RNA, or both. To do so, helicases cycle between “open” and “closed” forms, with conformational changes of as much as 15 Å. That dynamism complicates structure-based drug design, and many screens have yielded false positives. An irreversible covalent inhibitor that remained bound to the enzyme through its gyrations would potentially be easier to optimize.

 

The protein nsp13 from SARS-CoV-2 is essential for viral replication and thus an attractive drug target. The researchers started by testing previously reported and reactive “scout fragments” in a functional assay. Compound 1 inhibited the enzyme, and mass-spectrometry (MS) assays revealed that it modified three sites on the protein. Although multiple modifications are not desirable, the enzyme does contain 26 cysteine residues, so it could be worse. Peptide mapping and mutagenesis experiments revealed that modification of cysteine 556 (C556) is responsible for the inhibitory activity of compound 1.

 

A series of analogs culminated in compound 3b, which had low micromolar activity after a four hour incubation and also seemed more selective than compound 1, with less modification of other cysteine residues. The enantiomer of compound 3b was at least 6-fold less potent, suggesting molecular recognition rather than simple reactivity. In addition to nsp13, the researchers examined two mammalian helicases with disease relevance, WRN and BLM, and found that compound 3b was modestly selective for nsp13. (The researchers find different inhibitors for these two enzymes, though these are weaker and not as extensively characterized as those for nsp13.)

Cysteine 556 is not in the ATP-binding site and does not seem to be involved with RNA binding, and the researchers suggest that compound 3b may act allosterically. It seems to be highly conserved too, which might mean mutational resistance is less likely to evolve.

 

As the researchers acknowledge, compound 3b contains a chloroacetamide warhead, which is likely too reactive and unstable to move forward into in vivo studies, let alone the clinic. Also, had I reviewed the manuscript I would have requested the researchers to provide k_inact/K_I values rather than merely IC₅₀ values; a rough calculation using the methodology in this paper suggests a modest 10 M^-1s^-1 for compound 3b. That said, the discovery that liganding C556 inhibits nsp13 means that working to develop more potent and selective molecules may be worth the effort.

Review of 2023 reviews

The annual Practical Fragments look-back on the preceding year may not be the most highly anticipated year-end tradition, but I hope you find something of interest in this twelfth edition.

 

I was fortunate to attend several conferences and wrote about CHI’s Discovery on Target in Boston and Drug Discovery Chemistry in San Diego. As for reviews, Louise Walsh and collaborators at Astex, Vrije Universiteit Amsterdam, Novartis, and Frontier Medicines (me!) published our annual analysis of fragment-to-lead success stories in J. Med. Chem., this one covering the year 2021. Some twenty other reviews of interest to this readership were also published. I’ll cover them thematically below.

 

Methods

Crystallography is the most popular fragment-finding technique, and in Expert Opin. Drug Disc. Wladek Minor and collaborators at University of Virginia and Jagiellonian University examine “the current role and evolution of X-ray crystallography in drug discovery and development.” At the start of 2023 the Protein Data Bank (PDB) contained more than 200,000 structures, which sounds impressive until you learn that the AlphaFold database contains more than 200 million predicted protein structures. But this is not experimentalist vs machine: the researchers note how machine learning approaches can be used to more rapidly refine and improve experimental data with resources such as CheckMyBlob and PDB-REDO.

 

For those wishing to dig deeper, two papers in Methods Enzymol. go into experimental detail. In the first, Natalie Tatum and colleagues at Newcastle University describe “crystallographic fragment screening in academic drug discovery.” May Sharpe and collaborators at the Swiss Light Source and University of Hohenheim describe their fast fragment-screening pipeline in a comprehensive (49 page) guide. The focus is on reproducibility, and there is plenty of practical advice. For example, “the authors have even been successful in flying with crystal plates,” though getting these through airport security may be easier in some countries than others.

 

Protein-detected NMR was the first truly practical fragment-based approach, and another paper in Methods Enzymol. by Brian Volkman, Brian Smith, and colleagues at Medical College of Wisconsin describes “fragment-screening by protein-detected NMR.” This distills eight years of effort building their internal protein-detected NMR fragment screening platform that has been applied to 16 proteins thus far. The chapter is particularly detailed on protein and library preparation and screening.

 

Compared with crystallography and NMR, virtual screening can be dramatically faster; we’ve highlighted multibillion-compound screens. In WIREs Comput. Mol. Sci., Artem Cherkasov, Francesco Gentile, and colleagues at University of British Columbia and University of Ottawa discuss (open access) how computational methods are “keeping pace with the explosive growth of chemical libraries.” They cover brute force methods, fragment-based virtual screening, and machine-learning based methods, all while avoiding hype, and conclude that it will take time for these methods to “have a real impact on practical drug discovery.”

 

Finally, Marianne Fillet and collaborators at University of Liege and University of Namur provide a general review in Trends Anal. Chem. covering multiple methods to detect non-covalent fragments. These include established techniques such as biochemical assays, ligand-observed NMR, crystallography, thermal shifts, and SPR, as well as less common ones such as WAC, microscale thermophoresis, ACE, and DEL. The paper includes several nice tables and even a decision tree to help choose among the various approaches.

 

Covalent fragments

Many techniques to detect noncovalent interactions also apply to reversible covalent inhibitors, the subject of a review in Med. Chem. Res. by Faridoon and collaborators at Genhouse Bio and Olema Oncology. The researchers focus on various warheads including cyanoacrylamides, nitriles, ketones and aldehydes, boronic acids, and others, and provide multiple examples for each.

 

In contrast, an open-access review in Pharmaceuticals by Monique Multeder and collaborators at Leiden University Medical Center discusses methods to detect both reversible as well as irreversible covalent protein-drug adducts. Crystallography is the most informative, but the researchers also delve into various mass-spectrometry techniques including top-down (with intact proteins) and bottom-up (after digestion of modified proteins). Also covered are activity-based protein profiling (ABPP) methods, NMR, and fluorescence-based approaches. The nearly 300 references make a useful compendium.

 

One of the most exciting recent developments is “proteome-wide fragment-based ligand and target discovery,” the subject of an open-access review in Isr. J. Chem. by Ines Forrest and Christopher Parker, both at Scripps. This concise, highly readable account covers a lot of ground, from ABPP to fully functionalized fragments (FFFs) to phenotypic screening.

 

If you’re doing covalent FBLD you’ll need a library of covalent fragments, and if you’re building one, I’d recommend a review in Prog. Med. Chem. by David Mann and colleagues at Imperial College London. The paper nicely summarizes design principles such as choice of warhead and the fact that reactivity can vary considerably even among compounds with the same warhead. Synthetic methods and screening approaches are also well covered, along with methods to distinguish specific binding from nonspecific reactivity.

 

Most covalent fragments target cysteine residues, but there at least nine other potentially reactive amino acids, and these are the subject of an open-access review by György Keserű and colleagues at Budapest University of Technology and Economics in Trends Pharm. Sci. Lysine, serine, threonine, tyrosine, and histidine are the most common targets, though some of the warheads are so reactive that specificity will be challenging, let alone reasonable pharmacokinetic properties. This is especially true for aspartic and glutamic acids, methionine, and tryptophan.

 

Finally, another article in Trends Pharm. Sci. by Carlo Ballatore and colleagues at University of California San Diego describes using covalent strategies to develop stabilizers and inhibitors of protein-protein interactions (PPIs). Site-directed fragment tethering with disulfide and imine chemistry is a focus, particularly in the context of 14-3-3 proteins. Proximity-enabled covalent strategies, in which warheads are grafted onto non-covalent molecules, are also covered. There is also a short section on covalent PROTACs – more on that topic below.

 

Targets

Keeping with the theme of protein-protein interactions, Ge-Fei Hao, Guang-Fu Yang, and collaborators at Central China Normal University and Guizhou University discuss fragment-based approaches against “undruggable” PPIs in Trends Biochem. Sci. After describing why protein-protein interactions can be difficult, the paper presents several successful case studies, including venetoclax, sotorasib, and targeting 14-3-3 proteins.

 

Targeted protein degradation continues to be a major focus for drug discovery, and this is commonly achieved by hijacking E3 ligases to cause them to ubiquitinate a target of interest. Iacovos Michaelides and Gavin Collie (AstraZeneca) describe how FBLD has been used to find ligands against E3s in an open-access J. Med. Chem. paper. There are more than 600 E3s, and because their biology relies on protein-protein interactions they are often tough targets. Fragment hits can be weak and difficult to advance, though the researchers do describe several success stories including against KEAP1 and XIAP/cIAP. Covalent fragments have the potential to permanently reprogram E3 ligases, and these are covered well too.

 

Another difficult type of target is RNA, the topic of two reviews. In an open-access Curr. Opin. Struct. Biol. paper Kevin Weeks and colleagues at University of North Carolina Chapel Hill provide a concise and beautifully illustrated overview of the field. They note that “RNA-targeted FBLD is in its infancy,” but given that the first report dates to 2002 it is a long childhood, and the paper does a good job of describing the challenges.

 

A more extensive treatment of “fragment-based approaches to identify RNA binders” is provided by Matthew Disney and colleagues at UF Scripps in J. Med. Chem. The paper describes many case studies, some of which we’ve covered, and also contains a handy table comparing the pros and cons of a dozen different methods for finding RNA-binding fragments.

 

Tuberculosis kills more than 1.5 million people each year, and fragment-based approaches have been applied against multiple targets within the pathogen, as reviewed by Baptiste Villemagne and colleagues at University Lille in Eur. J. Med. Chem. We’ve covered many of these studies on Practical Fragments, but as the paper notes none have advanced to the clinic. This is attributed in part to cell permeability, and the researchers suggest turning to phenotypic screens (see below).

 

Other

Fragment linking can be difficult but highly effective, especially for difficult targets. An overview of published linkers is provided by Isabelle Krimm and collaborators at Université Claude Bernard Lyon and Université Montpellier in Expert Opin. Drug Disc. The paper includes a table summarizing 40 fragment linking stories, noting that most linkers are short and flexible. Another table summarizes 19 examples of target-guided synthesis, including dynamic combinatorial chemistry. As the paper notes, all of these are small model studies based on known compounds. In silico approaches, the last topic covered, will probably prove more practical.

 

And on the subject of practical, Dean Brown (Jnana Therapeutics) provides an “analysis of successful hit-to-clinical candidate pairs” in J. Med. Chem. This is an update to his 2018 article and captures 156 clinical candidates reported in the journal between 2018 and 2021. Of these, 14 had fragments in their lineage. Most of these drugs appear in our list of fragment-derived clinical candidates (though berotralstat does not – I’ll need to look closer). The paper contains lots of interesting analyses. For example, of the 138 oral drugs, 39 had a molecular weight > 500 Da, 24 had Clog > 5, and 17 had more than 10 hydrogen bond acceptors (HBA). On the other hand, none had more than 5 HBD, emphasizing that you should be parsimonious with hydrogen bond donors.

 

Finally, veteran drug hunter Nicholas Meanwell provides “reflections on a 40-year career in drug design and discovery” (open access) in Med. Chem. Rev. Those of you who saw his talk earlier this year at the CHI DDC meeting will know what to expect, and those of you who didn’t will be in for a treat. A personal and entertaining romp through pharma starting in the early 1980s, the paper is full of surprises, such as the pursuit of minor impurities in a phenotypic screen that ultimately led to the hepatitis C drug daclatasvir. Nicholas notes that “you discover what you screen for, so screen design is of paramount importance.”

 

The paper also reveals a passion for medicinal chemistry: “In a search for inspiration for design concepts, I sat down one Saturday afternoon in early October of 1987 and perused every molecule in the United States Adopted Names (USAN) dictionary.” And, as he notes near the end, “Decision making in drug discovery and development is a delicate balancing act, inherently flawed based on absence of predictive accuracy, and knowing when to conclude a discovery program with grace is also an important trait.” That said, he provides examples of successful programs that were almost killed multiple times – and others that were killed at Bristol Myers Squibb but subsequently succeeded elsewhere. While this is frustrating on one level, Nicholas takes satisfaction in the fact that “the science that we conducted and the molecules and pharmacophores that we defined have been of benefit to mankind.”

 

There are still a couple weeks left in the year, but that’s it for Practical Fragments for 2023. Thanks for reading, and special thanks for commenting. And if you live in one of the 70+ countries with elections in 2024, please vote.