Review of 2024 reviews

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

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

 

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

 

Structure-based methods

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

 

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

 

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

 

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

 

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

 

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

 

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

 

Other methods

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

 

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

 

Covalent Fragments

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

 

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

 

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

 

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

 

Targets

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

 

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

 

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

 

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

 

Other

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

 

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

 

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

 

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

 

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

Covalent fragments vs BFL1: a selective chemical probe

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

 

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

 

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

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

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

 

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

 

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

 

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

 

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

 

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

How to build a covalent fragment library

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

 

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

 

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

 

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

 

The design principles are summarized as:

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

 

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

 

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

 

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

 

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

They may be cons, but they’re our CONS

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

 

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

 

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

 

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

 

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

 

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

 

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

Mapping protein conformations with fragments

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

 

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

 

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

 

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

 

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

 

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

 

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