Review of 2021 reviews

As the year winds down SARS-CoV-2 continues its relentless drive through Greek letters and the planet. But there is hope: vaccines seem to be holding, for those who have access, and two oral drugs have been granted emergency use authorization by the US FDA, one of which (PF-07321332) is covalent and looks remarkably effective. As is our custom, Practical Fragments ends the year by highlighting conferences and reviews.

 

Conferences started the year online only (CHI’s Sixteenth Annual Fragment-based Drug Discovery), moved to hybrid (CHI’s Nineteenth Annual Discovery on Target) and sadly returned to virtual (Pacifichem 2021).

 

This year produced more than twenty FBLD-related reviews, and these are grouped thematically: NMR and crystallography, computational methods, targets, library design, and covalent fragments. The most general is the sixth installment in a series of annual reviews in J. Med. Chem. covering fragment-to-lead success stories from the previous year. Iwan de Esch (Vrije Universiteit Amsterdam) took the lead (pardon the pun) on the most recent review, which details 21 examples from 2020. In addition to the centerpiece table showing fragment, lead, and key parameters, this open-access paper also includes an analysis on the molecular complexity of fragment hits.

 

NMR and crystallography

Consistent with its central role in FBLD, several reviews covered NMR. Ben Davis (Vernalis), one of the leading practitioners, discusses fragment screening in Methods Mol. Biol. The chapter is written for a non-specialist, so you won’t see detailed pulse sequences. Instead, Ben provides a very accessible and practical guide covering everything from sample preparation through data analysis and validation.

 

A more detailed description of solution NMR in drug discovery by Li Shi and Naixia Zhang (Shanghai Institute of Materia and Medica) is published (open access) in Molecules. With 180 references, this review covers considerable ground, including various ligand-detected and protein-detected methods for screening as well as for hit-to-lead and mechanistic studies. The paper also includes a nice summary of in-cell (!) NMR.

 

The Pacifichem meeting had several talks on fluorine NMR, and speaker Will Pomerantz, together with Caroline Buchholz, has published a thorough, open-access review in RSC Chem. Biol. Will has been a leading developer of protein-observed ¹⁹F NMR, so naturally this topic is well-covered, but there is plenty on ligand-observed ¹⁹F NMR as well as a good background section and musings on the future of the field.

 

And if you’re looking for a detailed how-to guide for NMR-based fragment screening, Harald Schwalbe and colleagues describe the platform they’ve built at the Center for Biomolecular Magnetic Resonance (BMRZ) at Johann Wolfgang Goethe-University Frankfurt in J. Vis. Exp. This open-access paper also describes quality control experiments of the iNEXT library, which we’ve discussed here.

 

Switching gears to crystallography, J. Vis. Exp. carries a paper by Frank von Delft and collaborators describing the XChem platform at the Diamond Light Source. This high-throughput fragment screening platform has delivered a 95% success rate on more than 150 screens, with hit rates varying from 1-30%. In addition to technical details, this open-access article also provides tips on successfully getting your screening proposal through peer review.

 

XChem has inspired similar efforts at other synchrotrons, including the Fast Fragment and Compound Screening (FFCS) platform at the Swiss Light Source. This is concisely described by May Sharpe and Justyna Wojdyla in Nihon Kessho Gakkaishi (open-access and published in English).

 

Private companies are also moving into high-throughput crystallography. Debanu Das and collaborators describe the platform at Accelero Biostructures, which is capable of screening ~500 fragments in two days. Screens against three nucleases are described in some detail in an open-access article in Prog. Biophys. Mol. Biol.; these and other components of the DNA damage response are the focus of XPose Therapeutics, Accelero’s sister company.

 

Computational methods

In addition to the experimental methods reviewed above, a couple papers describe computational approaches. In Drug Disc. Today: Tech., FragNet alum Moira Rachman and collaborators from UCSF, Universitat de Barcelona, and elsewhere focus on “fragment-to-lead tailored in silico design.” This is a nice review of the recent literature and emphasizes the fact that much of the heavy design lifting is still done by medicinal chemists – at least for now.

 

Predicting the energies of modified fragments has long been a challenge, and one promising approach is free energy perturbation, in which one ligand is “perturbed” into another and the relative energy differences calculated. Barbara Zarzycka and colleagues at Vrije Universiteit Amsterdam provide a concise review for aficionados in Drug Disc. Today: Tech.

 

Targets

Three reviews cover applications of FBLD to various target classes. Kinases have been particularly successful, with four of the six approved fragment-derived drugs targeting these enzymes. In Trends Pharm. Sci., Ge-Fei Hao and collaborators, mostly at Central China Normal University, review the state of the art. In addition to background and several case studies, the paper includes a nice table showing structures and summaries of clinical-stage kinase inhibitors.

 

Epigenetics has been another fruitful area, and in J. Med. Chem. Miguel Vaidergorn, Flavio da Silva Emery (both University of São Paulo) and Ganesan (University of East Anglia) detail the “successful union of epigenetic and fragment based drug discovery (EPIDD + FBDD).” This thorough summary (with 165 structures!) of the literature is particularly detailed when it comes to bromodomains, four inhibitors of which have entered the clinic with the help of fragments. The researchers point out that EPIDD and FBDD both began around the same time, and in fact the oncology drug vorinostat could be described as “a unique case of solvent-based drug discovery.”

 

RNA has long been a target of FBLD, and in ChemMedChem Mads Clausen (Technical University of Denmark) and collaborators review the state of the art. The various established and emerging methods to find fragment hits are covered in depth, and there is also a nice discussion as to whether RNA-focused fragment libraries will be useful.

 

Library design and molecular properties

In Expert Opin. Drug Discov. Zenon Konteatis (Agios) asks “what makes a good fragment in fragment-based drug discovery?” His answers provide a concise summary touching on the rule of three, molecular complexity, “three-dimensionality”, and other topics.

 

The topic of three-dimensional fragments is covered in several other reviews. In Drug Disc. Today: Tech., Iwan de Esch and collaborators at Vrije Universiteit Amsterdam and University of York assess 25 so-called 3D libraries reported in the literature, mostly since 2015. The researchers manually drew all 897 fragments so they could calculate various properties. While most of the molecules are rule-of-three compliant, just under half could be called 3D by both plane of best fit (PBF) and principal moment of inertia (PMI). PBF and PMI measurements correlated with one another, while Fsp³ correlated with neither measurement, leading to the conclusion that “Fsp³ is a poor measure of 3D shape.”

 

Shapely or not, sp³-rich fragments are interesting from a diversity point of view, and in Chem. Sci. Max Caplin and Dan Foley (University of Canterbury) discuss synthetic methods for advancing these. This is an excellent open-access review of the recent literature around C-H bond functionalization and well worth reading for the chemists in the audience.

 

3D fragments are often chiral, and the importance of chirality in drug discovery is the focus of a paper in ACS Med. Chem. Lett. by Ilaria Silvestri and Paul Colbon (University of Liverpool). The researchers note an opportunity for chemical suppliers: only 245 of 9751 heterocyclic building blocks offered by Sigma-Aldrich are chirally pure.

 

“Library design strategies to accelerate fragment-based drug discovery” is the topic of a Chem. Eur. J. review by Nikolaj Troelsen and Mads Clausen (Technical University of Denmark). The researchers provide a highly accessible overview of different libraries appropriate for different fragment-finding methods, including covalent approaches.

 

Covalent fragments

This year saw the approval of sotorasib, the first covalent fragment-derived drug, so it is no surprise that several papers focus on this topic. Sara Buhrlage, Jarrod Marto, and colleagues at Dana-Farber Cancer Institute provide a thorough introduction to “chemoproteomic methods for covalent drug discovery” in Chem. Soc. Rev. The review covers both isolated protein screening as well as proteome-wide methods and includes multiple case studies.

 

Nir London and colleagues at The Weizmann Institute of Science focus on “covalent fragment screening” in Ann. Rep. Med. Chem. This is an excellent review of the recent literature and also includes an analysis of six commercial covalent fragment libraries.

 

And finally, in RSC Chem. Biol. (open access), Nathanael Gray and collaborators mostly at Dana-Farber Cancer Institute discuss strategies for “fragment-based covalent ligand discovery”, including computer-aided approaches, as well as target classes and new modalities such as PROTACs. They end by asking whether sotorasib was “a lucky, one-off case” or “a preview of continued and increased impacts that these approaches will have on drug discovery as the improved methods, larger libraries, and increased focus start to bear fruit.”

 

I’m betting on the latter.

 

And that’s it for 2021. Thanks for reading, special thanks for commenting, and here’s hoping we’ll be able to meet in person in 2022.

Pacifichem 2021

Pacifichem, the last significant conference of 2021, has just ended. Traditionally held every five years, these meetings usually bring thousands of visitors from Pacific Rim countries to Honolulu. They are planned years in advance: symposia proposals were due in early 2018. Pacifichem 2015 saw the first symposium dedicated to FBLD, and that was so popular that a few of us planned one for 2020. The conference organizers decided to postpone the 2020 meeting in the hope that we could all meet in person. But SARS-CoV-2 had other plans, and instead of meeting in Hawaii we met on Zoom.

 

Time zones were challenging. Four-hour sessions started in the morning or evening Hawaiian time, which translated to 02:00 in Shanghai or 23:00 in Boston, respectively. In contrast to other virtual meetings almost all the presentations were live and not recorded, which meant that you only had one chance to see a talk.

 

Despite these challenges and universal Zoom-fatigue, the event came off quite well. With more than two dozen presentations I won’t attempt to cover everything but will instead just touch on a few themes.

 

Methods

Quite a few talks focused on methods, with NMR being especially well-represented. The symposium started with Will Pomerantz (University of Minnesota) discussing Protein-Observed Fluorine (PrOF) NMR, in which fluorinated amino acids are introduced into proteins. We’ve written about this previously, including Will’s longstanding interest in assessing shapely fragments. After reading about Mads Clausen’s fluorinated Fsp³-rich library, Will established a collaboration and has found 8 hits from 79 fragments screened against BET bromodomain proteins. He has also been able to optimize potent leads selective for either the BD1 or BD2 domains of BRD4.

 

Scott Prosser (University of Toronto) is also using NMR to study fluorine-labeled proteins, in this case GPCRs. And Michael Overduin (University of Alberta), one of the symposium organizers, is also studying membrane-bound proteins using NMR techniques.

 

On the extreme side of spectrometers, Chojiro Kojima described the 950 MHz NMR at Osaka University. This enables a ¹H-¹³C HSQC experiment on protein as dilute as 0.2 micromolar, which could be valuable for insoluble or hard-to-purify proteins. The facility is open to international collaborators. Chojiro also described isotopically labeling proteins with transglutaminase and ¹H{¹⁹F} STD NMR, which works even when the fluorine peak itself is broadened to invisibility.

 

But you don’t need a big magnet to do good science. Brian Stockman’s group at Adelphi University is composed entirely of undergraduates who use substrate-detected NMR to follow enzymatic reactions to find inhibitors of neglected parasitic infections.

 

All techniques can give false positives, and NMR can be very effective at weeding these out. As we described two years ago Steven LaPlante (NMX) has been developing methods to rapidly identify aggregators and has been assembling something of a taxonomy; more later.

 

But the conference was not all NMR all the time. Rebecca Whitehouse (Monash University) described a 91-compound “MicroFrag” library consisting of fragments containing 5-8 atoms, “somewhere in the land between solvents and fragments.” NMR and crystallographic screens of the difficult antimicrobial target DsbA gave very high hit rates, and both techniques successfully identified the large but shallow substrate-binding groove. In contrast, screening actual solvents or using the well-established FTMap computational approach did not clearly identify this groove.

 

The push in crystallography is towards increased speed, and Debanu Das described the high-throughput platform at Acclero BioStructures, which is capable of screening 1000 fragments per week, similar to XChem. But if even that is too slow for you, Marius Schmidt (University of Wisconsin-Madison) described mix-and-inject experiments using the European X-ray free-electron laser (XFEL). Much of the focus with this technique has been on high-speed enzymology, but since 100 datasets can be collected in 10 hours it can be used for high-throughput crystallographic screening too. An upgrade next year will increase this to 1000 datasets in 3 hours, though the resulting petabytes of data will no doubt create headaches for IT departments.

 

It’s not enough to find hits, you need to figure out what to do with them, and symposium organizer Martin Scanlon (Monash University) discussed a computational approach (GRADe, similar to Fragment Network) as well as the experimental (REFiL) approach we’ve discussed previously. Across 9 projects the techniques were successful at improving affinity, in some cases from unmeasurable levels.

 

Covalent Fragments

Several talks focused on covalent FBLD. Alexander Statsyuk (University of Houston) proposed five rules for covalent fragments: 1) ease of synthesis; 2) non-promiscuous electrophiles; 3) intrinsic reactivity should be the same within the library; 4) a given library should use the same electrophile; and 5) the electrophile should be on the end of the molecule with a minimal linker connecting it to the variable fragment. Some of these make sense, but it would have been fun to debate others over Mai Tais.

 

Dan Nomura (UC Berkeley) described using covalent fragments in chemoproteomic experiments, where he has identified more than 100,000 potentially ligandable hotspots in more than 16,000 human proteins. Among other applications, this allows him to make bifunctional molecules to bring two proteins together. A clear application is PROTACs, where the electrophilic fragment targets an E3 ligase, but he also described targeting the deubiquitinase OTUB1 to stabilize proteins.

 

Earlier this year we celebrated the approval of the KRAS^G12C inhibitor sotorasib. This target had long resisted drug discovery efforts; Ratmir Derda (University of Alberta) evocatively mentioned “waves and waves of medicinal chemists washing off its shore for 30 years.” Success was finally enabled using disulfide Tethering, and David Turner (Frederick National Laboratory) is now using this approach to interrogate nearly every surface-exposed residue by systematically mutating them to cysteines and screening against more than 1000 disulfide-containing fragments. He is well over half-way through the 85 mutants, and the resulting dataset should be valuable not just for drug discovery but for understanding molecular interactions.

 

Success Stories

With more than 50 drugs in the clinic derived from fragments, there were several success stories. Masakazu Atobe (Asahi Kasei) presented the discovery of the potent PKCζ inhibitors we wrote about here. And Chaohong Sun (AbbVie) described inhibitors of TNFα (see here), emphasizing the importance of robust biophysics and early committed chemistry.

 

Finally, Emiliano Tamanini (Astex) presented a nice fragment-to-lead effort to find a selective inhibitor of HDAC2. Despite some successes, histone deacetylase inhibitors tend to be non-specific and come with side effects. Emiliano described a fragment screen that identified a new metal chelator and used fragment merging to develop a molecule capable of crossing the blood-brain-barrier.

 

These last two stories in particular are examples of pursuing difficult targets, another theme throughout the conference. When asked about the challenges of targeting cancer-resistance-causing glucuronosyltransferases, Katherine Borden (University of Montreal) responded, “if you don’t try, where will you be?”

 

Bright words for these darkling days.

Fragments vs AXL, with help from an ELF

The receptor tyrosine kinase AXL has been implicated in multiple cancers, and more recently as a possible target for COVID-19. The protein has been pursued by many groups, mostly starting from high-throughput screens or existing inhibitors. In a recent Bioorg. Med. Chem. paper Pearly Ng, Alvin Hung, and collaborators at Experimental Drug Development Centre Singapore, University of Sussex, and LigatureTX describe a fragment-based approach.

 

The researchers started with a biochemical screen of 1700 fragments at 500 µM. Subsequent dose-response experiments yielded 16 fragment hits with IC₅₀ < 400 µM, all of which are shown in the paper. Four of these were based on 7-azaindole, a kinase hinge-binding motif that has previously been used in AXL inhibitors.

 

Seeking novelty, the researchers focused on the remaining 12 fragments and turned to their “expanded library of fragments (ELF),” which they have been building for several years. Though not described in depth, this collection was designed such that near-neighbors of their primary library could be rapidly screened to establish SAR and identify productive growth vectors. Compound 11, an indazole, led to several analogs from the ELF such as compound 24. This was docked into a publicly available crystal structure of AXL, and structure-based design led to compound 32. Further optimization ultimately led to molecules such as compound 52, the most potent in the paper.

 

Although compounds in this series were potent in the biochemical assay, they were typically 100-1000 less potent in a cell-based NanoBRET target-engagement assay, an annoying but not uncommon phenomenon. Several of the compounds showed high nanomolar activity in an ovarian cancer cell line with AXL overexpression. Interestingly, two compounds showed considerably more potent antiproliferative activity than target-engagement, and the researchers speculate they may be hitting other kinases. In support of this hypothesis, the researchers found that one of their compounds inhibited 12 of 97 kinases by >90% at 1 µM. This is not surprising given that indazoles are privileged fragments for kinases. Mouse pharmacokinetic studies revealed poor oral exposure for most of the compounds, though this could be improved by decreasing the basicity of the molecules.

 

A nice aspect of this paper is that most of the work was done without the benefit of structural information. Crystallography attempts with AXL and any of the ligands were unsuccessful, though the researchers were eventually able to obtain a structure of one of their more potent molecules bound to a related kinase. And just in time for the holidays, the paper also illustrates the utility of having an ELF on your shelf.

Merging fat fragments for fat mass obesity-associated protein

Despite its name, fat mass obesity-associated protein (FTO) is implicated not just in obesity but also cancer and neurological disorders. The DNA/RNA demethylase is an “epigenetic eraser” that removes methyl groups from nucleic acids, thereby regulating multiple genes. Several inhibitors have been reported, but many of these are weak and nonspecific. In a recent J. Med. Chem. paper, Takayoshi Suzuki and collaborators at Kyoto Prefectural University of Medicine, Osaka University, and Kyoto University describe merging two of these to create a more potent molecule.

 

The researchers started with four known inhibitors, all of which had structures bound to FTO deposited in the Protein Data Bank. These structures were then used to merge HZ (below in red) with the other (sometimes barely) fragment-sized molecules. For two approaches the resulting merged molecules were inactive, but when HZ was merged with MA (below in blue) the resulting molecule was active in a biochemical assay and showed high affinity as assessed by isothermal titration calorimetry (ITC).

 

Modeling studies on compound 11b suggested why it might have better affinity than the starting molecules. Additional modeling also suggested why the other merged molecules were inactive.

 

Given its highly polar nature compound 11b was inactive in cells, but transforming the carboxylic acid into an ester produced a prodrug that caused cell death in a cancer cell line in which FTO is overexpressed. The prodrug also caused an increase in N⁶-methyladenosine in mRNA and caused changes in transcription consistent with FTO inhibition. Although the potency is too low for a chemical probe, and the molecule contains a number of chemical liabilities, this on-target activity is encouraging.

 

This paper exemplifies that fragment merging is not necessarily easy, and I give plaudits to the researchers for describing designs that did not work – too often papers only trumpet successes. Moreover, as the researchers acknowledge, even the successfully merged compound has a lower ligand efficiency than the parent compounds. This is another illustration of why it is important to start with the best fragments possible – not just in terms of various metrics but in terms of overall chemical attractiveness as well. It will be fun to see follow-up work.

DeepFrag: fragment optimization by machine learning

Machine learning is becoming increasingly common in drug discovery. Just a few months ago we highlighted its use to design a library of privileged fragments. However, constructing a library is usually done infrequently (though continued renovation of a library is always a good idea). In two papers from earlier this year, Jacob Durrant and colleagues at University of Pittsburgh use machine learning to tackle the more common task of lead optimization.

 

The first paper, in Chem. Sci., describes DeepFrag, a “deep convolutional neural network for fragment-based lead optimization.” The researchers started with the Binding MOAD database, a collection of nearly 39,000 high-quality protein-ligand complex structures from the Protein Data Bank. Ligands were computationally fragmented by chopping off terminal appendages less than 150 Da. The fragments were then converted into molecular fingerprints encoding their structures. Meanwhile, the protein region around each ligand was converted into a three-dimensional grid of voxels, akin to how images used for computer vision training are processed.

 

The researchers describe the goal as follows. “We propose a new ‘fragment reconstruction’ task where we take a ligand/receptor complex, remove a portion of the ligand, and ask the question ‘what molecular fragment should go here.’”

 

About 60% of the data were used in a training model for the machine learning algorithm. This was then evaluated on 20% of the data and further refined before the final evaluation on the remaining 20% of the data. The details are beyond the scope of this post (and frankly beyond me as well) but DeepFrag recapitulated known fragments about 60% of the time. Importantly, the model worked for diverse types of fragments, including both polar and hydrophobic examples. Even “wrong” answers were often similar to the “correct” responses, for example a methyl group instead of a chlorine atom. In some cases where DeepFrag’s predictions differed from the original ligand the researchers note that these may be acceptable alternatives, a hypothesis supported by subsequent molecular docking studies.

 

Of course, the goal for most of us is not to recapitulate known ligands but to optimize them, so the researchers applied DeepFrag to crystallographically identified ligands of the main protease from SARS-CoV-2. Many of them docked well, though they have yet to be synthesized and tested.

 

Laudably, the model and source code have been released and can be accessed here. However, as these require a certain amount of computer savvy to use, Harrison Green and Jacob Durrant have also created an open-source browser app which is described in an open-access application note in J. Chem. Inf. Mod.

 

The browser app runs entirely on a local computer, without requiring users to upload possibly sensitive data. The application note describes using the app to recapitulate an example from the original paper. It also describes using it on a fragment bound to antibacterial target GyrB, a fragment-to-lead success story we blogged about last year. DeepFrag correctly predicted some of the same fragment additions that were described in that paper.

 

The app is incredibly easy to use: just load a protein and ligand (from a pdb file, for example) and the structure appears in a viewer. Click the “Select Atom as Growing Point” button, choose an atom, and hit “Start DeepFrag.” The ranked results are provided as SMILES strings and chemical structures, and the coordinates can also be downloaded. You can also delete atoms before growing if you would like to replace a fragment.

 

In my own cursory evaluation, DeepFrag correctly suggested adding a second hydroxyl to the ethamivan fragment bound to Hsp90 (see here). It did not suggest an isopropyl replacement for the methoxy group, but it did suggest methyl. Trying a newer example unlikely to have been part of the training set did not recapitulate the ethoxy in the BTK ligand compound 18 (see here), but did suggest a number of interesting and plausible rings. Calculations took a few minutes on my aging personal Windows laptop using Firefox.

 

In contrast to the hyperbolic claims too often seen in the field, the researchers conclude the Chem. Sci. paper modestly: “though not a substitute for a trained medicinal chemist, DeepFrag is highly effective for hypothesis generation.”

 

Indeed – I recommend playing around with it. We may still be some way from SkyFragNet, but we’re making progress.

Selective fragments vs GPCRs, guided by modeling

Earlier this year we highlighted a fragment optimization success story against a G protein-coupled receptor (GPCR) which made no use of structural information. Due to the difficulty of crystallizing these membrane-bound proteins, structures have been rare for this large class of drug targets. Advances in crystallography are starting to change that. In a recent open-access Chem. Commun. paper, Jens Carlsson and collaborators at Uppsala University and the US National Institutes of Health make use of the increasing availability of such structures to develop potent, selective inhibitors.

 

The researchers were interested in A₁ and A_2A adenosine receptors (A₁AR and A_2AAR), targets for a variety of ailments from cancer to cardiovascular diseases. (A_2AAR was the subject of this blog post a few months ago.) In the current study, the researchers wanted to know whether structures and molecular dynamics (MD) simulations could guide production of selective inhibitors.

 

Previous computational and experimental work from the authors had yielded compound 1, with low micromolar activity against A₁AR and 7-fold selectivity over A_2AAR. Crystal structures of both these proteins are available, though not bound to the small molecule. Docking studies suggested that the ligand would make similar interactions to both proteins, but that there might be an opportunity for increased selectivity towards A₁AR due to the presence of a smaller threonine residue compared with a methionine in A_2AAR. Nine analogs were designed to grow into this lipophilic pocket, and free energy perturbation and MD simulations suggested that they would have improved affinity for A₁AR. This turned out to be the case when the molecules were made and tested in radioligand binding assays.

 

Although compounds 5 and 9 were more potent, selectivity was not improved. MD simulations suggested this might be due to the small size of the fragments, which could be accommodated in A_2AAR by slight shifts in the binding modes. To try to anchor compounds within the pocket, the researchers grew off the phenyl ring, leading to molecules such as compound 15. Borrowing from this molecule and compound 9 led to compound 22, the most potent and selective molecule in the series. (A separate effort led to a somewhat weaker but A_2AAR-selective ligand.) Both molecules were found to be antagonists when tested in cells, which was expected given that the crystal structures used for modeling were in the inactive conformation.

 

The correlation between predicted and measured binding energies was respectable, with a mean unsigned error (MUE) of 1.08 kcal/mol and Spearman’s rank correlation coefficient (ρ) of 0.8 for 24 compounds. Selectivity predictions were also impressive at MUE = 0.48 kcal/mol and ρ = 0.85.

 

This is a nice illustration of using computational methods to improve the affinity of a fragment by more than three orders of magnitude while also increasing selectivity. This particular system is probably on the easier side; we blogged about previous research from this group on A_2AAR back in 2013. The researchers note that proteins with larger binding sites and weaker ligands are likely to be more challenging. It will be fun to see efforts towards Class B GPCRs, for example.

Fragments vs SETD2: a chemical probe

Among the various epigenetic “writers,” only one is capable of trimethylating lysine 36 of histone H3. SET domain-containing protein 2 (SETD2) is thought to be a tumor suppressor, but some evidence suggests it may have the opposite effect in certain cancers. A chemical probe would be useful to resolve these conflicting ideas, and in an (open access) ACS Med. Chem. Lett. paper Neil Farrow and colleagues at Epizyme describe one.

 

Epizyme has been pursuing epigenetic targets for years and has built a methyltransfersase-biased compound collection. A radiometric screen of this library yielded compound 1 and a related molecule. Both were weak inhibitors, but a co-crystal structure with the enzyme revealed the indole buried deep in the substrate binding pocket. Tweaking this led to compound 4, with low micromolar activity.

 

 

Substitution off the indole and phenyl moieties ultimately led to compound 25, with low nanomolar biochemical and cell activity. However, this molecule also had low aqueous solubility and poor pharmacokinetics in mice. Recognizing that the lipophilic and aromatic nature of the molecule were likely responsible, the researchers returned to the initial hit. Replacing the phenyl with a cyclohexyl moiety and making a few more modifications ultimately led to EPX-719.

 

The pharmacokinetics of EPX-719 in mice are reasonable, and the molecule is >8000-fold selective against a panel of 14 other histone methyltransferases. It is also fairly clean against a panel of 47 off-targets and 45 kinases. EPX-719 showed antiproliferative activity in two multiple myeloma cell lines, and more detailed biological studies are promised in a future paper.

 

This is a nice hit to lead story. As the researchers note, “close attention to the physical chemical properties of the inhibitors, in particular basicity, lipophilicity, and aromatic character, led to compounds with attractive cellular activities and in vivo exposures.” Interestingly though, the word “fragment” does not appear once in the paper. Although compounds 1 and 4 venture a bit beyond the rule of three, I would argue that starting with small, low affinity binders and focusing closely on molecular properties is the very definition of fragment-based lead discovery.

 

A quarter-century of FBLD has influenced the scientific zeitgeist, and a fragment by any other name is still as sweet.

Fragments in the clinic: 2021 edition

Since our last clinical update in 2020 two new fragment-derived drugs, asciminib and sotorasib, have been approved, bringing the total to six.

The current list contains 52 molecules, with 22 approved or in active trials. As always, this table includes compounds whether or not they are still in development (indeed, some of the companies no longer even exist). Because of this, the Phase 1 list contains a higher proportion of compounds that are no longer progressing. 

 

Drugs reported as still active in clinicaltrials.gov, company websites, or other sources are in bold, and those that have been discussed on Practical Fragments are hyperlinked to the most relevant post. The list is almost certainly incomplete, particularly for Phase 1 compounds. If you know of any others (and can mention them) please leave a comment.

Drug	Company	Target
Approved!
Asciminib	Novartis	BCR-ABL1
Erdafitinib	Astex/J&J	FGFR1-4
Pexidartinib	Plexxikon	CSF1R, KIT
Sotorasib	Amgen	KRASG12C
Vemurafenib	Plexxikon	B-RAFV600E
Venetoclax	AbbVie/Genentech	Selective BCL-2
Phase 3
Capivasertib	AstraZeneca/Astex/CR-UK	AKT
Lanabecestat	Astex/AstraZeneca/Lilly	BACE1
Pelabresib (CP-0610)	Constellation	BET
Verubecestat	Merck	BACE1
Phase 2
ASTX029	Astex	ERK1,2
ASTX660	Astex	XIAP/cIAP1
AT7519	Astex	CDK1,2,4,5,9
AT9283	Astex	Aurora, JAK2
AUY-922	Vernalis/Novartis	HSP90
AZD5991	AstraZeneca	MCL1
DG-051	deCODE	LTA4H
eFT508	eFFECTOR	MNK1/2
Indeglitazar	Plexxikon	pan-PPAR agonist
LY2886721	Lilly	BACE1
LY3202626	Lilly	BACE1
LY517717	Lilly/Protherics	FXa
LYS006	Novartis	LTA4H
MAK683	Novartis	PRC2 EED
Navitoclax (ABT-263)	Abbott	BCL-2/BCLxL
Onalespib	Astex	HSP90
PF-06650833	Pfizer	IRAK4
PF-06835919	Pfizer	KHK
PLX51107	Plexxikon	BET
S64315	Vernalis/Servier/Novartis	MCL1
VK-2019	Cullinan Oncology / Wistar	EBNA1
Phase 1
AG-270	Agios/Servier	MAT2A
ABBV-744	Abbott	BD2-selective BET
ABT-518	Abbott	MMP-2 & 9
ABT-737	Abbott	BCL-2/BCLxL
AT13148	Astex	AKT, p70S6K, ROCK
AZD3839	AstraZeneca	BACE1
AZD5099	AstraZeneca	Bacterial topoisomerase II
BI 691751	Boehringer Ingelheim	LTA4H
ETC-206	D3	MNK1/2
GDC-0994	Genentech/Array	ERK2
HTL0014242	Sosei Heptares	mGlu5 NAM
IC-776	Lilly/ICOS	LFA-1
LP-261	Locus	Tubulin
LY2811376	Lilly	BACE1
Mivebresib	AbbVie	BRD2-4
Navoximod	New Link/Genentech	IDO1
PLX5568	Plexxikon	RAF
SGX-393	SGX	BCR-ABL
SGX-523	SGX	MET
SNS-314	Sunesis	Aurora
TAK-020	Takeda	BTK

With only two phase 3 molecules in active development it may be some time before the next fragment-derived drug is approved. Then again, in 2020 sotorasib was only in phase 2. While long timelines are common in our industry, good drugs can make remarkably rapid progress.

Asciminib: the sixth fragment-derived drug approved

Yesterday, on October 29, the US FDA approved asciminib (ABL001, from Novartis) for two subsets of patients with chronic myeloid leukemia (CML), making it the sixth fragment-derived drug to reach the market.

 

In common with the five other approved fragment-based drugs, asciminib is a cancer therapeutic. Like three of them, it is a kinase inhibitor. But there the resemblance ends. As we discussed at length in 2018, asciminib targets not the hinge region of BCR-ABL1, but an allosteric myristoyl-binding pocket on the protein. This unique mechanism of action provides improved selectivity over conventional kinase inhibitors, which could be part of the reason the drug causes fewer side effects than other BCR-ABL1 inhibitors.

 

Another advantage of targeting the allosteric pocket is to sidestep resistance. One group for which asciminib was approved is for patients with the BCR-ABL1 T315I mutation, which causes resistance to other approved therapeutics. Combining asciminib with other drugs might prevent resistance from emerging in the first place.

 

The approval of sotorasib in May was a study in speed, with less than three years spent in the clinic. In contrast, asciminib was first dosed in 2014. Even getting there was far from certain: as Wolfgang Jahnke recounted five years ago, the project started as a grass-roots effort and was halted twice. Imatinib, which targets the hinge region of BCR-ABL1, also faced a fraught journey to the clinic before being approved twenty years ago.

 

These stories of persistence paid off, and today humanity has a new weapon against CML. And this is just the beginning: a dozen clinical trials with asciminib are either announced or in progress. Practical Fragments wishes to offer everyone involved congratulations, luck, and thanks.

Fragments vs TIM-3

In order to thrive, cancer cells need to evade the immune system. Preventing them from doing so is the goal of cancer immunotherapy. Although it has not entirely lived up to its initial hopes, this promising approach has generated multiple new targets, such as T-cell immunoglobulin and mucin domain-containing molecule 3 (TIM-3), whose upregulation correlates with tumor progression. Several antibodies targeting this protein are working their way through the clinic, but small molecules may have advantages in terms of oral dosing and improved tumor penetration. The discovery of one small molecule binder is reported in a new J. Med. Chem. paper by Stephen Fesik and colleagues at Vanderbilt University.

 

As is customary for this group, the project began with a two-dimensional (¹H/¹⁵N HMQC) NMR screen of 13,824 fragments, each at 0.8 mM in pools of 12. This yielded 101 hits, a respectable 0.7% hit rate, and higher than might be expected for this immunoglobulin-like protein. The hits belonged to 11 chemotypes, and 18 had dissociation constants better than 1 mM and ligand efficiencies (LE) better than 0.25 kcal mol^-1 per heavy atom. All of the fragments caused similar resonance perturbations, suggesting a common binding pocket, though as specific backbone resonance assignments were not known the exact location was unclear. Compound 1 was pursued due to its (relatively) high affinity, LE, and chemical tractability.

 

Substitutions off two vectors of the molecule improved affinity, and combining these substituents led to compound 22. This molecule bound sufficiently tightly that NMR could no longer be used to measure the dissociation constant. At this point the researchers were able to solve a crystal structure of the compound bound to TIM-3, revealing that it binds to a protein loop with the tricyclic core sandwiched between two tryptophan residues. The structure also revealed a portion of the molecule that extended toward solvent, and this insight was used to construct a fluorescent probe for use in a fluorescence polarization anisotropy (FPA) competition assay to accurately measure binding of more potent molecules.

 

 

With the probe results and crystal structure in hand, the researchers continued to optimize the molecule by growing towards a couple arginine and aspartic acid residues. This led to compound 34, which again started bottoming out the FPA assay and necessitated constructing yet another fluorescent probe. Further optimization using structure-based design ultimately led to compound 38, the most potent molecule in the series. NMR experiments revealed that compound 38 causes a rigidification of the TIM-3 loop where it binds.

 

And that’s where things stand. Unfortunately no data are presented as to whether compound 38 blocks binding of TIM-3 to its biological partners. The binding site is actually somewhat distant from where natural ligands bind, suggesting that the compounds would likely need to act allosterically. Moreover, the researchers note that many of the compounds are not particularly soluble. Still, whether the compounds move forward or not, this is a nice example of finding fragments that bind to a novel target and using diverse insights to improve them by several orders of magnitude.

Fragments vs the SARS-CoV-2 main protease – this time by NMR

Last week we highlighted NMR screens against RNA from SARS-CoV-2. As we noted then, much of the drug discovery action has focused on the virus’s main protease, called M^pro or 3CLp. These efforts have included two separate screens by crystallography and/or mass spectrometry. A new (open-access) paper in Angew. Chem. Int. Ed. by Benoit Deprez, Xavier Hanoulle, and collaborators at CNRS and University Lille describes an NMR screen against this same protein.

 

The main protease is 306 amino acid residues long and forms a 67.6 kDa dimer in solution. Proteins this large are challenging for protein-detected NMR, both because the number of potentially overlapping resonances increases and because of line broadening. Nonetheless, the researchers used several sophisticated NMR techniques to assign more than 60% of backbone resonances as well as quite a few main-chain and side-chain hydrogens to gain information on binding locations.

 

A library of 960 fragments was purchased from Life Chemicals and Maybridge. These were pooled in groups of five, with each fragment at 377 µM, and screened by Water-LOGSY and, for 427 fluorinated fragments, ¹⁹F line broadening and chemical shift perturbation. This exercise yielded 159 hits.

 

These hits were validated in a protein-detected ¹H, ¹⁵N TROSY-HSQC experiment, which confirmed 38 fragments, giving an overall hit rate of around 4%, comparable to that seen in the crystallographic fragment screen against M^pro. Also in common with the previous screen is the fact that most of the fragments (32) bind somewhere in the active site, while a few (5) bind at the dimerization interface. Fragment hits tended to be both larger and more lipophilic than those in the overall library.

 

Earlier this year we highlighted an NMR study of non-covalent fragment hits from the crystallographic fragment screen, which found that only two of them had measurable affinities, and both were weak (K_D = 1.6 mM at best). In contrast, one of the new fragments, F01, has a dissociation constant of 73 µM. With a molecular weight of 287 Da and 20 non-hydrogen atoms this is a somewhat portly fragment, but it does have a ligand efficiency of 0.3 kcal mol^-1 per heavy atom. It also shows functional activity with an IC₅₀ = 54 µM in a biochemical assay and EC₅₀ = 150 µM in an antiviral assay. The researchers further characterized their molecule crystallographically, which confirmed that it binds to the active site; it makes three hydrogen bond interactions and multiple hydrophobic contacts with the protein.

 

Although crystallography has been receiving increasing attention among fragment-screening techniques, this paper is a reminder than NMR remains highly relevant, even for larger proteins that crystallize easily. And at the end of the day, it’s not how you screen but what you find and what you do next. Hopefully folks will follow up on F01. While PF-07321332 is making rapid progress in the clinic against this enzyme and the COVID Moonshot effort is moving molecules into animal studies, HIV has taught us that we’ll need multiple small molecule drugs.

Fragments vs SARS-CoV-2 RNA

The first mention of SARS-CoV-2 on Practical Fragments in early March of last year highlighted a crystallographic fragment screen against the main viral protease. As discussed last week this effort has now led to compounds with nanomolar activity in cells. We’ve also highlighted a separate crystallographic screen against this target as well as a screen against the Nsp3 macrodomain. But proteins are not the only potential viral targets.

 

A recent (open access) paper in Angew. Chem. Int. Ed. by Harald Schwalbe and a large group of collaborators mostly at Johann Wolfgang Goethe-University focuses not on proteins but on RNA. Harald also presented this work at Discovery on Target last week, where he noted that the effort is part of the COVID19-NMR project, a collaboration of 240 people in 18 countries.

 

The researchers investigated 15 RNA regulatory elements that are conserved between SARS-CoV-2 and SARS-CoV, ranging from 29-90 nucleotides, as well as 5 larger multielement RNAs (118-472 nucleotides). These were screened against the DSI-poised library (discussed here): 768 fragments designed for rapid follow-up chemistry.

 

Three different ligand-detected NMR methods were used for screening: chemical shift perturbation (CSP) or line-broadening, waterLOGSY, and T2-relaxation. Fragments were screened at 200 µM in pools of 12 against 10 µM RNA. Compounds that hit in at least two assays were investigated individually.

 

In total 40 fragments bound to one or more of the 15 shorter RNAs, and an additional 29 fragments bound to the five longer RNAs. Between 5 and 49 hits were found for all but two of the RNAs. Selectivity varied: some fragments bound to just one RNA while one fragment bound to 18 of 20.

 

Given the negatively-charged phosphate backbone of RNA, it is not surprising that many of the fragment hits are positively charged at physiological pH. Nearly one-third of the 40 hits against the shorter RNAs contain a basic amine; pyrimidine and benzimidazole moieties are enriched, and not one of the hits contain a carboxylic acid. All the hits have at least one aromatic ring and most have two or three, perhaps suggesting intercalation. Moreover, as seen in a previous ambitious RNA screen from the same group, hits tend to have fewer sp³-carbons than non-hits.

 

The highest affinity fragment had a dissociation constant of just 64 µM but an impressive ligand efficiency of 0.38 kcal/mol/atom. A search of commercial analogs yielded a compound with low micromolar affinity against two RNA targets. In his presentation Harald noted that this series has since been optimized to a 200 nM binder.

 

This paper is a tour de force, but as I have noted, there remains a dearth of high-affinity specific RNA binders. The researchers also note another potential problem: viral RNA accounts for roughly two-thirds of total RNA in cells infected with SARS-CoV-2. Would this necessitate high concentrations of drug for effective antiviral activity?

 

Whether or not the work leads to drugs, it should further basic research. Laudably, structures of all the hits and non-hits are provided in the paper, and the extensive supporting information provides more details. Hopefully we will soon see whether fragments poised for ready elaboration really will enable rapid progress against RNA.

Nineteenth Annual Discovery on Target Meeting

Cambridge Healthtech Institute held its annual Discovery on Target meeting last week. For the first time the event was hybrid, with slightly fewer than half the attendees in Boston and the rest online, and I’m happy to report that it was quite successful. In-person attendees were required to show proof of vaccination against COVID-19, and masks and social distancing guidelines were observed. Ten of the individual tracks were hybrid, while four were virtual only. However, even in these cases it was valuable to attend in person; after one vendor presentation I immediately went from my hotel room to the exhibit hall to find out more.

 

For many of us this was the first in-person conference we had attended in nearly two years, and the return to some semblance of normalcy. At the same time, the fact that in-person talks were broadcast opened the conference to people unable to travel. One of the most active Q&A participants in one track was in Singapore, despite the 12 hour time difference.

 

Another nice feature of the virtual or hybrid model is reduction in FOMO; if you find it difficult to choose between the seven concurrent talks you can watch some later. But, as our 2020 poll showed, speakers may be less forthcoming with newer, more speculative results in a recorded format.

 

With the heavy focus on biology there seemed to be fewer “conventional” fragment stories, though Lars Neumann (Proteros) did discuss the identification and optimization of a kinase inhibitor that does not interact with the hinge region. Novel targets were represented in work from Harald Schwalbe (Johann Wolfgang Goethe University), who described fragment screens against RNA; I’ll post more on this later this month.

 

We’ve previously discussed the COVID Moonshot Consortium to rapidly discover drugs for SARS-CoV-2. Annette von Delft (Oxford University) provided an update, noting that fragments from a crystallographic screen have been advanced to compounds with mid-nanomolar biochemical and cellular activity. DMPK properties are reasonable, though this is an area of continued optimization. Annette mentioned the goal is to enter clinical development in 2023. Progress has been accelerated by the crowd-sourced nature of the initiative, with nearly 40 groups and 150 individuals working together. She also noted that many of the molecules are active against other coronaviruses.

 

The main series being advanced by the COVID Moonshot are noncovalent inhibitors of the SARS-CoV-2 main protease M^Pro. However, covalent molecules against this target are also moving forward. Matthew Reese described Pfizer’s oral PF-07321332, which is currently in several phase 3 trials. The program began on March 16 of last year and the clinical compound was first synthesized just four months later. Clinical trials began in February of this year, a mere 11 months after the program began. This is astonishingly rapid, though the researchers did benefit from previous work on SARS-CoV-1 and even earlier work from the 1990s on rhinovirus inhibitors. It is worth re-reading Glyn Williams’ 2020 discussion of HIV protease inhibitors for more historical context and insights.

 

Although PF-07321332 did not come from FBLD, fragments capable of forming covalent bonds were well represented. We’ve previously discussed fully-functionalized fragments (FFFs, or PhABits), which in addition to having a photoreactive group also contain an alkyne handle so that any target they bind can be captured and identified. Aarti Kawatkar and Jenna Bradley described using these at AstraZeneca to identify new targets. They’ve constructed a library of just under 500 FFFs and are using these to do phenotypic screening, particularly in hard-to-get cells such as primary tissue samples. They are also making the FFF library available through their open innovation initiative.

 

Fully functionalized fragments are just one flavor of covalent fragments. Indeed, unlike the light-activated warhead of FFFs, most covalent fragments have a moiety that reacts selectively with amino acid residues such as cysteines. Steve Gygi (Harvard) and Dan Nomura (UC Berkeley) both described covalent screening in cells to identify starting points against challenging targets. The approach is also gaining traction in industry; Heather Murrey described how Scorpion is using covalent fragments, and noted that Vividion (mentioned here) was recently acquired by Bayer for up to $2 billion.

 

A prominent recent success story from covalent fragments is sotorasib, which was approved earlier this year to treat certain non-small cell lung cancer patients whose tumors carry the G12C mutant form of KRAS. Sotorasib binds to a mostly cryptic pocket, and the protein itself has low ligandability. To improve the odds of finding new fragments, Mela Mulvihill described how she and her colleagues at Genentech have developed antibodies that stabilize the so-called Switch II loop in an “open” conformation more accessible to small molecules. An SPR-based fragment screen in the presence of the antibody led to more than twice as many hits, many of which could bind more tightly than without the antibody. Darryl McConnell (Boehringer-Ingelheim) also described using fragment-based methods to pursue KRAS, including mutants other than G12C.

 

In addition to inhibitors, Darryl also described bifunctional molecules that selectively cause degradation of KRAS by bringing it to the proteasome via E3 ligases. In his opinion PROTACs are “the best thing since sliced bread.” PROTACs and targeted protein degradation were in fact the subject of two tracks that spread across all three days of the conference, and were also covered in a pre-conference short course taught by Stewart Fisher (C4 Therapeutics) and Alexander Statsyuk (University of Houston). Here too fragments are playing an increasing role; in a second talk Dan Nomura described how he has been using chemoproteomic fragment approaches to identify ligands for E3 ligases.

 

The recent excitement around PROTACs is probably justified, but as our post last week noted, new technologies are not necessarily fast or inevitable. PROTACs were first described in 2001; Adam Gilbert (Pfizer) puckishly described them as a “20-year overnight success story.” But by the end of this year there will be roughly a dozen PROTACs in the clinic, with more likely to join them soon.

 

I’ll end on this positive note, but welcome your thoughts on science or experience with hybrid conferences. I look forward to seeing you at one in the near future!

Success in drug discovery is not necessarily fast or inevitable

The biotech industry rightly prizes speed: every day people die of diseases we are trying to prevent or cure. And developments can indeed happen quickly. Just eight years elapsed from the demonstration that a mutant form of KRAS was druggable to the approval of sotorasib, with less than three of those years spent in the clinic. Even more dramatically, it took less than a year from the first reports of SARS-CoV-2 to develop effective vaccines. But as two recent pieces in Nature Rev. Drug Disc. demonstrate, such speed is not necessarily the norm.

 

The first, by Asher Mullard, is entitled “FDA approves 100^th monoclonal antibody product.” This is a nice review of a remarkably successful therapeutic approach. But this triumph was not a foregone conclusion. Mullard traces the field’s origin to the mid-1970s, and while the first drug was approved in 1986, it took another eight years for the second. The article includes a timeline showing approvals by year, and it is interesting to compare this with FBDD-derived drug approvals since the 1996 publication of the seminal SAR by NMR paper. In the chart below, the first year on the x-axis is for antibody drugs; the second is for FBDD-derived drugs.

 

 

A quarter century after work began, new antibody approvals were still uncommon; Mullard notes that “antibody approvals have only been an annual event since 2006.”

 

Antibody-drug conjugates (ADCs) are an interesting subset that – as their name suggests – comprise an antibody linked to a small molecule, usually a toxin intended to kill cancer cells. Ten of these have been approved in the US, but while the first (gemtuzumab ozogamicin) was approved in 2000 most of the rest are recent, with six of them coming since the beginning of 2019.

 

By these standards the fact that only five fragment-derived drugs have been approved thus far isn’t surprising. Indeed, antibodies have some advantages: “whereas medicinal chemists can toil for years to find small molecules with activity against a given target, antibody discovery can take a matter of months.” Moreover, as the article continues, success in the clinic is roughly double that of small molecules.

 

The second article is by Christopher Austin, until recently Director of the National Center for Advancing Translational Sciences at the US National Institutes of Health. Titled “Translational Misconceptions,” it briefly enumerates and debunks false beliefs about translating new discoveries into drugs, which include:

 

- Translation does not exist 

 

- Translation is a “thermodynamically favored” process 

 

- Translation is straightforward and does not qualify as science 

 

- Translation is a unidirectional process 

 

- Once an investigational therapy gets into humans for the first time, regulatory approval

  and marketing are all but assured 

 

- Regulatory approval is the end of the translational process

 

Those of us in industry would probably dismiss these statements as naïve, but such perceptions are widespread. Indeed, Austin himself acknowledges that he “once believed unquestioningly in all of them.”

 

Each of these misconceptions invites discussion. To take just the last, the first approved ADC was pulled from the US market in 2010 when confirmatory trials showed that patients on the drug actually did worse than those on placebo. It was reapproved in 2017 after a better dosing schedule was established. In other words, it took 17 years after initial approval to figure out how to effectively use gemtuzumab ozogamicin, and 26 years from the beginning of the project.

 

Returning to the two successes mentioned at the top of this post reveals that their apparent rapidity does not tell the full story. The Tethering technology that eventually led to sotorasib was initially published more than twenty years ago, and researchers first used mRNA packaged in liposomes to transfect cells way back in 1989.

 

Amidst rapid visible progress it is easy to lose sight of the fact that much research goes nowhere very slowly. Even when successful, it might take decades to help patients. As Austin concludes, “only by advancing our common understanding of the complexity of translation, translational research and translational science will translational gaps be narrowed and eventually eliminated.”