Twentieth-Fourth Annual Discovery on Target Meeting

Cambridge Healthtech Institute held its annual Discovery on Target (DoT) meeting in Boston last week. Attendance may have set a record, with over 1000 participants. There were sessions on antibodies, conjugates, and radioligands, but I’ll just touch on some of the small molecule themes most relevant to this blog.

 

Covalent approaches

As with last year, covalent approaches to drug discovery were well-represented. Dhirendra Simanshu (Frederick National Laboratory) described several drugs birthed in the NCI RAS Initiative, including ones targeting the G12C mutant of KRAS and C242 on the RAS-binding domain of PI3Kα. He also described how various KRAS mutants cause resistance to drugs, finding that most of them affect the K_I term (binding) more than the k_inact term (bonding). Continuing on the KRAS theme, Jack Sloan discussed efforts at BMS to discover inhibitors targeting both the on- and off-states of KRAS G12C.

 

Kasia Handing (Tango Therapeutics) presented both covalent and non-covalent approaches against VRK1, a synthetic lethal target for glioblastoma. A biochemical screen yielded covalent inhibitors that were characterized crystallographically, but they turned out to be too non-selective to progress. In the meantime, an in-cell DNA-encoded library (DEL) screen led to more advanceable molecules.

 

DEL was indeed a major topic of the conference, and here too covalent approaches are being explored. Elizabeth D’ambrosio described how GSK is identifying covalent binders from DEL screens, a process which includes testing for time-dependence as well as aggressive washing steps under denaturing conditions to quench unreacted molecules and wash away any sticky, non-covalently bound compounds. And Xiaojie Bruce Lu (Chinese Academy of Sciences) described applying DEL to a variety of targets, including some covalently targeting non-cysteine residues.

 

One of the advantages of DEL is being able to screen large libraries, and increasing throughput is also of interest to Harvard’s Steve Gygi, who is screening covalent fragments against cells and cell lysates. In 2024 we described how he was using tandem mass tag (TMT) multiplexing to analyze 18 samples in a single mass-spectrometry run, and he has now extended this to 35 samples. What’s more, Steve is combining 16 different fragments in each sample and introducing clever deconvolution approaches to further accelerate the process.

 

Not everyone is targeting cysteine residues. Mark Tye described the discovery of Revolution Medicines’ zoldonrasib, which uses an aziridine warhead to target the G12D mutant of KRAS. And Balyn Zaro (University of California San Francisco) described using ninhydrin derivatives to target arginine. Most organic chemists are familiar with ninhydrin’s reaction with primary amines such as the N-termini of peptides, but Balyn has been able to optimize selectivity for arginine and has used the resulting chemical probes to identify more than 7000 arginine residues in cells, about 5% of which are hyperreactive. Interestingly, some cell lines seem to have a greater proportion of reactive arginine residues than others.

 

Direct-to-Biology (D2B) approaches

Another recurring theme was direct-to-biology, in which crude reaction mixtures are screened directly in various assays without purification. Indeed, Nicholas Bland of Domainex said that after looking at the agenda of the meeting he considered skipping the introductory slides – and this was on the first official day of the conference. Nicholas described using D2B for optimizing PROTACs as well as for improving fragment hits against the GPCR A_2A receptor.

 

Jack Sadowsky, who also presented at DDC this past spring, described how he and his colleagues at Kimia are using D2B in combination with machine learning to develop selective inhibitors of kinases while also improving their in vitro ADME properties. This effort entailed making and testing tens of thousands of compounds from a virtual space of some 80 million molecules.

 

But even small-scale D2B can be valuable. Trevor Sherwood described how he and his colleagues at BMS used D2B to make and test 663 molecules to improve the potency of an EP4 agonist identified from a high-throughput screen. This involved multiple reaction types, including photoredox chemistry, for which they built a custom-designed reactor to evenly heat and illuminate the microtiter plates. The D2B work was done in parallel with medicinal chemistry, but interestingly the development candidate BMS-986526 came directly from the D2B work and has entered a phase 1/2a trial for rheumatoid arthritis.

 

There were other talks and posters on D2B that I missed, but I did attend two breakfast roundtables on the topic that were quite lively despite happening at 7:30 – a testament to the interest in the topic.

 

“Traditional” fragment-based drug discovery

At a plenary panel moderated by Drug Hunter’s Dennis Hu on drugging difficult targets, Biogen’s Klaus Urbahns asked the other panelists if they were still doing fragment-based lead discovery. John Tallarico of Novartis and Ian Storer of AstraZeneca immediately said yes. And in a separate presentation, Kris Borzilleri described how she and her Pfizer colleagues used fluorine-detected NMR and fragment optimization to identify ligands for an orphan GPCR.

 

Debanu Das described using high-throughput crystallography screening at XPose Therapeutics, now part of Mid-Atlantic BioTherapeutics, to identify inhibitors of DNA damage response pathway proteins. Although the initial hits had no detectable activity, SAR by catalog was able to get to nanomolar inhibitors of the protein APE1, which is overexpressed in ovarian cancer, in a matter of weeks.

 

But perhaps the most robust validation of FBDD was provided by Steve Fesik of Vanderbilt, who described a decades-long effort to drug the MYC pathway, which is probably dysregulated in almost all cancers. We described some of his published work on WDR5 here, but he is now making progress against MYC itself. This has not been easy: MYC is intrinsically disordered, and SAR-by-NMR screens against both the transactivation domain and the DNA-binding domain failed. However, MYC forms a structured leucine-zipper heterodimer with the protein MAX, and screening this complex yielded hits at the DNA binding interface. (Earlier this year we described work from Novartis on a different leucine zipper.) The hits have been optimized to low nanomolar binders that disrupt DNA binding, unlike a slide full of other (alleged) MYC inhibitors, including high-profile commercially available compounds such as MYCi361 and MYCi975.

 

I’ll close – hopefully before you stop reading. If you attended, what talks or posters stood out? And if you wish you had attended, mark your calendar for Oct. 18-21 next year, when DoT returns to Boston in time for peak leaf-peeping season.

Covalent allosteric inhibitors of NLRP3

NLRP3 has received considerable attention as an inflammatory disease target. The protein normally exists in an auto-inhibited conformation, but in response to various stimuli it changes shape to help form a multimeric complex called the inflammasome. This in turn activates various inflammatory pathways, including cell death. Several drugs targeting NLRP3 have gone into the clinic, but none have been approved. In a new open-access Br. J. Pharmacol. paper, Brian Cook, Gabriel Simon, and colleagues at Vividion describe a covalent inhibitor that binds to a previously unknown site.

 

As we wrote nearly a decade ago, Vividion pioneered the use of chemoproteomics to identify covalent binders in cells and cell lysates. While exploring the mechanism of the previously reported NLRP3 inhibitor MCC950, they found that cysteine 280 became resistant to modification by electrophiles in the presence of the ligand. MCC950 does not contain an electrophile, arguing against direct interaction with Cys280 and suggesting instead that binding causes a conformational change to the protein which makes Cys280 less accessible. In other words, protein inhibition correlated with Cys280 being less exposed.

 

A screen of Vividion’s library yielded no hits against Cys280 but more than 300 against cysteine 463. Interestingly, modification of C463 also caused a decrease in reactivity of Cys280, suggesting a conformational change similar to the one caused by MCC950. Thus, the researchers pursued Cys463 binders.

 

Two parameters were used to guide compound optimization, target engagement and promiscuity. The first number, TE₅₀, measures the extent of covalent engagement of a given cysteine after 1 hour in cell lysates. The second, P₅₀₀, measures the percentage of all measured cysteines in the sample labeled by at least 40% after incubation with 500 µM compound for 1 hour. One of the initial hits, VVD-124476, gave a low micromolar value for TE₅₀ but labeled some 20% of the measured proteome. Indeed, it was previously reported as a covalent ligand for a completely different protein. To be useful as a NLRP3 inhibitor, the researchers needed to reduce promiscuity, and the most straightforward approach is to reduce general reactivity.

 

To decrease reactivity, the researchers swapped the acrylamide warhead with a butynamide (VVD-142397). Butynamides are reported to be less reactive than acrylamides, a result corroborated at Vividion with chemoproteomic data on 400 matched molecular pairs. The acrylamide to butynamide swap improved selectivity some 20-fold, though it also decreased potency against NLRP3. Increasing the core ring size regained activity, and rigidifying the molecule led to further improvements. Further optimization ultimately led to VVD-338213 and several related molecules.

 

Although the researchers do not report k_inact/K_I values, a back of the envelope calculation puts it around 1200 M^-1s^-1 for VVD-338213, a potency where cell activity might be expected. Happily this turned out to be the case: the molecule blocked IL-1β secretion from human cells at lower concentrations than MCC950. Importantly, in cells where Cys463 had been mutated to alanine, MCC950 was still active while the covalent molecules were inactive.

 

In addition to promising cell activity, many of the covalent molecules had reasonable stability in whole blood, good permeability, and low efflux. (I do wish the ligand efficiency reactivities of the molecules were reported; after all Vividion, introduced this metric.) Some of the molecules also showed good brain penetration. In vivo pharmacodynamic studies are complicated by the fact that Cys463 is not conserved in mice, but fortunately the researchers could run studies on transgenic mice in which human NLRP3 had been introduced. In these animals, IL-1β was decreased by both VVD-338213 and by MCC950.

 

The researchers were also able to obtain a cryo-EM structure of one of their molecules bound to NLRP3, which confirmed covalent binding to Cys463. More importantly, it revealed that the binding site is in a cryptic pocket some 20 Å from where MCC950 binds. (One wonders if the computational technique we discussed last week would have been able to predict this pocket.) Similarly to the WRN inhibitor Vividion has taken into the clinic, the molecule appears to make only hydrophobic contacts with the protein, with no polar interactions. Mechanistically, the “Cys463 ligands act as molecular doorstops, stabilizing the inactive conformation and preventing the structural rearrangements necessary for transition to the active inflammasome disc.”

 

This is a lovely paper and a case study in transforming a highly reactive molecule into a selective molecule with in vivo activity. It is interesting that the starting molecule, with 28 non-hydrogen atoms, flagrantly violates the rule of three. Last year we asked whether covalent fragments need to be larger than their non-covalent counterparts, and this paper provides another example where the answer is yes. Whether or not this chemical series ultimately advances, it is nice to have another chemical probe for this target.

Differential scanning fluorimetry (DSF) for covalent ligands

Differential scanning fluorimetry (DSF), also known as a thermal shift assay, is one of the most common techniques for fragment screening according to the most recent methods poll on Practical Fragments. This popularity is in part due to the simplicity of the assay: just mix your protein with a dye such as SYPRO Orange, which binds to the hydrophobic core of unfolded proteins, heat the solution in a PCR thermocycler, and measure the change in fluorescence. Ligands that bind specifically to proteins often stabilize them, increasing the melting temperature. As we noted in 2017, some companies even use DSF to assess the ligandability of new targets.

 

Most of the focus on DSF has involved non-covalent ligands. But the approach also works for covalent ligands, according to a new open-access paper in the British Journal of Pharmacology by Nir London and colleagues at the Weizmann Institute of Science.

 

The researchers acknowledge that they are not the first to explore DSF on covalent ligands; last year we discussed a screen of 47 covalent fragments against 47 kinases, and just last month we highlighted a DSF screen that identified an unexpectedly covalent fragment hit.

 

The new paper starts by performing DSF on five drug targets (BTK, the G12C mutant of KRAS, SARS-CoV-2 MPro, Pin1, and Keap1), each with well-characterized covalent ligands (for example, ibrutinib, acalabrutinib, and evobrutinib for BTK, sotorasib and adagrasib for KRAS^G12C, etc.) For four of the proteins they also tested non-covalent or covalent reversible ligands. Almost all of the ligands increased thermal stability of the bound protein, and covalent ligands tended to have a greater effect.

 

In addition to testing specific ligands, the researchers also performed DSF experiments on the five proteins with three reactive, non-specific covalent ligands: iodoacetamide, ethyl 2-(bromomethyl)acrylate, and a chloroacetamide called RA13. Intact protein mass spectrometry confirmed that these molecules gave essentially complete modification of the five proteins, in some cases at multiple sites.

 

In contrast to the specific ligands, the reactive alkylators tended either to have marginal effects on the melting temperatures of the proteins or to actually destabilize the proteins, sometimes dramatically. That said, there were cases of stabilization. The researchers suggest that “reactive, non-specific, irreversible binders may act as destabilizers, since they form an irreversible covalent bond regardless of molecular recognition, which may result in deformation of the protein if the rest of the binder cannot be accommodated near the modified amino acid.”

 

To assess how well DSF works for covalent fragments more generally, the researchers acquired 2612 acrylamides from Enamine and screened them (each at 8 µM in pools of 5) against Keap1 for 24 hours at 4 ºC. Mass spectrometry showed that just over 100 gave at least 50% modification, which a back of the envelope calculation suggests a covalent efficiency roughly 1 M^-1s^-1. Next, 84 compounds with differing degrees of modification were tested by DSF, and, in contrast to the kinase paper we discussed last year, there was a correlation between extent of modification and either stabilization or destabilization of the protein. But consistent with the specific ligands discussed above, the more reactive fragments (as assessed by glutathione reactivity) were the only ones that caused destabilization.

 

The authors conclude by proposing “DSF as a fast and complementary follow-up approach for electrophilic fragment screening, to prioritize productive and selective covalent binders over promiscuous reactive fragments.” This seems reasonable – what do you think?

Fragments in the clinic: VVD-214

Just over two years ago we highlighted a new clinical candidate targeting WRN, a covalent inhibitor then called VVD-133214. An open-access paper published near the end of last year in J. Med. Chem. from Shota Kikuchi, David Weinstein, and colleagues at Vividion describes in detail the optimization of the covalent fragment hit to the clinical compound. (Shota presented some of this work at the 2024 DDC meeting.) This paper is also an interesting contrast to non-covalent fragment-finding approaches against this target we wrote about earlier this month.

 

The 2024 post described the chemoproteomic screening that identified compound 1a, which covalently binds to C727 in WRN. An early observation was that some molecules were cooperative with ATP while others were competitive. Given the high concentration of ATP in cells, the researchers prioritized the former category, which led to compound 1f. (Note that while I’m showing only the k_inact/K_I values, the researchers used biochemical and cell-based assays to drive SAR).

 

The vinyl sulfone warhead is unusual amongst covalent clinical compounds, so the researchers sought to characterize it. The rate of reaction with glutathione for compound 1f is comparable to the approved drug osimertinib: reactive, though acceptable. To try to lower the reactivity and also prevent isomerization of the double bond, the researchers introduced a methyl group. Compound 2a not only showed increased stability but also improved activity against WRN and sub-micromolar activity in a cell-based assay. A crystal structure of a later molecule revealed interactions with a hydrophobic patch on the protein, explaining the improvement in potency. Importantly, the other enantiomer was much less potent.

 

Other rings were tried, unsuccessfully, to replace the pyrimidine and the phenyl moieties. However, changing the cyclopentyl ring to a tert-butyl moiety (compound 5d) further improved the potency to the point where the compound could be tested in vivo, where it proved to be active in a mouse xenograft study. Mass spectrometry experiments revealed prolonged occupancy of C727 out to 24 hours after compound dosing even though the compound itself had been cleared, consistent with a long half-life of the WRN protein. The researchers note that high target engagement (TE) at 24 hours was predictive of tumor growth inhibition, which streamlines optimization since it is easier to run a one-day TE experiment than a multi-week efficacy study.

 

Further optimization of ADME properties ultimately led to VVD-214, which was active in a mouse xenograft study and showed good oral bioavailability and pharmacokinetics in mouse, rat, dog, and monkey. This compound was also profiled in a chemoproteomic assay and found to be quite selective for the C727 of WRN.

 

There are several important lessons in this paper. First, the initial fragment is larger than prescribed by the rule of three, consistent with an analysis of covalent inhibitors last year. Second, much of the SAR was empirical; crystallography was not used until relatively late in the campaign. When a crystal structure was finally solved of VVD-214 bound to WRN it revealed no polar contacts between the ligand and the protein, only hydrophobic interactions, which is rare for fragments, let alone drugs. Perhaps because of the lack of polar interactions, it was impossible to measure the inhibition constant (K_I), and saturating the warhead to make it unreactive completely abolished activity. In other words, the binding is largely driven by reactivity, but specific reactivity for WRN rather than generic chemical reactivity.

 

In 2024 just two WRN inhibitors had entered the clinic, the other being a noncovalent molecule called HRO761. We quoted the Vividion team as saying that “this presents a rare opportunity to compare two small molecule oncology drugs targeting the same protein by different mechanisms.” Since then, HRO761 has been quietly discontinued, as has another noncovalent drug, IDE275. Meanwhile, development of VVD-214 is ongoing, and another covalent compound, MOMA-341, which we mentioned here, has also begun human testing. (To be fair, so has yet another non-covalent molecule NDI-219216. And the binding mechanism of a sixth WRN drug, EIK1005, is undisclosed.) While it’s still early in the match, covalent drugs seem to be punching above their weight. May the best drug(s) prevail - Practical Fragments is rooting for them all.

From noncovalent fragments to covalent WRN inhibitors

Last week we described the discovery and early optimization of noncovalent inhibitors against the the DNA helicase WRN, an interesting oncology target both for its conformational flexibility as well as a ‘synthetic lethal’ approach to cancer drugs. Today we continue the theme with a Bioorg. Med. Chem. Lett. paper from Geoffrey Smith and colleagues at CHARM Therapeutics.

 

At the time the project began, no specific WRN inhibitors had been reported, but a crystal structure of the ADP-bound form of the protein had been published. The computational tool Fpocket, which we wrote about here, was used to identify several ligandable pockets.

 

To find actual ligands, the researchers crystallographically screened a library of 860 "poised" fragments from Enamine and used PanDDA (see here) to identify binders, even those with low occupancies. Several ligands occupied pockets that had been identified by Fpocket. More interestingly, five fragments bound in a previously cryptic site that had not been predicted. This pocket was formed by rotation of two phenylalanine residues as well as peptide backbone movements, consistent with sites able to support high affinity ligands, as we discussed in 2024. Thus, the focus turned to ligands that bind here.

 

Because of its low occupancy in the crystal, the orientation of compound 3 was ambiguous, so the researchers turned to a machine-learning-based protein-ligand co-folding algorithm called DragonFold. This revealed that the fragment binds in close proximity to a cysteine residue, C727, known to be reactive. Scaffold hopping and addition of a covalent warhead led to compound 4d. While the initial compound 3 showed no activity in a WRN helicase assay, compound 4d showed micromolar activity. Moreover, a crystal structure revealed binding to C727. Further SAR led to molecules such as compound 9b, the most potent WRN inhibitor reported in the paper.

 

Compounds were also tested against the closely related helicase Bloom syndrome protein, or BLM, and most of them were active, though the SAR varied between WRN and BLM. The activity against BLM is odd given that the residue corresponding to C727 is a serine, but the researchers note that the molecules might bind to other cysteine residues in BLM. Although no chemical reactivity data are provided for the ligands, I suspect they are somewhat reactive.

 

Several important lessons can be drawn from this brief paper. The fact that an experimental screen was able to identify cryptic pockets missed in a computational screen justifies empirical approaches. The identification of these pockets is all the more impressive given that pre-formed crystals were sufficiently flexible to undergo significant conformational changes. But computational approaches did prove their utility in refining the binding mode of the ligand. And finally, this is another example of appending a covalent warhead onto a non-covalent ligand.

 

Next week we’ll conclude this WRN trilogy with a covalent-first example.

Twenty-First Annual Fragment-Based Drug Discovery Meeting

Last week some 875 people attended the CHI Drug Discovery Chemistry (DDC) meeting in San Diego. I can’t do justice to the 40 or so presentations I attended over four days but can highlight some of the main themes.

 

Reversible fragments

Membrane targets such as G protein-coupled receptors (GPCRs) pose a challenge for biophysical methods, but three talks presented progress. Matthew Eddy (University of Florida Gainesville) described high-resolution magic angle spinning (HRMAS) NMR, which entails spinning isolated cellular membranes containing GPCRs at high speed (4 kHz!), which miraculously yields sharp NMR signals for bound ligands. Matthew demonstrated applications with the human adenosine A_2A receptor and weak (mM) ligands. He noted that the technique can work with native, poorly expressed proteins, though data collection times can be upwards of 30 minutes.

 

Kris Borzilleri described using ¹⁹F NMR to find ligands against an orphan GPCR at Pfizer. The 2287 fragments screened yielded 87 hits, of which 38 confirmed by SPR. SAR studies eventually yielded low micromolar ligands, but these were difficult to advance in the absence of structure (see here for a more successful example from Merck).

 

Vanessa Porkolab (Eurofins Cerep) described using the Nanotemper Spectral Shift technology to screen 826 fragments against the adenosine A_2A receptor at 300 µM, with a 9.2% hit rate. Many of these ligands stabilized the GPCR in a thermal shift assay and seven were even active (as antagonists) in a cellular assay.

 

Turning to soluble proteins, Paola Di Lello presented a case study from Genentech and Vernalis applying ligand-observed NMR to the protein phosphatase PTPN22. Subsequent protein-observed NMR revealed that most of the 16 validated hits bound to two pockets some distance from the active site. The fragments were optimized to mid-micromolar affinity but showed no functional activity.

 

And Charlotte Hodson presented the eIF4E story from Astex. As we discussed last year, this yielded a low nanomolar ligand that did not have the desired cellular effects. Charlotte noted that subsequent genetic experiments were consistent with the limited efficacy. Still, the target was sufficiently interesting that a chemical probe would have been pursued even knowing it would be high-risk.

 

Covalent ligands

Covalent approaches made appearances throughout the conference. Keriann Backus (UCLA) described chemoproteomic approaches to find cysteine-targeting ligands; she noted that gain of cysteine residues (such as G12C in KRAS) are the most common missense variants in cancer. Keriann also warned how covalent compounds can cause potentially misleading effects in cells, as she described in Nat. Chem. Biol. last year.

 

In 2021 we wrote about the SpotXplorer fragment library from György Keserű (Hungarian Research Centre for Natural Sciences). György has now prepared a PhotoXplorer library, which uses diazirine tags for photochemical screening, which we described here. The new library has produced high hit rates across a variety of targets. György also described a new sulfozone-based photoprobe that is easier to prepare than diazirines.

 

Kelly Craft recounted a DNA-encoded library (DEL) screen at AbbVie against the target BCL2A1, also known as BFL1. This produced an aldehyde-containing low micromolar binder that formed an imine with buried lysine 102. Uncomfortable progressing an aldehyde, the researchers sought to covalently engage cysteine 55, the same cysteine targeted by AstraZeneca, as we wrote about here. The progression included at least one dual-warhead molecule which was crystallographically confirmed to bind both the lysine and cysteine. The effort ultimately yielded cysteine-selective leads.

 

Earlier this year I described the dDRTC method we developed at Frontier Medicines for determining k_inact/K_I, and Svetlana Kholodar presented a nice overview of its scope and utility. My colleague Johannes Hermann spoke in more detail about our covalent technologies, particularly those using AI.

 

Chemical space and the exploration thereof

Brian Shoichet (UCSF) gave an entertaining and wide-ranging account of “directed and random walks in chemical space.” Brian has consistently been on the bleeding edge of high-throughput in silico screens, from 67,000 compounds in 2009 to 138 million molecules in 2019 to 4 billion molecules today. When docking artifacts are avoided (as we discussed here), bigger libraries consistently produce more potent hits for more targets – an observation strikingly consistent with Alex Shaginian’s in 2023 as HitGen expanded their DEL libraries from billions to more than a trillion molecules. Brian is developing methods to computationally screen the >4 trillion make-on-demand molecules now available from companies such as Enamine.

 

Direct-to-biology (DTB) approaches, which rely on microscale chemical reactions screened without purification, have become increasingly popular methods for exploring chemical space. Jack Sadowsky correctly stated that Carmot was the first company formed around this approach; we previously wrote about the role Chemotype Evolution played in the discovery of sotorasib. Jack described how Kimia, which spun out of Carmot, has continued to advance the technology, applying it to find inhibitors selective for single members of closely related kinase families.

 

Allan Jordan described how Sygnature Discovery is applying DTB in a variety of assays including microsome stability and crystallography. (We wrote about crude reaction screening by crystallography earlier this year.) Expanding beyond DTB, Allan called their platform direct-to-discovery, and discussed how it led to a preclinical candidate with STORM Therapeutics in just 18 months.

 

WuXi Apptec is also using DTB. Peichuan Zhang described starting with ligands derived from fragment and DEL screens against the E3 ligase GID4 to make PROTACs to degrade BRD4; DTB was used to explore a wide range of different linkers. And Daniel Blair (St. Jude) described using DTB and affinity selection-mass spectrometry (AS-MS) to find new molecular glues for the oncology target LCK.

 

Computers, DEL, and DTB are not the only way to explore chemical space. Last year we covered Tom Kodadek’s bead-based screening approach at University of Florida Scripps, and Tom presented two talks on the topic, one using macrocycles to find binders to difficult targets such as PTP1B and one using small molecules to find molecular glues.

 

Speaking of PROTACs and glues, plenary keynote speaker Alessio Ciulli (University of Dundee) discussed the “evolution and future of targeted protein degradation.” Alessio noted that there are >25 PROTAC degraders and >10 glues in the clinic, though these collectively target only a small number of E3 ligands, so there is plenty of opportunity for the area to expand.

 

For many of us in industry, drugs represent the most privileged points in chemical space, and these often look quite different than we assume, as Dean Brown (Jnana) noted in his recent analysis of 104 oral small molecule drugs approved by the FDA from 2020 to 2024 (which we mentioned here). Some drugs contain eye-raising moieties such as acetylenes, styrenes, N-O bonds, and nitro groups. Indeed, it is worth remembering that venetoclax, arguably the most successful fragment-derived drug, sports a nitro group.

 

But before getting too complacent, Jonathan Baell (Manas) warned about frequent hitters in libraries of FDA-approved drugs. He notes in Eur. J. Med. Chem. earlier this year that many commercial libraries are actually enriched for molecules that cause spurious biological activity. Jonathan calls on library vendors to remove particularly egregious compounds, though I’d settle for world peace.

 

I’ll close on that pleasant thought, but please feel free to comment. I hope to see you in San Diego next year April 19-22 for the twenty-second iteration of DDC.

From noncovalent fragment to (non)covalent leads against PLPro

The most successful drug against COVID-19, nirmatrelvir, targets the main protease of SARS-CoV-2. As we discussed just last year, this protein has received considerable attention. But the genome for SARS-CoV-2 also encodes a second cysteine protease, papain-like protease, or PLPro. Despite this enzyme being essential for viral replication, the only previously disclosed chemical series targeting it dates from 2008 efforts against the original SARS. Two new papers in J. Med. Chem. from Stephen Fesik and colleagues at Vanderbilt University introduce new molecules, covalent and non-covalent.

 

One reason progress has been slow against PLPro is that it is inherently challenging. It recognizes the sequence LXGG, where X = Arg, Lys, or Asn. The two glycine residues thread a narrow channel to access the catalytic cysteine, while leucine and the “X” residue bind in solvent-exposed subsites. In 2024 the Fesik group published an open-access paper in ACS Med. Chem. Lett. describing the results of a protein-observed NMR fragment screen. Out of 13,824 fragments screened in pools of 12 at 0.8 mM each, 77 confirmed when tested individually, and 22 had affinities better than 1 mM.

 

An attractive feature of protein-observed NMR is that it tells you where the fragments bind, and in this case there were two main binding sites. One set of fragments bound in the S3 and S4 pockets, where the leucine and lysine residues of the substrate would normally fit, while another group of fragments bound some distance from the active site. Representatives from both groups were tested for inhibition of enzymatic activity. Only those in the first group were active, so these were prioritized.

 

In the first open-access J. Med. Chem. paper this year, the researchers started with compound 11, which fulfills rule-of-three fragment criteria, binds in the S4 pocket, and inhibits the enzyme with high-micromolar activity. Adding a couple methyl groups (compound 15) improved the potency by roughly 10-fold, and building towards the S3 pocket yielded compound 37, with low micromolar activity. Addition of a basic nitrogen, as in compound 46, improved the potency to submicromolar activity, and crystallography revealed that the basic nitrogen interacts with a glutamic acid side chain. This molecule was active in a cellular assay at submicromolar concentration.

 

An increasingly popular strategy for addressing difficult targets is through covalent inhibitors, and this is the subject of the second open-access J. Med. Chem. paper this year. The researchers synthesized and tested 25 analogs based on molecules such as compound 37 in which various warheads were appended by flexible linkers. Although some of these were active at low micromolar concentrations, only a few showed time-dependent activity as would be expected for an irreversible covalent inhibitor.

 

These few were optimized with the aid of molecular dynamics and structure-based design to molecules such as compound 45. Interestingly, despite being nearly 10-fold more potent than the best non-covalent molecule, it was less active in cells; the researchers attribute lower-than-hoped activity to the two hydrogen-bond donors in the diacetylhydrzaine linker. Unfortunately, these turned out to be essential for covalent binding; crystallography revealed that the compound forms four hydrogen bonds in the glycine channel. Indeed, this particular linker-warhead combination had been previously reported, and the inability to improve on it emphasizes the restrictive requirements for this particular protein.

 

This is a nice series of papers that shows how a single fragment can lead to multiple leads. The last paper is also a useful reminder that adding a warhead to a high-affinity binder is not always easy, nor does it necessarily lead to superior molecules. Indeed, neither the covalent nor the noncovalent leads have any reported in vitro ADME or pharmacokinetic data. It would be fun to screen PLPro against a library of covalent fragments to look for even more starting points.

Ligand reactivity efficiency (LRE)

As covalent drug discovery continues to rise, the demand for metrics to help guide lead optimization is increasing. Last year we discussed covalent ligand efficiency (CLE). In an open-access paper just published in J. Med. Chem., Benjamin Horning, Brian Cook, and colleagues at Vividion Therapeutics describe ligand reactivity efficiency (LRE). (Benjamin presented LRE at the DDC meeting in 2024.)

 

A key challenge when developing covalent ligands is maximizing specific reactivity towards the target of interest while minimizing intrinsic reactivity towards other proteins; the two types of reactivity are not the same, as we wrote about last year. For molecules that target cysteine residues, intrinsic reactivity is usually determined by assessing reactivity against the small molecule glutathione, which is abundant in cells.

 

For lead optimization more generally, a common metric is lipophilic efficiency (LLE or LipE, see here and here), in which the logP of a molecule is subtracted from the negative log of the IC₅₀ (pIC₅₀). More lipophilic molecules have higher logP values, so maximizing LLE helps to minimize increases in lipophilicity.

 

By analogy, the researchers defined LRE to help minimize increases in intrinsic reactivity. However, distinguishing specific from intrinsic activity is not necessarily straightforward. As we previously discussed, IC₅₀ alone is an inappropriate measurement for covalent inhibitors; the incubation time before the IC₅₀ is measured is an essential variable. The most rigorous value is k_inact/K_I, and although this ratio has been historically time-consuming to determine, we described an easier method earlier this year. Yet an even simpler measurement is the TE_{50(target, 1h)}, the concentration of compound necessary to label 50% of a target after one hour, which is a function of k_inact/K_I. The researchers thus defined LRE as:

 

    LRE = pTE_{50(target, 1h)} – pTE_{50(GSH, 1h)}

 

The variable in the second term, pTE_{50(GSH, 1h)}, is calculated from the reaction rate of the ligand with glutathione; intrinsically reactive ligands have higher rates.

 

In the case of LLE, values above 5 or 6 are generally considered acceptable for advanced leads, and the same is true for LRE. For example, a molecule with TE_{50(target, 1h)} = 10 nM and a (low) GSH reactivity of 0.01 M^-1s^-1 would have an LRE = 6.3. Also analogous to LLE, one can generate plots with pTE_{50(GSH, 1h)} on the x-axis and pTE_{50(target, 1h)} on the y-axis to assess whether LRE values are improving during a lead optimization campaign.

 

In my view, LRE is superior to previously discussed CLE because it explicitly considers the time component. A one hour incubation is practical; a ligand with k_inact/K_I = 10,000 M^-1s^-1 would have TE_{50(target, 1h)} = 19 nM. Also, LRE is more intuitive for medicinal chemists than CLE due to its similarity to LLE.

 

On the minus side, the researchers note that some of the assumptions break down for ligands with high non-covalent affinity (low K_I). Also, some folks may take issue with metrics that take the logarithm of a measurement that has units.

 

The researchers note another alternative metric, the reactivity enhancement factor (REF), which I briefly discussed here. REF is simply the ratio of the specific reactivity to the intrinsic reactivity, which is conceptually simpler to me than LRE. Nonetheless, the researchers state that LRE is commonly used at Vividion, which has put several covalent drugs into the clinic, so clearly it can be useful. Whether REF, LRE, or CLE, ultimately the choice of metric is less important than the ultimate goal: maximizing specific reactivity while minimizing intrinsic reactivity.

A new tool for covalent ligands: kinact/KI made easy with dDRTC

As covalent drug discovery becomes increasingly common, researchers are becoming more rigorous in how they characterize their molecules. The simple IC₅₀ values used for reversible inhibitors are meaningless for irreversible ligands unless the incubation times are also disclosed. And as molecules become more potent during the course of optimization, the incubation time may need to be shortened. An early hit might require treatment overnight to give 50% protein modification, while a potent lead might completely modify the protein in seconds. How do you quantitatively compare these?

 

The most rigorous parameter to characterize irreversible ligands is k_inact/K_I, sometimes called covalent efficiency, which we recently discussed here and here. Unfortunately, determining k_inact/K_I is a pain: it requires running multiple dose-response studies at multiple time points, and is thus typically only done for key compounds. In a new (open-access) Nat. Commun. paper, Robert Everley and colleagues at Frontier Medicines (including yours truly) provide a shortcut.

 

The new method relies on the fact that, especially for low-affinity fragments, much of the data collected in a conventional dose-response time course (DRTC) is redundant, providing little additional value. For example, if a compound at one concentration gives virtually no modification after 8 hours, it also won’t modify after 1 hour. The trick is to collect just the most informative data in a “diagonal” dose-response time course, or dDRTC.

 

I won’t go into the mathematics and full implementation details since the paper is open-access, but suffice it to say that dDRTC lowers the number of required data points by a factor of eight, thus saving both time and reagents – including precious protein.

 

The paper appropriately notes limitations, such as the fact that for compounds with better affinities (K_I < 50 µM), the values derived from dDRTC can underestimate the true k_inact/K_I. However, this situation is uncommon for fragments, and indeed the potencies for even some clinical compounds such as sotorasib and VVD-133214 are largely driven by (specific) k_inact rather than K_I. The paper shows good agreement for k_inact/K_I values determined using dDRTC with those determined using the conventional approach for compounds having k_inact/K_I from 1 to 2000 M^-1s^-1.

 

Perhaps most relevant for this blog, dDRTC is a practical solution for collecting important data. The next time you’re running a covalent program, give it a try! 

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.

Covalent ligand efficiency

Ligand efficiency (LE) was proposed more than two decades ago as “a useful metric for lead selection.” The concept is simple: divide the binding energy of a ligand by the number of non-hydrogen (or heavy) atoms (HA). The higher the number, the higher the binding energy per atom, and thus the more “efficiently” the ligand binds to the protein. LE is particularly useful in fragment-based lead discovery when prioritizing among differently sized hits to ensure that small, weak molecules are not overlooked. While some have criticized the metric’s dependence on standard state, drug hunters have repeatedly found it to be useful, as we’ve discussed here, here, and here.

 

Irreversible covalent drugs are a horse of a different color - or perhaps a different species entirely. Because of their two-step mechanism, binding followed by bonding, time is an essential parameter, and the proper way to characterize them is with the ratio k_inact/K_I. Is it possible to develop a covalent ligand efficiency metric? This is the task that György Ferenczy and György Keserű at HUN-REN Research Centre for Natural Sciences and Budapest University of Technology and Economics set for themselves in a recent (open-access) Drug Discovery Today paper.

 

As we wrote just a couple months ago, an important distinction for covalent drugs is specific vs chemical reactivity: you want the first to be high and the second to be low. For cysteine-reactive molecules, this distinction is often assessed by measuring the rate of reaction with the abundant cellular thiol glutathione (GSH). The researchers sought to incorporate this parameter into their definition of covalent ligand efficiency (CLE) as follows:

 

CLE = LE – LE(GSH) = (-1.4*log₁₀(IC_50,t)/HA) - (1.4*log₁₀(k^2nd_sur*t)/HA)

Where IC_50,t is half maximal inhibitory concentration at time “t” and k^2nd_sur is the second-order rate constant of the ligand reacting with a surrogate nucleophile such as GSH.

 

The researchers cataloged multiple covalent modifiers from the literature. Some had reported glutathione reactivity data. For the rest, the researchers estimated these values based on analogs. They went on to calculate CLE values for the protein-ligand pairs. Laudably, all of these data are provided in the supplementary data.

 

So, how useful would CLE have been in prior lead discovery campaigns? The researchers calculated CLE values for the BFL1 covalent fragment hits we wrote about here. The potencies of the six reported fragment hits varied, reflected in k_inact/K_I values, from 0.7 to 9.5 M^-1s^-1. But their CLE values spanned a narrower range, from 0.08 to 0.12. The fragment that was successfully optimized was one of the most potent, with a k_inact/K_I of 7.5 M^-1s^-1, but had a CLE of just 0.09. If anything, CLE would have deprioritized this fragment, at odds with the stated goal that “CLE is designed to support compound priorization.”

 

As we discussed earlier this year, the researchers previously proposed that covalent fragments may need to be larger than reversible fragments. If this is true, then normalizing for size may be less important for covalent ligands than for noncovalent ones, which can be very small and weak yet still valuable. Indeed, the researchers’ analysis of covalent ligands from the literature shows a smaller range of CLE values than LE values.

 

The researchers acknowledge other oddities too: “there is no smooth transition from CLE to LE as the reactivity of ligands decreases. Moreover, CLE can take negative values for compounds with low affinity and high reactivity.”

 

But for me, the biggest liability is the fact that – unlike LE for reversible binders or k_inact/K_I – the value of CLE depends on the time the measurement was taken. (In the paper, the researchers use a 1 hour incubation, so I would propose the annotation CLE_1h.) This makes it difficult to compare CLE values taken at different time points.

 

The first word of this blog is “practical,” and I’m not convinced this adjective applies to CLE, though I applaud the effort. The popularity of LE spawned a cottage industry of other metrics, some of which we summarized in a 2011 post. I confess that I had nearly forgotten about some of them, but I think they were a useful way for the field to grapple with what characteristics mattered. As covalent drug discovery becomes increasingly popular, perhaps we will see a similar proliferation of metrics. (Indeed, we already wrote about another one here.) It will be interesting to revisit these a decade hence to see which ones have caught on.

The importance of specific reactivity for covalent drugs

As we noted in our thousandth post, covalent drugs are becoming increasingly popular, particularly for tackling tough targets. But finding and optimizing covalent ligands entails unique challenges, as discussed in a new paper by Bharath Srinivasan at Cancer Research UK. (Derek Lowe also recently blogged about this.)

 

Interactions between noncovalent drugs and their targets are characterized by dissociation or inhibition constants K_D or K_{I ,} where lower numbers mean stronger binding. In contrast, irreversible covalent drugs are characterized by a ratio we discussed last year, k_inact/K_I, where the rate constant k_inact represents the covalent modification step. (Side note: although the term k_inact is commonly used, covalent modulators can also be activators; my company Frontier Medicines recently announced a covalent activator of p53^Y220C. Perhaps k_cov would be more general?)

 

To explain k_inact/K_I, Srinivasan draws a useful analogy to enzymes, which are mechanistically described by the specificity constant k_cat/K_m in Michaelis-Menten kinetics. In both cases, higher numbers mean more rapid modification or greater catalytic efficiency. A study of several thousand enzymes found the median k_cat/K_m to be around 100,000 M^-1s^-1, with 60% between 1,000 and 1,000,000 M^-1s^-1. Enzymes operate by stabilizing the transition state of the reaction, which means that the affinities for the substrates do not necessarily have to be high, particularly if the structures of the substrates differ from the transition states.

 

Just as catalytic efficiency for enzymes can be increased either by increasing k_cat or lowering K_m, the inactivation efficiency of covalent drugs can be optimized either by increasing k_inact or by decreasing K_I. Historically, drug hunters have focused on the latter; we previously described the discovery of TAK-020 in which the affinity of a fragment for the kinase BTK was first optimized and then a covalent warhead was appended.

 

However, focusing on k_inact can also be productive, and Srinivasan argues this is particularly true for challenging targets with shallow pockets where noncovalent affinity is difficult to obtain. As a case in point he discusses covalent KRAS^G12C inhibitors such as sotorasib, which I wrote about here. Just as residues within enzyme active sites stabilize the transition state of a reaction, a lysine residue in KRAS forms a hydrogen bond to the carbonyl of the acrylamide electrophile, thereby increasing its reactivity for the protein.

 

Srinivasan emphasizes that k_inact is specific for each particular protein-ligand pair as well as distinct from intrinsic or chemical reactivity. This is a critical point. Newcomers to the field often worry that a high k_inact value means a molecule is generically reactive and thus likely to react with many proteins, but this is not necessarily true. For example, sotorasib’s favorable k_inact/K_I is driven by a high k_inact for KRAS^G12C but it is still quite specific. Indeed, Srinivasan points out that even a chemically reactive molecule may not react with a protein if the geometry isn’t right.

 

A nice way of assessing specific reactivity (which unfortunately is not cited) is the reactivity enhancement factor, or REF, as defined by Alan Armstrong, David Mann, and colleagues at Imperial College London in an (open-access) 2020 ChemBioChem paper. Akin to the k_cat/k_uncat ratio used to assess rate enhancement for enzymes, REF is defined as the rate of reaction for a specific protein divided by the rate of reaction for glutathione, an abundant cellular thiol. The higher the REF score, the higher the specific reactivity for the protein of interest.

 

Srinivasan also considers tradeoffs between k_inact and K_I as k_inact/K_I approaches the rate of diffusion, suggesting that above 1,000,000 M^-1s^-1 or so any further improvement in affinity will come at the cost of specific reactivity. While this is theoretically interesting, from a practical perspective you can have a perfectly fine drug with a k_inact/K_I of just 10,000 M^-1s^-1.

 

Covalent drugs will only become more important as we pursue increasingly hard targets that have resisted previous efforts. For these targets in particular, focusing on specific reactivity will be rewarding.

Twentieth Annual Fragment-Based Drug Discovery Meeting

Last week’s CHI Drug Discovery Chemistry (DDC) meeting was held as usual in San Diego. More than 850 people attended, 96% in person, with 70% from industry and 28% from outside the US. I personally attended more than three dozen talks over the four days and will just touch on some broad themes.

 

Noncovalent approaches

Steve Fesik (Vanderbilt) gave two talks, the first of which was focused on “FBDD tips for success.” This opinionated and entertaining romp revealed lessons learned across several projects on difficult targets such as KRAS. Another holy grail oncology target is MYC, which is largely disordered. A two-dimensional NMR screen against the protein failed to yield any hits, but a screen of the MYC:MAX heterodimer provided hits which have been optimized to high nanomolar potency and are able to block DNA binding.

 

The second talk was focused on E3 ligases, a target class Steve has been pursuing for the past decade. Steve is particularly interested in E3 ligases such as CBL-C, TRAF4, and KLHL12 that are differentially expressed in certain tissues. In the case of KLHL12, which is not found in heart tissue, an NMR-based screen led to fragment hits that were ultimately optimized to mid-nanomolar binders and could be turned into bivalent degraders for Bcl-xL and β-catenin.

 

When asked about his second favorite fragment-finding method after protein-detected NMR, Steve mentioned SPR. The throughput for SPR has historically been modest, but John Quinn (Genentech) described the new Carterra Ultra, which is capable of screening 96 proteins simultaneously while retaining good sensitivity. John screened 3000 fragments at 500 µM against multiple proteins in just two weeks, which provided an immediate assessment of both protein ligandability and fragment selectivity. Interestingly, and in contrast to some other analyses, shapelier fragments had similar hit rates to flatter fragments.

 

Several talks focused on fragment-to-lead success stories, some of which we’ve covered on Practical Fragments, such as RIP2 kinase inhibitors that started from flat fragments and were evolved to more three-dimensional leads as described by Mark Elban (GSK). John Taylor discussed pan-RAS inhibitors discovered at Cancer Research Horizons, the subject of an upcoming post. Andrew Judd (AbbVie) described the discovery of ABBV-973, a potent STING agonist that could be useful for certain types of cancer. And Justyna Sikorska described the discovery of a non-covalent WRN inhibitor at Merck. This is a nice complement to Vividion’s covalent WRN inhibitor, which we wrote about here and which was presented by Shota Kikuchi. Interestingly, structural biology was not enabled until late in this project.

 

One of the earliest arguments for fragment linking was the concept of avidity, and this underlies the basis of a technology discussed by Tom Kodadek and Isuru Jayalath at University of Florida Scripps. The idea is to immobilize fragments onto TentaGel beads, each the size of a red blood cell. These can be screened against multivalent proteins using either simple plate-based assays or FACS, the idea being that even if an individual protein-ligand interaction is weak, a multimeric protein can interact with several ligands on a single bead for enhanced binding. The researchers validated the concept with streptavidin, and also used it to find millimolar binders to the proteasome subunit Rpn13.

 

Last year we wrote about using photoaffinity crosslinking with fully functionalized fragments (FFFs) to identify non-covalent ligands to thousands of proteins in cells, and this was the subject of several talks. Chris Parker (Scripps) has mapped more than 7000 binding sites and described the discovery of an inhibitor against the inflammatory target SLC15A4. Interestingly, the molecule binds what appears to be a disordered region, though Chris speculated that it adopts a more defined structure in cells.

 

Belharra has gone all in on using FFFs, and Jarrett Remsberg and Andrew Wang described the construction of a diverse >11,000-membered FFF library, 88% of which consists of enantiomers. This has been screened against 13 different oncology and immunology cell lines to identify enantioselective or chemoselective hits against >4000 proteins including STAT3, IRF3, and AR.

 

Covalent approaches

The FFF approach uses covalent bond formation to trap a noncovalent ligand, but of course covalent ligands are all the rage these days, as we noted just last week. Dan Nomura (UC Berkeley) described the identification of stereoselective covalent ligands against a disordered region of cMYC that seem to work by destabilizing the protein in cells. Similarly, covalent ligands against the largely disordered AR-V7 also seem to destabilize the protein. It will be interesting to explore the mechanism of these molecules to see whether the proteins are more ordered inside cells.

 

Jin Wang (Baylor College of Medicine) described a chemoproteomic approach called Fragment Probe Protein Enrichment (FraPPE) which entails linking covalent fragments to a desthiobiotin tag. Labeled proteins are then pulled down, proteolyzed, and analyzed by mass spectrometry. In contrast, competition methods such as those described last year pull down labeled peptides after proteolysis. The advantage of FraPPE is that it can capture multiple peptides from each pulled-down protein, leading to fewer false negatives.

 

Of course, not every application of covalent discovery involves chemoproteomics. Joe Patel, who co-organized FBLD 2016, described the Nexo Therapeutics platform. They’ve built from scratch a library of >12,000 fragments, a third of which contain stereocenters. Each member is rule-of-three compliant before adding the warhead, meaning that the final molecules can be larger, which as we noted earlier this month is probably a good idea. To date Nexo has successfully screened more than a dozen targets using intact protein mass spectrometry.

 

The Nexo library targets not only cysteines but other residues as well, and Maurizio Pellecchia (UC Riverside) described using sulfonyl fluorides and fluorosulfates to target histidine residues. He and his group screened a library of 600 fluorosulfate-containing fragments (MW 250-350 Da) against the oncology target MCL1 and found several that stabilized the protein towards thermal denaturation. Crystallography confirmed covalent bond formation.

 

Most covalent fragments are electrophilic so that they can react with nucleophilic protein residues, but as we noted in 2022 it is possible to do the reverse. Megan Matthews (University of Pennsylvania) described how she used chemoproteomics to discover the mechanism of action for hydralazine, a drug that has been used since 1949 to treat hypertension. This fragment-sized (MW 160 Da!) molecule irreversibly alkylates a histidine residue within the active site of the enzyme ADO, a target that has also been implicated in gliobastoma.

 

Plenary Keynotes

The approval of the covalent BTK inhibitor ibrutinib in 2013 arguably marks the start of the modern era of covalent drug discovery, and Chris Helal described Biogen’s efforts against this target using reversible inhibitors, irreversible inhibitors, and degraders. Chris traced the origin of their phase-2 BIIB091 to a collaboration with Sunesis that used Tethering, so perhaps we should include this molecule in our list of fragment-derived clinical compounds.

 

Phil Baran of Scripps, who last spoke at the conference in 2020, gave the secondary plenary keynote. After stating that “medicinal chemists are the backbone of society,” he then detailed multiple examples of how they’ve been doing things wrong. Fortunately, he provided useful chemistry solutions, with “useful” defined as reactions that are operationally simple, have wide scope, and require only readily available reagents. Rather than deploying tedious protecting group installations and deprotections, Phil uses radical chemistry to directly generate carbon-carbon bonds between or within complicated molecules. His goal is to make the chemistry so simple and practical as to be boring, and he illustrated the point by showing his teenage daughter successfully running a reaction.

 

I’ll end here, but please leave comments. And mark your calendar for April 13-16 next year, when DDC returns to San Diego.