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.

Noncovalent fragments vs WRN

Werner syndrome helicase, or WRN, is an interesting target both for its biological mechanism and its flexible structure. Two years ago we highlighted work out of Vividion describing the discovery of a clinical-stage covalent WRN inhibitor. In a Nat. Commun. paper published earlier this year, Sandra Gabelli, Daniel Wyss, and collaborators at Merck and Proteros describe their noncovalent efforts against this target.

 

Inhibiting WRN kills cancer cells that are already defective for certain DNA repair pathways. It is an example of a “synthetic lethal” approach to drug discovery that expands the number of cancer targets by focusing on oncogenic cells rendered vulnerable by pre-existing mutations. As an ATP-dependent helicase, WRN acts as a molecular machine to unwind DNA. This requires the multidomain protein to undergo dramatic conformational changes, which makes finding ligands challenging: how do you know which conformation(s) to target? Moreover, WRN enzymatic assays are particularly prone to false positives; a paper published in 2024 demonstrated that some previously disclosed inhibitors are at best nonspecific, and at worst downright artifacts. Thus, the researchers chose to use biophysics to identify fragments.

 

A library of 1020 fluorine-containing fragments was screened in pools of up to 21 compounds using ¹⁹F NMR T₂ CPMG experiments. The 31 primary hits were re-screened as pure compounds in this assay as well as three more ligand-detected NMR assays, leading to seven hits taken into crystallography, of which three yielded structures. A separate SPR screen of 500 non-fluorinated fragments followed by confirmation by NMR led to three additional fragments characterized crystallographically. None of the validated fragments from either screen showed functional activity in an enzymatic assay.

 

The fragments bound in three different sites on the protein, which itself underwent significant conformational changes to accommodate the fragments. Fragments 1 and 2 bound in the same site and could be partially superimposed on one another, and these were used to generate a virtual library, of which 17 compounds were made and tested. Compound 4 had the best affinity as assessed by SPR and was also active in a functional assay.

Crystal structures of some of the other compounds bound to WRN were also determined, and these showed significant protein domain rearrangements, even when the compounds themselves were structurally similar. The researchers include a nice movie (link to download here) and suggest that “these structures capture only a few states of WRN as it translocates along the DNA and conducts its helicase and exonuclease functions.”

 

This paper nicely illustrates the challenges of finding ligands, particularly noncovalent ones, against conformationally flexible proteins. We’ll revisit this topic next week.

(Not) getting misled by crystal structures part 6: low ligand occupancies

It’s been a while since our last “getting misled by crystal structures” post. That one described unrecognized conformational heterogeneity of ligands. A more basic issue is ligand occupancy. It’s normally assumed that every protein in the crystal lattice has a bound ligand. A new paper in Structure by Timothy Stachowski and Marcus Fischer at St. Jude Children’s Research Hospital reveals that this is not always the case.

 

The researchers selected roughly 10,000 protein-ligand structures from the Protein Data Bank (PDB) and did a simple re-refinement of the ligand occupancies and B-factors (measures of conformational heterogeneity and modeling errors). 10% of the structures already presumed ligand occupancies at or below 0.9, but re-refinement saw the fraction jump to 35%, more than three-fold higher. There were no overall differences between covalent and non-covalent ligands, but 37% of fragments (defined as having MW <300 Da) saw decreased occupancy in re-refinement compared to just 22% of larger ligands. A few structures even saw occupancy drop completely. The authors wrote that “manual reviewing these revealed that ligands were built into spurious electron-density.”

 

Crystallographers use several metrics to assess the quality of their structures. In addition to B-factors, unique to each residue or atom, real-space correlation coefficients (RSCC) and real-space R values are commonly used, and the researchers compared these parameters before and after re-refinement. In many cases the metrics improved with decreased occupancy, but not for all metrics, and not always meaningfully. This means that standard assessments do not always flag partially occupied ligands.

 

OK, so a ligand binds at only 80% occupancy rather than the 100% assumed: does this matter? The researchers describe three categories where the answer may be yes. In the first, correcting ligand occupancy reveals alternative conformations of protein side chains, which could be informative for understanding the mechanism of binding. In the second, correcting ligand occupancy can reveal water molecules that interact with the ligand and/or protein. As we noted more than a decade ago, water is an essential player in protein-ligand interactions, and a single water molecule can make the difference between a binder and a non-binder. Finally, correcting ligand occupancy can reveal alternative binding modes for the ligand, and even ligands binding to other sites.

 

Importantly, this analysis was done on individually refined structures in the PDB, and it seems likely that the issues would be even more severe for structures batch-refined in high-throughput crystallographic fragment screens. As we wrote last year, the community needs to figure out how to deal with the increasing number of these structures.

 

The fact that roughly a third of PDB structures have less than 100% ligand occupancy has implications for training AI models. It also has implications for individual targets. As the researchers note, “non-crystallographers who rely on the PDB often assume that deposition itself is an implicit stamp of approval. Structural biologists, however, know that this is not always the case.” Before using a structure, it would be wise to re-refine it yourself.