Discovery on Target 2024

Last week Boston hosted CHI's 22^nd Annual Discovery on Target. With dozens of talks spread across seven or eight concurrent tracks over three days, and an additional day of pre-conference symposia, I’ll just touch on a few themes.

 

Computational Approaches

Artificial intelligence and machine learning were well represented. Brandon White described an ML model built at Axiom to predict liver toxicity, responsible for a quarter of clinical trial failures. As we noted last week, good ML models require lots of data, and Axiom has tested 50,000 small molecules in primary human hepatocytes from multiple donors using assays including high-content imaging. Just input a chemical structure and the model will predict toxicity. When run against the FDA’s database of drug-induced liver injury, the model performed with 74% sensitivity and 97% specificity, and even gave good dose predictions.

 

Woody Sherman (Psivant) laid out a series of “grand challenges for computers in drug discovery.” This is the working title for a publication he is spearheading to focus attention on key problems. They fall into five categories: chemistry (including synthesis, stability, and covalency), structure predictions (including protein-ligand structures, dynamics, and cryptic pockets), energetics (including affinity, selectivity, and kinetics), ADME (including everything from solubility and aggregation to bioavailability), and pharmacology (including toxicity). A sixth category, human considerations (including intellectual property and interpreting experimental data), is also being considered.

 

The success of AlphaFold to predict protein structures shows what computers can achieve, but in that case the effort was enabled by massive amounts of high-quality public data in the Protein Data Bank. Few of these challenges can draw on anything approaching the PDB. Indeed, even parameters as seemingly simple as solubility can change dramatically depending on crystal form and subtle changes to pH.

 

Because these computational challenges are so daunting, collecting them into one forum may prove salutary. And other categories may be worth including, such as target discovery. Woody is looking for co-authors, so reach out to him if you’re interested.

 

Covalent approaches

Covalent approaches to drug discovery have gone mainstream, at least if this conference is any indication. But they are not without risk: Doug Johnson (Biogen) described research implicating the piperidine acrylamide pharmacophore in approved BTK inhibitors with inhibition of ALDH1A1 and possible liver injury.

 

Several talks focused on methodologies. Alexander Federation (Talus) described data-independent acquisition (DIA) mass spectrometry methods, which can be more comprehensive than the more commonly used data-dependent acquisition (DDA) methods in identifying peptides in chemoproteomic studies, which we first discussed here. Talus is focused specifically on transcription factors.

 

As we noted earlier this year, Steve Gygi (Harvard) has been at the forefront of increasing the throughput of mass spectrometry methods, and he described how to increase the number of samples that can be analyzed simultaneously from 18 to 35. He also described two approaches, GoDig and CysDig, to look for up to 200 pre-specified proteins in a sample, ensuring identification of even low-abundance targets.

 

Turning to specific targets, Wai Cheung Adrian Chan described work done at Harvard to find covalent inhibitors against deubiquitinating enzymes (DUBs), reporting that screens of a small library of 178 covalent fragments in cell lysates found hits against several dozen DUBs. (We previously wrote about non-covalent USP7 inhibitors.)

 

Brooke Brauer described the optimization of a covalent inhibitor of Bfl-1 at AstraZeneca, an interesting oncology target. AZ has published some nice papers on this project which I’ll write about soon.

 

Last week we mentioned work Michelle Arkin and collaborators had done on 14-3-3 proteins, and Lynn McGregor described work done at Novartis on the same system. A screen of 6000 covalent compounds identified hits that modified a specific cysteine in 14-3-3 more rapidly in the presence of a peptide derived from the estrogen receptor. Stabilizing this interaction could be useful for treating certain cancers.

 

Not everyone is focused on cysteine: Andrea Zuhl described work done at Hyku Biosciences, which as the name suggests is targeting histidine, tyrosine, and lysine. This has necessitated building a fragment library of more than 6000 compounds, more than 70% of which are stable in buffer. Andrea presented one example targeting the catalytic lysine residue of the oncogenic ALK fusion protein, though the selectivity against other kinases was not disclosed.

 

All of these examples focused on covalent molecules in which the warhead is maintained during optimization. But as we first wrote about here, fully functionalized fragments (FFFs) contain a photoreactive moiety that reacts covalently with nearby proteins but is subsequently discarded. Sherry Niessen described how Belharra has industrialized this process by creating a library of about 11,000 FFF probes. Because of the low efficiency of protein crosslinking (typically <5%), most of the library consists of enantiomeric pairs to facilitate hit identification. Also, the average molecular weight of the library is around 350 Da, and these super-sized fragments tend to perform better than the strictly rule-of-three compliant molecules.

 

Covalent success stories

At least two presentations covered covalent fragment-based drug candidates. Shota Kikuchi (Vividion) described the discovery of VVD-214/RO7589831, a WRN inhibitor we wrote about earlier this year. As I speculated at the time, the cyclopropyl group was introduced to lower the reactivity of the vinyl sulfone warhead. Interestingly though, even early molecules were quite selective for WRN. Like sotorasib, binding is largely driven by the k_inact term of k_inact/K_i, again demonstrating that high reactivity for the target does not necessarily mean high chemical reactivity.

 

Finally, in his plenary keynote Steve Fesik (Vanderbilt University) covered multiple success stories, including the discovery of the KRAS^G12C inhibitor BI 1823911, which we wrote about here. Boehringer Ingelheim has since published molecules that hit multiple KRAS mutants as well as KRAS degraders, and Steve noted that all of these contain the same “squirrely-looking” fragment identified from SAR by NMR, an illustration of the power of fragment-based methods to explore new regions of chemical space.

 

I’ll close there, but please add your thoughts. There are is still at least one good conference coming up this year, and 2025 is quickly approaching.