Last week Boston hosted CHI's 22nd
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 kinact term of kinact/Ki,
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 KRASG12C 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.