AI News, 33 Pharma Companies Using Artificial Intelligence in Drug Discovery artificial intelligence

An AI-Generated Drug?

There were some headlines the other day about the “first AI-discovered drug”, so that should send us to the work in question to see what’s going on.

“It’s like the Big Pharma companies come into a casino, put a million-dollar coin into a slot machine and with some probability like 10% or something, they get a win.”  Instead of gambling to get at the fruit higher up on the tree, Frey built Deep Genomics, a company using artificial intelligence to discover new disease targets as well as the best compounds to drug them.

My problem with it is that everyone has seen slot machines and knows the low probabilities that they will deliver a huge payoff, and using them as a metaphor makes it sound like we in drug research just randomly throw things around until something delivers.

That one-in-ten figure is indeed the success rate for drug candidates going into clinical trials and making it to eventual approval, but that’s our success rate after we’ve put as much thought as possible into possible targets, modes of action, screening cascades, and the drug candidates themselves.

Here’s the paper authored by the Deep Genomics team with the details on what they’ve done in this case: the story is on Wilson’s disease, a rare copper-storage pathology known to be caused by various mutations in the ATP7B gene, which codes for a copper-transporting protein in hepatocytes.

At any rate, it appears from the literature cited in the Deep Genomics paper above that (outside of Spain) perhaps one out of fifty or so Wilson’s patients have this mutation (there is a paper studying several dozen unrelated Spanish patients that found about half of them with this mutation, though, so there are very strong variations in this number).

When you’re developing a rare disease therapy, some big considerations are: (1) is there an actionable target for therapy, (2) what are the chances of developing an agent that hits this target, (3) how many patients are in need of such a therapy, and (4) can these patients be identified (and identified in time to help them)?

If you can’t find the patients, you can’t treat them, so Deep Genomics is going to have to make sure that every new Wilson’s patient gets sequenced (which, to be sure, is increasingly common) and that they know about it and can find the patients they want (which is not common at all).

The problem is that no one gets sequenced for Wilson’s until they’re already showing overt symptoms of the disease, and I would expect any drug that restores functional protein would mainly stop the damage from getting worse.

Since one can already predict a list of potential steric-blocking antisense oligos from such sequences, it is a relatively small step to generate candidates, at least compared to running a small-molecule discovery effort (especially one in this area!) But keep in mind that the development of such an agent is fraught with difficulty –

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News Insights

With extraordinary speed, Healx, a start-up using AI to drive the repurposing of drugs for neglected diseases, has progressed a project from inception to having a clinical candidate in phase 2a trials within 18 months.

Increasing collaboration In the past year, one of the most well established AI start-ups, BenevolentAI has announced several partnerships, including with AstraZeneca – to target new treatments for chronic kidney disease (CKD) and idiopathic pulmonary fibrosis (IPF), and with Novartis – to investigate indications and responder groups for oncology assets currently in clinical development.

Many start-ups and big pharma companies alike are making multiple partnerships across different disease areas, allowing for a healthily competitive environment for multiple co-existing AI service providers.

In an unprecedented move, 17 partners (including 10 pharmaceutical companies) have come together to create MELLODDY, a consortium hoping to establish a platform allowing machine learning from multiple sets of proprietary data.

It seems that in the near future, increased data sharing and collaboration will help to push this area of the pharmaceutical industry forwards, possibly even into a core position in the pharmaceutical research pipeline.

Implications for intellectual property Some are heralding the emergence of AI in the pharmaceutical industry as the beginning of a new era of drug discovery, where computers will be able to design drugs from start to finish.

Should AI techniques in pharma become exceptionally well established and, for example, automated repurposing of drugs become commonplace, one could imagine that patent claims directed towards second and further medical uses might find themselves falling below the obviousness threshold for patentability more frequently.

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