The advances in drug discovery have been almost miraculous as we now have cures for such intractable diseases like hepatitis C and have continued to make strides with a myriad of cancers. But of course, there is certainly much that needs to be done. The fact is that drug companies are having more troubles coming up with treatments. Here’s just one example: In March 2019, Biogen announced that one of its drugs for Alzheimer’s, which was in Phase III trials, failed to show meaningful results. On the news, the company’s shares plunged by 29%, wiping out $18 billion of market value.27
Consider that traditional drug development often involves much trial and error, which can be time consuming. Then might there be a better way?
Increasingly, researchers are looking to AI for help. We are seeing a variety of startups spring up that are focusing on the opportunity.
One is Insitro. The company, which got its start in 2019, had little trouble raising a staggering $100 million in its Series A round. Some of the investors included Alexandria Venture Investments, Bezos Expeditions (which is the investment firm of Amazon.com’s Jeff Bezos), Mubadala Investment Company, Two Sigma Ventures, and Verily.
Even though the team is relatively small—with about 30 employees—they all are brilliant researchers who span areas like data science, deep learning, software engineering, bioengineering, and chemistry. The CEO and founder, Daphne Koller, has the rare blend of experience in advanced computer science and health sciences, having led Google’s healthcare business, Calico.
As a testament to Insitro’s prowess, the company has already struck a partnership with mega drug operator Gilead. It involves potential payments of over $1 billion for research on nonalcoholic steatohepatitis (NASH), which is a serious liver disease.28 A key is that Gilead has been able to assemble a large amount of data, which can train the models. This will be done using cells outside of a person’s body—that is, with an in vitro system. Gilead has some urgency for looking at alternative approaches since one of its NASH treatments, selonsertib, failed in its clinical trials (it was for those who had the disease in the later stages).
The promise of AI is that it will speed up drug discovery because deep learning should be able to identify complex patterns. But the technology could also turn out to be helpful in developing personalized treatments—such as geared to a person’s genetic make-up—which is likely to be critical for curing certain diseases.
Regardless, it is probably best to temper expectations. There will be major hurdles to deal with as the healthcare industry will need to undergo changes because there will be increased education for AI. This will take time, and there will likely be resistance.
Next, deep learning is generally a “black box” when it comes to understanding how the algorithms really work. This could prove difficult in getting regulatory approval for new drugs as the FDA focuses on causal relationships.
Finally, the human body is highly sophisticated, and we still are learning about how it works. And besides, as we have seen with innovations like the decoding of the Human Genome, it usually takes considerable time to understand new approaches.
As a sign of the complexities, consider the situation of IBM’s Watson. Even though the company has some of the most talented AI researchers and has spent billions on the technology, it recently announced that it would no longer sell Watson for drug discovery purposes.29
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