Insilico Medicine, an A.I-based platform for drug development and discovery announced $255 million in Series C financing on Tuesday. The massive round is reflective of a recent breakthrough for the company: proof that it’s A.I based platform can create a new target for a disease, develop a bespoke molecule to address it, and begin the clinical trial process.
It’s also yet another indicator that A.I and drug discovery continues to be especially attractive for investors.
Insilico Medicine is a Hong Kong-based company founded in 2014 around one central premise: that A.I assisted systems can identify novel drug targets for untreated diseases, assist in the development of new treatments, and eventually predict how well those treatments may perform in clinical trials. Previously, the company had raised $51.3 million in funding, according to Crunchbase.
Insilico Medicine’s aim to use A.I to drive drug development isn’t particularly new, but there is some data to suggest that the company might actually accomplish that gauntlet of discovery all the way through trial prediction. In 2020, the company identified a novel drug target for idiopathic pulmonary fibrosis, a disease in which tiny air sacs in the lungs become scarred, which makes breathing laborious.
Two A.I-based platforms first identified 20 potential targets, narrowed it down to one, and then designed a small molecule treatment that showed promise in animal studies. The company is currently filing an investigational new drug application with the FDA and will begin human dosing this year, with aims to begin a clinical trial late this year or early next year.
The focus here isn’t on the drug, though, it’s on the process. This project condensed the process of preclinical drug development that typically takes multiple years and hundreds of millions of dollars into just 18 months, for a total cost of about $2.6 million. Still, founder Alex Zhavoronkov doesn’t think that Insilico Medicine’s strengths lie primarily in accelerating preclinical drug development or reducing costs: its main appeal is in eliminating an element of guesswork in drug discovery, he suggests.
“Currently we have 16 therapeutic assets, not just IPF,” he says. “It definitely raised some eyebrows.”
“It’s about the probability of success,” he continues. “So the probability of success of connecting the right target to the right disease with a great molecule is very, very low. The fact that we managed to do it in IPF and other diseases I can’t talk about yet – it increases confidence in A.I in general.”
Bolstered partially by the proof-of-concept developed by the IPF project and enthusiasm around A.I based drug development, Insilico Medicine attracted a long list of investors in this most recent round.
The round is led by Warburg Pincus, but also includes investment from Qiming Venture Partners, Pavilion Capital, Eight Roads Ventures, Lilly Asia Ventures, Sinovation Ventures, BOLD Capital Partners, Formic Ventures, Baidu Ventures, and new investors. Those include CPE, OrbiMed, Mirae Asset Capital, B Capital Group, Deerfield Management, Maison Capital, Lake Bleu Capital, President International Development Corporation, Sequoia Capital China and Sage Partners.
This current round was oversubscribed four-fold, according to Zhavoronkov.
A 2018 study of 63 drugs approved by the FDA between 2009 and 2018 found that the median capitalized research and development investment needed to bring a drug to market was $985 million, which also includes the cost of failed clinical trials.
Those costs and the low likelihood of getting a drug approved has initially slowed the process of drug development. R&D returns for biopharmaceuticals hit a low of 1.6 percent in 2019, and bounced back to a measly 2.5 percent in 2020 according to a 2021 Deloitte report.
Ideally, Zhavoronkov imagines an A.I-based platform trained on rich data that can cut down on the amount of failed trials. There are two major pieces of that puzzle: PandaOmics, an A.I platform that can identify those targets; and Chemistry 42, a platform that can manufacture a molecule to bind to that target.
“We have a tool, which incorporates more than 60 philosophies for target discovery,” he says.
“You are betting something that is novel, but at the same time you have some pockets of evidence that strengthen your hypothesis. That’s what our A.I does very well.”
Although the IPF project has not been fully published in a peer-reviewed journal, a similar project published in Nature Biotechnology was. In that paper, Insilco’s deep learning model was able to identify potential compounds in just 21 days.
The IPF project is a scale-up of this idea. Zhavoronkov doesn’t just want to identify molecules for known targets, he wants to find new ones and shepherd them all the way through clinical trials. And, indeed, also to continue to collect data during those clinical trials that might improve future drug discovery projects.
“So far nobody has challenged us to solve a disease in partnership” he says. “If that happens, I’ll be a very happy man.”
That said, Insilico Medicine’s approach to novel target discovery has been used piecemeal, too. For instance, Insilico Medicine has collaborated with Pfizer on novel target discovery, and Johnson and Johnson on small molecule design and done both with Taisho Pharmaceuticals. Today, the company also announced a new partnership with Teva Branded Pharmaceutical Products R&D, Inc. Teva will aim to use PandaOmics to identify new drug targets.
That said, it’s not just Insilico Medicine raking in money and partnerships. The whole field of A.I-based novel targets has been experiencing significant hype.
In 2019 Nature noted that at least 20 partnerships between major drug companies and A.I drug discovery tech companies had been reported. In 2020, investment in A.I companies pursuing drug development increased to $13.9 billion, a four-fold increase from 2019, per Stanford University’s Artificial Intelligence Index annual report. R&D cost
Drug discovery projects received the greatest amount of private A.I investment in 2020, a trend that can partially be attributed to the pandemic’s need for rapid drug development. However, the roots of the hype predate Covid-19.
Zhavorokov is aware that A.I based drug development is riding a bit of a hype wave right now. “Companies without substantial evidence supporting their A.I powered drug discovery claims manage to raise very quickly,” he notes.
Insilico Medicine, he says, can distinguish itself based on the quality of its investors. “Our investors don’t gamble,” he says.
But like so many other A.I-based drug discovery platforms, we’ll have to see whether they make it through the clinical trial churn.