Decades of work, exponentially increased computing power and the availability and accessibility of enormous data sets is leading to dramatic changes and dramatic increases in drug discovery, improvements in clinical trial design and speed and precision in treatment of the end patient. “I expect that the development of new capabilities is going to go way past my lifetime,” said Andrew Radin, CEO and co-founder of Aria Pharmaceuticals and a computer scientist by training. “It’s an approach that’s been on people’s minds for quite a long time now, and will be for many decades to come.” The application of artificial intelligence in biopharma is expected to create an additional $50 billion market in the next decade.
“It would be difficult to overestimate how important machine learning is to making that all work, a completely new paradigm of drug discovery, from the acquisition of genomic data, to annotating those data, to querying them, to figuring out how to take silent biosynthetic genes and activate them, to figuring out which molecule is the right molecule,” said Greg Verdine, Ph.D., founder of FogPharma and LifeMine Therapeutics. “I just don’t think any of what we’re doing at this time would be doable without AI.”
Morgan Stanley Research believes that modest improvements in early-stage drug development success rates enabled by the use of AI and machine learning could lead to an additional 50 novel therapies over a 10-year period – how they calculated the creation of a $50 billion market mentioned earlier. Morgan Stanley also reported that “a 20% to 40% reduction in costs for preclinical development across a subset of U.S. biotech companies could generate the cost savings needed to fund the successful development of four to eight novel molecules.” This would represent as much as a 15% increase of approved therapies over the total number of novel drug approvals in 2021 – a significant impact from the application of artificial intelligence in biopharma.
There are nearly 270 companies working in the AI-driven drug discovery industry, with more than 50% of the companies based in the U.S. AstraZeneca’s collaboration with BenevolentAI resulted in the identification of multiple new targets in idiopathic pulmonary fibrosis with subsequent broadening of the scope to other therapeutic areas. Sumitomo Dainippon Pharma worked with Exscientia to identify DSP-1181 for obsessive compulsive disorder in less than a quarter of the time typically taken for drug discovery – under 12 months versus 4 ½ years.
Clinical Trials & Patient Treatment
“Predictive diagnostics, enhanced by data, present a significant near-term opportunity for the life sciences industry, says Tejas Savant of Morgan Stanley Research. “…these trials can generate better outcomes. They can also deliver sizable cost savings by enabling earlier identification and treatment of higher-risk patients.” Technological advances have made the capture of digital patient data much easier and much more accessible. This trove of genomic data, health records and medical imaging can be mined by artificial intelligence in the biopharma industry to assess patient’s risk and detect disease earlier.
Drug hunter Exscientia demonstrated that an AI-powered precision medicine platform could propose which therapy would be the most effective for a patient with late-stage blood cancer. The platform made the conclusion based on a high-resolution analysis of the patient’s individual cells and how they fared when tested against as many as 139 different treatments. “We’re starting to show for the first time that an AI algorithm can actually improve outcomes and survival in blood cancers,“ said Exscientia CEO Andrew Hopkins.