AI drug discovery: leading to new drugs


For biotech companies, much of the traditional drug discovery process is costly guesswork. But a new wave of drug development platforms, enabled by artificial intelligence, are helping companies use large datasets to quickly identify markers of patient response and develop viable drug targets more cheaply and efficiently. .

The results could be transformative not only for medical providers and patients with difficult-to-treat diseases, but also for the biotech industry: Morgan Stanley Research estimates that modest improvements in early-stage drug development success rates made possible by the use of artificial intelligence and machine learning could lead to 50 additional new therapies over a 10-year period, which could translate into an opportunity of more than 50 billion dollars.

“Predictive, data-enhanced diagnostics present a significant near-term opportunity for the life sciences industry,” says Tejas Savant, who covers life science tools and diagnostics at Morgan Stanley Research. “It is also likely to resonate with payers, as these trials can generate better outcomes. They can also generate significant cost savings by allowing earlier identification and treatment of high-risk patients.

Technological advances in recent years have made it easier to capture and store vast amounts of digital patient data. This has resulted in rich troves of genomic data, health records, medical imaging, and other patient information that AI platforms can leverage to help develop drugs faster and with more chances of success in the early stages of creation.

Matthew Harrison and Vikram Purohit, biotech analysts at Morgan Stanley Research, estimate that “a 20-40% reduction in preclinical development costs at a subset of U.S. biotech companies could generate the savings needed to fund successful development. four to eight new molecules. ”

This would represent a 15% increase in approved therapies compared to the total number of new drug approvals in 2021, demonstrating the potential of biotechnologies to generate new revenue while helping more patients.

The combination of AI and big data could help patients in other ways. In addition to drug discovery and development, advanced data analytics capabilities and richer datasets could help healthcare professionals assess patient risk and detect disease earlier.

For biotech companies, it may take a hit drug discovery just to break even. The median investment required to bring a new drug to market is estimated at nearly $1 billion, while the actual research and development cost can be as high as $2.5 billion per marketed therapy, based on trials discontinued and clinical failures.

This means the cost savings from AI could offer significant value. But with the high risks involved in creating biologically feasible treatments and the limited track record of the technology platforms involved, investors will need to see strong evidence of real-world use cases for AI-based drug discovery.

Morgan Stanley Research analysts anticipate an inflection point for the sector, driven by drug trial data readings over the next two years. An increase in collaboration between AI drug developers and large biopharmaceutical companies could also make a difference.

“If initial readings are consecutively strong, we believe equities across the space could rise as investors gain confidence in a well-defined total addressable market for AI-enabled drug development,” Purohit says. , which covers small and medium-sized biotechnologies. “In addition to strong data, we expect the market to look for concrete breakthroughs with biopharmaceutical partnerships as proof of validation.”

An AI drug development platform could generate significant revenue growth through partnerships, assuming modest annual increases in AI investments in biopharmaceutical research and development budgets.

Along with new data and progress in partnerships, investors will have to weigh how individual companies are using AI and machine learning to develop medicines. They must also consider the biotech industry’s range of business models, with revenue coming from a mix of proprietary pipeline development and a mix of milestone and royalty payments from programs developed with partners. biopharmaceuticals.

For more Morgan Stanley research on the implications for investors of the potential impact of artificial intelligence on biotechnology, ask your Morgan Stanley representative or Financial Advisor for the full report, “Putting the ‘Tech’ in Biotech: Assessing the Potential for AI in Drug Development” (June 27, 2022). Morgan Stanley Research clients can directly access the report here. And more Ideas opinion leaders from Morgan Stanley.


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