For biotech companies, much of the traditional process of discovering new drugs is an expensive guesswork. But a new wave of drug development platforms, enabled by artificial intelligence, is helping companies use massive data sets to quickly identify indicators of patient response and develop viable drug targets at lower cost and greater efficiency.
Results could be transformative not only for medical providers and patients with difficult-to-treat diseases, but for the biotech sector: Morgan Stanley Research believes modest improvements in early-stage drug development success rates are enabled by the use of artificial intelligence and learning can lead automated to 50 additional new treatments over a 10-year period, which could translate to more than $50 billion.
“Predictive diagnostics, enhanced by data, presents an important 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 its payers, because these trials can lead to better outcomes. They can also provide significant cost savings by enabling early identification and treatment of high-risk patients.”
Technological advances in recent years have made it easier to capture and store packets of digital patient data. This has resulted in a rich set of genomic data, health records, medical imaging, and other patient information that AI platforms can mine to aid faster drug development with a greater chance of success in the early stages of creation.
Morgan Stanley Research biotechnology analysts Matthew Harrison and Vikram Buruhit estimate that “a 20% to 40% reduction in preclinical development costs across a subset of US biotechnology companies could generate the cost savings needed to fund the successful development of four to eight new molecules.”
This would represent a 15% increase in approved treatments based on the total number of new drug approvals in 2021, showing the potential for biotechnology to generate new revenue while helping more patients.
The coupling of artificial intelligence and big data can help patients in other ways. In addition to drug discovery and development, advanced data analysis capabilities and richer data sets can help medical professionals assess patient risks and detect disease early.
For biotech companies, it can take massive drug discovery just to break even. The average investment required to bring a new drug to market is estimated to be around $1 billion, while the true cost of research and development could be as high as $2.5 billion for each marketed treatment, when abandoned trials and clinical failures are taken into account.
This means that the savings from AI can offer significant value. But with the high risks involved in creating biologically feasible treatments, and the limited history of the technology platforms involved, investors will need to see solid evidence of real-world use cases for drug discovery with AI.
Morgan Stanley Research analysts anticipate an inflection point for the sector, driven by readings of data from drug trials over the next two years. An increase in collaboration between AI drug developers and large biopharma companies could also make a difference.
“If the initial readings are straight strong, we believe stocks across the space could rise as investors gain confidence in a well-defined, addressable overall market for AI-assisted drug development,” says Buruhit, who covers small and medium-sized biotech companies. “In addition to the strong data, we expect the market to look for concrete steps forward with biopharmaceutical partnerships as evidence of validation.”
An AI drug development platform can drive significant revenue growth through partnerships, assuming modest annual increases in AI investment within biopharma research and development budgets.
Along with new data and progress in partnerships, investors will have to assess how individual companies are using artificial intelligence and machine learning to develop drugs. They must also consider the biotech industry’s suite of business models, where revenue comes from a combination of proprietary pipeline development and a mix of payments and royalties from programs developed with biopharma partners.
For more Morgan Stanley Research research on the implications for investors of the potential impact of artificial intelligence on biotechnology, ask a Morgan Stanley representative or financial consultant For the full report, “Putting ‘technology’ into biotechnology: Assessing the potential of artificial intelligence in drug development” (27 June 2022). Morgan Stanley Research clients can access the report directly over here. Plus more Ideas One of the thought leaders at Morgan Stanley.