Artificial intelligence is poised to significantly impact the drug development process, potentially reducing development times by about half. This is according to a report from the Information Technology and Innovation Foundation (ITIF), a prominent think tank focused on science and technology policy.
The traditional drug development timeline can span 15 to 16 years from discovery to delivery, with substantial costs involved. The ITIF report outlines how AI is transforming this timeline by enhancing efficiency at every stage, thereby making therapies more accessible.
Sandra Barbosu, associate director of ITIF’s Center for Life Sciences Innovation and author of the report, stated: “Drug development is a laborious, costly, and risky process. It can take up to a decade and a half from start to finish, and in the end only 8 percent of early-stage drug candidates make it to market, so average R&D expenses for new drugs can exceed $2.8 billion. But AI tools can accelerate each step in the process by helping researchers identify drug targets faster, optimizing clinical trial designs, and enhancing manufacturing efficiency.”
AI's role in cutting down trial-and-error processes in clinical research could lead to increased productivity—a crucial change amid recent declines in biopharmaceutical productivity. However, ITIF emphasizes that supportive policies are needed for AI to achieve its full potential. These include privacy-enhancing data-sharing standards, public research funding expansion, regulatory clarity, and workforce training.
The report showcases companies like Genentech, Johnson & Johnson, Gilead, and Asimov as examples of AI's potential in boosting biopharma productivity. Genentech's "lab-in-the-loop" platform accelerates drug discovery by refining predictions with real-time lab results. Johnson & Johnson uses AI tools to improve clinical trial inclusivity across diverse populations. Gilead employs AI to identify underdiagnosed patients in underserved communities for earlier intervention while Asimov enhances gene therapy manufacturing with AI.
ITIF suggests several policy recommendations:
- Encouraging privacy-enhancing data sharing while maintaining patient privacy.
- Expanding public funding for foundational research.
- Developing risk-based regulatory standards tailored to each AI application.
- Strengthening workforce training programs for scientists and healthcare professionals.
“AI has the power to make drug development more efficient and effective—but it needs the right policy support to deliver,” Barbosu noted. “The future of biopharmaceutical innovation depends on getting these policies right.”
Contact: Austin Slater
