AI Can Transform Drug Development, New ITIF Report Finds

WASHINGTON—Artificial intelligence can transform drug development and may slash development times by roughly half, according to early evidence. AI can enhance productivity across the entire development pipeline, from discovery to preclinical testing, clinical trials, regulatory reviews, and manufacturing, according to a new report from the Information Technology and Innovation Foundation (ITIF), the leading think tank for science and technology policy.

The drug development process is notoriously long and expensive, often taking 15 to 16 years from discovery to delivery. ITIF’s latest report details how AI is reshaping this timeline by boosting efficiency at every stage, ultimately making life-saving therapies more accessible to patients.

“Drug development is a laborious, costly, and risky process,” said Sandra Barbosu, associate director of ITIF’s Center for Life Sciences Innovation, who authored the new report. “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.”

With AI cutting down trial-and-error in clinical research and streamlining workflows, drug development is set to become far more productive—a crucial shift given recent declines in biopharmaceutical productivity. But ITIF argues that more supportive policies are necessary for AI to reach its full potential—especially in privacy-enhancing data-sharing standards, public research funding, regulatory clarity, and workforce training.

ITIF’s report highlights how companies like Genentech, Johnson & Johnson, Gilead, and Asimov are already demonstrating AI’s potential to boost biopharma productivity. Genentech’s AI-driven “lab-in-the-loop” platform accelerates drug discovery by continuously refining predictions based on real-time lab results, cutting down discovery timelines significantly. Johnson & Johnson’s AI tools improve clinical trial inclusivity, helping to develop therapies that are effective across diverse populations more quickly. Gilead uses AI to identify underdiagnosed patients in underserved communities, reducing health disparities through earlier intervention, while Asimov’s AI-enhanced gene therapy manufacturing boosts scalability and quality. These examples show that AI is more than just a technological upgrade; it’s a catalyst for faster, more efficient, and accessible drug development.

ITIF’s policy recommendations include:

• Encouraging privacy-enhancing data sharing to support AI-enabled research while maintaining patient privacy.
• Expanding public funding for foundational research to fuel AI’s role in drug development and make R&D more efficient.
• Developing risk-based regulatory standards that reflect the risk each AI application poses to patients, fostering innovation while ensuring patient safety.
• Strengthening workforce training programs to prepare scientists and healthcare professionals for AI’s integration in biopharmaceutical R&D.

“AI has the power to make drug development more efficient and effective—but it needs the right policy support to deliver,” said Barbosu. “The future of biopharmaceutical innovation depends on getting these policies right.”

Contact: Austin Slater, [email protected]