The Promise of Data-Driven Drug Development

From screening chemical compounds to optimizing clinical trials to improving post-market surveillance of drugs, the increased use of data and better analytical tools such as artificial intelligence (AI) hold the potential to transform drug development, leading to new treatments, improved patient outcomes, and lower costs. However, achieving the full promise of data-driven drug development will require the U.S. federal government to address a number of obstacles. This should be a priority for policymakers for two main reasons. First, enabling data-driven drug development will accelerate access to more effective and affordable treatments. Second, the competitiveness of the U.S. biopharmaceutical industry is at risk so long as these obstacles exist. As other nations, particularly China, pursue data-driven innovation, especially greater use of AI, foreign life sciences firms could become more competitive at
drug development.

Policymakers should recognize that the potential of data-driven drug development is crucial to the well-being of Americans as well as U.S. competitiveness, and develop policies to accelerate this transformation.

To that end, policymakers should prioritize data-driven drug development. Overall, policymakers’ highest priority should be to dramatically increase the availability of data for drug development—and the most effective way to do that would be to support the creation of a National Health Data Research Exchange to prioritize the collection and sharing of patient medical data for research purposes.
Policymakers should take other steps, including:

• Implementing a unique patient identifier to improve data integrity throughout the health care system;
• Better enforcing the publication of data from clinical trial results by being diligent about penalizing noncompliance;
• Developing guidance for the use of new kinds of data sources in the drug development lifecycle;
• Developing mechanisms to facilitate data sharing by biopharmaceutical stakeholders by establishing data trusts that protect sensitive and proprietary data while still making it available to researchers;
• Requiring and funding the Food and Drug Administration (FDA) to improve the reliability of data used in drug development outside the United States;
• Developing best practices for data collection in health care to ensure equitable outcomes, such as strategies to increase coverage of underrepresented populations;
• Fully funding the U.S. National Institutes of Health (NIH) to accelerate the development of the All of Us Research Program’s million-person research cohort; and
• Increasing the number of workers—including high-skilled foreign-born workers—with AI skills and computer science education at all levels.