Brian W. Corrigan, Eric Anderson, Andrew Tredennick, Luis Martinez Lomeli, Megan Cala Pane, Tyler Dunlap, Brian Davis, James Rogers, Marc R. Gastonguay
First published 05 September 2026 in Clinical and Translational Science, Volume 19, Issue 9
Despite integration of artificial intelligence (AI) into drug discovery and an expanding global medicines pipeline, new drug approvals have declined, highlighting a paradox in biopharma: More drugs discovered, but less approvals, higher costs, and longer timelines. Reasons for the decreased research and development (R&D) efficiency are multifactorial, in part driven by the complexity of new modalities, difficult targets and indications, and persistence of cognitive biases in clinical decision-making. One proposed solution to address R&D productivity challenges includes the adoption of organization-wide quantitative decision frameworks (QDFs). QDFs have the potential to increase R&D productivity by integrating quantitative assessments of program risk and value, clinical development costs, time, and probability of success into product valuations.