Expanding Quantitative Medicines' Reach: Systems and Methods to Enhance R&D Productivity and Decision-Making in the Agentic-Era.

Authors

Brian W Corrigan, Eric Anderson, Andrew Tredennick, Luis Martinez Lomeli, Megan Cala Pane, Tyler Dunlap, Brian Davis, James Rogers, Marc R Gastonguay

Published in

Clinical and translational science. Volume 19. Issue 9. Pages e70721.

Abstract

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. A QDF integrates emerging clinical characteristics of the product through model-predicted efficacy and safety and links them to common valuation models to quantify the impact of product risk and uncertainty on value at different development stages. Context-aware AI can dynamically incorporate relevant unstructured information including clinical, competitor, market, and regulatory data into a QDF. The framework may be applied to compare clinical development scenarios for a single program, evaluate trade-offs between programs, and support portfolio-level decision making. Application of comprehensive QDFs in drug development promotes organizational alignment and transparency in product valuations, thereby supporting rationale decision-making, investment partnership negotiations, and product reimbursement assessment.

PMID:
42698299
Bibliographic data and abstract were imported from PubMed on 05 Sep 2026.

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