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

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.