Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design.

Area of Science:

• Biopharmaceutical Product Development
• Real-World Data Science
• Health Informatics

Background:

• Growing interest in real-world data (RWD) and real-world evidence (RWE) is driven by policy changes, regulatory acceptance, payer demands, and clinical trial limitations.
• While regulatory applications of RWE are prominent, its internal use within biopharma is less explored.

Purpose of the Study:

• To review how biopharmaceutical companies can leverage RWE for internal decision-making across the product development lifecycle.
• To highlight novel RWD sources and advanced analytics for enhancing RWE generation and application.

Main Methods:

• Review of RWD applications in pipeline strategy, product development, and clinical trial design.
• Discussion of novel data types (e.g., molecular, imaging, patient-derived xenografts) and advanced analytics (AI, data tokenization).
• Consideration of data quality, methodological transparency, and RWE's role in trial enrichment and diversity.

Main Results:

• RWD can inform pipeline and portfolio strategy, clinical trial criteria, population enrichment, endpoint selection, sample size estimation, and participant diversity.
• Novel RWD sources and advanced analytical techniques, including AI, are increasingly important for harnessing RWE.
• Data quality and methodological transparency are crucial for reliable RWE in internal decision-making.

Conclusions:

• Biopharmaceutical companies can strategically integrate RWE into internal processes to expedite and enrich product development.
• Leveraging diverse RWD sources and advanced analytics enhances the value and application of RWE.
• Ensuring data quality and transparency is paramount for the effective use of RWE in biopharma.