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Moving Toward a Question-Centric Approach for Regulatory Decision Making in the Context of Drug Assessment.

Flora T MusuambaS Y Amy CheungPieter J ColinElin H DaviesJeffrey S BarretFrancesco PappalardoMichael ChappellJean-Michel DogneAdriana CeciOscar Della PasquaIne S Rusten
Published in: Clinical pharmacology and therapeutics (2023)
The most intuitive question for market access for medicinal products is the benefit/risk (B/R) balance. The B/R assessment can conceptually be divided into subquestions related to establishing efficacy and safety. There are additional layers to the B/R ratio for medical products, including questions related to dose selection, clinical and nonclinical pharmacology, and drug quality. Explicitly stating the actual questions and how they contribute to the overall B/R provides a structure that fosters better informed cross-domain discussions. There is currently no systematic approach in the regulatory setting to assess and establish the acceptability of alternative methods and data sources. In most cases, the medicinal product sponsors tend to prioritize traditional data types and methods, which are well accepted by regulators for inclusion in regulatory submissions. This, in addition to the absence of rigor in the use and validation of new data types and methods, and the limited training of assessors in related fields can lead to increased regulatory skepticism toward new data types and methods. A data-knowledge backbone is needed to mitigate the uncertainty in efficacy and safety characterization. This white paper discusses the value of explicitly redefining and restructuring the regulatory scientific decision making around the scientific question to be addressed. The ecosystem proposed is based on three pillars: (i) a repository connecting questions, data, and methods; (ii) the development and validation of high-quality standards for data and methods; and (iii) credibility assessment. The ecosystem is applied to four use cases for illustration. The need for training and regulatory guidance is also discussed.
Keyphrases
  • electronic health record
  • transcription factor
  • big data
  • healthcare
  • decision making
  • risk assessment
  • deep learning
  • virtual reality