Validation framework for in vivo digital measures - Baran, Szczepan W.; Bolin, Susan E.; Gaburro, Stefano; van Gaalen, Marcel M.; LaFollette, Megan R.; Liu, Chang-Ning; Maguire, Sean; Noldus, Lucas P. J. J.; Bratcher-Petersen, Natalie; Berridge, Brian R. (Frontiers in Toxicology, 2025)
The adoption of in vivo digital measures in pharmaceutical research and development (R&D) presents an opportunity to enhance the efficiency and effectiveness of discovering and developing new therapeutics. For clinical measures, the Digital Medicine Society’s (DiMe) V3 Framework is a comprehensive validation framework that encompasses verification, analytical validation, and clinical validation. This manuscript describes collaborative efforts to adapt this framework to ensure the reliability and relevance of digital measures for a preclinical context. Verification ensures that digital technologies accurately capture and store raw data. Analytical validation assesses the precision and accuracy of algorithms that transform raw data into meaningful biological metrics. Clinical validation confirms that these digital measures accurately reflect the biological or functional states in animal models relevant to their context of use. By widely adopting this structured approach, stakeholders—including researchers, technology developers, and regulators—can enhance the reliability and applicability of digital measures in preclinical research, ultimately supporting more robust and translatable drug discovery and development processes.