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REVIEW article
Front. Toxicol.
Sec. Computational Toxicology and Informatics
Volume 6 - 2024 |
doi: 10.3389/ftox.2024.1484895
This article is part of the Research Topic Application of Image- and AI-based digital caging technology in toxicology and safety pharmacology testing View all 3 articles
Validation framework for in vivo digital measures
Provisionally accepted
Szczepan W Baran
1*
Susan E Bolin
2
Stefano Gaburro
3
Marcel M Van Gaalen
4
Megan Renee LaFollette
5,6
Chang-Ning Liu
7
Sean Maguire
8
Lucas P.J.J Noldus
10,9*
Natalie Bratcher-Petersen
11*
Brian Berridge
11- 1 VeriSIM Life, San Francisco, United States
- 2 AbbVie (United States), North Chicago, Illinois, United States
- 3 Tecniplast, Buguggiate, Italy
- 4 Evotec, Goettingen, Germany
- 5 The North American 3Rs Collaborative, Denver, United States
- 6 The 3Rs Collaborative (3RsC), Denver, United States
- 7 Pfizer, Groton, United States
- 8 GSK, Collegeville, United States
- 9 Noldus Information Technology, Wageningen, Netherlands
- 10 Radboud University, Nijmegen, Gelderland, Netherlands
- 11 Digital In Vivo Alliance (DIVA), Redwood City, California, United States
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.
Keywords: Digital Biomarkers1, 3Rs (reduce replace refine)2, Preclinical3, translation4, drug discovery and development5, rodents6, validation7, verification8. (Min
Received: 22 Aug 2024; Accepted: 09 Dec 2024.
Copyright: © 2024 Baran, Bolin, Gaburro, Van Gaalen, LaFollette, Liu, Maguire, Noldus, Bratcher-Petersen and Berridge. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Szczepan W Baran, VeriSIM Life, San Francisco, 94104, United States
Lucas P.J.J Noldus, Noldus Information Technology, Wageningen, Netherlands
Natalie Bratcher-Petersen, Digital In Vivo Alliance (DIVA), Redwood City, California, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.