Leveraging biomarkers and translational medicine for preclinical safety - Lessons for advancing the validation of alternatives to animal testing

转化医学 转化研究 临床前试验 医学 动物试验 临床前研究 医学物理学 计算机科学 生物 病理 生态学
作者
Thomas Härtung,Nicholas M. P. King,Nicole Kleinstreuer,Marcel Leist,Danilo A. Tagle
出处
期刊:Alternatives to animal experimentation [Springer Nature]
卷期号:41 (4): 545-566 被引量:5
标识
DOI:10.14573/altex.2410011
摘要

This article explores the potential of principles established in translational medicine for the use of bio-markers to advance the validation of alternatives to animal testing in preclinical safety assessment. It examines especially how such principles can enhance the predictive power, mechanistic under-standing, and human relevance of new approach methodologies (NAMs). Key concepts from translational medicine, such as fit-for-purpose validation, evidence-based approaches, and inte-grated testing strategies, are already being applied to the development and validation of NAMs. The article discusses challenges in implementing biomarker-based approaches, including standardi-zation, demonstration of relevance, regulatory acceptance, and addressing biological complexity. It also highlights opportunities for advancement through collaborative efforts, technological inno-vations, and regulatory evolution. Case studies demonstrate successful applications of biomarkers in preclinical safety, while future perspectives explore emerging trends like multi-omics integration, microphysiological systems, and artificial intelligence. The article emphasizes the potential of bio-markers and translational science approaches in creating more predictive, efficient, and ethical preclinical safety assessment paradigms in the use of NAMs. Use of biomarkers can enable the mechanistic validation of human-relevant models and provide a means to relate changes in NAMs to animal or clinical study results. By leveraging these tools, the field can work towards reducing reliance on animal testing while improving the accuracy and human relevance of safety predictions.
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