Considerations for Regulatory Reusability of Dynamic Tools in the New Drug Development

监管科学 基于生理学的药代动力学模型 药物开发 背景(考古学) 计算机科学 风险分析(工程) 重新使用 透明度(行为) 过程管理 管理科学 医学 药品 药理学 工程类 药代动力学 生物 古生物学 计算机安全 病理 废物管理
作者
Jiang Liu,Yuching Yang,Jogarao Gobburu,Cynthia J. Musante,Martin Klein,Liang Zhao,Rajanikanth Madabushi,Hao Zhu
出处
期刊:Pharmaceutical Research [Springer Science+Business Media]
标识
DOI:10.1007/s11095-025-03831-5
摘要

Model-informed drug development (MIDD) approaches have become indispensable for new drug development and to address regulatory challenges. Dynamic tools, such as population pharmacokinetics (popPK), physiologically-based pharmacokinetics (PBPK), and quantitative systems pharmacology (QSP) models, are routinely employed to enhance the efficiency of drug development. Recently, the Fit-for-Purpose (FFP) initiative and the Model Master File (MMF) framework have emerged to support model reusability and sharing in regulatory settings. In this manuscript we share key insights from the Session "Pathways for Regulatory Acceptance of Dynamic Tools in the New Drug Space" of Workshop "Considerations and Potential Regulatory Applications for a Model Master File", hosted by the U.S. Food and Drug Administration (FDA) and the Center for Research on Complex Generics (CRCG) and discuss the considerations for regulatory acceptance of dynamic modeling tools. Presentations at the workshop explored current practices in PBPK model evaluation, the potential for popPK models in bioequivalence (BE) assessments, and the implications of reusing models. Challenges such as context-specific validation, version control, and the impact of scientific and technological advancements on model reuse were emphasized. The workshop underscored the importance of clear regulatory pathways and structured frameworks for the consistent application of reusable models. The MMF's potential to streamline reviews and reduce redundancies was noted, although operational details require further elaboration. Continued collaboration among stakeholders is essential to refine model-sharing practices, enhance model validation processes, and promote transparency, ensuring that MIDD approaches remain robust and adaptable to evolving regulatory needs.
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