Clinical Pharmacology Modeling and Simulation, Amgen Inc., South San Francisco, California, USA.
Quantitative Clinical Pharmacology, Takeda Pharmaceuticals International Co., Cambridge, Massachusetts, USA.
Clin Pharmacol Ther. 2019 Nov;106(5):981-992. doi: 10.1002/cpt.1462. Epub 2019 Jun 14.
Model-based meta-analysis (MBMA) is a valuable component of the quantitative pharmacology toolkit for model-informed drug discovery and development. It enables principled decision making with a totality of evidence mindset through integration of internal and external data across multiple dimensions (e.g., targets/mechanisms, molecules/drugs, doses/regimens, diseases/indications, populations, endpoints, and clinical trial designs). MBMA distinguishes itself from traditional meta-analysis by infusing pharmacologic plausibility into the statistical rigor that typifies meta-analytic data integration. This is possible through mechanism-informed formulation of pharmacologically inspired cause-effect and dose-response relationships, time course of treatment effects, and interrelationships between proximal and distal outcomes of modulation of disease biology and pathophysiology. In this review, we offer a question-based approach to enhance appreciation of the value of MBMA across the continuum from drug discovery and translational research through clinical development, comparative effectiveness research, and postapproval optimization of therapeutics using illustrative examples across therapeutic areas.
基于模型的荟萃分析（MBMA）是定量药理学工具包中的一个有价值的组成部分，可用于基于模型的药物发现和开发。它通过整合多个维度（例如靶点/机制、分子/药物、剂量/方案、疾病/适应证、人群、终点和临床试验设计）的内部和外部数据，实现了基于整体证据的原则性决策。MBMA 通过将药理学合理性融入典型的荟萃分析数据整合的统计严谨性，从而与传统的荟萃分析区分开来。这可以通过机制知情的药理学启发的因果关系和剂量反应关系、治疗效果的时间过程以及疾病生物学和病理生理学调节的近端和远端结果之间的相互关系的形式来实现。在这篇综述中，我们提供了一种基于问题的方法，通过治疗领域的实例，增强了对 MBMA 在从药物发现和转化研究到临床开发、比较疗效研究以及治疗后优化的整个过程中的价值的认识。
