基于模型的荟萃分析一般考虑

随着药品研发及临床试验成本的不断增加，如何充分利用各类数据指引临床试验高效地开展，以提高新药的研发效率，并制定科学合理的用药方案，具有重要意义。基于模型的荟萃分析（model based meta-analysis, MBMA）是将数学建模与荟萃分析（meta-analysis）相结合，对多种来源（如临床前和临床各阶段研究数据等）和多个维度（靶标/机制、药代/药效动力学、疾病/适应症、人群特征、给药方案、生物标志物/药效指标/安全性指标等）的信息进行整合，不仅为药物研发各个关键环节的决策制定提供重要依据，也可为临床合理用药以及药物经济学中的成本-效益分析提供有效信息。传统的Meta分析对数据的同质性要求较高，而MBMA通过建模可将不同剂量、不同疗程和不同人群的异质性数据合并分析，从而对药物的剂量效应、时间效应和影响因素进行量化，预测以往研究中不曾涉及的剂量、时间和协变量水平下的药效或安全特征。尽管MBMA的建模和模拟技术与群体药动学/药效学（population pharmacokinetics/pharmacodynamics, Pop PK/PD）相似，但相比Pop PK/PD，MBMA最大的优势是可对文献数据进行充分利用，不仅提高了结论的证据强度，更可回答单个研究不能回答的问题。目前MBMA已成为模型引导的药物研发（model-informed drug development, MIDD）策略中的重要方法之一。本文将就MBMA的应用价值、分析计划、数据采集与处理、分析方法以及报告要点进行说明，旨在为MBMA在新药研发和临床实践中的应用提供参考。

General considerations of model-based meta-analysis

With the increasing cost of drug development and clinical trials, it is of great value to make full use of all kinds of data to improve the efficiency of drug development and to provide valid information for medication guidelines. Model-based meta-analysis (MBMA) combines mathematical models with meta-analysis to integrate information from multiple sources (preclinical and clinical data, etc.) and multiple dimensions (targets/mechanisms, pharmacokinetics/pharmacodynamics, diseases/indications, populations, regimens, biomarkers/efficacy/safety, etc.), which not only provides decision-making for all key points of drug development, but also provides effective information for rational drug use and cost-effectiveness analysis. The classical meta-analysis requires high homogeneity of the data, while MBMA can combine and analyze the heterogeneous data of different doses, different time courses, and different populations through modeling, so as to quantify the dose-effect relationship, time-effect relationship, and the relevant impact factors, and thus the efficacy or safety features at the level of dose, time and covariable that have not been involved in previous studies. Although the modeling and simulation methods of MBMA are similar to population pharmacokinetics/pharmacodynamics (Pop PK/PD), compared with Pop PK/PD, the advantage of MBMA is that it can make full use of literature data, which not only improves the strength of evidence, but also can answer the questions that have not been proved or can not be answered by a single study. At present, MBMA has become one of the important methods in the strategy of model-informed drug development (MIDD). This paper will focus on the application value, data analysis plan, data acquisition and processing, data analysis and reporting of MBMA, in order to provide reference for the application of MBMA in drug development and clinical practice.