Hamon Jérémy, Renner Maria, Jamei Masoud, Lukas Arno, Kopp-Schneider Annette, Bois Frédéric Y
Mathematical Modeling for Systems Toxicology, Université de Technologie de Compiègne, BP 20529, 60205 Compiègne Cedex, France.
Division of Biostatistics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany.
Toxicol In Vitro. 2015 Dec 25;30(1 Pt A):203-16. doi: 10.1016/j.tiv.2015.01.011. Epub 2015 Feb 9.
Predicting repeated-dosing in vivo drug toxicity from in vitro testing and omics data gathering requires significant support in bioinformatics, mathematical modeling and statistics. We present here the major aspects of the work devoted within the framework of the European integrated Predict-IV to pharmacokinetic modeling of in vitro experiments, physiologically based pharmacokinetic (PBPK) modeling, mechanistic models of toxicity for the kidney and brain, large scale dose-response analyses methods and biomarker discovery tools. All of those methods have been applied to various extent to the drug datasets developed by the project's partners. Our approach is rather generic and could be adapted to other drugs or drug candidates. It marks a successful integration of the work of the different teams toward a common goal of predictive quantitative in vitro to in vivo extrapolation.
从体外试验和组学数据收集预测体内重复给药的药物毒性，需要生物信息学、数学建模和统计学方面的大力支持。在此，我们介绍在欧洲综合项目Predict-IV框架内开展的工作的主要方面，包括体外实验的药代动力学建模、生理药代动力学（PBPK）建模、肾脏和大脑毒性的机制模型、大规模剂量反应分析方法以及生物标志物发现工具。所有这些方法都已在不同程度上应用于该项目合作伙伴开发的药物数据集。我们的方法具有相当的通用性，可适用于其他药物或候选药物。它标志着不同团队为实现从体外到体内的预测性定量外推这一共同目标而开展的工作成功整合。
