Boulenc Xavier, Barberan Olivier
Drug Safety and Animal Research, Drug Disposition Domain, Sanofi Research and Development, Montpellier, France.
Drug Metabol Drug Interact. 2011;26(4):147-68. doi: 10.1515/DMDI.2011.031.
Prediction of in vivo drug-drug interactions (DDIs) from in vitro and in vivo data, also named in vitro in vivo extrapolation (IVIVE), is of interest to scientists involved in the discovery and development of drugs. To avoid detrimental DDIs in humans, new drug candidates should be evaluated for their possible interaction with other drugs as soon as possible, not only as an inhibitor or inducer (perpetrator) but also as a substrate (victim). DDI risk assessment is addressed along the drug development program through an iterative process as the features of the new compound entity are revealed. Both in vitro and preclinical/clinical outcomes are taken into account to better understand the behavior of the developed compound and to refine DDI predictions. During the last decades, several equations have been proposed in the literature to predict DDIs, from a quantitative point of view, showing a substantial improvement in the ability to predict metabolism-based in vivo DDIs. Mechanistic and dynamic approaches have been proposed to predict the magnitude of metabolic-based DDIs. The purpose of this article is to provide an overview of the current equations and methods, the pros and cons of each method, the required input data for each of them, as well as the mechanisms (i.e., reversible inhibition, mechanism-based inhibition, induction) underlying metabolic-based DDIs. In particular, this review outlines how the methods (static and dynamic) can be used in a complementary manner during drug development. The discussion of the limitations and advantages associated with the various approaches, as well as regulatory requirements in that field, can give the reader a helpful overview of this growing area.
从体外和体内数据预测体内药物-药物相互作用（DDIs），也称为体外-体内外推法（IVIVE），是参与药物发现和开发的科学家们所关注的。为避免在人体中出现有害的药物相互作用，新药候选物应尽快评估其与其他药物可能的相互作用，不仅要评估其作为抑制剂或诱导剂（引发剂）的作用，还要评估其作为底物（受害者）的作用。随着新化合物实体特征的揭示，在药物开发过程中通过迭代过程进行药物相互作用风险评估。同时考虑体外和临床前/临床结果，以更好地了解所开发化合物的行为并完善药物相互作用预测。在过去几十年中，文献中提出了几个方程，从定量角度预测药物相互作用，在预测基于代谢的体内药物相互作用的能力方面有了显著提高。已经提出了机制和动力学方法来预测基于代谢的药物相互作用的程度。本文的目的是概述当前的方程和方法、每种方法的优缺点、每种方法所需的输入数据，以及基于代谢的药物相互作用背后的机制（即可逆抑制、基于机制的抑制、诱导）。特别是，本综述概述了在药物开发过程中如何以互补方式使用这些方法（静态和动态）。对各种方法相关的局限性和优势以及该领域的监管要求的讨论，可以为读者提供这个不断发展的领域的有用概述。
