Metabolic-based drug-drug interactions prediction, recent approaches for risk assessment along drug development
Metabolic-based drug-drug interactions prediction, recent approaches for risk assessment along drug development
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DOI：
10.1515/dmdi.2011.031
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发表时间：
2011-12-01
期刊：
影响因子：
--
通讯作者：
Barberan, Olivier
中科院分区：
文献类型：
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作者：
Boulenc, Xavier;Barberan, Olivier
从体外和体内数据预测体内药物相互作用(DDiS)，也称为体外体内外推(IVE)，是参与药物发现和开发的科学家感兴趣的问题。为了避免对人体有害的DDIS，应尽快评估新的候选药物与其他药物的可能相互作用，不仅作为抑制剂或诱导剂(施暴者)，而且作为底物(受害者)。随着新化合物实体的特征被揭示，DDI风险评估通过迭代过程在药物开发计划中进行。体外和临床前/临床结果都被考虑在内，以更好地了解所开发化合物的行为并改进DDI预测。在过去的几十年里，文献中已经提出了几个公式来从定量的角度来预测DDIS，这表明在体内基于代谢的DDIS的预测能力有了实质性的提高。已经提出了机制和动态方法来预测基于代谢的DDIS的大小。本文的目的是概述目前的方程式和方法，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DDIS的潜在机制(即，可逆抑制、基于机制的抑制、诱导)。特别是，这项审查概述了在药物开发过程中如何以互补的方式使用这些方法(静态和动态)。讨论与各种方法相关的限制和优势，以及该领域的监管要求，可以让读者对这一不断增长的领域有一个有用的概述。
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.