Jones Hannah M, Dickins Maurice, Youdim Kuresh, Gosset James R, Attkins Neil J, Hay Tanya L, Gurrell Ian K, Logan Y Raj, Bungay Peter J, Jones Barry C, Gardner Iain B
Pfizer Worldwide R&D, Department of Pharmacokinetics, Dynamics and Metabolism, Sandwich, Kent, UK.
Xenobiotica. 2012 Jan;42(1):94-106. doi: 10.3109/00498254.2011.627477. Epub 2011 Oct 30.
Early prediction of human pharmacokinetics (PK) and drug-drug interactions (DDI) in drug discovery and development allows for more informed decision making. Physiologically based pharmacokinetic (PBPK) modelling can be used to answer a number of questions throughout the process of drug discovery and development and is thus becoming a very popular tool. PBPK models provide the opportunity to integrate key input parameters from different sources to not only estimate PK parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. Using examples from the literature and our own company, we have shown how PBPK techniques can be utilized through the stages of drug discovery and development to increase efficiency, reduce the need for animal studies, replace clinical trials and to increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however, some limitations need to be addressed to realize its application and utility more broadly.
在药物研发过程中，对人体药代动力学（PK）和药物-药物相互作用（DDI）进行早期预测有助于做出更明智的决策。基于生理的药代动力学（PBPK）模型可用于回答药物研发过程中的诸多问题，因此正成为一种非常流行的工具。PBPK模型提供了整合来自不同来源的关键输入参数的机会，不仅可以估计PK参数和血浆浓度-时间曲线，还能深入了解化合物的性质。通过引用文献和我们公司的实例，我们展示了如何在药物研发的各个阶段利用PBPK技术来提高效率、减少动物研究需求、替代临床试验并增进对PK的理解。鉴于这些模型的机制特性，PBPK模型在药物研发中的未来应用前景广阔，然而，要更广泛地实现其应用和效用，还需要解决一些局限性。
