Systems Modelling and Simulation Group, Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide R&D, 35 Cambridgepark Drive, Cambridge, MA 02140, USA.
AAPS J. 2013 Apr;15(2):377-87. doi: 10.1208/s12248-012-9446-2. Epub 2012 Dec 27.
Physiologically based pharmacokinetic (PBPK) models are built using differential equations to describe the physiology/anatomy of different biological systems. Readily available in vitro and in vivo preclinical data can be incorporated into these models to not only estimate pharmacokinetic (PK) parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. They provide a mechanistic framework to understand and extrapolate PK and dose across in vitro and in vivo systems and across different species, populations and disease states. Using small molecule and large molecule examples from the literature and our own company, we have shown how PBPK techniques can be utilised for human PK and dose prediction. Such approaches have the potential to increase efficiency, reduce the need for animal studies, replace clinical trials and 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 realise its application and utility more broadly.
生理药代动力学（PBPK）模型是使用微分方程构建的，用于描述不同生物系统的生理学/解剖学。这些模型可以整合易于获得的体外和体内临床前数据，不仅可以估计药代动力学（PK）参数和血浆浓度-时间曲线，还可以深入了解化合物的性质。它们提供了一个机制框架，用于理解和推断体外和体内系统以及不同物种、人群和疾病状态下的 PK 和剂量。我们使用来自文献和我们自己公司的小分子和大分子的例子，展示了如何利用 PBPK 技术进行人体 PK 和剂量预测。这些方法有可能提高效率、减少对动物研究的需求、替代临床试验并增加对 PK 的理解。鉴于这些模型的机制性质，PBPK 建模在药物发现和开发中的未来应用前景广阔，但需要解决一些限制因素，才能更广泛地实现其应用和实用性。
