Systems Modeling and Translational Biology, GlaxoSmithKline R&D, Ware, Hertfordshire, UK.
Department of Clinical Pharmacology, Array BioPharma, Boulder, CO, USA.
Clin Pharmacokinet. 2019 Jun;58(6):727-746. doi: 10.1007/s40262-019-00741-9.
Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug-drug interactions. However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-in-human pharmacokinetic predictions. A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-in-human physiologically based pharmacokinetic model-building strategy. Based on the experience of scientists from multiple companies participating in the GastroPlus™ User Group Steering Committee, new Absorption, Distribution, Metabolism and Excretion knowledge is integrated and decision trees proposed for each essential component of a first-in-human prediction. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.
基于生理学的药代动力学模型在制药行业中已经得到广泛应用,并被监管机构所接受,可用于预测药物相互作用。然而,基于生理学的药代动力学模型在解决更广泛的药物应用问题方面具有重要价值,并且随着其全面应用,预计将产生新的监管影响。例如,基于生理学的药代动力学模型已经在药物发现过程中常规用于体外到体内的转化和临床前物种的药代动力学建模,从而可以应用经过验证的模型进行人体首剂量药代动力学预测。在这一应用领域采用一致的跨行业策略将增强人们对该方法的信心,并促进进一步的学习。有鉴于此,本文旨在增强之前发表的人体首剂量基于生理学的药代动力学模型构建策略。基于来自多家公司的科学家在 GastroPlus™ 用户组指导委员会中的经验,本文整合了新的吸收、分布、代谢和排泄知识,并针对人体首剂量预测的每个基本组成部分提出了决策树。我们查阅了许多相关科学出版物,以确定新的发现,并强调需要解决的差距。最后,通过四个具有挑战性的化合物的行业案例研究,说明了和突出了该策略的关键组成部分。