Santos Laura G A, Jaiswal Swati, Chen Kuan-Fu, Jones Hannah M, Templeton Ian E
Simcyp Division, Certara UK, Ltd, Princeton, New Jersey.
Simcyp Division, Certara UK, Ltd, Princeton, New Jersey.
Drug Metab Dispos. 2025 Jan;53(1):100015. doi: 10.1124/dmd.122.001036. Epub 2024 Nov 22.
The utility of physiologically based pharmacokinetic (PBPK) models in support of drug development has been well documented. During the discovery stage, PBPK modeling has increasingly been applied for early risk assessment, prediction of human dose, toxicokinetic dose projection, and early formulation assessment. Previous review articles have proposed model-building and application strategies for PBPK-based first-in-human predictions with comprehensive descriptions of the individual components of PBPK models. This includes the generation of decision trees based on literature reviews to guide the application of PBPK models in the discovery setting. The goal of this minireview is to provide additional guidance on the real-world application of PBPK models in support of the discovery stage of drug development, to assist in decision making. We have illustrated our recommended approach through description of case examples where PBPK models have been successfully applied to aid in human pharmacokinetic projection, candidate selection, and prediction of drug interaction liability for parent and metabolite. Through these case studies, we have highlighted fundamental issues, including preverification in preclinical species, the application of empirical scalars in the prediction of in vivo clearance from in vitro systems, in silico prediction of permeability, and the exploration of aqueous and biorelevant solubility data to predict dissolution. In addition, current knowledge gaps have been highlighted and future directions proposed. SIGNIFICANCE STATEMENT: Through description of 3 case studies, this minireview highlights the fundamental principles of physiologically based pharmacokinetic application during drug discovery. These include preverification of the model in preclinical species, application of empirical scalars where necessary in the prediction of clearance, in silico prediction of permeability, and the exploration of aqueous and biorelevant solubility data to predict dissolution. In addition, current knowledge gaps have been highlighted and future directions proposed.
基于生理的药代动力学（PBPK）模型在支持药物研发方面的实用性已有充分记录。在发现阶段，PBPK建模越来越多地应用于早期风险评估、人体剂量预测、毒代动力学剂量推算以及早期制剂评估。先前的综述文章已经提出了基于PBPK的首次人体预测的模型构建和应用策略，并对PBPK模型的各个组成部分进行了全面描述。这包括基于文献综述生成决策树，以指导PBPK模型在发现阶段的应用。本小型综述的目的是为PBPK模型在支持药物研发发现阶段的实际应用提供额外指导，以协助决策。我们通过描述案例示例来说明我们推荐的方法，在这些案例中，PBPK模型已成功应用于辅助人体药代动力学预测、候选药物选择以及母体和代谢物药物相互作用可能性的预测。通过这些案例研究，我们强调了一些基本问题，包括临床前物种的预验证、在从体外系统预测体内清除率时经验标量的应用、渗透性的计算机模拟预测以及探索水性和生物相关溶解度数据以预测溶解。此外，还强调了当前的知识差距并提出了未来的方向。重要声明：通过描述3个案例研究，本小型综述突出了药物发现过程中基于生理的药代动力学应用的基本原理。这些原理包括在临床前物种中对模型进行预验证、在预测清除率时必要时应用经验标量、渗透性的计算机模拟预测以及探索水性和生物相关溶解度数据以预测溶解。此外，还强调了当前的知识差距并提出了未来的方向。
