Environmental Engineering Laboratory, Departament d'Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain.
Pere Virgili Health Research Institute (IISPV), Hospital Universitari Sant Joan de Reus, Universitat Rovira i Virgili, 43204 Reus, Catalonia, Spain.
Int J Environ Res Public Health. 2023 Feb 16;20(4):3473. doi: 10.3390/ijerph20043473.
Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.
生理药代动力学（PBPK）模型是一种机制工具，通常用于制药行业和环境健康风险评估。这些模型被监管机构认可，可用于预测外来物质的器官浓度-时间曲线、药代动力学和每日摄入量。将 PBPK 模型扩展到捕获敏感人群（如儿科、老年、孕妇、胎儿等）和患病人群（如肾功能不全、肝硬化等）是必要的。然而，目前的建模实践和现有模型还不够成熟，无法自信地预测这些人群的风险。临床医生、实验和模型科学家之间的多学科合作对于提高生理学和生物化学参数的计算对于整合知识和改进现有 PBPK 模型至关重要。需要特定的 PBPK 涵盖脑脊髓液和海马等隔室，以获得对外来物质在这些亚部分中分布的机制理解。PBPK 模型有助于构建定量不良结局途径 (qAOP)，用于多个终点，如发育神经毒性 (DNT)、肝毒性和心脏毒性。机器学习算法可以预测需要开发计算模型的物理化学参数，而这些模型缺乏实验数据。将机器学习与 PBPK 集成有可能彻底改变药物发现和开发以及环境风险的领域。总的来说，这篇综述试图总结最近在计算模型、qAOP 构建和使用机器学习来改进现有模型方面的进展，以及监管视角。对于希望在动力学建模领域建立职业生涯的毒理学家来说，这篇综述可以作为指南。
