In vitro-in vivo extrapolations and physiologically-based pharmacokinetic modeling: Predicting permeability, clearance, metabolic interactions and interindivual variability from in vitro data

体外-体内外推和基于生理学的药代动力学模型：根据体外数据预测渗透性、清除率、代谢相互作用和个体间差异

• 批准号: RGPIN-2015-05577
• 负责人: Haddad, Sami
• 金额: $ 1.75万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=613633
• 关键词: vitro; vivo; extrapolations; physiologically; based

In vitro data of different processes of toxicokinetics related to absorption, distribution metabolism and excretion can be generated very rapidly and in large quantity (i.e., high throughput). Currently there is a lot of enthusiasm towards the use of such data towards quantitative predictions of the in vivo toxicokinetics of xenobiotics. Although there are still some hurdles, there have been some scientific advancements/breakthroughs related to extrapolating in vitro data to the in vivo situation especially related to the prediction of hepatic clearance from in vitro data. But there is still quite some room for improvement and validation. We still do not know how to adequately deal with chemicals binding to multiple plasma proteins that include albumin? How should we deal with these chemicals? Are these newly developed clearance models also adequate for in vitro-in vivo extrapolation (IVIVE) of chemical-chemical interactions at the level of metabolism and protein binding displacements? Can they be used to predict variability in clearance in the population. Also, one of the current greatest difficulties in qunatitatively predicting xenobiotic in vivo toxicokinetics is the distribution of compounds that have low membrane permeability, i.e., diffusion limited tissue distribution. There is simply no approach existing to predict the in vivo permeability in tissues when it is the rate limiting factor of tissue uptake. The proposed research program therefore to to increase our ability to predict in vivo tissue dosimetry and toxicokinetics from in vitro data using PBPK modeling. The program will be divided in 2 specific objectives: 1) to develop a physiologically based approach to enable IVIVE of membrane permeability for a better predictability of diffusion limited tissue uptake; and 2) to refine IVIVE clearance models for compounds alone and for mixtures to ameliorate in vivo predictions and estimate variability in a population. On the long-term, our goal is to have a validated PBPK model framework that will allow IVIVE of different toxicokinetic determinants and to increase confidence in the use of in vitro data for the prediction of in vivo toxicokinetics. By acquiring clearance and permeability data from in vitro and in vivo experimental systems, we will derive mechanistic relationship to enhance and/or enhance IVIVE approaches in toxicokinetics and hence refine PBPK modeling for predictive toxicology. The success of this research program will inevitably decrease our need to use live animals in toxicokinetic data and increase our ability to predict  in vivo toxicokinetics from simple in vitro experiments that can be generated in highthrouput. This will facilitate and accelerate decision making in risk assessment of environmental contaminants as well as in drug development.

与吸收、分布、代谢和排泄相关的不同毒代动力学过程的体外数据可以非常快速和大量地生成（即，高吞吐量）。目前，有很多的热情对使用这些数据对定量预测的外源性物质的体内毒代动力学。尽管仍存在一些障碍，但在将体外数据外推至体内情况方面已取得了一些科学进步/突破，尤其是在根据体外数据预测肝脏清除率方面。但仍有相当大的改进和验证空间。我们仍然不知道如何充分处理与包括白蛋白在内的多种血浆蛋白结合的化学物质？我们应该如何处理这些化学物质？这些新开发的清除模型是否也适用于代谢和蛋白结合置换水平的化学-化学相互作用的体外-体内外推（IVIVE）？它们能否用于预测人群清除率的变异性。此外，目前在定量预测异生物质体内毒代动力学中最大的困难之一是具有低膜渗透性的化合物的分布，即，扩散限制组织分布。当组织渗透性是组织吸收的速率限制因素时，根本没有方法可以预测组织的体内渗透性。因此，拟议的研究计划，以提高我们的能力，预测在体内组织剂量和毒代动力学的体外数据使用PBPK建模。该计划将分为2个具体目标：1）开发一种基于生理学的方法，使IVIVE具有膜渗透性，从而更好地预测扩散限制的组织摄取; 2）完善单独化合物和混合物的IVIVE清除模型，以改善体内预测并估计群体的变异性。从长远来看，我们的目标是建立一个经过验证的PBPK模型框架，使IVIVE能够分析不同的毒代动力学决定因素，并增加使用体外数据预测体内毒代动力学的信心。通过从体外和体内实验系统中获得清除率和渗透性数据，我们将推导出增强和/或增强IVIVE方法在毒理学中的机制关系，从而改进PBPK模型用于预测毒理学。这项研究计划的成功将不可避免地减少我们在毒代动力学数据中使用活动物的需求，并提高我们从简单的体外实验中预测体内毒代动力学的能力，这些实验可以在高负荷下进行。这将促进和加速环境污染物风险评估以及药物开发方面的决策。