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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679785
• 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.

可以非常迅速和大量（即高吞吐量）生成与遭受痛苦，分布代谢和极端相关的毒动力学不同过程的体外数据。目前，对使用此类数据的使用很热情，以定量预测异生物生物的体内毒性。尽管仍然存在一些障碍，但与将体外数据推送到体内情况有关的科学进步/突破，尤其是与体外数据预测肝炎清除率有关的情况。但是，仍然有很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还足以适合代谢和蛋白质结合位移水平上化学化学相互作用的体内外推（IVIVE）？他们可以用来预测人口清除的可变性吗？同样，定量预测体内毒性动物学的最大困难之一是膜渗透性较低的化合物的分布，即差异有限的组织分布。当它是组织摄取的速率限制因素时，根本没有任何方法可以预测组织中体内渗透性。因此，拟议的研究计划旨在提高我们使用PBPK建模从体外数据中预测体内组织剂量法和毒素的能力。该程序将分为两个特定目标：1）开发一种基于物理的方法，以使膜渗透性能够更好地预测扩散有限的组织摄取； 2）单独使用化合物和混合物来改善体内预测并估计种群变异性的混合物。从长远来看，我们的目标是拥有经过验证的PBPK模型框架，该框架将允许不同的有毒动力学决定剂，并增加对体外数据使用的信心，以预测体内毒理学。通过从体外和体内实验系统中获取清除率和渗透率数据，我们将得出机械关系，以增强和/或增强毒理学中的静脉曲张方法，从而提高预测毒理学的PBPK建模。该研究计划的成功将不可避免地减少我们在有毒动力学数据中使用活动物的需求，并提高我们从高肿瘤中可以产生的简单体外实验中预测体内毒性的能力。这将促进和加速对环境污染物以及药物开发的风险评估的决策。