In silico resources to assist in the development and evaluation of physiologically-based kinetic models

被引:37
|
作者
Madden J.C. [1 ]
Pawar G. [1 ,2 ]
Cronin M.T.D. [1 ]
Webb S. [3 ]
Tan Y.-M. [4 ]
Paini A. [5 ]
机构
[1] School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool
[2] Pharmacy and Therapeutics Section, School of Clinical and Experimental Medicine, Medical School Building, University of Birmingham, Edgbaston
[3] Department of Applied Mathematics, Liverpool John Moores University, Byrom Street, Liverpool
[4] U.S. Environmental Protection Agency, Office of Pesticide Programs, Health Effects Division, 109 TW Alexander Dr, Research Triangle Park, 27709, NC
[5] European Commission Joint Research Centre (JRC), Ispra, VA
来源
Computational Toxicology | 2019年 / 11卷
关键词
ADME prediction; In silico tools; PBK; PBPK; PBTK;
D O I
10.1016/j.comtox.2019.03.001
中图分类号
学科分类号
摘要
Since their inception in pharmaceutical applications, physiologically-based kinetic (PBK) models are increasingly being used across a range of sectors, such as safety assessment of cosmetics, food additives, consumer goods, pesticides and other chemicals. Such models can be used to construct organ-level concentration-time profiles of xenobiotics. These models are essential in determining the overall internal exposure to a chemical and hence its ability to elicit a biological response. There are a multitude of in silico resources available to assist in the construction and evaluation of PBK models. An overview of these resources is presented herein, encompassing all attributes required for PBK modelling. These include predictive tools and databases for physico-chemical properties and absorption, distribution, metabolism and elimination (ADME) related properties. Data sources for existing PBK models, bespoke PBK software and generic software that can assist in model development are also identified. On-going efforts to harmonise approaches to PBK model construction, evaluation and reporting that would help increase the uptake and acceptance of these models are also discussed. © 2019 Elsevier B.V.
引用
收藏
页码:33 / 49
页数:16
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