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

被引:36
|
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
相关论文
共 50 条
  • [1] Development and application of physiologically-based kinetic (PBK) models
    Madden, Judith
    Tan, Yu-Mei
    Blaauboer, Bas
    Paini, Alicia
    Computational Toxicology, 2020, 13
  • [4] Physiologically-Based Kinetic (PBK) Models and Applications In Read-Across
    Madden, J.
    Thompson, C.
    Webb, S.
    Penson, P.
    Tan, C.
    Paini, A.
    TOXICOLOGY LETTERS, 2023, 384 : S32 - S33
  • [5] Fundamentals of physiologically-based toxicokinetic models
    Watanabe, KH
    ENDOCRINE DISRUPTERS AND CARCINOGENIC RISK ASSESSMENT, 2002, 340 : 271 - 280
  • [6] An introduction to physiologically-based pharmacokinetic models
    Upton, Richard N.
    Foster, David J. R.
    Abuhelwa, Ahmad Y.
    PEDIATRIC ANESTHESIA, 2016, 26 (11) : 1036 - 1046
  • [7] INCORPORATION OF TEMPORAL FACTORS INTO PHYSIOLOGICALLY-BASED KINETIC-MODELS FOR RISK ASSESSMENT
    OFLAHERTY, EJ
    POLAK, J
    ANDRIOT, MD
    INHALATION TOXICOLOGY, 1995, 7 (06) : 917 - 925
  • [8] Using read-across to build physiologically-based kinetic models: Part 1. Development of a KNIME workflow to assist analogue selection for PBK modelling
    V. Thompson, Courtney
    Webb, Steven D.
    Leedale, Joseph A.
    Penson, Peter E.
    Paini, Alicia
    Ebbrell, David
    Madden, Judith C.
    COMPUTATIONAL TOXICOLOGY, 2024, 29
  • [9] A Systematic Review of Published Physiologically-based Kinetic Models and an Assessment of their Chemical Space Coverage
    Thompson, Courtney, V
    Firman, James W.
    Goldsmith, Michael R.
    Grulke, Christopher M.
    Tan, Yu-Mei
    Paini, Alicia
    Penson, Peter E.
    Sayre, Risa R.
    Webb, Steven
    Madden, Judith C.
    ATLA-ALTERNATIVES TO LABORATORY ANIMALS, 2021, 49 (05): : 197 - 208
  • [10] Examination of Physiologically-Based Pharmacokinetic Models of Rosuvastatin
    Bowman, Christine M.
    Ma, Fang
    Mao, Jialin
    Chen, Yuan
    CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, 2021, 10 (01): : 5 - 17