Literature DB >> 23535119

Case study illustrating the WHO IPCS guidance on characterization and application of physiologically based pharmacokinetic models in risk assessment.

M E Bette Meek1, Hugh A Barton, Jos G Bessems, John C Lipscomb, Kannan Krishnan.   

Abstract

The World Health Organization (WHO) International Programme on Chemical Safety (IPCS) Guidance on Characterization and Application of Physiologically Based Pharmacokinetic Models in Risk Assessment (IPCS, 2010) describes key principles for risk assessors and model developers. In the WHO Guidance, a template for model documentation was developed and a case study included. Here the WHO Guidance, including the template, is summarized and an additional case study is presented to illustrate its application, based upon an existing risk assessment for 2-butoxyethanol (CAS NO. 111-76-2). The goal of the WHO Guidance and the current paper is to increase regulatory acceptance of complex biologically descriptive pharmacokinetic (or toxicokinetic) models, such as PBPK models, by facilitating communication and successful interaction between modelers and risk assessors.
Copyright © 2013 Elsevier Inc. All rights reserved.

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Year:  2013        PMID: 23535119     DOI: 10.1016/j.yrtph.2013.03.005

Source DB:  PubMed          Journal:  Regul Toxicol Pharmacol        ISSN: 0273-2300            Impact factor:   3.271


  8 in total

1.  Exploring Mechanistic Toxicity of Mixtures Using PBPK Modeling and Computational Systems Biology.

Authors:  Patricia Ruiz; Claude Emond; Evad D McLanahan; Shivanjali Joshi-Barr; Moiz Mumtaz
Journal:  Toxicol Sci       Date:  2020-03-01       Impact factor: 4.849

2.  Application of Physiologically Based Pharmacokinetic Modeling in Preclinical Studies: A Feasible Strategy to Practice the Principles of 3Rs.

Authors:  Yawen Yuan; Qingfeng He; Shunguo Zhang; Min Li; Zhijia Tang; Xiao Zhu; Zheng Jiao; Weimin Cai; Xiaoqiang Xiang
Journal:  Front Pharmacol       Date:  2022-05-12       Impact factor: 5.988

Review 3.  PBPK model reporting template for chemical risk assessment applications.

Authors:  Yu-Mei Tan; Melissa Chan; Amechi Chukwudebe; Jeanne Domoradzki; Jeffrey Fisher; C Eric Hack; Paul Hinderliter; Kota Hirasawa; Jeremy Leonard; Annie Lumen; Alicia Paini; Hua Qian; Patricia Ruiz; John Wambaugh; Fagen Zhang; Michelle Embry
Journal:  Regul Toxicol Pharmacol       Date:  2020-06-02       Impact factor: 3.271

4.  Physiologically based toxicokinetic modelling as a tool to support risk assessment: three case studies.

Authors:  Hans Mielke; Ursula Gundert-Remy
Journal:  J Toxicol       Date:  2012-05-09

5.  How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology.

Authors:  Clemens Wittwehr; Hristo Aladjov; Gerald Ankley; Hugh J Byrne; Joop de Knecht; Elmar Heinzle; Günter Klambauer; Brigitte Landesmann; Mirjam Luijten; Cameron MacKay; Gavin Maxwell; M E Bette Meek; Alicia Paini; Edward Perkins; Tomasz Sobanski; Dan Villeneuve; Katrina M Waters; Maurice Whelan
Journal:  Toxicol Sci       Date:  2016-12-19       Impact factor: 4.849

6.  Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits.

Authors:  Panteleimon D Mavroudis; Helen E Hermes; Donato Teutonico; Thomas G Preuss; Sebastian Schneckener
Journal:  PLoS One       Date:  2018-03-21       Impact factor: 3.240

7.  Towards a qAOP framework for predictive toxicology - Linking data to decisions.

Authors:  Alicia Paini; Ivana Campia; Mark T D Cronin; David Asturiol; Lidia Ceriani; Thomas E Exner; Wang Gao; Caroline Gomes; Johannes Kruisselbrink; Marvin Martens; M E Bette Meek; David Pamies; Julia Pletz; Stefan Scholz; Andreas Schüttler; Nicoleta Spînu; Daniel L Villeneuve; Clemens Wittwehr; Andrew Worth; Mirjam Luijten
Journal:  Comput Toxicol       Date:  2022-02

8.  A latent variable approach to account for correlated inputs in global sensitivity analysis.

Authors:  Nicola Melillo; Adam S Darwich
Journal:  J Pharmacokinet Pharmacodyn       Date:  2021-05-25       Impact factor: 2.745

  8 in total

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