Literature DB >> 17467128

TIMES-SS--a promising tool for the assessment of skin sensitization hazard. A characterization with respect to the OECD validation principles for (Q)SARs and an external evaluation for predictivity.

Grace Patlewicz1, Sabcho D Dimitrov, Lawrence K Low, Petra S Kern, Gergana D Dimitrova, Mike I H Comber, Aynur O Aptula, Richard D Phillips, Jay Niemelä, Charlotte Madsen, Eva B Wedebye, David W Roberts, Paul T Bailey, Ovanes G Mekenyan.   

Abstract

The TImes MEtabolism Simulator platform used for predicting Skin Sensitization (TIMES-SS) is a hybrid expert system that was developed at Bourgas University using funding and data from a Consortium comprising industry and regulators. The model was developed with the aim of minimizing animal testing and to be scientifically valid in accordance with the OECD principles for (Q)SAR validation. TIMES-SS encodes structure-toxicity and structure-skin metabolism relationships through a number of transformations, some of which are underpinned by mechanistic 3D QSARs. Here, we describe the extent to which the five OECD principles are met and in particular the results from an external evaluation exercise that was recently carried out. As part of this exercise, data were generated for 40 new chemicals in the murine local lymph node assay (LLNA) and then compared with predictions made by TIMES-SS. The results were promising with an overall good concordance (83%) between experimental and predicted values. Further evaluation of these results highlighted certain inconsistencies which were rationalized by a consideration of reaction chemistry principles for sensitization. Improvements for TIMES-SS were proposed where appropriate. TIMES-SS is a promising tool to aid in the evaluation of skin sensitization hazard under legislative programs such as REACH.

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Year:  2007        PMID: 17467128     DOI: 10.1016/j.yrtph.2007.03.003

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


  8 in total

1.  Fragment-based prediction of skin sensitization using recursive partitioning.

Authors:  Jing Lu; Mingyue Zheng; Yong Wang; Qiancheng Shen; Xiaomin Luo; Hualiang Jiang; Kaixian Chen
Journal:  J Comput Aided Mol Des       Date:  2011-09-20       Impact factor: 3.686

2.  Application of a combined aggregate exposure pathway and adverse outcome pathway (AEP-AOP) approach to inform a cumulative risk assessment: A case study with phthalates.

Authors:  Rebecca A Clewell; Jeremy A Leonard; Chantel I Nicolas; Jerry L Campbell; Miyoung Yoon; Alina Y Efremenko; Patrick D McMullen; Melvin E Andersen; Harvey J Clewell; Katherine A Phillips; Yu-Mei Tan
Journal:  Toxicol In Vitro       Date:  2020-04-08       Impact factor: 3.500

3.  An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential.

Authors:  J M Fitzpatrick; D W Roberts; G Patlewicz
Journal:  SAR QSAR Environ Res       Date:  2018-04-20       Impact factor: 3.000

Review 4.  In Silico Models for Skin Sensitization and Irritation.

Authors:  Gianluca Selvestrel; Federica Robino; Matteo Zanotti Russo
Journal:  Methods Mol Biol       Date:  2022

Review 5.  In Silico Models for Hepatotoxicity.

Authors:  Claire Ellison; Mark Hewitt; Katarzyna Przybylak
Journal:  Methods Mol Biol       Date:  2022

Review 6.  Non-animal methods to predict skin sensitization (II): an assessment of defined approaches *.

Authors:  Nicole C Kleinstreuer; Sebastian Hoffmann; Nathalie Alépée; David Allen; Takao Ashikaga; Warren Casey; Elodie Clouet; Magalie Cluzel; Bertrand Desprez; Nichola Gellatly; Carsten Göbel; Petra S Kern; Martina Klaric; Jochen Kühnl; Silvia Martinozzi-Teissier; Karsten Mewes; Masaaki Miyazawa; Judy Strickland; Erwin van Vliet; Qingda Zang; Dirk Petersohn
Journal:  Crit Rev Toxicol       Date:  2018-02-23       Impact factor: 5.635

7.  Analysis of publically available skin sensitization data from REACH registrations 2008-2014.

Authors:  Thomas Luechtefeld; Alexandra Maertens; Daniel P Russo; Costanza Rovida; Hao Zhu; Thomas Hartung
Journal:  ALTEX       Date:  2016-02-11       Impact factor: 6.043

8.  Interpretation of murine local lymph node assay (LLNA) data for skin sensitization: Overload effects, danger signals and chemistry-based read-across.

Authors:  David W Roberts
Journal:  Curr Res Toxicol       Date:  2021-01-21
  8 in total

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