Literature DB >> 14565764

Computer-aided knowledge generation for understanding skin sensitization mechanisms: the TOPS-MODE approach.

Ernesto Estrada1, Grace Patlewicz, Mark Chamberlain, David Basketter, Sue Larbey.   

Abstract

The TOPS-MODE (topological substructural molecular descriptors) approach is used to derive models for understanding the molecular structural contribution to skin sensitization. A data set of 93 compounds was used in the development of the models; 29 new skin sensitization values (EC3) are reported here for the first time. The models developed possess high predictivity and have been validated through the use of cross-validation and external validation sets. The models have enabled the formulation of potential new structural alerts far faster and using less data than typically required by traditional approaches. Structural contributions to skin sensitization for various classes of chemicals are presented on the basis of bond contributions. The models have also been able to identify potential structural alerts for chemicals requiring metabolic activation.

Mesh:

Year:  2003        PMID: 14565764     DOI: 10.1021/tx034093k

Source DB:  PubMed          Journal:  Chem Res Toxicol        ISSN: 0893-228X            Impact factor:   3.739


  10 in total

1.  True prediction of lowest observed adverse effect levels.

Authors:  R García-Domenech; J V de Julián-Ortiz; E Besalú
Journal:  Mol Divers       Date:  2006-05-24       Impact factor: 2.943

2.  Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds.

Authors:  Vinicius M Alves; Eugene Muratov; Denis Fourches; Judy Strickland; Nicole Kleinstreuer; Carolina H Andrade; Alexander Tropsha
Journal:  Toxicol Appl Pharmacol       Date:  2015-01-03       Impact factor: 4.219

3.  Prediction of skin sensitization potency using machine learning approaches.

Authors:  Qingda Zang; Michael Paris; David M Lehmann; Shannon Bell; Nicole Kleinstreuer; David Allen; Joanna Matheson; Abigail Jacobs; Warren Casey; Judy Strickland
Journal:  J Appl Toxicol       Date:  2017-01-10       Impact factor: 3.446

4.  Integrated decision strategies for skin sensitization hazard.

Authors:  Judy Strickland; Qingda Zang; Nicole Kleinstreuer; Michael Paris; David M Lehmann; Neepa Choksi; Joanna Matheson; Abigail Jacobs; Anna Lowit; David Allen; Warren Casey
Journal:  J Appl Toxicol       Date:  2016-02-06       Impact factor: 3.446

5.  Evaluating Confidence in Toxicity Assessments Based on Experimental Data and In Silico Predictions.

Authors:  Candice Johnson; Lennart T Anger; Romualdo Benigni; David Bower; Frank Bringezu; Kevin M Crofton; Mark T D Cronin; Kevin P Cross; Magdalena Dettwiler; Markus Frericks; Fjodor Melnikov; Scott Miller; David W Roberts; Diana Suarez-Rodriguez; Alessandra Roncaglioni; Elena Lo Piparo; Raymond R Tice; Craig Zwickl; Glenn J Myatt
Journal:  Comput Toxicol       Date:  2021-11-08

6.  Multivariate models for prediction of human skin sensitization hazard.

Authors:  Judy Strickland; Qingda Zang; Michael Paris; David M Lehmann; David Allen; Neepa Choksi; Joanna Matheson; Abigail Jacobs; Warren Casey; Nicole Kleinstreuer
Journal:  J Appl Toxicol       Date:  2016-08-02       Impact factor: 3.446

7.  Modeling skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol compounds with quantum mechanistic properties.

Authors:  Qin Ouyang; Lirong Wang; Ying Mu; Xiang-Qun Xie
Journal:  BMC Pharmacol Toxicol       Date:  2014-12-24       Impact factor: 2.483

8.  Prediction of skin sensitization with a particle swarm optimized support vector machine.

Authors:  Hua Yuan; Jianping Huang; Chenzhong Cao
Journal:  Int J Mol Sci       Date:  2009-07-17       Impact factor: 6.208

9.  QSAR models of human data can enrich or replace LLNA testing for human skin sensitization.

Authors:  Vinicius M Alves; Stephen J Capuzzi; Eugene Muratov; Rodolpho C Braga; Thomas Thornton; Denis Fourches; Judy Strickland; Nicole Kleinstreuer; Carolina H Andrade; Alexander Tropsha
Journal:  Green Chem       Date:  2016-10-06       Impact factor: 10.182

10.  Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms.

Authors:  Humberto González-Díaz; Lázaro G Pérez-Montoto; Florencio M Ubeira
Journal:  J Immunol Res       Date:  2014-01-12       Impact factor: 4.818

  10 in total

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