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

Fitzpatrick, JM, Roberts, DW ORCID: 0000-0001-6112-5868 and Patlewicz, G ORCID: 0000-0003-3863-9689 (2018) An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential. SAR and QSAR in Environmental Research, 29 (6). pp. 439-468. ISSN 1062-936X

Preview

Text An evaluation of selected (Q)SARsexpert systems for predicting skin sensitisation potential.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview

Abstract

Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, have provided a strong incentive to develop non-animal alternatives, such as expert systems software. Here we selected three different types of expert systems: VEGA (statistical), Derek Nexus (knowledge-based) and TIMES-SS (hybrid), and evaluated their performance using two large sets of animal data: one set of 1249 substances from eChemportal and a second set of 515 substances from NICEATM. A model was considered successful at predicting skin sensitisation potential if it had at least the same balanced accuracy as the LLNA and the GPMT had in predicting the other outcomes, which ranged from 79% to 86%. We found that the highest balanced accuracy of any of the expert systems evaluated was 65% when making global predictions. For substances within the domain of TIMES-SS, however, balanced accuracies for the two datasets were found to be 79% and 82%. In those cases where a chemical was within the TIMES-SS domain, the TIMES-SS skin sensitisation hazard prediction had the same confidence as the result from LLNA or GPMT.

Item Type:	Article
Additional Information:	This is an Accepted Manuscript version of the following article, accepted for publication in SAR and QSAR in Environmental Research. Fitzpatrick, J. M., Roberts, D. W., & Patlewicz, G. (2018). An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential. SAR and QSAR in Environmental Research, 29(6), 439–468. https://doi.org/10.1080/1062936X.2018.1455223. It is deposited under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License ( Deed - Attribution-NonCommercial-NoDerivatives 4.0 International - Creative Commons ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way
Uncontrolled Keywords:	Science & Technology; Physical Sciences; Technology; Life Sciences & Biomedicine; Chemistry, Multidisciplinary; Computer Science, Interdisciplinary Applications; Environmental Sciences; Mathematical & Computational Biology; Toxicology; Chemistry; Computer Science; Environmental Sciences & Ecology; (Q)SAR; expert system; TIMES-SS; VEGA; Derek Nexus; skin sensitisation; ALLERGIC CONTACT-DERMATITIS; APPLICABILITY DOMAIN; POTENCY; REPRODUCIBILITY; CHEMISTRY; CHEMICALS; TOXICITY; MODELS; ASSAY; (Q)SAR; expert system; TIMES-SS; VEGA; Derek Nexus; skin sensitisation; Skin; Animals; Guinea Pigs; Mice; Dermatitis, Allergic Contact; Local Lymph Node Assay; Structure-Activity Relationship; Quantitative Structure-Activity Relationship; Animal Testing Alternatives; Expert Systems; (Q)SAR; Derek Nexus; TIMES-SS; VEGA; expert system; skin sensitisation; Animal Testing Alternatives; Animals; Dermatitis, Allergic Contact; Expert Systems; Guinea Pigs; Local Lymph Node Assay; Mice; Quantitative Structure-Activity Relationship; Skin; Structure-Activity Relationship; 31 Biological Sciences; 41 Environmental Sciences; 34 Chemical Sciences; Animal Testing Alternatives; Animals; Dermatitis, Allergic Contact; Expert Systems; Guinea Pigs; Local Lymph Node Assay; Mice; Quantitative Structure-Activity Relationship; Skin; Structure-Activity Relationship; 03 Chemical Sciences; 05 Environmental Sciences; 06 Biological Sciences; Medicinal & Biomolecular Chemistry; 31 Biological sciences; 34 Chemical sciences; 41 Environmental sciences
Subjects:	R Medicine > RL Dermatology R Medicine > RS Pharmacy and materia medica
Divisions:	Pharmacy and Biomolecular Sciences
Publisher:	Taylor & Francis Group
Date of acceptance:	17 March 2018
Date of first compliant Open Access:	6 February 2026
Date Deposited:	06 Feb 2026 10:33
Last Modified:	06 Feb 2026 10:33
DOI or ID number:	10.1080/1062936X.2018.1455223
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28067

View Item