{"title":"重新配置SkinSensPred在线工具,用于预测农药的皮肤致敏性。","authors":"Chia-Chi Wang, Shan-Shan Wang, Chun-Lin Liao, Wei-Ren Tsai, Chun-Wei Tung","doi":"10.1584/jpestics.D22-043","DOIUrl":null,"url":null,"abstract":"<p><p>Adverse outcome pathway (AOP)-based computational models provide state-of-the-art prediction for human skin sensitizers and are promising alternatives to animal testing. However, little is known about their applicability to pesticides due to scarce pesticide data for evaluation. Moreover, pesticides traditionally have been tested on animals without human data, making validation difficult. Direct application of AOP-based models to pesticides may be inappropriate since their original applicability domains were designed to maximize reliability for human response prediction on diverse chemicals but not pesticides. This study proposed to identify a consensus chemical space with concordant human responses predicted by the SkinSensPred online tool and animal testing data to reduce animal testing. The identified consensus chemical space for non-sensitizers achieved high concordance of 85% and 100% for the cross-validation and independent test, respectively. The reconfigured SkinSensPred can be applied as the first-tier tool for identifying non-sensitizers to reduce. animal testing for pesticides by 19.6%.</p>","PeriodicalId":16712,"journal":{"name":"Journal of Pesticide Science","volume":"47 4","pages":"184-189"},"PeriodicalIF":1.5000,"publicationDate":"2022-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/dc/72/jps-47-4-D22-043.PMC9716044.pdf","citationCount":"0","resultStr":"{\"title\":\"Reconfiguring the online tool of SkinSensPred for predicting skin sensitization of pesticides.\",\"authors\":\"Chia-Chi Wang, Shan-Shan Wang, Chun-Lin Liao, Wei-Ren Tsai, Chun-Wei Tung\",\"doi\":\"10.1584/jpestics.D22-043\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Adverse outcome pathway (AOP)-based computational models provide state-of-the-art prediction for human skin sensitizers and are promising alternatives to animal testing. However, little is known about their applicability to pesticides due to scarce pesticide data for evaluation. Moreover, pesticides traditionally have been tested on animals without human data, making validation difficult. Direct application of AOP-based models to pesticides may be inappropriate since their original applicability domains were designed to maximize reliability for human response prediction on diverse chemicals but not pesticides. This study proposed to identify a consensus chemical space with concordant human responses predicted by the SkinSensPred online tool and animal testing data to reduce animal testing. The identified consensus chemical space for non-sensitizers achieved high concordance of 85% and 100% for the cross-validation and independent test, respectively. The reconfigured SkinSensPred can be applied as the first-tier tool for identifying non-sensitizers to reduce. animal testing for pesticides by 19.6%.</p>\",\"PeriodicalId\":16712,\"journal\":{\"name\":\"Journal of Pesticide Science\",\"volume\":\"47 4\",\"pages\":\"184-189\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2022-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/dc/72/jps-47-4-D22-043.PMC9716044.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Pesticide Science\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1584/jpestics.D22-043\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENTOMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Pesticide Science","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1584/jpestics.D22-043","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENTOMOLOGY","Score":null,"Total":0}
Reconfiguring the online tool of SkinSensPred for predicting skin sensitization of pesticides.
Adverse outcome pathway (AOP)-based computational models provide state-of-the-art prediction for human skin sensitizers and are promising alternatives to animal testing. However, little is known about their applicability to pesticides due to scarce pesticide data for evaluation. Moreover, pesticides traditionally have been tested on animals without human data, making validation difficult. Direct application of AOP-based models to pesticides may be inappropriate since their original applicability domains were designed to maximize reliability for human response prediction on diverse chemicals but not pesticides. This study proposed to identify a consensus chemical space with concordant human responses predicted by the SkinSensPred online tool and animal testing data to reduce animal testing. The identified consensus chemical space for non-sensitizers achieved high concordance of 85% and 100% for the cross-validation and independent test, respectively. The reconfigured SkinSensPred can be applied as the first-tier tool for identifying non-sensitizers to reduce. animal testing for pesticides by 19.6%.
期刊介绍:
The Journal of Pesticide Science publishes the results of original research regarding the chemistry and biochemistry of pesticides including bio-based materials. It also covers their metabolism, toxicology, environmental fate and formulation.