Thomas B. Knudsen , Richard M. Spencer , Jocylin D. Pierro , Nancy C. Baker
{"title":"Computational biology and in silico toxicodynamics","authors":"Thomas B. Knudsen , Richard M. Spencer , Jocylin D. Pierro , Nancy C. Baker","doi":"10.1016/j.cotox.2020.11.001","DOIUrl":null,"url":null,"abstract":"<div><p>New approach methodologies (NAMs) refer to any non-animal technology, methodology, approach, or combination thereof that can be used to provide information on chemical hazard and risk assessment that avoids the use of intact animals. A spectrum of <em>in silico</em><span> models is needed for the integrated analysis of various domains in toxicology to improve predictivity and reduce animal testing. This review focuses on </span><em>in silico</em><span> approaches, computer models, and computational intelligence for developmental and reproductive toxicity<span> (predictive DART), providing a means to measure toxicodynamics in simulated systems for quantitative prediction of adverse outcomes phenotypes.</span></span></p></div>","PeriodicalId":93968,"journal":{"name":"Current opinion in toxicology","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.cotox.2020.11.001","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current opinion in toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S246820202030067X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
New approach methodologies (NAMs) refer to any non-animal technology, methodology, approach, or combination thereof that can be used to provide information on chemical hazard and risk assessment that avoids the use of intact animals. A spectrum of in silico models is needed for the integrated analysis of various domains in toxicology to improve predictivity and reduce animal testing. This review focuses on in silico approaches, computer models, and computational intelligence for developmental and reproductive toxicity (predictive DART), providing a means to measure toxicodynamics in simulated systems for quantitative prediction of adverse outcomes phenotypes.