| Literature DB >> 35368437 |
Raymond R Tice1, Arianna Bassan2, Alexander Amberg3, Lennart T Anger4, Marc A Beal5, Phillip Bellion6, Romualdo Benigni7, Jeffrey Birmingham8, Alessandro Brigo9, Frank Bringezu10, Lidia Ceriani11, Ian Crooks12, Kevin Cross13, Rosalie Elespuru14, David M Faulkner15, Marie C Fortin16, Paul Fowler17, Markus Frericks18, Helga H J Gerets19, Gloria D Jahnke20, David R Jones21, Naomi L Kruhlak22, Elena Lo Piparo23, Juan Lopez-Belmonte24, Amarjit Luniwal25, Alice Luu5, Federica Madia26, Serena Manganelli23, Balasubramanian Manickam27, Jordi Mestres28, Amy L Mihalchik-Burhans29, Louise Neilson30, Arun Pandiri20, Manuela Pavan2, Cynthia V Rider20, John P Rooney31, Alejandra Trejo-Martin32, Karen H Watanabe-Sailor33, Angela T White8, David Woolley34, Glenn J Myatt13.
Abstract
Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.Entities:
Keywords: (Q)SAR; Cancer; Carcinogenesis; Computational Toxicology; Expert Alerts; Hazard Identification; In Silico; Key Characteristics; Read-across; Risk Assessment
Year: 2021 PMID: 35368437 PMCID: PMC8967183 DOI: 10.1016/j.comtox.2021.100191
Source DB: PubMed Journal: Comput Toxicol ISSN: 2468-1113