In silico approaches in carcinogenicity hazard assessment: Current status and future needs

Raymond R. Tice, Arianna Bassan, Alexander Amberg, Lennart T. Anger, Marc A. Beal, Phillip Bellion, Romualdo Benigni, Jeffrey Birmingham, Alessandro Brigo, Frank Bringezu, Lidia Ceriani, Ian Crooks, Kevin Cross, Rosalie Elespuru, David M. Faulkner, Marie C. Fortin, Paul Fowler, Markus Frericks, Helga H.J. Gerets, Gloria D. JahnkeDavid R. Jones, Naomi L. Kruhlak, Elena Lo Piparo, Juan Lopez-Belmonte, Amarjit Luniwal, Alice Luu, Federica Madia, Serena Manganelli, Balasubramanian Manickam, Jordi Mestres, Amy L. Mihalchik-Burhans, Louise Neilson, Arun Pandiri, Manuela Pavan, Cynthia V. Rider, John P. Rooney, Alejandra Trejo-Martin, Karen H. Watanabe-Sailor, Angela T. White, David Woolley, Glenn J. Myatt

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Article number100191
JournalComputational Toxicology
Volume20
DOIs
StatePublished - Nov 2021

Keywords

  • (Q)SAR
  • Cancer
  • Carcinogenesis
  • Computational toxicology
  • Expert alerts
  • Hazard Identification
  • In Silico
  • Key characteristics
  • Read-across
  • Risk assessment

ASJC Scopus subject areas

  • Toxicology
  • Computer Science Applications
  • Health, Toxicology and Mutagenesis

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