In Silico Prediction of Chemically Induced Mutagenicity: A Weight of Evidence Approach Integrating Information from QSAR Models and Read-Across Predictions

Enrico Mombelli (1) , Giuseppa Raitano (2) , Emilio Benfenati (2)

1 INERIS - Institut National de l'Environnement Industriel et des Risques
2 IRCCS - Istituto di Ricerche Farmacologiche "Mario Negri" [Milan, Italy]

Information on genotoxicity is an essential piece of information in the framework of several regulations aimed at evaluating chemical toxicity. In this context, QSAR models that can predict Ames genotoxicity can conveniently provide relevant information. Indeed, they can be straightforwardly and rapidly used for predicting the presence or absence of genotoxic hazards associated with the interactions of chemicals with DNA. Nevertheless, and despite their ease of use, the main interpretative challenge is related to a critical assessment of the information that can be gathered, thanks to these tools. This chapter provides guidance on how to use freely available QSAR and read-across tools provided by VEGA HUB and on how to interpret their predictions according to a weight-of-evidence approach.

Domains: Life Sciences [q-bio]; Toxicology

Enrico Mombelli, Giuseppa Raitano, Emilio Benfenati. In Silico Prediction of Chemically Induced Mutagenicity: A Weight of Evidence Approach Integrating Information from QSAR Models and Read-Across Predictions. Benfenati, Emilio. In Silico Methods for Predicting Drug Toxicity, 2425, Springer, pp.149-183, 2022, Methods in Molecular Biology, ⟨10.1007/978-1-0716-1960-5_7⟩. ⟨ineris-03889594⟩