In Silico Hazard Assessment of Ototoxicants Through Machine Learning and Computational Systems Biology.

ABSTRACT: Individuals across their lifespan may experience hearing loss from medications or chemicals, prompting concern about ototoxic environmental exposures. This study applies computational modeling as a screening-level hazard identification and chemical prioritization approach and is not intended to constitute a human health risk assessment or to estimate exposure- or dose-dependent ototoxic risk. We evaluated in silico drug-induced ototoxicity models on 80 environmental chemicals, excluding 4 with known ototoxicity, and analyzed 76 chemicals using fingerprinting, similarity assessment, and machine learning classification. We compared predicted environmental ototoxicants with ototoxic drugs, paired select polychlorinated biphenyls with the antineoplastic drug mitotane, and used PCB 177 as a cas

SUBMITTER: Luan S 

PROVIDER: S-EPMC12845763 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature