Open AccessA compound attributes-based predictive model for drug induced liver injury in humans
Author(s) -
Yang Liu,
Hua Gao,
Yudong He
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0231252
Subject(s) - adverse event reporting system , drug , in silico , benchmark (surveying) , liver injury , support vector machine , adverse outcome pathway , computer science , medicine , adverse effect , food and drug administration , drug development , pharmacology , data mining , machine learning , computational biology , biology , biochemistry , geodesy , gene , geography
Drug induced liver injury (DILI) is one of the key safety concerns in drug development. To assess the likelihood of drug candidates with potential adverse reactions of liver, we propose a compound attributes-based approach to predicting hepatobiliary disorders that are routinely reported to US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Specifically, we developed a support vector machine (SVM) model with recursive feature extraction, based on physicochemical and structural properties of compounds as model input. Cross validation demonstrates that the predictive model has a robust performance with averaged 70% of both sensitivity and specificity over 500 trials. An independent validation was performed on public benchmark drugs and the results suggest potential utility of our model for identifying safety alerts. This in silico approach, upon further validation, would ultimately be implemented, together with other in vitro safety assays, for screening compounds early in drug development.