Predicting drug-induced liver injury
- Author(s)
- Eleni Kotsampasakou, Floriane Montanari, Gerhard F Ecker
- Abstract
Drug-induced liver injury (DILI) is a major issue for both patients and pharmaceutical industry due to insufficient means of prevention/prediction. In the current work we present a 2-class classification model for DILI, generated with Random Forest and 2D molecular descriptors on a dataset of 966 compounds. In addition, predicted transporter inhibition profiles were also included into the models. The initially compiled dataset of 1773 compounds was reduced via a 2-step approach to 966 compounds, resulting in a significant increase (p-value<0.05) in model performance. The models have been validated via 10-fold cross-validation and against three external test sets of 921, 341 and 96 compounds, respectively. The final model showed an accuracy of 64% (AUC 68%) for 10-fold cross-validation (average of 50 iterations) and comparable values for two test sets (AUC 59%, 71% and 66%, respectively). In the study we also examined whether the predictions of our in-house transporter inhibition models for BSEP, BCRP, P-glycoprotein, and OATP1B1 and 1B3 contributed in improvement of the DILI mode. Finally, the model was implemented with open-source 2D RDKit descriptors in order to be provided to the community as a Python script.
- Organisation(s)
- Journal
- Toxicology
- Volume
- 389
- Pages
- 139-145
- No. of pages
- 7
- ISSN
- 0300-483X
- DOI
- https://doi.org/10.1016/j.tox.2017.06.003
- Publication date
- 08-2017
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 301207 Pharmaceutical chemistry, 301211 Toxicology
- Keywords
- ASJC Scopus subject areas
- Toxicology
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/predicting-druginduced-liver-injury(b67c4c8c-feb8-4fb0-8f37-9b351c7f7e98).html