Predicting drug-induced liver injury: The importance of data curation

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.

The development and application of in silico models for drug induced liver injury

“…Drug-induced liver injury (DILI) is the term used for liver damage caused by drugs, herbal agents or nutritional supplements. 1 DILI has become one of the most important concerns in modern drug development as it is a leading cause of drugs failing clinical trials and being withdrawn from the market. 2 DILI is also an important issue in traditional Chinese medicines (TCMs), [3][4][5] which have been widely used in the ethnic Chinese population and have become increasingly popular in Western society.…”

Prediction and mechanistic analysis of Drug-Induced Liver Injury (DILI) based on chemical structure

“…1, S1, and S2) despite showing similar chemical similarity distribution to the training set as seen for the external test set (Fig. 2b) (9) which demonstrated that careful data curation can lead to improved performance, it should be noted that the sample size of the external test set and in particular the FDA validation set (49 compounds) were small. This makes it difficult to accurately evaluate model performance and accordingly also to confidently compare models (Fig.…”

“…Major challenges associated with the prediction of DILI using in vitro approaches lie in identifying relevant assays (5) and extrapolating from assay concentrations to in vivo blood concentrations associated with a hepatotoxic risk (6). Numerous in silico models have been generated based on molecular structure (7)(8)(9)(10)(11)(12) and in vitro readouts, such as bioactivity (13) and gene expression (14) in cell culture, which are able to predict DILI better than random, but with a…”

“…In the case of computational predictions, DILI is often simplified to a classification problem, i.e. separating compounds with or without this annotation in a data set (7)(8)(9)11,13). These labels, however, do not provide information on important factors such as dose-dependency or affected patient population, and consequently the practical applicability of such models is limited.…”

Improving the Safety Assessment of Chemicals and Drug Candidates by the Integration of Bioinformatics and Chemoinformatics Data

“…Data mining approaches to investigate underlying relationships or data extraction for the creation of in silico tools are only feasible if individual terms (e.g. specific histologies of liver findings) can be found and attributed to the harmonized expression .…”