Abstract
Drug-induced liver injury (DILI) is one of the most cited reasons for the high drug attrition rate and drug withdrawal from the market. The accumulated large amount of high throughput transcriptomic profiles and advances in deep learning provide an unprecedented opportunity to improve the suboptimal performance of DILI prediction. In this study, we developed an eight-layer Deep Neural Network (DNN) model for DILI prediction using transcriptomic profiles of human cell lines (LINCS L1000 dataset) with the current largest binary DILI annotation data [i.e., DILI severity and toxicity (DILIst)]. The developed models were evaluated by Monte Carlo cross-validation (MCCV), permutation test, and an independent validation (IV) set. The developed DNN model achieved the area under the receiver operating characteristic curve (AUC) of 0.802 and 0.798, and balanced accuracy of 0.741 and 0.721 for training and an IV set, respectively, outperforming the conventional machine learning algorithms, including K-nearest neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). Moreover, the developed DNN model provided a more balanced sensitivity of 0.839 and specificity of 0.603. Besides, we found the developed DNN model had a superior predictive performance for oncology drugs. Also, the functional and network analysis of genes driving the predictions revealed their relevance to the underlying mechanisms of DILI. The proposed DNN model could be a promising tool for early detection of DILI potential in the pre-clinical setting.
Highlights
Drug-induced liver injury (DILI) has been recognized as a significant cause of drug attrition, resulting in drug withdrawal from any stage of the drug development processes and post-marketing (Hoofnagle and Björnsson, 2019; Weaver et al, 2020)
Besides the clinical-driven approaches initialized by consortiums such as DILI Network (DILIN) (Fontana et al, 2009), considerable efforts have been made for enhancing DILI prediction in a pre-clinical setting (Walker et al, 2020)
We developed a Deep Neural Network (DNN) model for DILI prediction based on the largest binary DILI classification dataset–DILI severity and toxicity (DILIst) as well as the transcriptomic profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset
Summary
Drug-induced liver injury (DILI) has been recognized as a significant cause of drug attrition, resulting in drug withdrawal from any stage of the drug development processes and post-marketing (Hoofnagle and Björnsson, 2019; Weaver et al, 2020). Besides the clinical-driven approaches initialized by consortiums such as DILI Network (DILIN) (Fontana et al, 2009), considerable efforts have been made for enhancing DILI prediction in a pre-clinical setting (Walker et al, 2020). The model yielded the Matthews correlation coefficients (MCC) of 0.56∼0.89. It is still elusive of the model performance in human DILI endpoints, it is a beneficial attempt. The developed PTGS tool was applied for human DILI prediction with different DILI annotation datasets and achieved a sensitivity of 72–86% without a loss of specificity
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