Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury

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.

The Promise of AI for DILI Prediction

“…Li et al (2020a) and Li et al (2020b) both used the DILIst dataset (Thakkar et al, 2020) to build predictive models. However, Li et al (2020a) split the dataset according to the availability of transcriptomic profiles, while Li et al (2020b) split it according to the initial year when the FDA approved the drugs. The predictive performance results obtained on these different splits cannot be compared.…”

“…The authors claimed that the dataset used, published in the context of the CMap Drug Safety Challenge 2018, was not rich enough to build predictive models for DILI. Li et al (2020a) proposed a DILI prediction model consisting of a deep neural network which leveraged transcriptomic profiles of human cell lines. It outperformed other shallow ML methods, namely k-nearest neighbors, support vector machines and random forests.…”

“…Chierici et al (2020) utilized a subset of the CMap dataset of two specific cell lines, as provided in the CMap Drug Safety Challenge 2018, 7 although the authors found that these data were insufficient for DILI prediction. Li et al (2020a) proposed a DILI prediction model based on a deep neural network, which takes as input transcriptomic profiles of human cell lines derived from the LINCS L1000 dataset (Subramanian et al, 2017). Xu et al (2008) developed an in vitro testing strategy for DILI prediction based on features measured by high-content cellular imaging in primary human hepatocyte cultures.…”

Prediction of Alternative Drug-Induced Liver Injury Classifications Using Molecular Descriptors, Gene Expression Perturbation, and Toxicology Reports

“…Challenge organizers provided several alternative classifications of DILI based on two different classification schemes: DILI severity score and commercial status of the drug (Chen et al, 2016 ; Li et al, 2020 ). Additionally, two further DILI decisions were provided.…”

“…Some drugs can injure the liver, and in extreme cases therapy can be more dangerous than the disease for which they are prescribed. DILI accounts for approximately half of the cases of acute liver failure (Li et al, 2020 ). DILI has diverse symptoms—it mimics all forms of acute and chronic liver disease (Thakkar et al, 2019 ).…”