Abstract
MotivationDrug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI can bring a significant reduction in the cost of clinical trials. In this work we examined whether occurrence of DILI can be predicted using gene expression profile in cancer cell lines and chemical properties of drugs.MethodsWe used gene expression profiles from 13 human cell lines, as well as molecular properties of drugs to build Machine Learning models of DILI. To this end, we have used a robust cross-validated protocol based on feature selection and Random Forest algorithm. In this protocol we first identify the most informative variables and then use them to build predictive models. The models are first built using data from single cell lines, and chemical properties. Then they are integrated using Super Learner method with several underlying methods for integration. The entire modelling process is performed using nested cross-validation.ResultsWe have obtained weakly predictive ML models when using either molecular descriptors, or some individual cell lines (AUC ∈(0.55−0.61)). Models obtained with the Super Learner approach have a significantly improved accuracy (AUC=0.73), which allows to divide substances in two categories: low-risk and high-risk.
Highlights
Drug-induced liver toxicity is a common cause of liver injury
Hyperparameters selection and feature number fixing In most cases, our feature selection methods reported no relevant variables in gene expression data sets
In the case of molecular descriptors obtained from the Mordred, the number of relevant variables obtained for the entire data set is 127 when FDR level 0.1 was applied, see Figures 5 and 6 in the Additional File 1
Summary
Drug-induced liver toxicity is a common cause of liver injury. It accounts for approximately half of the cases of acute liver failure. What is more, it mimics all forms of acute and chronic liver disease. DILI often presents as acute hepatitis and/or cholestasis; virtually any clinicalpathological pattern of acute or chronic liver disease can occur. Each drug associated with hepatotoxicity tends to have a characteristic signature regarding latency and pattern of injury [1]. The mechanism can be arise due to drug metabolism or it can be related to the chemical properties of the drug molecule itself [2].
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