Machine-Learning Prediction of Oral Drug-Induced Liver Injury (DILI) via Multiple Features and Endpoints.

Xiaobin Liu,Zuquan Weng,Zhaofan Xia,Yi Zhong,Heng Luo,Danhua Zheng

doi:10.1155/2020/4795140

Xiaobin Liu, Zuquan Weng + Show 4 more

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https://doi.org/10.1155/2020/4795140

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Abstract

Drug discovery is a costly process which usually takes more than 10 years and billions of dollars for one successful drug to enter the market. Despite all the safety tests, drugs may still cause adverse reactions and be restricted in use or even withdrawn from the market. Drug-induced liver injury (DILI) is one of the major adverse drug reactions, and computational models may be used to predict and reduce it. To assess the computational prediction performance of DILI, we curated DILI endpoints from three databases and prepared drug features including chemical descriptors, therapeutic classifications, gene expressions, and binding proteins. We trained machine-learning models to predict the various DILI endpoints using different drug features. Using the optimal feature sets, the top-performing models obtained areas under the receiver operating characteristic curve (AUC) around 0.8 for some DILI endpoints. We found that some features, including therapeutic classifications and proteins, have good prediction performance towards DILI. We also discovered that the severity of DILI endpoints as well as the selection of negative samples may significantly affect the prediction results. Overall, our study provided a comprehensive collection, curation, and prediction of DILI endpoints using various drug features, which may help the drug researchers to better understand and prevent DILI during the drug discovery process.

Highlights

The drug discovery process is both time-consuming and costly
Since some endpoints have very few or zero positive samples during the independent test and produced abnormally high or zero AUC values, we focused our analysis based on the results of 10-fold cross-validations and provided the independent test results as additional references in Supplementary Tables 4-6
We found that the combination generally improved the model performance than using a single type of features, indicating the usefulness of combining various types of features (Figure 2 and Supplementary Figs. 1-5)

Summary

Introduction

The drug discovery process is both time-consuming and costly. It typically takes 10-17 years and costs $2.6 billion to develop a new drug [1, 2]. Even after a drug passes all the clinical trials and enters the market, it can still cause adverse drug reactions, which may result in restricted uses or even withdrawal [3, 4]. In the history of drug development, drug-induced liver injury (DILI) is one of the major factors to cause withdrawal of new drugs [5,6,7]. As an effort to reduce DILI, researchers have developed computational models to predict it [8, 9]. Since predicting DILI may help to improve drug safety and reduce loss, this field is attracting interests from both the academia and the pharmaceutical industry

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