Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data

IEEE/ACM Trans Comput Biol Bioinform. Jul-Aug 2019;16(4):1231-1239. doi: 10.1109/TCBB.2018.2858756. Epub 2018 Jul 23.

Abstract

Drug-induced hepatotoxicity may cause acute and chronic liver disease, leading to great concern for patient safety. It is also one of the main reasons for drug withdrawal from the market. Toxicogenomics data has been widely used in hepatotoxicity prediction. In our study, we proposed a multi-dose computational model to predict the drug-induced hepatotoxicity based on gene expression and toxicity data. The dose/concentration information after drug treatment is fully utilized in our study based on the dose-response curve, thus a more informative representative of the dose-response relationship is considered. We also proposed a new feature selection method, named MEMO, which is also one important aspect of our multi-dose model in our study, to deal with the high-dimensional toxicogenomics data. We validated the proposed model using the TG-GATEs, which is a large database recording toxicogenomics data from multiple views. The experimental results show that the drug-induced hepatotoxicity can be predicted with high accuracy and efficiency using the proposed predictive model.

MeSH terms

  • Algorithms
  • Chemical and Drug Induced Liver Injury*
  • Computational Biology / instrumentation*
  • Computational Biology / methods
  • Computer Simulation
  • Databases, Factual*
  • Drug-Related Side Effects and Adverse Reactions*
  • Gene Expression
  • Humans
  • Liver / drug effects
  • Models, Statistical
  • Oligonucleotide Array Sequence Analysis
  • Reproducibility of Results
  • Support Vector Machine
  • Toxicogenetics / statistics & numerical data*