Computational models for the prediction of adverse cardiovascular drug reactions

Abstract

Abstract Background Predicting adverse drug reactions (ADRs) has become very important owing to the huge global health burden and failure of drugs. This indicates a need for prior prediction of probable ADRs in preclinical stages which can improve drug failures and reduce the time and cost of development thus providing efficient and safer therapeutic options for patients. Though several approaches have been put forward for in silico ADR prediction, there is still room for improvement. Methods In the present work, we have used machine learning based approach for cardiovascular (CV) ADRs prediction by integrating different features of drugs, biological (drug transporters, targets and enzymes), chemical (substructure fingerprints) and phenotypic (therapeutic indications and other identified ADRs), and their two and three level combinations. To recognize quality and important features, we used minimum redundancy maximum relevance approach while synthetic minority over-sampling technique balancing method was used to introduce a balance in the training sets. Results This is a rigorous and comprehensive study which involved the generation of a total of 504 computational models for 36 CV ADRs using two state-of-the-art machine-learning algorithms: random forest and sequential minimization optimization. All the models had an accuracy of around 90% and the biological and chemical features models were more informative as compared to the models generated using chemical features. Conclusions The results obtained demonstrated that the predictive models generated in the present study were highly accurate, and the phenotypic information of the drugs played the most important role in drug ADRs prediction. Furthermore, the results also showed that using the proposed method, different drugs properties can be combined to build computational predictive models which can effectively predict potential ADRs during early stages of drug development.

Keywords

• Adverse drug reactions
• Machine learning
• Random forest
• Sequential minimization optimization
• Feature selection