Abstract
BackgroundPredicting 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.MethodsIn 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.ResultsThis 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.ConclusionsThe 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.
Highlights
Predicting adverse drug reactions (ADRs) has become very important owing to the huge global health burden and failure of drugs
Prediction of potential ADRs has become of extreme importance and several machine learning based methods have been proposed for the prediction of potential ADRs in pre-clinical stages using the chemical features of compounds, drug targets, enzymes, transporters and pathways and information on drug side effects and therapeutic indications
In the present study, a total of 504 machine-learning based classifier models were generated for 36 CV ADRs using chemical, biological, and phenotypic features and their two and three level combinations for 842 drugs
Summary
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 providing efficient and safer therapeutic options for patients. Liu et al [10] carried out large scale prediction of ADRs integrating several features of drugs that included drug targets and pathways, chemical properties of drugs, therapeutic indications, and data from other known ADRs. Huang et al [11] generated SVMs and logistic regression prediction models trained on the combination of drug targets, gene ontology annotations, and protein–protein interaction networks. Zhang et al [12] considered ADR prediction as a multi-label learning task and proposed a novel approach, ‘feature selectionbased multi-label k-nearest neighbor method’ that led to simultaneous predictions of relevant features, as well as the generation of highly accurate prediction models
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