Abstract
Adverse drug reactions (ADRs) are responsible for drug failure in clinical trials and affect life quality of patients. The identification of ADRs during the early phases of drug development is an important task. Therefore, predicting potential protein targets eliciting ADRs is essential for understanding the pathogenesis of ADRs. In this study, we proposed a computational algorithm,Integrated Network for Protein-ADR relations (INPADR), to infer potential protein-ADR relations based on an integrated network. First, the integrated network was constructed by connecting the protein-protein interaction network and the ADR similarity network using known protein-ADR relations. Then, candidate protein-ADR relations were further prioritized by performing a random walk with restart on this integrated network. Leave-one-out cross validation was used to evaluate the ability of the INPADR. An AUC of 0.8486 was obtained, which was a significant improvement compared to previous methods. We also applied the INPADR to two ADRs to evaluate its accuracy. The results suggested that the INPADR is capable of finding novel protein-ADR relations. This study provides new insight to our understanding of ADRs. The predicted ADR-related proteins will provide a reference for preclinical safety pharmacology studies and facilitate the identification of ADRs during the early phases of drug development.
Highlights
Adverse drug reactions (ADRs) are responsible for drug failure in clinical trials and affect life quality of patients
The identification of ADR-related proteins can be used to explain the molecular mechanisms of reported drug-ADR pairs and can be helpful for in vitro ADR assessment at an early phase of drug development
In this study, based on the hypothesis that similar ADRs are caused by proteins that interacted with each other in the protein-protein interactions (PPI) network, a computational predictor, Integrated Network for Protein-ADR relations (INPADR), was developed to identify the potential protein-ADR relations
Summary
Adverse drug reactions (ADRs) are responsible for drug failure in clinical trials and affect life quality of patients. Molecular docking methods cannot be applied when the 3D structures of the target proteins are unknown[8] These approaches have focused on relatively few ADRs. Later, Kuhn et al used known drug-protein and drug-ADR relations to identify systematically overrepresented protein-ADR pairs through the enrichment analysis[7]. Kuhn et al used known drug-protein and drug-ADR relations to identify systematically overrepresented protein-ADR pairs through the enrichment analysis[7] This method is dependent on the availability of drug-target interaction data. The goal of this study is to develop a new strategy for systematically predicting the relations between proteins and ADRs. Currently, many studies used similarity as a measure to investigate the relations between drugs, targets and ADRs9–12. An AUC of 0.8486 was obtained, which suggested that the INPADR is superior to previous methods and capable of predicting ADR-related proteins. This study provides a practical method to detect ADR-related proteins and will be valuable for ADR screening in clinical drug-discovery trials
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