Systematic Prediction of Pharmacodynamic Drug-Drug Interactions through Protein-Protein-Interaction Network

Identifying drug-drug interactions (DDIs) is a major challenge in drug development. Previous attempts have established formal approaches for pharmacokinetic (PK) DDIs, but there is not a feasible solution for pharmacodynamic (PD) DDIs because the endpoint is often a serious adverse event rather than a measurable change in drug concentration. Here, we developed a metric “S-score” that measures the strength of network connection between drug targets to predict PD DDIs. Utilizing known PD DDIs as golden standard positives (GSPs), we observed a significant correlation between S-score and the likelihood a PD DDI occurs. Our prediction was robust and surpassed existing methods as validated by two independent GSPs. Analysis of clinical side effect data suggested that the drugs having predicted DDIs have similar side effects. We further incorporated this clinical side effects evidence with S-score to increase the prediction specificity and sensitivity through a Bayesian probabilistic model. We have predicted 9,626 potential PD DDIs at the accuracy of 82% and the recall of 62%. Importantly, our algorithm provided opportunities for better understanding the potential molecular mechanisms or physiological effects underlying DDIs, as illustrated by the case studies. Author Summary: Drug-drug interaction (DDI) is an important problem in clinical practice. In this study, we developed a novel algorithm for systematically predicting pharmacodynamic (PD) DDIs through protein-protein-interaction (PPI) networks. We calculated a score to predict potential PD DDIs by integrating the information from drug-target associations, PPI network topology and cross-tissue gene expression correlations. The scoring system was validated by known PD DDIs and agreed with similarities in drug clinical side effects, which we further integrated to increase the prediction performance. Our approach not only outperformed previously published methods in predicting DDIs, but also provided opportunities for better understanding the potential molecular mechanisms or physiological consequences underlying DDIs.