From random to predictive: a context-specific interaction framework improves selection of drug protein-protein interactions for unknown drug pathways

Abstract

With high drug attrition, interaction network methods are increasingly attractive as quick and inexpensive methods for prediction of drug safety and efficacy effects when a drug pathway is unknown. However, these methods suffer from high false positive rates for selecting drug phenotypic effects, their performance is often no better than random (AUROC ~0.5), and this limits the use of network methods in regulatory and industrial decision making. In contrast to many network engineering approaches that apply mathematical thresholds to discover phenotype associations, we hypothesized that interaction networks associated with true positive drug phenotypes are context specific. We tested this hypothesis on 16 designated medical event (DMEs) phenotypes which are a subset of adverse events that are of upmost concern to FDA review using a novel data set extracted from drug labels. We demonstrated that context-specific interactions (CSIs) distinguished true from false positive DMEs with an 50% improvement over non-context-specific approaches (AUROC 0.77 compared to 0.51). By reducing false positives, CSI analysis has the potential to advance network techniques to influence decision making in regulatory and industry settings. Author summaryDrugs bind proteins that interact with multiple downstream proteins and these protein networks are responsible for drug efficacy and safety. Protein interaction network methods predict drug effects aggregating information about proteins around drug-binding protein targets. However, many frameworks exist for identifying proteins relevant to a drugs effect. We consider three frameworks for selecting these proteins and show increased performance from a context-specific approach on selecting proteins relevant to severe drug side effects. The context-specific approach leverages the idea that the proteins responsible for a drug side effect are specific to each side-effect. By discovering the relevant proteins, we can better understand downstream effects of drugs and better anticipate drug side effects for new drugs in development. Further, we focus on designated medical events, a subset of the most severe drug side-effects that are high priority for regulatory review.