In some cases, drug combinations affect adverse outcome phenotypes by binding the same protein; however, drug-binding proteins are associated through proteinprotein interaction (PPI) networks within the cell, suggesting that drug phenotypes may result from long-range network effects. We first used PPI network analysis to classify drugs based on proteins downstream of their targets and next predicted drug combination effects where drugs shared network proteins but had distinct binding proteins (e.g., targets, enzymes, or transporters). By classifying drugs using their downstream proteins, we had an 80.7% sensitivity for predicting rare drug combination effects documented in gold-standard datasets. We further measured the effect of predicted drug combinations on adverse outcome phenotypes using novel observational studies in the electronic health record. We tested predictions for 60 network-drug classes on seven adverse outcomes and measured changes in clinical outcomes for predicted combinations. These results demonstrate a novel paradigm for anticipating drug synergistic effects using proteins downstream of drug targets.
Study Highlights
WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?Current knowledge of drug-drug interactions (DDIs) emphasize the drug target level by identifying shared transporters, enzymes, or pharmacodynamic targets, and do not prioritize proteins downstream of targets.
WHAT QUESTION DID THIS STUDY ADDRESS?Here, we sought to address if proteins downstream of drug targets were sufficient to predict DDIs; we used protein interaction network analysis and real-world evidence to predict and detect rare DDIs mediated by downstream proteins.
WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?These results provide evidence that downstream proteins are sufficient for anticipating drug-drug effects.