A network paradigm predicts drug synergistic effects using downstream protein–protein interactions

Abstract

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.

Identification of Molecular Markers Associated with Prostate Cancer Subtypes: An Integrative Bioinformatics Approach

“…Overall, they are involved in multiple significant connections supported by low enrichment p-values, which indicate that these proteins have more interactions than expected and are, thus, biologically connected (Figure S6). This result follows the therapeutic trend of selecting that, by targeting central proteins involved in many physical and functional connections, may have an impact on multiple pathways [72].…”

“…thus, biologically connected (Figure S6). This result follows the therapeutic trend of selecting compounds that, by targeting central proteins involved in many physical and functional connections, may have an impact on multiple pathways [72].…”

Preclinical side effect prediction through pathway engineering of protein interaction network models

“…In this study, we utilized a dataset, called the “drug toxicity dataset,” which comprises pairs of active ingredients and their corresponding side effects extracted from drug labels. 22 , 30 The dataset contained 1970 drugs and 34 side effects, emphasizing only severe drug‐induced pathway phenotypes that could affect a drug development program. The entire dataset is provided with the original publication 30 and with this study (see Data availability statement ).…”

“… 22 , 30 The dataset contained 1970 drugs and 34 side effects, emphasizing only severe drug‐induced pathway phenotypes that could affect a drug development program. The entire dataset is provided with the original publication 30 and with this study (see Data availability statement ). For the purpose of this study, we selected and highlighted a subset of side effects to demonstrate the effectiveness of our proposed approaches.…”