Network-based prediction of drug combinations

TLDR

Drug combinations enhance therapeutic efficacy and reduce toxicity, yet identifying effective pairs is hampered by combinatorial explosion from numerous drug and dosage combinations. The authors aim to develop a network-based method to pinpoint clinically efficacious drug combinations for specific diseases. They employ a network-based approach that quantifies relationships between drug targets and disease proteins within the human protein–protein interactome. The study identifies six distinct drug–drug–disease classes in the human protein–protein interactome, showing that only combinations where both drugs target the disease module in separate neighborhoods are therapeutically effective, enabling validation of antihypertensive pairs and demonstrating a general network approach for efficacious drug combinations.

Abstract

Abstract Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating multiple complex diseases. Yet, our ability to identify and validate effective combinations is limited by a combinatorial explosion, driven by both the large number of drug pairs as well as dosage combinations. Here we propose a network-based methodology to identify clinically efficacious drug combinations for specific diseases. By quantifying the network-based relationship between drug targets and disease proteins in the human protein–protein interactome, we show the existence of six distinct classes of drug–drug–disease combinations. Relying on approved drug combinations for hypertension and cancer, we find that only one of the six classes correlates with therapeutic effects: if the targets of the drugs both hit disease module, but target separate neighborhoods. This finding allows us to identify and validate antihypertensive combinations, offering a generic, powerful network methodology to identify efficacious combination therapies in drug development.