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.
Highlights
Combination therapies are widely used in the treatment of multiple complex diseases, from hypertension 4 to cancer[5] and infectious diseases[6,7]
We hypothesize that exploring the network-based relationship between two drugs and their targets, and the disease proteins in the disease module would help clarify the mechanism-of-action of effective drug combinations while minimizing adverse effects (Supplementary Fig. 1)
We find that the z-score cannot discriminate FDAapproved pairwise combinations or clinically reported adverse drug interactions from random drug pairs (Supplementary Fig. 3)
Summary
Combination therapies are widely used in the treatment of multiple complex diseases, from hypertension 4 to cancer[5] and infectious diseases[6,7]. Network-based approaches have already offered a promising framework to identify novel insights to accelerate drug discovery[13], helping us quantify both disease–disease[14] and drug–disease[15,16] relationships. These methodological advances have raised the possibility of moving beyond the “one-drug, one-target” paradigm and exploring the “multipledrugs, multiple-targets” possibilities offered by aiming at simultaneously modulating multiple disease proteins within the same disease module, while minimizing toxicity profiles[17,18,19,20,21]. We quantify the relationship between drug targets and disease proteins in the human protein–protein interactome, leading to a rational, network-based drug combination design strategy
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