Network-based prediction of drug combinations

By Feixiong Cheng, István A. Kovács, Albert-László Barabási. Nature Communications, 2019

699 citations recorded by the source corpus.

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. Combination therapy holds great promise, but discovery remains challenging. Here, the authors propose a method to identify efficacious drug combinations for specific diseases, and find that successful combinations tend to target separate neighbourhoods of the disease module in the human interactome.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/4f9f2cf6343976371f93a4e0ef0ad33b783e1868

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Citations in the corpus

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2 (2020, methodology): “Our work over the last decade has demonstrated how network strategies can, for example, be used to identify effective repurposable drugs 13, [22] [23] [24] [25] [26] [27] and drug combinations 28 for multiple human diseases.”
Graph Representation Learning in Biomedicine and Healthcare (2021, positive): “Graph theoretic techniques have fuelled many discoveries, from uncovering relationships between diseases 43–46 to repurposing drugs 6,47,48 .”