Network-based prediction of drug combinations

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.

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

“…This methodology allows to identify several candidate repurposable drugs for Ebola virus 11,14 . 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. For example, network-based drug-disease proximity sheds light on the relationship between drugs (e.g., drug targets) and disease modules (molecular determinants in disease pathobiology modules within the PPIs), and can serve as a useful tool for efficient screening of potentially new indications for approved drugs, as well as drug combinations, as demonstrated in our recent studies 13,23,27,28 .…”

Network-based Drug Repurposing for Human Coronavirus

“…Preclinical studies are warranted to evaluate in vivo efficiency and side effects before clinical trials. Furthermore, we only limited to predict pairwise drug combinations based on our previous network-based framework [25] .…”

“…The basis for the proposed network-based drug repurposing methodologies rests on the notions that the proteins that associate with and functionally govern a viral infection are localized in the corresponding subnetwork ( Figure 1A) within the comprehensive human interactome network. For a drug with multiple targets to be effective against an HCoV, its target proteins should be within or in the immediate vicinity of the corresponding subnetwork in the human interactome (Figure 1), as we demonstrated in multiple diseases [12,21,22,25] using this drug repurposing strategy. We used a state-ofthe-art network proximity measure to quantify the relationship between HCoV-specific subnetwork ( Figure 3A) and drug targets in the human protein-protein interactome.…”

“…Our key methodology is that a drug combination is therapeutically effective only if it follows a specific relationship to the disease module, as captured by Complementary Exposure patterns in targets' modules of both drugs without overlapping toxic mechanisms. [25]…”

Machine Learning for Cancer Drug Combination

“…Besides, Cheng et al . proposed a network‐based approach to identify drug combinations ( Figure d ). They used the bootstrapping algorithm, z‐score, and separation measurement to analyze the distance between drug targets and disease proteins.…”