Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

TL;DR

This study investigates how drug targets, especially those with side effects, influence the human interactome's perturbation spread, revealing that drug targets with side effects are more effective spreaders, which could inform safer drug development.

Contribution

It demonstrates that drug targets with side effects are more effective at spreading perturbations in the human interactome than those without side effects, providing insights into drug safety and network dynamics.

Findings

Drug targets are better spreaders than non-target proteins.

Targets of drugs with side effects are more effective spreaders than those without.

Differences in network distance between drug targets and disease proteins vary by disease.

Abstract

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.