Total controllability analysis discovers explainable drugs for Covid-19 treatment
Xinru Wei, Chunyu Pan, Xizhe Zhang, and Weixiong Zhang

TL;DR
This paper introduces a novel total controllability framework with control hubs for identifying explainable drug targets against Covid-19, leading to effective drug repurposing and insights into therapeutic mechanisms.
Contribution
It extends structural controllability theory to total controllability, develops an efficient algorithm for control hub detection, and applies it to identify druggable targets for Covid-19 treatment.
Findings
Identified 65 druggable control hubs related to Covid-19 pathways.
Found drugs targeting these hubs, including Fostamatinib, improve patient outcomes.
Revealed new potential drug targets not previously explored.
Abstract
Network medicine has been pursued for Covid-19 drug repurposing. One such approach adopts structural controllability, a theory for controlling a network (the cell). Motivated to protect the cell from viral infections, we extended this theory to total controllability and introduced a new concept of control hubs. Perturbation to any control hub renders the cell uncontrollable by exogenous stimuli, e.g., viral infections, so control hubs are ideal drug targets. We developed an efficient algorithm for finding all control hubs and applied it to the largest homogenous human protein-protein interaction network. Our new method outperforms several popular gene-selection methods, including that based on structural controllability. The final 65 druggable control hubs are enriched with functions of cell proliferation, regulation of apoptosis, and responses to cellular stress and nutrient levels,…
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Taxonomy
TopicsComputational Drug Discovery Methods · SARS-CoV-2 and COVID-19 Research · Bioinformatics and Genomic Networks
