Causal Network Models of SARS-CoV-2 Expression and Aging to Identify Candidates for Drug Repurposing

TL;DR

This study develops a causal network model integrating multi-omics data to identify potential drug targets for COVID-19, emphasizing age-related factors and highlighting kinases as promising candidates for drug repurposing.

Contribution

It introduces a novel causal framework combining transcriptomic, proteomic, and structural data for drug discovery in SARS-CoV-2, incorporating aging signatures.

Findings

Kinases are key targets intersecting SARS-CoV-2 and aging pathways.

The platform identifies candidate drugs for repurposing based on multi-modal data.

Serine/threonine and tyrosine kinases are potential therapeutic targets.

Abstract

Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs.