A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization

Bruce Schultz,Holger Fröhlich,Mark Strivens,Carsten Claussen,Lauren Nicole Delong,Christian Ebeling,Philip Gribbon ,Daniel Domingo‐Fernándéz ,Vanessa Lage-Rupprecht,Manuel C Peitsch ,Sandra Ciesek,Andrea Zaliani,Roland Eils,Steve Gardner,Denisa Bojkova,Shounak Baksi,David Van Dijk,Neal G Ravindra,Yojana Gadiya,Jindrich Cinatl,Söeren Lukassen ,Gerd Geißlinger ,Marc Jacobs,Alpha Tom Kodamullil,Reagon Karki,Manuel Lentzen,Sayoni Das,Julia Hoeng,Tamara Raschka,Jeanette Reinshagen,Martin Hofmann‐Apitius

doi:10.1038/s41598-021-90296-2

Abstract

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community’s massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

Highlights

The SARS-CoV-2 pandemic has challenged researchers at a global scale
Of the 47 drug combinations we were able to check within the COVID-19 PHARMACOME, we found that the pairs of drugs known to have a synergistic effect in the treatment of SARS-CoV-2 had an average shortest path length of 2.43, while antagonistic combinations were found to be farther apart with an average shortest path length of 4.0 (Supplementary Table 7)
Our experiments revealed that the HIV-Protease inhibitor Nelfinavir, which already appeared to be active against viral post-entry fusion steps of both SARS-CoV43 and SARS-CoV-244, displayed synergistic effects when combined with high concentrations of Raloxifene

Summary

Introduction

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community’s massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. In the field of computational biology, several initiatives have started generating disease maps that represent the current knowledge pertaining to COVID-19 m echanisms[8,9,10,11]. With the rapidly increasing generation of data (e.g. transcriptome17, interactome[18], and proteome[19] data), we are in the position to challenge and validate these COVID-19 pathophysiology knowledge graphs with experimental data. This is of particular interest as validation of these knowledge graphs bears the potential to identify those disease mechanisms that are highly relevant for targeting in drug repurposing approaches

Methods

Results

Conclusion