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

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.

Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases

“…To identify the shared molecular mechanisms between COVID-19, AD, and PD, we leveraged several resources listed in Supplementary Table 3. These were combined into two independent Knowledge Graphs (KGs) following the harmonization procedure described in our previous work [52] and [15] . By doing so, we combined disease specific molecular interactions pertaining to COVID-19 and two neurological indications (i.e., AD and PD) into graph structures: one for COVID-19 and one for AD and PD.…”

A hybrid approach unveils drug repurposing candidates targeting an Alzheimer pathophysiology mechanism

“…Our strategy is generalizable and applicable to a wide variety of conditions as we have shown in Schultz et al., 60 where we used a similar drug target mechanism graph (COVID-19 PHARMACOME) to predict the synergistic effect of specific drug pairs for combination therapy of SARS-CoV-2 infections. Our method is also flexible in terms of the entry point: from an in silico perspective, the context-enriched, qualitative biomedical knowledge graph is used to mine data related to drugs or targets to generate mechanistic hypotheses that can be validated through experimental testing.…”

Ethical considerations for precision psychiatry: A roadmap for research and clinical practice

“…genes, proteins), clinical outcomes or normative data and their causal relationships ( Slater, 2014 ). Although multimodal models have been used to support putative disease mechanisms via brain simulation ( Stefanovski et al, 2021 ;Triebkorn et al, 2021 ), decision support based on patient 'avatars' ( Emon et al, 2020 ;Khatami et al, 2020 ;Schultz et al, 2021 ), drug repurposing (Lars V. Kessing et al, 2019 ; L.V. Kessing et al, 2019 ) or the development of novel modes of action ( Rivas-Barragan et al, 2020 ), they are not yet clinically validated for implementation in precision psychiatry.…”