Optimal COVID-19 therapeutic candidate discovery using the CANDO platform

Abstract

The worldwide outbreak of SARS-CoV-2 in early 2020 caused numerous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability of our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.

Effective holistic characterization of small molecule effects using heterogeneous biological networks

“…Here we describe the use of the Computational Analysis of Novel Drug Opportunities (CANDO) platform for both drug indication as well as ADR prediction. CANDO is a shotgun multiscale drug repurposing, discovery, and design platform whose fundamental tenet or paradigm is to assess the biological or therapeutic potential of small molecule chemical compounds based on their interactions to higher scale entities such as proteins, proteomes, and pathways ( Minie et al, 2014 ; Sethi et al, 2015 ; Chopra et al, 2016 ; Chopra and Samudrala, 2016 ; Falls et al, 2019 ; Mangione and Samudrala, 2019 ; Schuler and Samudrala, 2019 ; Mangione et al, 2020a ; Mangione et al, 2020b ; Hudson and Samudrala, 2021 ; Overhoff et al, 2021 ; Schuler et al, 2021 ; Falls et al, 2022 ; Mammen et al, 2022 ; Mangione et al, 2022 ; Moukheiber et al, 2022 ; Bruggemann et al, 2023 ). Our hypotheses are that compound behavior is describable in terms of their interaction signatures, which are real value vectors representing interactions between a given compound and a library of proteins, pathways, cells, etc.…”

Drug repurposing: databases and pipelines

“…In total, 51 of the 276 molecules predicted by this platform against SARS-CoV-2 are explored in clinical studies and have demonstrated promising activity. 47 KsRepo 48 (https://github.com/adam-sam-brown/ksRepo) is an R-based open-source expression-level platform for drug repurposing that identifies potential candidates enrichment scores. KsRepo analyses and compares RNA-seq data to known gene-drug interactions and also exhibits flexibility in data set types and can also predict drug candidates with limited information on drug-gene interactions.…”

“…In CANDO drug–protein interaction signatures are generated from an extensive library of known interaction mappings and compared and screened based on their similarities to the drugs used for the same disease. In total, 51 of the 276 molecules predicted by this platform against SARS-CoV-2 are explored in clinical studies and have demonstrated promising activity 47 …”