A genetically based computational drug repurposing framework for rapid identification of candidate compounds: application to COVID-19
Georgios Voloudakis, Kyung Min Lee, James M Vicari, Wen Zhang, Daisy Hoagland, Sanan Venkatesh, Jiantao Bian, Marios Anyfantakis, Zhenyi Wu, Samir Rahman, Lina Gao, Kelly Cho, Jennifer S Lee, Sudha K Iyengar, Shiuh-Wen Luoh, Themistocles L Assimes, Gabriel E Hoffman, Benjamin R Tenoever, John F Fullard, Julie A Lynch, Panos Roussos
doi:10.1101/2025.01.10.25320348
Background The development and approval of novel drugs are typically time-intensive and expensive. Leveraging a computational drug repurposing framework that integrates disease-relevant genetically regulated gene expression (GReX) and large longitudinal electronic medical record (EMR) databases can expedite the repositioning of existing medications. However, validating computational predictions of the drug repurposing framework remains a challenge.
Methods To benchmark the drug repurposing framework, we first performed a 5-method-rank-based computational drug prioritization pipeline by integrating multi-tissue GReX associated with COVID-19-related hospitalization, with drug transcriptional signature libraries from the Library of Integrated Network-Based Cellular Signatures. We prioritized FDA-approved medications from the 10 top-ranked compounds, and assessed their association with COVID-19 incidence within the Veterans Health Administration (VHA) cohort (~9 million individuals). In parallel, we evaluated in vitro SARS-CoV-2 replication inhibition in human lung epithelial cells for the selected candidates.
Results Our in silico pipeline identified seven FDA-approved drugs among the top ten candidates. Six (imiquimod, nelfinavir and saquinavir, everolimus, azathioprine, and retinol) had sufficient prescribing rates or feasibility for further testing. In the VHA cohort, azathioprine (odds ratio [OR]=0.69, 95% CI 0.62-0.77) and retinol (OR=0.81, 95% CI 0.72-0.92) were significantly associated with reduced COVID-19 incidence. Conversely, nelfinavir and saquinavir demonstrated potent SARS-CoV-2 inhibition in vitro (~95% and ~65% viral load reduction, respectively). No single compound showed robust protection in both in vivo and in vitro settings. Conclusions These findings underscore the power of GReX-based drug repurposing in rapidly identifying existing therapies with potential clinical relevance; four out of six compounds showed a protective effect in one of the two validation approaches. Crucially, our results highlight how a complementary evaluation-combining epidemiological data and in vitro assays-helps refine
Abbreviations
Abbreviation Definition COVID-19 Coronavirus Disease 2019 SARS-CoV-2 Severe Acute Respiratory Syndrome Coronavirus 2
Supplementary Information Additional File 1: Tables S1-11 Author information Contributions Conceptualization and study design: GV, JFF, JAL, PR. Data contribution or analysis tools: GV, KML, JMV, WZ, DH, SV, JB, MA, ZW, SR, LG, KC, JSL, SKI, S-WL, TLA, GEH, BRtO, JFF, JAL, PR. GV, KML, JMV, WZ, DH, SV, JB performed the analyses. GV, KML, JMV, JFF, JAL, PR wrote the manuscript with input from all authors.
Ethics declarations
Ethics approval and consent to participate Analysis of national VA data was conducted under the protocol, "Leveraging Electronic Health Information to Advance Precision Medicine (LEAP)", which was approved by VA Central Institutional Review Board and by the Research & Development Committees at Palo Alto, Salt Lake City, and West Haven VA Medical Centers.
Consent for publication
Not applicable
Competing interests The authors declare no competing interests.
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'abstract': '<jats:p>Background The development and approval of novel drugs are typically time-intensive '
'and expensive. Leveraging a computational drug repurposing framework that integrates '
'disease-relevant genetically regulated gene expression (GReX) and large longitudinal '
'electronic medical record (EMR) databases can expedite the repositioning of existing '
'medications. However, validating computational predictions of the drug repurposing framework '
'remains a challenge. Methods To benchmark the drug repurposing framework, we first performed '
'a 5-method-rank-based computational drug prioritization pipeline by integrating multi-tissue '
'GReX associated with COVID-19-related hospitalization, with drug transcriptional signature '
'libraries from the Library of Integrated Network-Based Cellular Signatures. We prioritized '
'FDA-approved medications from the 10 top-ranked compounds, and assessed their association '
'with COVID-19 incidence within the Veterans Health Administration (VHA) cohort (~9 million '
'individuals). In parallel, we evaluated in vitro SARS-CoV-2 replication inhibition in human '
'lung epithelial cells for the selected candidates. Results Our in silico pipeline identified '
'seven FDA-approved drugs among the top ten candidates. Six (imiquimod, nelfinavir and '
'saquinavir, everolimus, azathioprine, and retinol) had sufficient prescribing rates or '
'feasibility for further testing. In the VHA cohort, azathioprine (odds ratio [OR]=0.69, 95% '
'CI 0.62-0.77) and retinol (OR=0.81, 95% CI 0.72-0.92) were significantly associated with '
'reduced COVID-19 incidence. Conversely, nelfinavir and saquinavir demonstrated potent '
'SARS-CoV-2 inhibition in vitro (~95% and ~65% viral load reduction, respectively). No single '
'compound showed robust protection in both in vivo and in vitro settings. Conclusions These '
'findings underscore the power of GReX-based drug repurposing in rapidly identifying existing '
'therapies with potential clinical relevance; four out of six compounds showed a protective '
'effect in one of the two validation approaches. Crucially, our results highlight how a '
'complementary evaluation-combining epidemiological data and in vitro assays-helps refine the '
'most promising candidates for subsequent mechanistic studies and clinical trials. This '
'integrated validation approach may prove vital for accelerating therapeutic development '
'against current and future health challenges.</jats:p>',
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