Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC), Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
Daniel J. Stein
Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC), Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
Daniel J.B. Clarke
Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC), Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
Eryk Kropiwnicki
Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC), Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
Kathleen M. Jagodnik
Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC), Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
Alon Bartal
Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC), Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
John E. Evangelista
Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC), Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
Jason Hom
Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC), Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
Minxuan Cheng
Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC), Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
Allison Bailey
Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC), Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
Abigail Zhou
Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC), Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
Laura B. Ferguson
Department of Neurology, Dell Medical School, University of Texas at Austin, 1601 Trinity Street, Bldg B, Austin, TX 78712, USA
Alexander Lachmann
Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC), Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
Avi Ma'ayan
Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Big Data to Knowledge, Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC), Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA; Corresponding author
Summary: In a short period, many research publications that report sets of experimentally validated drugs as potential COVID-19 therapies have emerged. To organize this accumulating knowledge, we developed the COVID-19 Drug and Gene Set Library (https://amp.pharm.mssm.edu/covid19/), a collection of drug and gene sets related to COVID-19 research from multiple sources. The platform enables users to view, download, analyze, visualize, and contribute drug and gene sets related to COVID-19 research. To evaluate the content of the library, we compared the results from six in vitro drug screens for COVID-19 repurposing candidates. Surprisingly, we observe low overlap across screens while highlighting overlapping candidates that should receive more attention as potential therapeutics for COVID-19. Overall, the COVID-19 Drug and Gene Set Library can be used to identify community consensus, make researchers and clinicians aware of new potential therapies, enable machine-learning applications, and facilitate the research community to work together toward a cure. The Bigger Picture: The COVID-19 pandemic requires rapid response by the research community to develop vaccines and therapeutics. While the development of vaccines may take years, drug repurposing can offer pandemic mitigation much quicker. In vitro drug screening is the first step toward identifying and prioritizing potential safe therapeutics for COVID-19. However, these screens are done by different laboratories across the world using different methods. As a result, these screens produce different lists of hits. Here, we attempted to consolidate the results from these drug screens to find out whether consensus emerges. In addition, we utilized machine-learning methods to further predict and prioritize the validity of the hits from these drug screens. Such analysis identified molecular mechanisms that may explain how some of these drugs interfere with viral replication inside human cells. As more SARS-CoV-2 drug screens are published, a clearer picture of the most promising drug candidates is expected to emerge.
