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BMC Biol ; 19(1): 12, 2021 01 22.
Article in English | MEDLINE | ID: covidwho-1044598


BACKGROUND: Pandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a "commons." Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases. However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modeled with entity schemas represented by Shape Expressions. RESULTS: As a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable is demonstrated by integrating data from NCBI (National Center for Biotechnology Information) Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates. CONCLUSIONS: Although this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, human coronavirus NL63, human coronavirus 229E, human coronavirus HKU1, human coronavirus OC4).

COVID-19/pathology , Genomics/methods , Knowledge Bases , Proteomics/methods , SARS-CoV-2/physiology , COVID-19/metabolism , COVID-19/virology , Coronavirus/genetics , Coronavirus/physiology , Coronavirus Infections/metabolism , Coronavirus Infections/pathology , Coronavirus Infections/virology , Genome, Viral , Humans , Internet , Pandemics , SARS-CoV-2/genetics , Viral Proteins/genetics , Viral Proteins/metabolism , Workflow
Nature ; 586(7827): 113-119, 2020 10.
Article in English | MEDLINE | ID: covidwho-672174


The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 has triggered an ongoing global pandemic of the severe pneumonia-like disease coronavirus disease 2019 (COVID-19)1. The development of a vaccine is likely to take at least 12-18 months, and the typical timeline for approval of a new antiviral therapeutic agent can exceed 10 years. Thus, repurposing of known drugs could substantially accelerate the deployment of new therapies for COVID-19. Here we profiled a library of drugs encompassing approximately 12,000 clinical-stage or Food and Drug Administration (FDA)-approved small molecules to identify candidate therapeutic drugs for COVID-19. We report the identification of 100 molecules that inhibit viral replication of SARS-CoV-2, including 21 drugs that exhibit dose-response relationships. Of these, thirteen were found to harbour effective concentrations commensurate with probable achievable therapeutic doses in patients, including the PIKfyve kinase inhibitor apilimod2-4 and the cysteine protease inhibitors MDL-28170, Z LVG CHN2, VBY-825 and ONO 5334. Notably, MDL-28170, ONO 5334 and apilimod were found to antagonize viral replication in human pneumocyte-like cells derived from induced pluripotent stem cells, and apilimod also demonstrated antiviral efficacy in a primary human lung explant model. Since most of the molecules identified in this study have already advanced into the clinic, their known pharmacological and human safety profiles will enable accelerated preclinical and clinical evaluation of these drugs for the treatment of COVID-19.

Antiviral Agents/analysis , Antiviral Agents/pharmacology , Betacoronavirus/drug effects , Coronavirus Infections/drug therapy , Coronavirus Infections/virology , Drug Evaluation, Preclinical , Drug Repositioning , Pneumonia, Viral/drug therapy , Pneumonia, Viral/virology , Adenosine Monophosphate/analogs & derivatives , Adenosine Monophosphate/pharmacology , Alanine/analogs & derivatives , Alanine/pharmacology , Alveolar Epithelial Cells/cytology , Alveolar Epithelial Cells/drug effects , Betacoronavirus/growth & development , COVID-19 , Cell Line , Cysteine Proteinase Inhibitors/analysis , Cysteine Proteinase Inhibitors/pharmacology , Dose-Response Relationship, Drug , Drug Synergism , Gene Expression Regulation/drug effects , Humans , Hydrazones , Induced Pluripotent Stem Cells/cytology , Models, Biological , Morpholines/analysis , Morpholines/pharmacology , Pandemics , Pyrimidines , Reproducibility of Results , SARS-CoV-2 , Small Molecule Libraries/analysis , Small Molecule Libraries/pharmacology , Triazines/analysis , Triazines/pharmacology , Virus Internalization/drug effects , Virus Replication/drug effects