A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.

Real-world evidence with a retrospective cohort of 15,968 Andalusian COVID-19 hospitalized patients suggests 21 new effective treatments and one drug that increases death risk.

Despite the extensive vaccination campaigns in many countries, COVID-19 is still a major worldwide health problem because of its associated morbidity and mortality. Therefore, finding efficient treatments as fast as possible is a pressing need. Drug repurposing constitutes a convenient alternative when the need for new drugs in an unexpected medical scenario is urgent, as is the case with COVID-19. Using data from a central registry of electronic health records (the Andalusian Population Health Database, BPS), the effect of prior consumption of drugs for other indications previous to the hospitalization with respect to patient survival was studied on a retrospective cohort of 15,968 individuals, comprising all COVID-19 patients hospitalized in Andalusia between January and November 2020. Covariate-adjusted hazard ratios and analysis of lymphocyte progression curves support a significant association between consumption of 21 different drugs and better patient survival. Contrarily, one drug, furosemide, displayed a significant increase in patient mortality.

Real world evidence of calcifediol use and mortality rate of COVID-19 hospitalized in a large cohort of 16,401 Andalusian patients

BackgroundCOVID-19 is a major worldwide health problem because of acute respiratory distress syndrome, and mortality. Several lines of evidence have suggested a relationship between the vitamin D endocrine system and severity of COVID-19. MethodsWe present a retrospective survival study that includes all Andalusian patients hospitalized between January and November 2020 because of COVID-19 infection. Based on a central registry of electronic health records (the Andalusian Population Health Database, BPS), prescription of vitamin D or its metabolites within 15-30 days before hospitalization were recorded. The effect of treatment with vitamin D metabolites for other indication previous to the hospitalization was studied with respect to patient survival by means of Kaplan-Meyer survival curves and Log Hazard Ratios, using a propensity score to compensate the disbalance of compared classes and the confounding factors. The availability of detailed patient data in the BPS allowed to obtain Real-World Evidence (RWE) of the effects of prior use of vitamin D or its metabolites on the mortality due to COVID-19 infection. FindingsA retrospective cohort of 16.401patients was extracted from the BPS, which includes all the patients hospitalized with COVID-19 diagnosis between January and November 2020 in Andalusia, one of the largest regions in Europe with the size of an average median country. A total of 358 patients were found with cholecalciferol, and 193 with calcifediol, prescriptions 15 days before hospitalization. For a period extended to 30 days before hospitalization, the numbers increase to 416 and 210 and, respectively. Kaplan-Meyer survival curves and hazard ratios support an association between consumption of these metabolites and patient survival. Such association was stronger in calcifediol (Log Hazard Ratio, LHR = -1.27{+/-}0.32) than in cholecalciferol (LHR= -0.56{+/-}0.15), when prescribed 15 days before hospitalization This effect decreases when a larger 30 days period is considered (calcifediol LHR= -1.01{+/-}0.27 and cholecalciferol LHR= -0.27{+/-}0.12), suggesting that the closer was the treatment to the hospitalization the stronger the association. ConclusionsA significant reduction in mortality in patients hospitalized with COVID-19 is associated with the prescription of vitamin D, especially calcifediol, within 15-30 days prior to hospitalization.

COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

Mechanistic modeling of the SARS-CoV-2 disease map

Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map in which the detailed activity of the human signaling circuits related to the viral infection and the different antiviral responses, including immune and inflammatory activities, can be inferred from gene expression experiments. Moreover, given to the mechanistic properties of the model, the effect of potential interventions, such as knock-downs, over-expression or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied in specific conditions. By providing a holistic, systems biology approach to the understanding of the complexities of the viral infection process, this tool will become an important asset in the search for efficient antiviral treatments. The tool is freely available at: http://hipathia.babelomics.org/covid19/