BACKGROUND Epidemiological studies indicate that as many as 20% of individuals who test positive for COVID-19 develop severe symptoms that can require hospitalization. These symptoms include low platelet count, severe hypoxia, increased inflammatory cytokines and reduced glomerular filtration rate. Additionally, severe COVID-19 is associated with several chronic co-morbidities, including cardiovascular disease, hypertension and type 2 diabetes mellitus. The identification of genetic risk factors that impact differential host responses to SARS-CoV-2, resulting in the development of severe COVID-19, is important in gaining greater understanding into the biological mechanisms underpinning life-threatening responses to the virus. These insights could be used in the identification of high-risk individuals and for the development of treatment strategies for these patients. METHODS As of June 6, 2020, there were 976 patients who tested positive for COVID-19 and were hospitalized, indicating they had a severe response to SARS-CoV-2. There were however too few patients with a mild form of COVID-19 to use this cohort as our control population. Instead we used similar control criteria to our previous study looking at shared genetic risk factors between severe COVID-19 and sepsis, selecting controls who had not developed sepsis despite having maximum co-morbidity risk and exposure to sepsis-causing pathogens. RESULTS Using a combinatorial (high-order epistasis) analysis approach, we identified 68 protein-coding genes that were highly associated with severe COVID-19. At the time of analysis, nine of these genes have been linked to differential response to SARS-CoV-2. We also found many novel targets that are involved in key biological pathways associated with the development of severe COVID-19, including production of pro-inflammatory cytokines, endothelial cell dysfunction, lipid droplets, neurodegeneration and viral susceptibility factors. CONCLUSION The variants we found in genes relating to immune response pathways and cytokine production cascades, were in equal proportions across all severe COVID-19 patients, regardless of their co-morbidities. This suggests that such variants are not associated with any specific co-morbidity, but are common amongst patients who develop severe COVID-19. Among the 68 severe COVID-19 risk-associated genes, we found several druggable protein targets and pathways. Nine are targeted by drugs that have reached at least Phase I clinical trials, and a further eight have active chemical starting points for novel drug development. Several of these targets were particularly enriched in specific co-morbidities, providing insights into shared pathological mechanisms underlying both the development of severe COVID-19, ARDS and these predisposing co-morbidities. We can use these insights to identify patients who are at greatest risk of contracting severe COVID-19 and develop targeted therapeutic strategies for them, with the aim of improving disease burden and survival rates.

The D (dissemination) phase of the ESID model has been often overlooked in our efforts to create innovative and widespread social change. The process of replicating successful social innovations is both a prerequisite for dissemination (in order to assess the consistency of effects) and an obvious outcome of a successful dissemination effort. Fidelity, the extent to which a replicated program is implemented in a manner consistent with the original program model, is an important dimension of replication. This study was designed to provide empirical data related to three questions. Can complex social programs be implemented with fidelity? How much fidelity is appropriate or desired? What are the organizational dynamics of adoption with fidelity? Data were collected from grantees of a national replication initiative funded by the Center for Substance Abuse Prevention. Data suggest that high fidelity can be achieved, at least in the context in which programs are mandated to do so as part of the funding agreement and are given technical assistance in achieving fidelity. Secondly, programs perceived high fidelity as having positive effects on the program and its participants, a finding consistent with a limited assessment of the relationship of program outcomes and fidelity. Finally, much was learned about the human and organizational dynamics of replicating with fidelity. Implications for policy and direction regarding replication are discussed.

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community’s massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

Background and Objectives: Precision medicine and drug repurposing provide an opportunity to ameliorate the challenges of declining pharmaceutical R&D productivity, rising costs of new drugs, and poor patient response rates to existing medications. Multifactorial “disease signatures” provide unique insights into the architecture of complex disease populations that can be used to better stratify patient groups, aiding the delivery of precision medicine. Methods: Analysis of a complex disease (breast cancer) population was undertaken to identify the combinations of single-nucleotide polymorphisms that are associated with different disease subgroups. Target genes associated with the disease risk of these subgroups were examined, followed by identification and evaluation of existing active chemical leads as drug repurposing candidates. Results: One hundred and seventy-five disease-associated gene targets relevant to different subpopulations of breast cancer patients were identified. Twenty-three of these genes were prioritized as both promising novel drug targets and repurposing candidates. Two targets, P4HA2 and TGM2, have high repurposing potential and a strong mechanistic link to breast cancer. Conclusions: This study showed that detailed analysis of combinatorial genomic (and other) features can be used to accurately stratify patient populations and identify highly plausible drug repurposing candidates systematically across all disease-associated targets.

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