Effects of canagliflozin on myocardial microvascular density, oxidative stress, and proteomic profile☆.

Introduction: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) are cardioprotective, and canagliflozin (CANA), an SGLT2i, has been shown to improve perfusion, AMPK signaling, and oxidative stress in chronically ischemic myocardium. The aim of this study is to determine the effects of CANA in nonischemic myocardium on coronary collateralization, oxidative stress, and other molecular pathways determined by proteomic profiling. Methods: Yorkshire swine underwent placement of an ameroid constrictor to the left circumflex artery. Two weeks later, pigs received no drug (CON, n = 8) or 300 mg CANA daily (n = 8). Treatment continued for five weeks, followed by tissue harvest of nonischemic myocardium. Results: CANA was associated with decreased capillary density (p = 0.05) compared to CON, without changes in arteriolar density. Reduced capillary density did not correlate with reduced perfusion. Oxidative stress was reduced with CANA (22 % decrease). In the CANA group, there was a trend towards increased p-eNOS and eNOS, without a change in p-eNOS/eNOS ratio, p-Akt, Akt, and p-Akt/Akt ratio. There was no change in p-ERK1/2, but a decrease in total ERK1/2 and increase in p-ERK1/2/ERK1/2 ratio. There were no changes in expression of p-AMPK, AMPK, with a trend towards increased ratio of p-AMPK/AMPK. Proteomics analysis identified 2819 common proteins, of which 120 were upregulated and 425 were downregulated with CANA. Pathway analysis demonstrated wide regulation of metabolic proteins. Conclusions: The effects of CANA on myocardial perfusion and AMPK signaling in chronically ischemic myocardium are not found in nonischemic territory, despite attenuation of oxidative stress. Metabolic proteins are widely regulated in nonischemic myocardium with CANA.

LACK OF ASSOCIATION BETWEEN CONVALESCENT PLASMA ADMINISTRATION AND LENGTH OF HOSPITAL STAY: A HOSPITAL-DAY STRATIFIED MULTI-CENTER RETROSPECTIVE COHORT STUDY

BackgroundEffects of timing of Convalescent plasma (CP) administration on hospitalized COVID-19 patients are not established. MethodsWe used the National COVID Cohort Collaborative data to perform a retrospective cohort study of hospitalized COVID-19 patients in the United States between 07-01-2020 and 12-19-2020. We stratified patients based on day of CP administration (Day 0, 1, 2, 3 and 4) from COVID-19 diagnosis. We used 35 predictors to frame matched cohorts accounting for clinical and sociodemographic characteristics. We used competing risk survival models to examine the association between CP administration and length of hospital stay with in-hospital death as a competing risk performing Grays test on the cumulative incidence function and Coxs regression on cause specific hazard ratios. ResultsIn a cohort of 4,003 hospitalized COVID-19 patients, 197 (4.9%) received CP within the first 5 days following COVID-19 diagnosis. After adjusting for potential confounding variables, there were no statistically significant associations between day of CP administration and length of hospital stay. Day 0 CP administration signallled lower mortality but was not statistically significant (HR 0.45 [0.19-1.03]). ConclusionsWe found no association between the timing of CP administration and length of stay among hospitalized COVID-19 patients.

Drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced COVID-19 mortality in 1.5M patient records

BackgroundDrug repositioning is a key component of COVID-19 pandemic response, through identification of existing drugs that can effectively disrupt COVID-19 disease processes, contributing valuable insights into disease pathways. Traditional non in silico drug repositioning approaches take substantial time and cost to discover effect and, crucially, to validate repositioned effects. MethodsUsing a novel in-silico quasi-quantum molecular simulation platform that analyzes energies and electron densities of both target proteins and candidate interruption compounds on High Performance Computing (HPC), we identified a list of FDA-approved compounds with potential to interrupt specific SARS-CoV-2 proteins. Subsequently we used 1.5M patient records from the National COVID Cohort Collaborative to create matched cohorts to refine our in-silico hits to those candidates that show statistically significant clinical effect. ResultsWe identified four drugs, Metformin, Triamcinolone, Amoxicillin and Hydrochlorothiazide, that were associated with reduced mortality by 27%, 26%, 26%, and 23%, respectively, in COVID-19 patients. ConclusionsTogether, these findings provide support to our hypothesis that in-silico simulation of active compounds against SARS-CoV-2 proteins followed by statistical analysis of electronic health data results in effective therapeutics identification.