ABSTRACT
The rampageous transmission of SARS-CoV-2 has been devastatingly impacting human life and public health since late 2019. The waves of pandemic events caused by distinct coronaviruses at present and over the past decades have prompted the need to develop broad-spectrum antiviral drugs against them. In this study, our Pentarlandir ultrapure and potent tannic acids (UPPTA) showed activities against two coronaviral strains, SARSCoV-2 and HCoV-OC43, the earliest-known coronaviruses. The mode of inhibition of Pentarlandir UPPTA is likely to act on 3-chymotrypsin-like protease (3CLpro) to prevent viral replication, as supported by results of biochemical analysis, a 3CLpro assay, and a "gain-of-function" 3CLpro overexpressed cell-based method. Even in the 3CLpro overexpressed environment, Pentarlandir UPPTA remained its antiviral characteristic. Utilizing cell-based virucidal and cytotoxicity assays, the 50% effective concentrations (EC50) and 50% cytotoxicity concentration (CC50) of Pentarlandir UPPTA were determined to be similar to 0.5 and 52.5 mu M against SARS-CoV-2, while they were 1.3 and 205.9 mu M against HCoV-OC43, respectively. In the pharmacokinetic studies, Pentarlandir UPPTA was distributable at a high level to the lung tissue with no accumulation in the body, although the distribution was affected by the food effect. With further investigation in toxicology, Pentarlandir UPPTA demonstrated an overall safe toxicology profile. Taking these findings together, Pentarlandir UPPTA is considered to be a safe and efficacious pancoronal antiviral drug candidate that has been advanced to clinical development.
ABSTRACT
Background: Immunothrombosis and coagulopathy in the lung microvasculature may lead to lung injury and disease progression in COVID-19. We aim to identify biomarkers of coagulation, endothelial function, and fibrinolysis that are associated with disease severity and may have prognostic potential. Aims: To identify biomarkers of coagulation, inflammation, and fibrinolysis that may predict clinical course and outcome of COVID-19 patients. Methods: We performed a single-center prospective study of 14 adult COVID-19(+) ICU patients who were age and sex-matched to 14 COVID-19(-) ICU patients, and healthy controls. Daily blood draws, clinical data, and patient characteristics were collected. Ten biomarkers of interest were subjected to linear discriminant analysis (LDA) to explore the discriminatory ability of biomarkers for COVID-19 status. Linear repeated measures mixed models were used to screen biomarkers for associations with mortality. Selected biomarkers were further explored and entered into an unsupervised longitudinal clustering machine learning algorithm to identify trends and targets that may be used for future predictive modelling efforts. Results: LDA identified high D-dimer as the strongest contributor in distinguishing COVID-19 status however D-dimer was not associated with survival. Variable selection identified clot lysis time, and antigen levels of soluble thrombomodulin (sTM), plasminogen activator inhibitor-1 (PAI-1), and plasminogen as biomarkers associated with death. Longitudinal multivariate k-means clustering on these biomarkers alone identified two clusters of COVID-19(+) patients -low (30%) and high (100%) mortality groups (Figure 1). Biomarker trajectories that characterized the high mortality cluster were higher clot lysis times (inhibited fibrinolysis), higher sTM and PAI-1 levels, and lower plasminogen levels. Conclusions: Longitudinal trajectories of clot lysis time, sTM, PAI-1, and plasminogen may have predictive ability for mortality in COVID-19.