Anti-COVID-19 Sulfonamides: A DFT, Docking and ADMET Study

Background: The development of a specific curative drug or prophylactic and vaccine is urgently required to cure COVID-19. Sulfonamide and its derivatives are famous for their multi-faceted antibiotic and antiviral activities against verities of a pathogen. Objective(s): The objective of this study is to find new potential molecules for COVID-19 treatment. We tested some sulfonamide molecules (including antiviral compounds) as SARS CoV-2 Mpro in-hibitors. Method(s): In this study, the Density Functional Theory (DFT) and Docking study have been util-ized for protein-small molecule affinity prediction. The SwissADME server was used for pharma-cokinetics and drug-like likeness prediction, and the Pred-hERG server was employed for cardio-toxicity prediction. Result(s): In this study, sixteen sulfonamides have been investigated in silico, with a perspective to obtaining a potential anti-covid compound. The sulfonamides have been subjected to molecular docking with SARS CoV-2 Mpro, mainly responsible for viral infection and replication. We discov-er the molecular flexibility and charge distribution profoundly affecting the binding of the compounds to the protein. Moderately flexible (six rotatable bond) and less polar (sufficient hydropho-bic) sulfonamide are favorable for strong binding with the enzyme. Here, the bioavailability proper-ties like adsorption, distribution, metabolism, excretion, pharmacokinetics, and potential toxicity of these compounds have also been checked. Conclusion(s): Low cardio-toxicity and high bioavailability make these sulfonamides a good anti-COVID-19 drug option. The sulfonamide 16 was found to be the best.Copyright © 2022 Bentham Science Publishers.

A metabolic readout of the urine metabolome of COVID-19 patients.

Analysis of urine samples from COVID-19 patients by 1H NMR reveals important metabolic alterations due to SAR-CoV-2 infection. Previous studies have identified biomarkers in urine that reflect metabolic alterations in COVID-19 patients. We have used 1H NMR to better define these metabolic alterations since this technique allows us to obtain a broad profile of the metabolites present in urine. This technique offers the advantage that sample preparation is very simple and gives us very complete information on the metabolites present. To detect these alterations, we have compared urine samples from COVID-19 patients (n = 35) with healthy people (n = 18). We used unsupervised (Robust PCA) and supervised (PLS-LDA) multivariate analysis methods to evaluate the differences between the two groups: COVID-19 and healthy controls. The differences focus on a group of metabolites related to energy metabolism (glucose, ketone bodies, glycine, creatinine, and citrate) and other processes related to bacterial flora (TMAO and formic acid) and detoxification (hippuric acid). The alterations in the urinary metabolome shown in this work indicate that SARS-CoV-2 causes a metabolic change from a normal situation of glucose consumption towards a gluconeogenic situation and possible insulin resistance.

Obesity and metabolic dysfunction drive sex-associated differential disease profiles in hACE2-mice challenged with SARS-CoV-2.

Severe outcomes from SARS-CoV-2 infection are highly associated with preexisting comorbid conditions like hypertension, diabetes, and obesity. We utilized the diet-induced obesity (DIO) model of metabolic dysfunction in K18-hACE2 transgenic mice to model obesity as a COVID-19 comorbidity. Female DIO, but not male DIO mice challenged with SARS-CoV-2 were observed to have shortened time to morbidity compared to controls. Increased susceptibility to SARS-CoV-2 in female DIO was associated with increased viral RNA burden and interferon production compared to males. Transcriptomic analysis of the lungs from all mouse cohorts revealed sex- and DIO-associated differential gene expression profiles. Male DIO mice after challenge had decreased expression of antibody-related genes compared to controls, suggesting antibody producing cell localization in the lung. Collectively, this study establishes a preclinical comorbidity model of COVID-19 in mice where we observed sex- and diet-specific responses that begin explaining the effects of obesity and metabolic disease on COVID-19 pathology.

Cystatin C is associated with adverse COVID-19 outcomes in diverse populations.

COVID-19 has highly variable clinical courses. The search for prognostic host factors for COVID-19 outcome is a priority. We performed logistic regression for ICU admission against a polygenic score (PGS) for Cystatin C (CyC) production in patients with COVID-19. We analyzed the predictive value of longitudinal plasma CyC levels in an independent cohort of patients hospitalized with COVID-19. In four cohorts spanning European and African ancestry populations, we identified a significant association between CyC-production PGS and odds of critical illness (n cases=2,319), with the strongest association captured in the UKB cohort (OR 2.13, 95% CI 1.58-2.87, p=7.12e-7). Plasma proteomics from an independent cohort of hospitalized COVID-19 patients (n cases = 131) demonstrated that CyC production was associated with COVID-specific mortality (p=0.0007). Our findings suggest that CyC may be useful for stratification of patients and it has functional role in the host response to COVID-19.

Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19.

The coronavirus disease-19 (COVID-19) pandemic has ravaged global healthcare with previously unseen levels of morbidity and mortality. In this study, we performed large-scale integrative multi-omics analyses of serum obtained from COVID-19 patients with the goal of uncovering novel pathogenic complexities of this disease and identifying molecular signatures that predict clinical outcomes. We assembled a network of protein-metabolite interactions through targeted metabolomic and proteomic profiling in 330 COVID-19 patients compared to 97 non-COVID, hospitalized controls. Our network identified distinct protein-metabolite cross talk related to immune modulation, energy and nucleotide metabolism, vascular homeostasis, and collagen catabolism. Additionally, our data linked multiple proteins and metabolites to clinical indices associated with long-term mortality and morbidity. Finally, we developed a novel composite outcome measure for COVID-19 disease severity based on metabolomics data. The model predicts severe disease with a concordance index of around 0.69, and shows high predictive power of 0.83-0.93 in two independent datasets.

SARS-CoV-2 Infection Dysregulates the Metabolomic and Lipidomic Profiles of Serum.

COVID-19 is a systemic infection that exerts significant impact on the metabolism. Yet, there is little information on how SARS-CoV-2 affects metabolism. Using NMR spectroscopy, we measured the metabolomic and lipidomic serum profile from 263 (training cohort) + 135 (validation cohort) symptomatic patients hospitalized after positive PCR testing for SARS-CoV-2 infection. We also established the profiles of 280 persons collected before the coronavirus pandemic started. Principal-component analysis discriminated both cohorts, highlighting the impact that the infection has on overall metabolism. The lipidomic analysis unraveled a pathogenic redistribution of the lipoprotein particle size and composition to increase the atherosclerotic risk. In turn, metabolomic analysis reveals abnormally high levels of ketone bodies (acetoacetic acid, 3-hydroxybutyric acid, and acetone) and 2-hydroxybutyric acid, a readout of hepatic glutathione synthesis and marker of oxidative stress. Our results are consistent with a model in which SARS-CoV-2 infection induces liver damage associated with dyslipidemia and oxidative stress.