Revealing the structural and molecular interaction landscape of the favipiravir-RTP and SARS-CoV-2 RdRp complex through integrative bioinformatics: Insights for developing potent drugs targeting SARS-CoV-2 and other viruses.

BACKGROUND: The global research community has made considerable progress in therapeutic and vaccine research during the COVID-19 pandemic. Several therapeutics have been repurposed for the treatment of COVID-19. One such compound is, favipiravir, which was approved for the treatment of influenza viruses, including drug-resistant influenza. Despite the limited information on its molecular activity, clinical trials have attempted to determine the effectiveness of favipiravir in patients with mild to moderate COVID-19. Here, we report the structural and molecular interaction landscape of the macromolecular complex of favipiravir-RTP and SARS-CoV-2 RdRp with the RNA chain. METHODS: Integrative bioinformatics was used to reveal the structural and molecular interaction landscapes of two macromolecular complexes retrieved from RCSB PDB. RESULTS: We analyzed the interactive residues, H-bonds, and interaction interfaces to evaluate the structural and molecular interaction landscapes of the two macromolecular complexes. We found seven and six H-bonds in the first and second interaction landscapes, respectively. The maximum bond length is 3.79 Å. In the hydrophobic interactions, five residues (Asp618, Asp760, Thr687, Asp623, and Val557) were associated with the first complex and two residues (Lys73 and Tyr217) were associated with the second complex. The mobilities, collective motion, and B-factor of the two macromolecular complexes were analyzed. Finally, we developed different models, including trees, clusters, and heat maps of antiviral molecules, to evaluate the therapeutic status of favipiravir as an antiviral drug. CONCLUSIONS: The results revealed the structural and molecular interaction landscape of the binding mode of favipiravir with the nsp7-nsp8-nsp12-RNA SARS-CoV-2 RdRp complex. Our findings can help future researchers in understanding the mechanism underlying viral action and guide the design of nucleotide analogs that mimic favipiravir and exhibit greater potency as antiviral drugs against SARS-CoV-2 and other infectious viruses. Thus, our work can help in preparing for future epidemics and pandemics.

A novel mutation-proof, next-generation vaccine to fight against upcoming SARS-CoV-2 variants and subvariants, designed through AI enabled approaches and tools, along with the machine learning based immune simulation: A vaccine breakthrough.

Emerging SARS-CoV-2 variants and subvariants are great concerns for their significant mutations, which are also responsible for vaccine escape. Therefore, the study was undertaken to develop a mutation-proof, next-generation vaccine to protect against all upcoming SARS-CoV-2 variants. We used advanced computational and bioinformatics approaches to develop a multi-epitopic vaccine, especially the AI model for mutation selection and machine learning (ML) strategies for immune simulation. AI enabled and the top-ranked antigenic selection approaches were used to select nine mutations from 835 RBD mutations. We selected twelve common antigenic B cell and T cell epitopes (CTL and HTL) containing the nine RBD mutations and joined them with the adjuvants, PADRE sequence, and suitable linkers. The constructs' binding affinity was confirmed through docking with TLR4/MD2 complex and showed significant binding free energy (-96.67 kcal mol-1) with positive binding affinity. Similarly, the calculated eigenvalue (2.428517e-05) from the NMA of the complex reveals proper molecular motion and superior residues' flexibility. Immune simulation shows that the candidate can induce a robust immune response. The designed mutation-proof, multi-epitopic vaccine could be a remarkable candidate for upcoming SARS-CoV-2 variants and subvariants. The study method might guide researchers in developing AI-ML and immunoinformatics-based vaccines for infectious disease.

Virtual Screening of Artemisia annua Phytochemicals as Potential Inhibitors of SARS-CoV-2 Main Protease Enzyme.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a human coronaviruses that emerged in China at Wuhan city, Hubei province during December 2019. Subsequently, SARS-CoV-2 has spread worldwide and caused millions of deaths around the globe. Several compounds and vaccines have been proposed to tackle this crisis. Novel recommended in silico approaches have been commonly used to screen for specific SARS-CoV-2 inhibitors of different types. Herein, the phytochemicals of Pakistani medicinal plants (especially Artemisia annua) were virtually screened to identify potential inhibitors of the SARS-CoV-2 main protease enzyme. The X-ray crystal structure of the main protease of SARS-CoV-2 with an N3 inhibitor was obtained from the protein data bank while A. annua phytochemicals were retrieved from different drug databases. The docking technique was carried out to assess the binding efficacy of the retrieved phytochemicals; the docking results revealed that several phytochemicals have potential to inhibit the SARS-CoV-2 main protease enzyme. Among the total docked compounds, the top-10 docked complexes were considered for further study and evaluated for their physiochemical and pharmacokinetic properties. The top-3 docked complexes with the best binding energies were as follows: the top-1 docked complex with a -7 kcal/mol binding energy score, the top-2 docked complex with a -6.9 kcal/mol binding energy score, and the top-3 docked complex with a -6.8 kcal/mol binding energy score. These complexes were subjected to a molecular dynamic simulation analysis for further validation to check the dynamic behavior of the selected top-complexes. During the whole simulation time, no major changes were observed in the docked complexes, which indicated complex stability. Additionally, the free binding energies for the selected docked complexes were also estimated via the MM-GB/PBSA approach, and the results revealed that the total delta energies of MMGBSA were -24.23 kcal/mol, -26.38 kcal/mol, and -25 kcal/mol for top-1, top-2, and top-3, respectively. MMPBSA calculated the delta total energy as -17.23 kcal/mol (top-1 complex), -24.75 kcal/mol (top-2 complex), and -24.86 kcal/mol (top-3 complex). This study explored in silico screened phytochemicals against the main protease of the SARS-CoV-2 virus; however, the findings require an experimentally based study to further validate the obtained results.

Selective COVID-19 Coinfections in Diabetic Patients with Concomitant Cardiovascular Comorbidities Are Associated with Increased Mortality.

Coinfections and comorbidities add additional layers of difficulties into the challenges of COVID-19 patient management strategies. However, studies examining these clinical conditions are limited. We have independently investigated the significance of associations of specific bacterial species and different comorbidities in the outcome and case fatality rates among 129 hospitalized comorbid COVID-19 patients. For the first time, to best of our knowledge, we report on the predominance of Klebsiella pneumoniae and Acinetobacter baumannii in COVID-19 non-survival diabetic patients The two species were significantly associated to COVID-19 case fatality rates (p-value = 0.02186). Coinfection rates of Klebsiella pneumoniae and Acinetobacter baumannii in non-survivors were 93% and 73%, respectively. Based on standard definitions for antimicrobial resistance, Klebsiella pneumoniae and Acinetobacter baumannii were classified as multidrug resistant and extremely drug resistant, respectively. All patients died at ICU with similar clinical characterisitics. Of the 28 major coinfections, 24 (85.7%) were in non-survivor diabetic patients, implying aggravating and worsening the course of COVID-19. The rates of other comorbidities varied: asthma (47%), hypertension (79.4%), ischemic heart disease (71%), chronic kidney disease (35%), and chronic liver disease (32%); however, the rates were higher in K. pneumoniae and were all concomitantly associated to diabetes. Other bacterial species and comorbidities did not have significant correlation to the outcomes. These findings have highly significant clinical implications in the treatment strategies of COVID-19 patients. Future vertical genomic studies would reveal more insights into the molecular and immunological mechanisms of these frequent bacterial species. Future large cohort multicenter studies would reveal more insights into the mechanisms of infection in COVID-19.

Mucormycosis in Indian COVID-19 Patients: Insight into Its Patho-Genesis, Clinical Manifestation, and Management Strategies.

Mucormycosis in patients who have COVID-19 or who are otherwise immunocompromised has become a global problem, causing significant morbidity and mortality. Infection is debilitating and fatal, leading to loss of organs and emotional trauma. Radiographic manifestations are not specific, but diagnosis can be made through microscopic examination of materials collected from necrotic lesions. Treatment requires multidisciplinary expertise, as the fungus enters through the eyes and nose and may even reach the brain. Use of the many antifungal drugs available is limited by considerations of resistance and toxicity, but nanoparticles can overcome such limitations by reducing toxicity and increasing bioavailability. The lipid formulation of amphotericin-B (liposomal Am-B) is the first-line treatment for mucormycosis in COVID-19 patients, but its high cost and low availability have prompted a shift toward surgery, so that surgical debridement to remove all necrotic lesions remains the hallmark of effective treatment of mucormycosis in COVID-19. This review highlights the pathogenesis, clinical manifestation, and management of mucormycosis in patients who have COVID-19.

COVID-19 convalescent plasma treatment of moderate and severe cases of SARS-CoV-2 infection: A multicenter interventional study.

OBJECTIVE: To study the effectiveness of COVID-19 convalescent plasma (CCP) therapy for patients with moderate and severe COVID-19 disease. METHODS: This non-randomized prospective cohort study was conducted from May 21 to June 30, 2020, at four major tertiary hospitals in Kuwait. CCP was administered to 135 patients. The control group comprised 233 patients who received standard treatment. All patients (N = 368, median age 54 [range 15-82]) had laboratory-confirmed SARS-CoV-2 infection and either moderate or severe COVID-19 disease. RESULTS: CCP treatment was associated with a higher rate of clinical improvement in patients with moderate or severe disease. Among those with moderate COVID-19 disease, time to clinical improvement was 7 days in the CCP group, versus 8 days in the control group (p = 0·006). For severe COVID-19 disease, time to clinical improvement was 7 days in the CCP group, versus 15.5 days in the control group (p = 0·003). In the adjusted analysis, patients with moderate disease treated with CCP had a significantly lower 30-day mortality rate. Compared to the control group, oxygen saturation improved within 3 days of CCP transfusion, and lymphocyte counts improved from day 7 in patients with moderate COVID-19 disease and day 11 in patients with severe disease. C-reactive protein levels declined throughout the first 14 days after CCP transfusion. None of the CCP patients developed a serious transfusion reaction. CONCLUSIONS: The data show that administration of CCP is a safe treatment option for patients with COVID-19 disease with a favorable outcome in the rate of, and time to, clinical improvement.