Potential activity of Linezolid against SARS-CoV-2 using electronic and molecular docking study

An eco-friendly and cost-effective HPTLC method for quantification of COVID-19 antiviral drug and co-administered medications in spiked human plasma

The coronavirus-2 has led to a global pandemic of COVID-19 with an outbreak of severe acute respiratory syndrome leading to worldwide quarantine measures and a rise in death rates. The objective of this study is to propose a green, sensitive, and selective densitometric method to simultaneously quantify remdesivir (REM) in the presence of the co-administered drug linezolid (LNZ) and rivaroxaban (RIV) in spiked human plasma. TLC silica gel aluminum plates 60 F254 were used as the stationary phase, and the mobile phase was composed of dichloromethane (DCM): acetone (8.5:1.5, v/v) with densitometric detection at 254 nm. Well-resolved peaks have been observed with retardation factors (Rf) of 0.23, 0.53, and 0.72 for REM, LNZ, and RIV, respectively. A validation study was conducted according to ICH Q2 (R1) Guidelines. The method was rectilinear over the concentration ranges of 0.2-5.5 μg/band, 0.2-4.5 μg/band and 0.1-3.0 μg/band for REM, LNZ and RIV, respectively. The sensitivities of REM, LIN, and RIV were outstanding, with quantitation limits of 128.8, 50.5, and 55.8 ng/band, respectively. The approach has shown outstanding recoveries ranging from 98.3 to 101.2% when applied to pharmaceutical formulations and spiked human plasma. The method's greenness was assessed using Analytical Eco-scale, GAPI, and AGREE metrics.

Ammonia quantum tunneling in cold rare-gas He and Ar clusters and factorial design approach for methodology evaluation

Quantum tunneling of the ammonia inversion motion and energy level splittings in He and Ar clusters were investigated. It was found that the double well potential (DWP) in He clusters is symmetrical and that the first layer of He atoms is able to model the system. The calculated tunneling splitting was in good agreement with the experimental, 36.4 and 24.6 cm[Formula: see text] respectively. For NH[Formula: see text] in Ar clusters, the DWP becomes slightly asymmetric, which is enough to decrease the resonance and make the symmetric DWP unable to model the system. An asymmetric potential was used and the result was in excellent agreement with the experimental splitting, of 9.0 and 10.6 cm[Formula: see text] respectively. Non-covalent interactions revealed that the asymmetry is caused by dissimilar interactions in each minimum of the double well potential. The effects of different methodologies were analyzed via a design of experiments approach. For the gas-phase NH[Formula: see text] molecule, only diffuse functions were statistically significant while for the NH[Formula: see text] embedded in He cluster both the MP2 method and polarization functions were significant. This tendency suggests higher order polarization functions may be essential to generate accurate barrier heights.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Plant Source Derived Compound Exhibited In Silico Inhibition of Membrane Glycoprotein In SARS-CoV-2: Paving the Way to Discover a New Class of Compound For Treatment of COVID-19

SARS-CoV-2 is the virus responsible for causing COVID-19 disease in humans, creating the recent pandemic across the world, where lower production of Type I Interferon (IFN-I) is associated with the deadly form of the disease. Membrane protein or SARS-CoV-2 M proteins are known to be the major reason behind the lower production of human IFN-I by suppressing the expression of IFNβ and Interferon Stimulated Genes. In this study, 7,832 compounds from 32 medicinal plants of India possessing traditional knowledge linkage with pneumonia-like disease treatment, were screened against the Homology-Modelled structure of SARS-CoV-2 M protein with the objective of identifying some active phytochemicals as inhibitors. The entire study was carried out using different modules of Schrodinger Suite 2020-3. During the docking of the phytochemicals against the SARS-CoV-2 M protein, a compound, ZIN1722 from Zingiber officinale showed the best binding affinity with the receptor with a Glide Docking Score of −5.752 and Glide gscore of −5.789. In order to study the binding stability, the complex between the SARS-CoV-2 M protein and ZIN1722 was subjected to 50 ns Molecular Dynamics simulation using Desmond module of Schrodinger suite 2020-3, during which the receptor-ligand complex showed substantial stability after 32 ns of MD Simulation. The molecule ZIN1722 also showed promising results during ADME-Tox analysis performed using Swiss ADME and pkCSM. With all the findings of this extensive computational study, the compound ZIN1722 is proposed as a potential inhibitor to the SARS-CoV-2 M protein, which may subsequently prevent the immunosuppression mechanism in the human body during the SARS-CoV-2 virus infection. Further studies based on this work would pave the way towards the identification of an effective therapeutic regime for the treatment and management of SARS-CoV-2 infection in a precise and sustainable manner.

Structure-based docking, pharmacokinetic evaluation, and molecular dynamics-guided evaluation of traditional formulation against SARS-CoV-2 spike protein receptor bind domain and ACE2 receptor complex

There is an urgent need for reliable cure and preventive measures in this hour of the outbreak of SARS-CoV-2. Siddha- and Ayurvedic-based classical formulations have antiviral properties and great potential therapeutic choice in this pandemic situation. In the current study, in silico-based analysis for the binding potential of phytoconstituents from the classical formulations suggested by the Ministry of Ayush (Kabasura Kudineer, Shwas Kuthar Rasa with Kantakari and pippali churna, Talisadi churna) to the interface domain of the SARS-CoV-2 receptor-binding domain and angiotensin-converting enzyme 2 was performed. Maestro software from Schrodinger and tools like Glide Docking, induced fit docking, MM-GBSA, molecular dynamics (MD) simulation, and thermal MM-GBSA was used to analyze the binding of protein PDB ID:6VW1 and the selected 133 ligands in comparison with drug molecules like favipiravir and ribavirin. QikProp-based ADMET evaluation of all the phytoconstituents found them nontoxic and with drug-like properties. Selection of top ten ligands was made based on docking score for further MM-GBSA analysis. After performing IFD of top five molecules iso-chlorogenic acid, taxiphyllin, vasicine, catechin and caffeic acid, MD simulation and thermal MM-GBSA were done. Iso-chlorogenic acid had formed more stable interaction with key residue among all phytoconstituents. Computational-based study has highlighted the potential of the many constituents of traditional medicine to interact with the SARS-CoV-2 RBD and ACE2, which might stop the viral entry into the cell. However, in vivo experiments and clinical trials are necessary for supporting this claim.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11696-021-01917-z.