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Essential role in replication and transcription of coronavirus makes the main protease of SARS-CoV-2 a great traget for drug design. The aim of this study was to predict structural interactions of compounds isolated from the Bosnian-Herzegovinian endemic plant Knautia sarajevensis (G. Beck) Szabo against the 3CLpro of SARS-CoV-2 virus. The three-dimensional crystal structure of SARS-CoV-2 main protease was retrieved from the RCSB Protein Data Bank and the three-dimensional structures of isolated compounds were obtained from the PubChem database. Active site was predicted using PrankWeb, while the preparation of protease and compounds was performed using AutoDock Tools and OpenBabel. Molecular docking was carried out using AutoDock Vina. Structural interactions are visualised and analyzed using PyMOL, LigPlus and UCSF Chimera. Apigenin, kaempferol, myricetin and quercetin showed the highest binding affinity for SARS-CoV-2 main protease and formed significant hydrogen bonds with the given protein. Results obtained in this study are in accordance with previous studies and showed that these compounds could potentially have antiviral effects against SARS-CoV-2. These findings indicate that K. sarajevensis could be potentially utilized as an adjuvant in the treatment of coronavirus disease 2019, but further pharmacological studies are required in order to prove the potential medicinal use of the plant.
ABSTRACT
COVID-19 is a disease caused by the new coronavirus, which has been spreading rapidly all over the world. There is no exact drug yet for the treatment of COVID-19 disease, and its treatment is tried to be provided with existing drugs. However, new drug research is being carried out to treat this disease. Topological indices are numerical descriptors based on the molecular graph of the molecular structure. Topological indices are used in modeling to predict the physicochemical properties and biological activities of molecules in the quantitative structure-property relationship (QSPR), quantitative structure-activity relationship (QSAR) studies. In this study, remdesivir, chloroquine, hydroxychloroquine, theaflavin, thalidomide, arbidol, lopinavir, ritonavir drugs used in the treatment of COVID-19 patients are studied. The QSPR model is designed using some degree-based indices, Mostar-type indices, and distance-based topological indices to predict the various physicochemical properties of these drugs. The relationship analyses between the physicochemical properties and the topological indices in the QSPR model are done by using the curvilinear regression method.
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The use of immunomodulators is one strategy in maintaining the immune system during the Covid-19 pandemic. Sungkai leaf extract from Peronema canecens keeps the immune system in good shape. Therefore, in this study, we formulated a self-emulsifying loaded sungkai leaves extract (SE-SLE) with oleic acid and virgin coconut oil (VCO) oil phases, span 80 and tween 80 as surfactants and co-surfactants in the form of PEG-400 and PG. Chemometric analysis was conducted by observing the typical pattern in each FTIR-ATR spectra. The pattern is divided into several groups based on the wavenumber and analyzed using principal component analysis (PCA) to identify the compounds contained therein. Grouping based on chemical properties via IR spectra on SE-SLE resulted in two large groups. The results obtained are beneficial as initial information in developing and optimizing the self-nano emulsifying drug delivery system formula.Copyright © RJPT All right reserved.
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The molecular structure of hydroxychloroquine (HCQ) used in the treatment of malaria is recently suggested for emergency used in COVID-19. The chemical compound of HCQ is produced by chemical alteration of ethylene oxide from human products, such as waxy maize starch. The molecular graph is a graph comprising of atoms called vertices and the chemical bond between molecules is called edges. A topological index is a numerical representation of a chemical structure which correlates certain physico-chemical characteristics of underlying chemical compounds besides its numerical representation. To distinguish the creation of entropy-based measures from the structure of chemical graphs, several graph properties have been utilized. For computing the structural information of chemical graphs, the graph entropies have become the information-theoretic quantities. The graph entropy measure has attracted the research community due to its potential application in discrete mathematics, biology, and chemistry. In this paper, our contribution is to explore graph entropies for molecular structure of HCQ based on novel information function, which is the number of different degree vertices along with the number of edges between various degree vertices. More precisely, we have explored the degree-based topological characteristics of hydroxyethyl starch conjugated with hydroxychloroquine (HCQ-HEC). Also, we computed entropies of this structure by making a relation of degree-based topological indices with the help of information function. Moreover, we presented the numerical and graphical comparison of the computed results.
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The novel coronavirus disease 2019 (Covid-19) is a mutating and recombining pandemic that potentially spreading through an infected person in droplet-generated forms that have affected more than 200 countries and endanger the entire globe. There is no clear strategy for the care of COVID-19 cases. Moreover, experts across the globe are working actively to develop medicinal or anti-virus drugs. On the basis of recent clinical findings and recommendations, the study examined a variety of new medications that have shown antiviral activity against SARS-CoV-2, among other drugs, antimalarial medications Chloroquine (CQ) and Hydroxychloroquine (HCQ) have gained significant publicity to have promising effects against SARS-CoV-2. Linking a bioactive substance to a biocompatible polymer typically provides various concerns, such as improved drug solubilization, improved modification, precise restriction, and controlled discharge. An enormous number of medical analyses have confirmed that the characteristics of medical drugs have a nearby connection with their atomic structure. Medication properties can be acquired by considering the atomic structure of relating drugs. The calculation of the topological index of a medication structure empowers researchers to have a superior comprehension of the physical science and bio-organic attributes of drugs. Ev-degree and ve-degree based topological indices are two novel degrees based indices as of late defined in graph theory. Ev-degree and ve-degree based topological indices have been defined as corresponding to their relating partners. In this paper, we have computed topological indices based on ev-degree and ve-degree for the Hydroxyethyl Starch and Hydroxychloroquine (HCQ-HEC) bioconjugate molecular structure.
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An outbreak of coronavirus disease 2019 (COVID-19) occurred in Wuhan and it has rapidly spread to almost all parts of the world. In the field of Medical Science, concerning the definition of the topological index on the molecular structure and corresponding medical, biological, chemical, pharmaceutical properties of drugs can be studied by the topological index calculation. In this paper, we compute some of the general topological properties of chloroquine and hydroxychloroquine used to inhibit the outbreak of coronavirus disease-19. The results in this paper may be useful in finding new drug and vaccine for the treatment and prevention of COVID-19.
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Objective: To explore the mechanism of Xiyanping injection in the treatment of human coronavirus infection based on network pharmacology and molecular docking method. Methods: The active components and targets of Xiyanping injection were screened by CNKI, SwissTarget Prediction and Targetnet. The Human Gene Database (Genecards), Online Human Mendelian Inheritance Database (OMIM) and Therapeutic Target Database (TTD) were searched to predict disease targets. Venny 2.1.0, Cytoscape 3.8.2 and STRING11.5 were used to construct "drug target-disease target Venn diagram", "drug-active ingredient-target-disease network" and "protein interaction network". The Database for Annotation, Visualization and Integrated Discovery (DAVID) and Bioinformatics, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for the enrichment analysis and visualization. Finally, molecular docking was performed by AutoDock Vina and PyMOL. Results: The active ingredient of Xiyanping injection was andrographolide, andrographolide had 140 targets, 1 812 potential targets of human coronavirus infection, and 35 common targets of Xiyanping and human coronavirus infection;PPI network analysis and molecular docking showed that MAPK9, MAPK8, TYK2, CDKI and interleukin (IL)-6 among the 35 common targets might be the key targets of Xiyanping injection in the treatment of human coronavirus infection. Lactone was tightly bound;enrichment analysis showed that key targets were closely related to protein phosphorylation, cell signal transduction, and gene expression regulation, and key targets were NOD-like receptor signaling pathway, Toll-like receptor signaling pathway, FOXO signaling pathway, there was also an important link in the TNF signaling pathway. Conclusion: The active ingredient of Xiyanping injection was andmgrapholide, and its treatment of human coronavirus infection might affect NOD-like receptor signaling pathway, Toll-like receptor signaling pathway and FOXO signaling by inhibiting the activities of MAPK9, MAPK8, TYK2, CDK1 and IL-6. The activation of the pathway and the TNF signaling pathway regulates protein phosphorylation, cell signal transduction and gene expression, thereby exerting anti-inflammatory effects.
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Scalable alternate end-game strategies for the synthesis of the anti-COVID drug molecule Nirmatrelvir (1, PF-07321332) have been described. The first involves a direct synthesis of 1 via amidation of the carboxylic acid 7 (suitably activated as a mixed anhydride with either pivaloyl chloride or T3P) with the amino-nitrile 10·HCl. T3P was found to be a more practical choice since the reagent promoted efficient and concomitant dehydration of the amide impurity 9 (derived from the amino-amide contaminant 8·HCl invariably present in 10·HCl) to 1. This observation allowed for the development of the second strategy, namely a continuous flow synthesis of 1 from 9 mediated by T3P. Under optimized conditions, this conversion could be achieved within 30 min in flow as opposed to 12–16 h in a traditional batch process. The final API had quality attributes comparable to those obtained in conventional flask processes.
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Curcumae Longae Rhizoma (CLR) is the rhizome of Curcuma longa L. Pharmacological studies show that CLR can be used to treat cervical cancer, lung cancer, lupus nephritis, and other conditions. In this paper, we review botany, traditional application, phytochemistry, pharmacological activity, and pharmacokinetics of CLR. The literature from 1981 to date was entirely collected from online databases, such as Web of Science, Google Scholar, China Academic Journals full-text database (CNKI), Wiley, Springer, PubMed, and ScienceDirect. The data were also obtained from ancient books, theses and dissertations, and Flora Reipublicae Popularis Sinicae. There are a total of 275 compounds that have been isolated from CLR, including phenolic compounds, volatile oils, and others. The therapeutic effect of turmeric has been expanded from breaking blood and activating qi in the traditional sense to antitumor, anti-inflammatory, antioxidation, neuroprotection, antibacterial, hypolipidemic effects, and other benefits. However, the active ingredients and mechanisms of action related to relieving disease remain ill defined, which requires more in-depth research and verification at a clinical level.
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Background: The 2'-O-methyltransferase is responsible for the capping of SARS-CoV-2 mRNA and consequently the evasion of the host's immune system. This study aims at identifying prospective natural inhibitors of the active site of SARS-CoV-2 2'O-methyltransferase (2'-OMT) through an in silico approach. Materials and Method: The target was docked against a library of natural compounds obtained from edible African plants using PyRx - virtual screening software. The antiviral agent, Dolutegravir which has a binding affinity score of -8.5 kcal mol-1 with the SARS-CoV-2 2'-OMT was used as a standard. Compounds were screened for bioavailability through the SWISSADME web server using their molecular descriptors. Screenings for pharmacokinetic properties and bioactivity were performed with PKCSM and Molinspiration web servers respectively. The PLIP and Fpocket webservers were used for the binding site analyses. The Galaxy webserver was used for simulating the time-resolved motions of the apo and holo forms of the target while the MDWeb web server was used for the analyses of the trajectory data.
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The severe acute respiratory syndrome coronavirus 2 (SARS - CoV2), the causative viral pathogen of the COVID-19 pandemic belongs to the family of Coronaviruses which are positive single stranded RNA viruses. The scientific fraternity has developed and developing various types of vaccines for prevention against COVID-19, such as inactivated virus vaccines, mRNA vaccines, replicating vector protein subunit vaccines, etc., Out of which ten vaccines namely Novovax, Covovax (protein subunit vaccines), Pfizer BNT16b2, Moderna mRNA 1273 (mRNA vaccines), Johnson & Johnson Ad26, Cov2.S, Astrazeneca AZD1222, Covishield (non-replicating viral vector vaccines), Covaxin, Sinopharm BBIBP-CorV, CoronoVac (inactivated vaccines) have been approved for clinical use by WHO. There is an urgent need for SARS-CoV2 specific therapeutics for the treatment of COVID-19 as there is the emergence of various variants such as Alpha, Beta, Gamma, Delta, Omicron, etc. The emergence of variants that possesses immune evading property and spike protein mutation have increased infectivity and more pathogenicity which impelled the need to develop various therapeutics for the treatment of COVID-19. This review compiles the information about potential antiviral candidates in preclinical trials intended for the treatment of COVID-19. The clinical development of such antivirals will be very crucial for the treatment of COVID-19 and also to curb the spread as the present scenario depends on the development of effective prophylactic vaccines.
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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes COVID-19 which is responsible for respiratory illness infection in humans. The virus was first identified in China in 2019 and later spread to other countries worldwide. This study aims to identify the bioactive compounds from mangosteen (Garcinia mangostana L.) as an antiviral agent via dual inhibitor mechanisms against two SARS-CoV-2 proteases through the in silico approach. The three-dimensional structure of various bioactive compounds of mangosteen from the database was examined. Furthermore, all the target compounds were analyzed for drug, antiviral activity prediction, virtual screening, molecular interactions, and threedimensional structure visualization. It aimed to determine the potential of the bioactive compounds from mangosteen that can serve as antiviral agents to fight SARS-CoV-2. Results showed that the bioactive compounds from mangosteen have the prospective to provide antiviral agents that contradict the virus via dual inhibitory mechanisms. In summary, the binding of the various bioactive compounds from mangosteen results in low binding energy and is expected to have the ability to induce any activity of the target protein binding reaction. Therefore, it allows various bioactive compounds from mangosteen to act as dual inhibitory mechanisms for COVID-19 infection.
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The adsorption of chloroquine (CQ) and hydroxychloroquine (HCQ) on BC3nanosheets was evaluated and compared in gas and water media. The most desired complexes were obtained when the drug is parallel to the BC3surface, with an adsorption energy of −1.69 and −1.77 eV for CQ/bare and CQ/hydrogenated BC3complexes, respectively. The corresponding adsorption energies for HCQ/bare and HCQ/hydrogenated BC3nanosheets are −1.78 and −1.99 eV, respectively. It was found that the BC3nanosheets could be a suitable carrier of CQ and HCQ drugs, considering the amount of adsorption energy in the gas phase and water environment. The hydrogenated BC3nanosheet is a more prominent nanocarrier for CQ and HCQ than the bare BC3monolayer.
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The molecular structure of 2-Deoxy D-Glucose is exactly like D-Glucose except for the fact that the former has no hydroxyl group on the carbon atom at the position 2 but instead has a hydrogen atom. Glucose can be of two types: one which turns the plane of the polarized light, when passed through its solution in water, towards right is called Dextrorotatory or D-Glucose and the other which turns it towards left is called Levorotatory or LGlucose. Chemists call glucose as an aldohexose, indicating that it is made up of six carbon atoms, one of which is an aldehyde group and the rest contain hydrogen atoms and hydroxyl groups. In other words, it has one oxygen atom less than D-Glucose and therefore, is called 2-Deoxy-D-Glucose. The Drugs Controller General of India granted approval (May 17 2021) for use of 2 DG drug after it went through too extensive successful clinical trials. Now, the drug has been recommended to be used in moderate cases which have to be taken orally mixed with water, 2 times a day for 5 to 7 days. It showed that the drug was able to reduce oxygen dependency by the third day and reduce the hospital stay considerably.
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Coronavirus disease, which is called as COVID-19 and that is one of the infectious diseases which is infected by newly found coronavirus. Maching Learning has the major role in predicting the drugs of the particular disease. Lalmuanawma et al. 2020 has given the application of machine learning and artificial intelligence in COVID'19. It is used to develop the model design, Regression is one of the supervised Machine Learning Techniques. It is used to predict the values based on the data given. In this research work, Quantitative structure activity relationship (QSAR) study has been developed for structurally similar to 2-acetamido-2-deoxy-beta-D-glucopyranose as inhibitors for COVID-19 causing targets using regression. QSAR models for complexity was created with 40 training compounds, 20 test compounds, and 21 different descriptors. The structurally 95% similar compound of 2-acetamido-2-deoxy-beta-D-glucopyranose has been collected from pubchem[13] and molinspiration.com. Using 40 compounds, the linear regression model has been developed. The predictive capability of the QSAR models was evaluated by Correlation coefficient, mean absolute error, Root mean squared error, Relative absolute error, Root relative squared error.
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Layered double hydroxide nanomaterials (LDH NMs) have been dragging the researchers' attention toward biomedical applications owing to their physiochemical properties, biocompatibility, environmental sensitivity and good cellular uptake mechanisms. Various synthetic methods have been presented in brief. This paper draws attention toward the modification and functionalization of LDH nanostructures for biomedical applications in targeted and controlled drug release, anticancer, bioimaging, bone therapy and regeneration, gene delivery, ophthalmic and antitumor activities. Further, it explains the properties of conjugated LDH NMs which put forward their possibilities to be used in synthesizing the most demanding vaccine for COVID-19 pandemic. Current scenario, challenges and future perspective of LDH NMs have also been discussed.
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BACKGROUND: The current coronavirus disease-19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global outbreak of a disease from a new coronavirus. Several databases have been published on this pandemic, but the research community still needs an easy way to get comprehensive information on COVID-19. OBJECTIVES: COVID-19 pandemic database (CO-19 PDB) aims to provide wonderful insights for COVID-19 researchers with the well-gathered of all the COVID-19 data to one platform, which is a global challenge for the research community these days. METHODS: We gathered 59 updated databases since December-2019 until May 2021 and divided them into six categories: digital image database, genomic database, literature database, visualization tools database, chemical structure database, and social science database. These categories focus on taking number of functions from the images, information from gene sequences, updates from relevant papers, essays, reports, articles, and books, the data or information in the form of maps, graphs, and charts, information of bonds between atoms, and updates about events of the physical and social environment, respectively. RESULTS: Users can search the information of interest in two ways including typing the name of the database in the search bar or by clicking the right category directly. Computer languages such as CSS, PHP, HTML, Java, etc. are utilized to construct CO-19 PDB. CONCLUSION: This article attempts to compile up-to-date appropriate COVID-19 datasets and resources that have not been compiled and given in such an accessible and user-friendly manner. As a result, the CO-19 PDB offers extensive open data sharing for both worldwide research communities and local people. Further, we have planned future development of new features, that will be awesome for future study.
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Discovering drug-target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug-target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.