A nicotinamide-based derivative was designed as an antiproliferative VEGFR-2 inhibitor with the key pharmacophoric features needed to interact with the VEGFR-2 catalytic pocket. The ability of the designed congener ((E)-N-(4-(1-(2-(4-benzamidobenzoyl)hydrazono)ethyl)phenyl)nicotinamide), compound 10, to bind with the VEGFR-2 enzyme was demonstrated by molecular docking studies. Furthermore, six various MD simulations studies established the excellent binding of compound 10 with VEGFR-2 over 100 ns, exhibiting optimum dynamics. MM-GBSA confirmed the proper binding with a total exact binding energy of −38.36 Kcal/Mol. MM-GBSA studies also revealed the crucial amino acids in the binding through the free binding energy decomposition and declared the interactions variation of compound 10 inside VEGFR-2 via the Protein–Ligand Interaction Profiler (PLIP). Being new, its molecular structure was optimized by DFT. The DFT studies also confirmed the binding mode of compound 10 with the VEGFR-2. ADMET (in silico) profiling indicated the examined compound’s acceptable range of drug-likeness. The designed compound was synthesized through the condensation of N-(4-(hydrazinecarbonyl)phenyl)benzamide with N-(4-acetylphenyl)nicotinamide, where the carbonyl group has been replaced by an imine group. The in-vitro studies were consonant with the obtained in silico results as compound 10 prohibited VEGFR-2 with an IC50 value of 51 nM. Compound 10 also showed antiproliferative effects against MCF-7 and HCT 116 cancer cell lines with IC50 values of 8.25 and 6.48 μM, revealing magnificent selectivity indexes of 12.89 and 16.41, respectively.

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Design, Synthesis, Docking, DFT, MD Simulation Studies of a New Nicotinamide-Based Derivative: In Vitro Anticancer and VEGFR-2 Inhibitory Effects

The inhibitory potential of Artemisia annua, a well-known antimalarial herb, against several viruses, including the coronavirus, is increasingly gaining recognition. The plant extract has shown significant activity against both the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and the novel SARS-CoV-2 that is currently ravaging the world. It is therefore necessary to evaluate individual chemicals of the plant for inhibitory potential against SARS-CoV-2 for the purpose of designing drugs for the treatment of COVID-19. In this study, we employed computational techniques comprising molecular docking, binding free energy calculations, pharmacophore modeling, induced-fit docking, molecular dynamics simulation, and ADMET predictions to identify potential inhibitors of the SARS-CoV-2 main protease (Mpro) from 168 bioactive compounds of Artemisia annua. Rhamnocitrin, isokaempferide, kaempferol, quercimeritrin, apigenin, penduletin, isoquercitrin, astragalin, luteolin-7-glucoside, and isorhamnetin were ranked the highest, with docking scores ranging from −7.84 to −7.15 kcal/mol compared with the −6.59 kcal/mol demonstrated by the standard ligand. Rhamnocitrin, Isokaempferide, and kaempferol, like the standard ligand, interacted with important active site amino acid residues like HIS 41, CYS 145, ASN 142, and GLU 166, among others. Rhamnocitrin demonstrated good stability in the active site of the protein as there were no significant conformational changes during the simulation process. These compounds also possess acceptable druglike properties and a good safety profile. Hence, they could be considered for experimental studies and further development of drugs against COVID-19.

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A Computational Approach to Elucidate the Interactions of Chemicals From Artemisia annua Targeted Toward SARS-CoV-2 Main Protease Inhibition for COVID-19 Treatment

In coronavirus disease 2019 (Covid-19) era, neuroinflammation may develop due to neuronal tropism of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) and/or associated immune activation, cytokine storm, and psychological stress. SARS-CoV-2 infection and linked cytokine storm may cause blood-brain barrier (BBB) injury through which activated immune cells and SARS-CoV-2 can pass into the brain causing activation of glial cells with subsequent neuroinflammation. Different therapeutic regimens were suggested to alleviate Covid-19-induced neuroinflammation. Since glibenclamide has anti-inflammatory and neuroprotective effects, it could be effective in mitigation of SARS-CoV-2 infection-induced neuroinflammation. Glibenclamide is a second-generation drug from the sulfonylurea family, which acts by inhibiting the adenosine triphosphate (ATP)-sensitive K channel in the regulatory subunit of type 1 sulfonylurea receptor (SUR-1) in pancreatic β cells. Glibenclamide reduces neuroinflammation and associated BBB injury by inhibiting the nod-like receptor pyrin 3 (NLRP3) inflammasome, oxidative stress, and microglial activation. Therefore, glibenclamide through inhibition of NLRP3 inflammasome, microglial activation, and oxidative stress may attenuate SARS-CoV-2-mediated neuroinflammation. 

/pdf/targeting-of-neuroinflammation-by-glibenclamide-in-covid-19-22gbdhzl.pdf

Targeting of neuroinflammation by glibenclamide in Covid-19: old weapon from arsenal

Deciphering the interactions of compounds from Allium sativum targeted towards identification of novel PTP 1B inhibitors in diabetes treatment: A computational approach

BACKGROUND Wedelia chinensis has been reported as a folk medicine for the treatment of different diseases including neurodegenerative disease. Although the plant has been studied well for diverse biological activities, the effect of this plant in neurological disorder is largely unknown. The present study was undertaken to evaluate the cholinesterase inhibitory and antioxidant potential of W. chinensis. METHODS The extract and fractions of the plant were evaluated for acetylcholinesterase and butyrylcholinesterase inhibitory activity by modified Ellman method. The antioxidant activity was assessed in several in vitro models/assays such as reducing power, total antioxidant capacity, total phenolic and flavonoid content, scavenging of 2,2'-diphenyl-1-picrylhydrazyl (DPPH) free radical and hydroxyl radical, and inhibition of brain lipid peroxidation. Chromatographic and spectroscopic methods were used to isolate and identify the active compound from the extract. RESULTS Among the fractions, aqueous fraction (AQF) and ethylacetate fraction (EAF) exhibited high inhibition against acetylcholinesterase (IC50: 40.02 ± 0.16 μg/ml and 57.76 ± 0.37 μg/ml) and butyrylcholinesterase (IC50: 31.79 ± 0.18 μg/ml and 48.41 ± 0.05 μg/ml). Similarly, the EAF and AQF had high content of phenolics and flavonoids and possess strong antioxidant activity in several antioxidant assays including DPPH and hydroxyl radical scavenging, reducing power and total antioxidant activity. They effectively inhibited the peroxidation of brain lipid in vitro with IC50 values of 45.20 ± 0.10 μg/ml and 25.53 ± 0.04 μg/ml, respectively. A significant correlation was observed between total flavonoids and antioxidant and cholinesterase inhibitory activity. Activity guided chromatographic separation led to the isolation of a major active compound from the EAF and its structure was elucidated as apigenin by spectral analysis. CONCLUSIONS The potential ability of W. chinensis to inhibit the cholinesterase activity and peroxidation of lipids suggest that the plant might be useful for the management of AD.

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Evaluation of cholinesterase inhibitory and antioxidant activity of Wedelia chinensis and isolation of apigenin as an active compound.

Neurodegenerative diseases, for example Alzheimer’s, are perceived as driven by hereditary, cellular, and multifaceted biochemical actions. Numerous plant products, for example flavonoids, are documented in studies for having the ability to pass the blood-brain barrier and moderate the development of such illnesses. Computer-aided drug design (CADD) has achieved importance in the drug discovery world; innovative developments in the aspects of structure identification and characterization, bio-computational science, and molecular biology have added to the preparation of new medications towards these ailments. In this study we evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies, in comparison with standard drugs. The results indicated that the pharmacophore models produced from structures of AChE, BChE and MAO could identify the active compounds, with a recuperation rate of the actives found near 100% in the complete ranked decoy database. Moreso, the robustness of the virtual screening method was accessed by well-established methods including enrichment factor (EF), receiver operating characteristic curve (ROC), Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and area under accumulation curve (AUAC). Most notably, the compounds’ pIC50 values were predicted by a machine learning-based model generated by the AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best models achieved for AChE, BChE and MAO were models kpls_radial_17 (R2 = 0.86 and Q2 = 0.73), pls_38 (R2 = 0.77 and Q2 = 0.72), kpls_desc_44 (R2 = 0.81 and Q2 = 0.81) and these externally validated models were utilized to predict the bioactivities of the lead compounds. The binding affinity results of the ligands against the three selected targets revealed that luteolin displayed the highest affinity score of −9.60 kcal/mol, closely followed by apigenin and ellagic acid with docking scores of −9.60 and −9.53 kcal/mol, respectively. The least binding affinity was attained by gallic acid (−6.30 kcal/mol). The docking scores of our standards were −10.40 and −7.93 kcal/mol for donepezil and galanthamine, respectively. The toxicity prediction revealed that none of the flavonoids presented toxicity and they all had good absorption parameters for the analyzed targets. Hence, these compounds can be considered as likely leads for drug improvement against the same.

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer's Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches.

Mary-Ann Chinyere Nwakama

Papers

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer's Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches.