In silico evaluation of molecular interactions between macrocyclic inhibitors with the HCV NS3 protease. Docking and identification of antiviral pharmacophore site

Anticancer activity, DFT study, ADMET prediction, and molecular docking of novel α-sulfamidophosphonates

A series of novel α-sulfamidophosphonate derivatives (3a-3 g) were synthesized and evaluated for anticancer activity against different human cancer cell lines (PRI, K562, and JURKAT). The antitumor activity of all compounds using the MTT test remains moderate compared to the standard drug chlorambucil. Compounds 3c and 3 g were found to be more active anticancer agent against PRI and K562 cells with IC50 value 0.056-0.097 and 0.182-0.133 mM, respectively. Molecular docking study related to binding affinity and binding mode analysis showed that synthesized compounds had potential to inhibit glutamate carboxypeptidase II (GCPII). Furthermore, computational analysis was performed through Density Functional Theory (DFT) utilizing the B3LYP 6-31 G (d, p) basis set and the theoretical results were correlated with experimental data. The ADME/toxicity analyses carried out by Swiss ADME and OSIRIS software show that all synthesized molecules exhibited good pharmacokinetics, bioavailability, and had no toxicity profile.

Elucidating the anti-cancer potential of Cinnamomum tamala essential oil against non-small cell lung cancer: A multifaceted approach involving GC-MS profiling, network pharmacology, and molecular dynamics simulations

Cinnamomum tamala (Buch.-Ham.) T.Nees & Eberm., or Indian Bay Leaf, is a well-known traditional ayurvedic medicine used to treat various ailments. However, the molecular mechanism of action of Cinnamomum tamala essential oil (CTEO) against non-small cell lung cancer (NSCLC) remains elusive. The present study aims to decipher the molecular targets and mechanism of CTEO in treating NSCLC. GC-MS analysis detected 49 constituents; 44 successfully passed the drug-likeness screening and were identified as active compounds. A total of 3961 CTEO targets and 4588 anti-NSCLC-related targets were acquired. JUN, P53, IL6, MAPK3, HIF1A, and CASP3 were determined as hub genes, while cinnamaldehyde, ethyl cinnamate and acetophenone were identified as core compounds. Enrichment analysis revealed that targets were mainly involved in apoptosis, TNF, IL17, pathways in cancer and MAPK signalling pathways. mRNA expression, pathological stage, survival analysis, immune infiltrate correlation and genetic alteration analysis of the core hub genes were carried out. Kaplan-Meier overall survival (OS) curve revealed that HIF1A and CASP3 are linked to worse overall survival in Lung Adenocarcinoma (LUAD) cancer patients compared to normal patients. Ethyl cinnamate and cinnamaldehyde showed high binding energy with the MAPK3 and formed stable interactions with MAPK3 during the molecular dynamic simulations for 100 ns. The MM/PBSA analysis revealed that van der Waals (VdW) contributions predominantly account for a significant portion of the compound interactions within the binding pocket of MAPK3. Density functional theory analysis showed cinnamaldehyde as the most reactive and least stable compound. CTEO exhibited selective cytotoxicity by inhibiting the proliferation of A549 cells while sparing normal HEK293 cells. CTEO triggered apoptosis by arresting the cell cycle, increasing ROS accumulation, causing mitochondrial depolarisation, and elevating caspase-3, caspase-8 and caspase-9 levels in A549 cells. The above study provides insights into the pharmacological mechanisms of action of Cinnamomum tamala essential oil against non-small cell lung cancer treatment, suggesting its potential as an adjuvant therapy.

Green synthesis, antibacterial and antifungal evaluation of new thiazolidine-2,4-dione derivatives: molecular dynamic simulation, POM study and identification of antitumor pharmacophore sites

In this study, a series of thiazolidine-2,4-dione derivatives 3a-i were synthesized and evaluated for antibacterial activity against Gram-positive and Gram-negative strains of Bacillus licheniformis, Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus. Newly prepared thiazolidine (TZD) derivatives were further screened separately for in vitro antifungal activity against cultures of fungal species, namely, Aspergillus niger, Alternaria brassicicola, Chaetomium murorum, Fusarium oxysporum, Lycopodium sp. and Penicillium notatum. The electron-donating substituents (-OH and -OCH3) and electron-withdrawing substituents (-Cl and -NO2) on the attached arylidene moieties of five-membered heterocyclic ring enhanced the broad spectrum of antimicrobial and antifungal activities. The molecular docking study has revealed that compound 3h strongly interacts with the catalytic residues of the active site of the β-carbonic anhydrase (P. aeruginosa) and has the best docking score. In silico pharmacokinetics studies showed the drug-likeness and non-toxic nature of the synthesized compounds, which indicates the combined antibacterial, antiviral and antitumor pharmacophore sites of the targeted drug. This work demonstrates that potential TZD derivatives bind to different types of bacterial and fungal pathogens for circumventing their activities and opens avenues for the development of newer drug candidates that can target bacterial and fungal pathogens.Communicated by Ramaswamy H. Sarma.

Computer Analysis of the Inhibition of ACE2 by Flavonoids and Identification of Their Potential Antiviral Pharmacophore Site

In the present study, we investigated the antiviral activities of 17 flavonoids as natural products. These derivatives were evaluated for their in vitro antiviral activities against HIV and SARS-CoV-2. Their antiviral activity was evaluated for the first time based on POM (Petra/Osiris/Molispiration) theory and docking analysis. POM calculation was used to analyze the atomic charge and geometric characteristics. The side effects, drug similarities, and drug scores were also assumed for the stable structure of each compound. These results correlated with the experimental values. The bioinformatics POM analyses of the relative antiviral activities of these derivatives are reported for the first time.

Synthesis, In Vitro α-Glucosidase Inhibitory Activity and Molecular Docking Study of New Benzotriazole-Based Bis-Schiff Base Derivatives

This study was carried out to synthesize benzotriazole-based bis-Schiff base scaffolds (1-20) and assess them in vitro for α-glucosidase inhibitory potentials. All the synthetics analogs based on benzotriazole-based bis-Schiff base scaffolds were found to display an outstanding inhibition profile on screening against the α-glucosidase enzyme. The synthetic scaffolds showed a varied range of inhibition profiles having IC₅₀ values ranging from 1.10 ± 0.05 µM to 28.30 ± 0.60 µM when compared to acarbose as a standard drug (IC₅₀ = 10.30 ± 0.20 µM). Among the series, fifteen scaffolds 1-3, 5, 6, 9-16, 18-20 were identified to be more potent than standard acarbose, while the five remaining scaffolds 4, 7, 8, 16, and 17, also showed potency against the α-glucosidase enzyme but were found to be less potent than standard acarbose. The structure of all the newly synthesized scaffolds was confirmed using different spectroscopic techniques such as HREI-MS and ¹H- and ¹³C- NMR spectroscopy. To find a structure-activity relationship, molecular docking studies were carried out to understand the binding mode of the active inhibitors with the active sites of the enzyme and the results supported the experimental data.

Potential SARS-CoV-2 RdRp inhibitors of cytidine derivatives: Molecular docking, molecular dynamic simulations, ADMET, and POM analyses for the identification of pharmacophore sites

The RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 is one of the optimum targets for antiviral drug design and development. The hydroxyl groups of cytidine structures were modified with different aliphatic and aromatic groups to obtain 5´-O-acyl and 2´,3´-di-O-acyl derivatives, and then, these derivatives were employed in molecular modeling, antiviral prediction, molecular docking, molecular dynamics, pharmacological and POM studies. Density functional theory (DFT) at the B3LYP/6-31G++ level analyzed biochemical behavior and molecular electrostatic potential (MESP) of the modified cytidine derivatives. The antiviral parameters of the mutated derivatives revealed promising drug properties compared with those of standard antiviral drugs. Molecular docking has determined binding affinities and interactions between the cytidine derivatives and SARS-CoV-2 RdRp. The modified derivatives strongly interacted with prime Pro620 and Lys621 residues. The binding conformation and interactions stability were investigated by 200 ns of molecular dynamics simulations and predicted the compounds to firmly dock inside the RdRp binding pocket. Interestingly, the binding residues of the derivatives were revealed in high equilibrium showing an enhanced binding affinity for the molecules. Intermolecular interactions are dominated by both Van der Waals and electrostatic energies. Finally, the pharmacokinetic characterization of the optimized inhibitors confirmed the safety of derivatives due to their improved kinetic properties. The selected cytidine derivatives can be suggested as potential inhibitors against SARS-CoV-2. The POM Theory supports the hypothesis above by confirming the existence of an antiviral (Oδ--O'δ-) pharmacophore site of Hits.

Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches

Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5-10% cases. Therefore, it is crucial to develop new antivirals against HCV. In this endeavor, we developed the "Anti-HCV" platform using machine learning and quantitative structure-activity relationship (QSAR) approaches to predict repurposed drugs targeting HCV non-structural (NS) proteins. We retrieved experimentally validated small molecules from the ChEMBL database with bioactivity (IC50/EC50) against HCV NS3 (454), NS3/4A (495), NS5A (494) and NS5B (1671) proteins. These unique compounds were divided into training/testing and independent validation datasets. Relevant molecular descriptors and fingerprints were selected using a recursive feature elimination algorithm. Different machine learning techniques viz. support vector machine, k-nearest neighbour, artificial neural network, and random forest were used to develop the predictive models. We achieved Pearson's correlation coefficients from 0.80 to 0.92 during 10-fold cross validation and similar performance on independent datasets using the best developed models. The robustness and reliability of developed predictive models were also supported by applicability domain, chemical diversity and decoy datasets analyses. The "Anti-HCV" predictive models were used to identify potential repurposing drugs. Representative candidates were further validated by molecular docking which displayed high binding affinities. Hence, this study identified promising repurposed drugs viz. naftifine, butalbital (NS3), vinorelbine, epicriptine (NS3/4A), pipecuronium, trimethaphan (NS5A), olodaterol and vemurafenib (NS5B) etc. targeting HCV NS proteins. These potential repurposed drugs may prove useful in antiviral drug development against HCV.