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Search Results (301)

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Keywords = ADMET analysis

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24 pages, 14052 KiB  
Article
Identification of DDR1 Inhibitors from Marine Compound Library Based on Pharmacophore Model and Scaffold Hopping
by Honghui Hu, Jiahua Tao and Lianxiang Luo
Int. J. Mol. Sci. 2025, 26(3), 1099; https://doi.org/10.3390/ijms26031099 - 27 Jan 2025
Abstract
Ulcerative colitis (UC) is a chronic inflammatory condition that affects the intestines. Research has shown that reducing the activity of DDR1 can help maintain intestinal barrier function in UC, making DDR1 a promising target for treatment. However, the development of DDR1 inhibitors as [...] Read more.
Ulcerative colitis (UC) is a chronic inflammatory condition that affects the intestines. Research has shown that reducing the activity of DDR1 can help maintain intestinal barrier function in UC, making DDR1 a promising target for treatment. However, the development of DDR1 inhibitors as drugs has been hindered by issues such as toxicity and poor binding stability. As a result, there are currently no DDR1-targeting drugs available for clinical use, highlighting the need for new inhibitors. In a recent study, a dataset of 85 DDR1 inhibitors was analyzed to identify key characteristics for effective inhibition. A pharmacophore model was constructed and validated to screen a library of marine natural products for potential DDR1 inhibitors. Through high-throughput virtual screening and precise docking, 17 promising compounds were identified from a pool of over 52,000 molecules in the marine database. To improve binding affinity and reduce potential toxicity, scaffold hopping was employed to modify the 17 compounds, resulting in the generation of 1070 new compounds. These new compounds were further evaluated through docking and ADMET analysis, leading to the identification of three compounds—39713a, 34346a, and 34419a—with superior predicted activity and drug-like properties compared to the original 17 compounds. Further analysis showed that the binding free energy values of the three candidate compounds were less than −12.200 kcal/mol, which was similar to or better than −12.377 kcal/mol of the known positive compound VU6015929, and the drug-like properties were better than those of the positive compounds. Molecular dynamics simulations were then conducted on these three candidate compounds, confirming their stable interactions with the target protein. In conclusion, compounds 39713a, 34346a, and 34419a show promise as potential DDR1 inhibitors for the treatment of ulcerative colitis. Full article
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<p>A flowchart of the strategy employed in the identification of potential inhibitors of DDR1. By constructing a pharmacophore model with multi-ligand common features to screen a compound library composed of three marine compound libraries to distinguish between active and inactive compounds, and through high-throughput virtual screening and precise docking, 17 promising potential DDR1 inhibitors were identified from the marine compound library. In order to improve the binding affinity and reduce the potential toxicity, we modified the 17 compounds by fragment substitution and re-evaluated the fragment-replaced compounds by precise docking and ADMET. Three potential DDR1 inhibitors were identified that were superior to the positive compounds, and their molecular dynamics simulations were carried out. The potential DDR1 inhibitors 39713a, 34346a, and 34419a with advantages were screened out through a comprehensive analysis of the above multiple perspectives.</p>
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<p>Comparative analysis of pharmacophore model interactions. (<b>A</b>) Successful alignment of pharmacophore model ADHRR_3 with active molecule 71624791; (<b>B</b>) ineffective alignment of pharmacophore model AARR_2 with inactive molecule 89884371; (<b>C</b>) receiver operating characteristic (ROC) curve for pharmacophore model ADHRR_3.</p>
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<p>Binding pattern of DI1 and 89884371 to the protein DDR1. (<b>A</b>) Two-dimensional images of DDR1 interacting with DI1. (<b>B</b>) Three-dimensional images of DDR1 interacting with DI1. (<b>C</b>) Two-dimensional images of DDR1 interacting with 89884371. (<b>D</b>) Three-dimensional images of DDR1 interacting with 89884371.</p>
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<p>Identification of 17 marine compounds superior to positive compound VU6015929 by high-throughput virtual screening and precision docking.</p>
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<p>Three-dimensional visualization of binding patterns in protein–ligand complexes (hydrogen bond interactions in violet, cation–π interactions in red, π-π interactions in green). (<b>A</b>) Binding pattern of compound 39713a; (<b>B</b>) binding pattern of compound 34346a; (<b>C</b>) binding pattern of compound 34419a; (<b>D</b>) binding pattern of positive control compound VU6015929.</p>
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<p>Two-dimensional images of DDR1 interacting with different compounds. (<b>A</b>) DDR1 and compound 39713a; (<b>B</b>) DDR1 and compound 34346a; (<b>C</b>) DDR1 and compound 34419a; (<b>D</b>) DDR1 and positive control compound VU6015929.</p>
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<p>Intestinal absorption models (red ellipses represent 95% confidence intervals for the HIA model; green ellipses represent 99% confidence intervals for the HIA model. Blue dots depict the values of ADMET_PSA_2D and ADMET_AlogP98 for the three active molecules, positive control compound, and negative compound) (A: 39713-a, B: 34346-a, C: 34419-a, D: VU6015929, E: 89884371).</p>
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<p>Molecular dynamics simulations of 3 ligand–protein complexes, positive control compound, and negative control compound. (<b>A</b>) RMSD values of the 3 ligand–protein complexes, positive control compound, and negative control compound over time; (<b>B</b>) schematic of the RMSF of the 3 ligand–protein complexes, positive control compound, and negative control compound; (<b>C</b>) schematic of the protein radius of gyration of the 3 ligand–protein complexes, positive control compound, and negative control compound; (<b>D</b>) overall potential energy of the 3 ligand–protein complexes, positive control compound, and negative control compound over time.</p>
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<p>Statistical analysis of interaction numbers for three ligand–protein complexes. (<b>A</b>) Time-dependent hydrogen bond formation in the 34419a–DDR1 complex; (<b>B</b>) time-dependent hydrogen bond formation in the 37913a–DDR1 complex; (<b>C</b>) time-dependent hydrogen bond formation in the 34346a–DDR1 complex; (<b>D</b>) time-dependent interaction counts in the 34419a–DDR1 complex; (<b>E</b>) time-dependent interaction counts in the 37913a–DDR1 complex; (<b>F</b>) time-dependent interaction counts in the 34346a–DDR1 complex.</p>
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<p>Schematic representation of principal component distributions and variance ratios of the different ligand and protein systems in the molecular dynamics simulation. Blue dots indicate early stages of the simulation, red dots indicate later stages of the simulation, and the blue to red colour gradient can help to observe the molecular trend over time during the simulation process. (<b>A</b>) Two-dimensional plot of PC1 versus PC2 for the 34419a–protein system; (<b>B</b>) 2D plot of PC2 versus PC3 for the 34419a–protein system; (<b>C</b>) 2D plot of PC1 versus PC3 for the 34419a–protein system; (<b>D</b>) plot of the eigenvalues versus the proportion of variance of the 34419a–protein system; (<b>E</b>) 2D plot of PC1 versus PC2 for the 37913a–protein system; (<b>F</b>) 2D plot of PC2 versus PC3 for the 37913a–protein system; (<b>G</b>) 2D plot of PC1 versus PC3 for the 37913a–protein system; (<b>H</b>) plot of eigenvalues versus variance scaling for the 37913a–protein system; (<b>I</b>) 2D plot of PC1 versus PC2 for the 34346a–protein system; (<b>J</b>) 2D plot of PC2 versus PC3 for the 34346a–protein system; (<b>K</b>) two-dimensional plot of PC1 versus PC3 for the 34346a–protein system; (<b>L</b>) plot of eigenvalues versus variance scaling for the 34346a–protein system.</p>
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<p>Graph of energy breakdown of protein residues at equilibrium for systems with different ligand and protein compositions. (<b>A</b>) 34419a; (<b>B</b>) 37913a; (<b>C</b>) 34346a.</p>
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36 pages, 8181 KiB  
Article
Environmentally Friendly Synthesis of New Mono- and Bis-Pyrazole Derivatives; In Vitro Antimicrobial, Antifungal, and Antioxidant Activity; and In Silico Studies: DFT, ADMETox, and Molecular Docking
by Oussama Merzouki, Nadia Arrousse, Elhachmia Ech-chihbi, Ashwag S. Alanazi, El Houssine Mabrouk, Mohamed Hefnawy, Abdelfattah El Moussaoui, Hanane Touijer, Azeddin El Barnossi and Mustapha Taleb
Pharmaceuticals 2025, 18(2), 167; https://doi.org/10.3390/ph18020167 - 26 Jan 2025
Abstract
Background/Objectives: Antimicrobial resistance and oxidative stress are major global health challenges, necessitating the development of novel therapeutic agents. Pyrazole derivatives, known for their diverse pharmacological properties, hold promise in addressing these issues. This study aimed to synthesize new mono- and bis-pyrazole derivatives using [...] Read more.
Background/Objectives: Antimicrobial resistance and oxidative stress are major global health challenges, necessitating the development of novel therapeutic agents. Pyrazole derivatives, known for their diverse pharmacological properties, hold promise in addressing these issues. This study aimed to synthesize new mono- and bis-pyrazole derivatives using an eco-friendly, catalyst-free approach and evaluate their antioxidant, antibacterial, and antifungal activities, supported by in silico ADMET profiling, molecular docking, and Density Functional Theory (DFT) analysis. Methods: The compounds were synthesized via a green condensation reaction and characterized using NMR and mass spectrometry, which was verified by DFT analysis. Biological activities were assessed through DPPH and FRAP antioxidant assays, as well as disk diffusion and MIC methods, against bacterial strains (Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli) and fungal strains (Candida albicans and Aspergillus niger). Computational ADMET profiling evaluated pharmacokinetics and toxicity, while molecular docking assessed interactions with target proteins, including catalase, topoisomerase IV, and CYP51. Results: Theoretical calculations using DFT were in agreement with the experimental results; regarding biological activities, O4 demonstrated the most significant antioxidant activity, with 80.14% DPPH radical scavenging and an IC50 value of 40.91 µg/mL. It exhibited potent antimicrobial activity, surpassing Streptomycin with a 30 mm inhibition zone against Pseudomonas aeruginosa and showing strong efficacy against Staphylococcus aureus and Candida albicans. Computational studies confirmed favorable pharmacokinetic properties, no AMES toxicity, and strong binding affinities. DFT analysis revealed O4’s stability and reactivity, further validating its potential as a therapeutic candidate. Conclusions: This study identified and characterized novel pyrazole derivatives with promising biological and pharmacological properties. O4 emerged as the most potent compound, demonstrating strong antioxidant and antimicrobial activities alongside favorable computational profiles. These findings highlight the potential of the synthetized compounds for therapeutic development and underscore the value of integrating green synthesis with computational techniques in drug discovery. Full article
(This article belongs to the Section Medicinal Chemistry)
7 pages, 3141 KiB  
Proceeding Paper
A Computational Investigation of Potential 5-HT 2C Receptor Inhibitors for Treating Schizophrenia by ADMET Profile Analysis, Molecular Docking, DFT, Network Pharmacology, and Molecular Dynamic Simulation
by Mohammed Raihan Uddin, Mahira Rahman, Mosammad Jannatun Nayem Rafin and Joya Datta Ripa
Chem. Proc. 2024, 16(1), 69; https://doi.org/10.3390/ecsoc-28-20242 - 16 Jan 2025
Viewed by 249
Abstract
Background: Schizophrenia manifests through behavioral abnormalities, suicidal ideation, and neuropsychological deficits. Hence, this study focused on 5-hydroxytryptamine (5-HT 2C) which influenced the modulation of the series of events that lead to schizophrenia. Methodology: Based on the computational study, the potential 5-HT 2C inhibitors [...] Read more.
Background: Schizophrenia manifests through behavioral abnormalities, suicidal ideation, and neuropsychological deficits. Hence, this study focused on 5-hydroxytryptamine (5-HT 2C) which influenced the modulation of the series of events that lead to schizophrenia. Methodology: Based on the computational study, the potential 5-HT 2C inhibitors such as Ephemeranthoquinone from Arundina graminifolia and Actinodaphnine from Litsea polyantha were determined. The candidate ligands were optimized using the Gaussian 16 software package and the DFT 6-31g (d,p) basis set. The interaction between the ligands and proteins was examined with PyRx 0.8. Additionally, pharmacokinetics was assessed using SwissADME, and Protox II for toxicity prediction. The network pharmacology study was examined by using the STRING database and the Cytoscape 3.10.1 tool. Moreover, a 100-nanosecond molecular dynamics simulation analysis using Desmond to ensure the stability of these two compounds was carried out. Result: This computational research observed that ephemeranthoquinone and actinodaphnine are the most selective 5-HT 2C inhibitors due to their docking score, optimization, and molecular dynamics simulation results. Conclusions: These compounds are required to be studied further to develop a useful 5-HT 2C inhibitors for the treatment of schizophrenia. Full article
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<p>Protein–ligand binding interaction of top two compounds based on binding score: (<b>a</b>) ephemeranthoquinone and (<b>b</b>) actinodaphnine.</p>
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<p>Network pharmacology analysis of 5-HT 2C protein (<b>a</b>) and top two compounds (<b>b</b>).</p>
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<p>The optimization structure of the top two compounds, (<b>a</b>) Actinodaphnine and (<b>b</b>) Ephemeranthoquinone.</p>
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<p>RMSD values of top 2 compounds.</p>
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<p>RMSF value of top 2 compounds.</p>
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<p>SASA value of top 2 compounds.</p>
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<p>rGyr value of top 2 compounds.</p>
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13 pages, 8562 KiB  
Article
Tribulus terrestris Fruit Extract: Bioactive Compounds, ADMET Analysis, and Molecular Docking with Penicillin-Binding Protein 2a Transpeptidase of Methicillin-Resistant Staphylococcus epidermidis
by Khalid J. Alzahrani
Curr. Issues Mol. Biol. 2025, 47(1), 52; https://doi.org/10.3390/cimb47010052 - 15 Jan 2025
Viewed by 506
Abstract
Tribulus terrestris is a rich source of bioactive molecules and thrives in Mediterranean and desert climate regions worldwide. In this study, Tribulus terrestris methanolic HPLC fractions were evaluated for bioactive compounds and PBP2a transpeptidase inhibitors against methicillin-resistant Staphylococcus epidermidis (MRSE). Among the collected [...] Read more.
Tribulus terrestris is a rich source of bioactive molecules and thrives in Mediterranean and desert climate regions worldwide. In this study, Tribulus terrestris methanolic HPLC fractions were evaluated for bioactive compounds and PBP2a transpeptidase inhibitors against methicillin-resistant Staphylococcus epidermidis (MRSE). Among the collected HPLC fractions, F02 of the methanol extract demonstrated potential activity against MRSE01 (15 ± 0.13 mm), MRSE02 (13 ± 0.21 mm), and MRSE03 (16 ± 0.14 mm) isolates. GC-MS analysis of the F02 fraction identified seventeen compounds. Among seventeen compounds, eight have favorable pharmacokinetics and medicinal chemistry; however, on the basis of in silico high water solubility, high GI absorption, blood–brain barrier non-permeability, lack of toxicity, and potential drug-likeness, 1-ethylsulfanylmethyl-2,8,9-trioxa-5-aza-1-sila-bicyclo[3.3.3]undecane and phthalimide, N-(1-hydroxy-2-propyl), were processed for molecular docking. 1-ethylsulfanylmethyl-2,8,9-trioxa-5-aza-1-sila-bicyclo[3.3.3]undecane formed three hydrogen bonds with Ser-452, Thr-584, and Asn-454 residues of the PBP2a transpeptidase. Similarly, phthalimide, N-(1-hydroxy-2-propyl)-formed four hydrogen bonds with Ser-396, Asn-454, Lys-399, and Ser-452 residues of PBP2a transpeptidase. These two compounds are proposed as novel putative PBP2a transpeptidase inhibitors. Further characterization of compounds extracted from Tribulus terrestris may aid in identifying novel PBP2a inhibitory agents for managing MRSE infections. Full article
(This article belongs to the Special Issue Biochemical Composition and Activity of Medicinal Plants and Food)
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<p>Phenotypic and molecular identification of MRSE: (<b>A</b>) phenotypic identification of MRSE; (<b>A1</b>) growth on blood agar; (<b>A2</b>) Gram staining showing Gram-positive cocci in clusters; (<b>A3</b>) antibiotic susceptibility on MHA agar; (<b>B</b>) molecular identification of MRSE; (<b>B1</b>) amplification of <span class="html-italic">rdr</span> (130 bp) for the detection of <span class="html-italic">S. epidermidis</span> (M: ladder; 1, 3, 4, 5: <span class="html-italic">rdr</span>-positive; 2: <span class="html-italic">rdr</span>-negative); (<b>B2</b>) amplification of <span class="html-italic">mecA</span> (533 bp) (M: ladder; 1: <span class="html-italic">mecA</span>-positive; 2: <span class="html-italic">mecA</span>-negative).</p>
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<p>GC-MS chromatogram of bioactive HPLC fraction of <span class="html-italic">Tribulus terrestris</span> fruit methanolic extract.</p>
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<p>Binding conformation and chemical interaction network of 1-ethylsulfanylmethyl-2,8,9-trioxa-5-aza-1-sila-bicyclo[3.3.3]undecane and phthalimide, N-(1-hydroxy-2-propyl)- within the binding pocket of PBP2a enzyme: (<b>A</b>) PBP2a surface; (<b>B</b>) interaction map of 1-ethylsulfanylmethyl-2,8,9-trioxa-5-aza-1-sila-bicyclo[3.3.3]undecane with active site residues of PBP2a; (<b>C</b>) phthalimide, N-(1-hydroxy-2-propyl)- with active site residues of PB.</p>
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23 pages, 7871 KiB  
Article
Resveratrol-Based Carbamates as Selective Butyrylcholinesterase Inhibitors: Design, Synthesis, Computational Study and Biometal Complexation Capability
by Maja Sviben, Ilijana Odak, Danijela Barić, Milena Mlakić, Ottó Horváth, Lajos Fodor, Sunčica Roca, Ivana Šagud and Irena Škorić
Molecules 2025, 30(2), 316; https://doi.org/10.3390/molecules30020316 - 15 Jan 2025
Viewed by 410
Abstract
Considering our previous experience in the design of new cholinesterase inhibitors, especially resveratrol analogs, in this research, the basic stilbene skeleton was used as a structural unit for new carbamates designed as potentially highly selective butyrylcholinesterase (BChE) inhibitors with excellent absorption, distribution, metabolism, [...] Read more.
Considering our previous experience in the design of new cholinesterase inhibitors, especially resveratrol analogs, in this research, the basic stilbene skeleton was used as a structural unit for new carbamates designed as potentially highly selective butyrylcholinesterase (BChE) inhibitors with excellent absorption, distribution, metabolism, excretion and toxicity ADMET properties. The inhibitory activity of newly prepared carbamates 113 was tested toward the enzymes acetylcholinesterase (AChE) and BChE. In the tested group of compounds, the leading inhibitors were 1 and 7, which achieved excellent selective inhibitory activity for BChE with IC50 values of 0.12 ± 0.09 μM and 0.38 ± 0.01 μM, respectively. Both were much more active than the standard inhibitor galantamine against BChE. Molecular docking of the most promising inhibitor candidates, compounds 1 and 7, revealed that stabilizing interactions between the active site residues of BChE and the ligands involve π-stacking, alkyl-π interactions, and, when the carbamate orientation allows, H-bond formation. MD analysis confirmed the stability of the obtained complexes. Some bioactive resveratrol-based carbamates displayed complex-forming capabilities with Fe3+ ions as metal centers. Spectrophotometric investigation indicated that they coordinate one or two metal ions, which is in accordance with their chemical structure, offering two binding sites: an amine and a carboxylic group in the carbamate moiety. Based on the obtained in silico, experimental and computational results on biological activity in the present work, new carbamates 1 and 7 represent potential selective BChE inhibitors as new therapeutics for neurological disorders. Full article
(This article belongs to the Special Issue Synthesis of Bioactive Compounds: Volume II)
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<p>Structures of rivastigmine, a progressive non-selective inhibitor of AChE and BChE; resveratrol analogs (<b>A</b>) exhibiting significant BChE inhibitory potential [<a href="#B30-molecules-30-00316" class="html-bibr">30</a>,<a href="#B32-molecules-30-00316" class="html-bibr">32</a>] and newly designed compounds from this work (<b>B</b>).</p>
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<p>Newly synthesized carbamates <b>1</b>–<b>13</b> bearing various functionalities (isolated yields for individual compounds are shown in parentheses).</p>
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<p>The portions of the <sup>1</sup>H NMR spectra of the most active carbamates as selective BChE inhibitors were compared: (<b>a</b>) <b>1</b>, (<b>b</b>) <b>5</b>, and (<b>c</b>) <b>8</b>.</p>
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<p>Structure of bambuterol.</p>
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<p>(<b>a</b>) Representative pose of the most populated cluster of molecule <b>1</b> docked into the active site of BChE. (<b>b</b>) The most stable pose of molecule <b>1</b> obtained by docking to BChE. Distances given in angstroms. Docked molecules are presented using ball-and-stick models.</p>
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<p>Representative pose of the most populated and most stable cluster of molecule <b>7</b> docked into the active site of BChE. Distances in angstroms. Docked molecules are presented using ball-and-stick models.</p>
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<p>Root mean square deviation values from molecular dynamics simulation of a protein–ligand complex of BChE with compounds <b>1</b> and <b>7</b>, respectively. Complexes BChE-<b>1a</b> and BChE-<b>1b</b> correspond to the ligand conformations presented in <a href="#molecules-30-00316-f005" class="html-fig">Figure 5</a>a and <a href="#molecules-30-00316-f005" class="html-fig">Figure 5</a>b, respectively.</p>
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<p>Root mean square fluctuation values from molecular dynamics simulation of a protein–ligand complex of BChE with compounds <b>1</b> (dotted violet and blue lines) and <b>7</b> (red line), respectively.</p>
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<p>Values of a radius of gyration from molecular dynamics simulation of a protein–ligand complex of BChE with compounds <b>1</b> (violet for conformation <b>1a</b>, and blue for <b>1b</b>) and <b>7</b> (red line), respectively.</p>
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<p>Distance between hydroxyl oxygen of the catalytic serine and carbonyl carbon of ligand <b>1</b> based on molecular dynamics simulation of a protein–ligand complex BChE-<b>1b</b>.</p>
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<p>Molar absorptivity spectra of compound <b>1</b> (black), <b>3</b> (blue), <b>6</b> (green) and <b>7</b> (red).</p>
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<p>Difference absorption spectra obtained through titration of 1.93 × 10<sup>–5</sup> M of Fe<sub>2</sub>(SO<sub>4</sub>)<sub>3</sub> aqueous solution with compound <b>6</b> in the 0–0.35 mM concentration range. The orange curve is the spectrum of the starting pure Fe<sub>2</sub>(SO<sub>4</sub>)<sub>3</sub> solution, while the green spectrum belongs to the pure ligand. The inset shows the changes in absorbance measured at 330 nm (blue) and the theoretical absorbance calculated for the ligand at the same wavelength (red) as a function of the concentration of <b>6</b>.</p>
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<p>Newly designed and synthesized carbamates <b>1</b>–<b>13</b>.</p>
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<p>Possible mechanism of action of carbamate <b>1</b> in the BChE enzyme active site by analogy with acetylcholine and rivastigmine.</p>
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18 pages, 4829 KiB  
Article
Identification of Two Flavonoids as New and Safe Inhibitors of Kynurenine Aminotransferase II via Computational and In Vitro Study
by Redouane Rebai, Luc Jasmin and Abdennacer Boudah
Pharmaceuticals 2025, 18(1), 76; https://doi.org/10.3390/ph18010076 - 10 Jan 2025
Viewed by 423
Abstract
Background/Objectives: Kynurenine aminotransferase II (KAT-II) is a target for treating several diseases characterized by an excess of kynurenic acid (KYNA). Although KAT-II inactivators are available, they often lead to adverse side effects due to their irreversible inhibition mechanism. This study aimed to identify [...] Read more.
Background/Objectives: Kynurenine aminotransferase II (KAT-II) is a target for treating several diseases characterized by an excess of kynurenic acid (KYNA). Although KAT-II inactivators are available, they often lead to adverse side effects due to their irreversible inhibition mechanism. This study aimed to identify potent and safe inhibitors of KAT-II using computational and in vitro approaches. Methods: Virtual screening, MM/GBSA, and molecular dynamics simulations were conducted to identify the top drug candidates, followed by kinetic measurements and in vitro cytotoxicity evaluation. Results: The study showed that two compounds, herbacetin and (-)-Epicatechin exhibited the best scores. Their Glide docking scores are −8.66 kcal/mol and −8.16 kcal/mol, respectively, and their MM/GBSA binding energies are −50.30 kcal/mol and −51.35 kcal/mol, respectively. These scores are superior to those of the standard inhibitor, PF-04859989, which has docking scores of −7.12 kcal/mol and binding energy of −38.41 kcal/mol. ADMET analysis revealed that the selected compounds have favorable pharmacokinetic parameters, moderate bioavailability, and a safe toxicity profile, which supports their potential use. Further, the kinetic study showed that herbacetin and (-)-Epicatechin are reversible KAT-II inhibitors and exhibit a competitive inhibition mechanism. Their half-maximal inhibitory concentrations (IC50) are 5.98 ± 0.18 µM and 8.76 ± 0.76 µM, respectively. The MTT assay for cell toxicity indicated that the two compounds do not affect HepG2 cell viability at the necessary concentration for KAT-II inhibition. Conclusions: These results suggest that herbacetin and (-)-Epicatechin are suitable for KAT-II inhibition and are promising candidates for further development of KAT-II inhibitors. Full article
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<p>2D interaction of the lead molecules and the standard inhibitor with KAT-II binding site residues; (<b>A</b>) herbacetin, (<b>B</b>) (-)-Epicatechin, (<b>C</b>) melilotoside, (<b>D</b>) sakakin, (<b>E</b>) eriodictyol, (<b>F</b>) PF-04859989.</p>
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<p>2D interaction of the lead molecules and the standard inhibitor with KAT-II binding site residues; (<b>A</b>) herbacetin, (<b>B</b>) (-)-Epicatechin, (<b>C</b>) melilotoside, (<b>D</b>) sakakin, (<b>E</b>) eriodictyol, (<b>F</b>) PF-04859989.</p>
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<p>2D interaction diagram of induced fit docking of: (<b>A</b>) herbacetin; (<b>B</b>) (-)-Epicatechin; (<b>C</b>) PF-04859989.</p>
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<p>MD simulation for herbacetin–KAT-II complex: (<b>A</b>) RMSD plot of herbacetin–KAT-II complex, (<b>B</b>) RMSF of herbacetin–KAT-II complex, (<b>C</b>) histogram of herbacetin–KAT-II complex.</p>
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<p>MD simulation for (-)-Epicatechin–KAT-II complex: (<b>A</b>) RMSD plot of (-)-Epicatechin–KAT-II complex, (<b>B</b>) RMSF of (-)-Epicatechin–KAT-II complex, (<b>C</b>) histogram of (-)-Epicatechin–KAT-II complex.</p>
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<p>Inhibitory activity of (<b>A</b>) herbacetin, (<b>B</b>) (-)-Epicatechin, and (<b>C</b>) PF-04859989 compounds in a dose-dependent manner. All experiments were performed in triplicate and plotted using GraphPad Prism v8.4.0.</p>
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<p>Lineweaver–Burk plots of inhibitory kinetics of herbacetin and (-)-Epicatechin towards KAT-II. Kinetics parameters of (<b>A</b>) herbacetin, (<b>B</b>) (-)-Epicatechin, and (<b>C</b>) PF-04859989 were evaluated using Lineweaver–Burk analysis. All experiments were performed in triplicate, and data are presented as mean ± SD.</p>
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<p>Cell viability (%) of HepG2 cells, measured by the MTT assay, after 72 h exposure to (<b>A</b>) herbacetin, (<b>B</b>) (-)-Epicatechin, and (<b>C</b>) PF-04859989.</p>
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21 pages, 8475 KiB  
Article
Identification of Novel LCN2 Inhibitors Based on Construction of Pharmacophore Models and Screening of Marine Compound Libraries by Fragment Design
by Ningying Zheng, Xuan Li, Nan Zhou and Lianxiang Luo
Mar. Drugs 2025, 23(1), 24; https://doi.org/10.3390/md23010024 - 5 Jan 2025
Viewed by 636
Abstract
LCN2, a member of the lipocalin family, is associated with various tumors and inflammatory conditions. Despite the availability of known inhibitors, none have been approved for clinical use. In this study, marine compounds were screened for their ability to inhibit LCN2 using pharmacophore [...] Read more.
LCN2, a member of the lipocalin family, is associated with various tumors and inflammatory conditions. Despite the availability of known inhibitors, none have been approved for clinical use. In this study, marine compounds were screened for their ability to inhibit LCN2 using pharmacophore models. Six compounds were optimized for protein binding after being docked against the positive control Compound A. Two compounds showed promising results in ADMET screening. Molecular dynamics simulations were utilized to predict binding mechanisms, with Compound 69081_50 identified as a potential LCN2 inhibitor. MM-PBSA analysis revealed key amino acid residues that are involved in interactions, suggesting that Compound 69081_50 could be a candidate for drug development. Full article
(This article belongs to the Special Issue Chemoinformatics for Marine Drug Discovery)
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<p>Hydrophobic group features are represented by blue spheres, aromatic ring features are represented by orange spheres, and hydrogen bond donor features are represented by green spheres. (<b>a</b>) RL_5, RL_7, and RL_9 pharmacophores. (<b>b</b>) Pharmacophore of RL_5. (<b>c</b>) ROC curve of RL_5. (<b>d</b>) Comparative fitness plots of active small molecules of RL_5, RL_7, and RL_9.</p>
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<p>Hydrophobic group features are indicated by blue spheres and hydrogen bond donor features are indicated by green spheres. (<b>a</b>) RL_6, RL_8, and RL_10 pharmacophores. (<b>b</b>) RL_8 pharmacophore. (<b>c</b>) ROC curve of RL_8. (<b>d</b>) Comparative fitness plots of active small molecules of RL_6, RL_8, and RL_10.</p>
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<p>(<b>a</b>) 2D interaction diagram of Compound 44879 with LCN2. The rosy-red part is 2-methylpropan-1-amine. (<b>b</b>) 2D interaction diagram of Compound 46563 with LCN2. The rosy-red portion is 3,6-dimethoxy-2-methyltetrahydro-2H-pyran-4-ol. (<b>c</b>) 2D interaction plot of Compound 50616 with LCN2. The rosy-red part is isopentane. (<b>d</b>) 2D interaction diagram of Compound 50617 with LCN2. (Z)-2-methyl-1-propylguanidine is the rosy-red portion. (<b>e</b>) 2D interaction diagram of Compound 50618 with LCN2. The rosy-red portion is 1,1-dimethyl-3-propylguanidine. (<b>f</b>) 2D interaction diagram of Compound 69081 with LCN2. The rosy-red portion is 3-methoxy-5-methylphenol.</p>
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<p>Hexagonal range distribution of ADME properties of candidate Compounds 44879_4, 69081_38, and 69081_50. (<b>a</b>) Distribution of ADME properties of Compound 44879_4; (<b>b</b>) Distribution of ADME properties of Compound 69081_38; (<b>c</b>) Distribution of ADME properties of Compound 69081_50.</p>
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<p>Results of the molecular dynamics simulations of protein–ligand complexes. (<b>a</b>) RMSD diagram of protein–ligand complex. (<b>b</b>) RMSF diagram of protein–ligand complex. (<b>c</b>) Radius of gyration (Rg) graph for complexes with respect to 100 ns of molecular dynamics. (<b>d</b>) The hydrogen bond of protein with Compound 69081_38. (<b>e</b>) The hydrogen bond of protein with Compound 69081_50. (<b>f</b>) The hydrogen bond of protein with Compound A.</p>
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<p>(<b>a</b>) Three-dimensional binding pattern of Compound 69081_50 to LCN2 protein. Carbon–hydrogen bonds are shown by pale green dashed lines, hydrogen bonds are shown by green dashed lines, alkyl bonds are shown by pink dashed lines, and Pi interactions are shown by magenta dashed lines. (<b>b</b>) The small green molecule is Compound 69081_50, showing the binding of Compound 69081_50 to the LCN2 protein pocket. (<b>c</b>) Schematic of the two-dimensional interaction of Compound 69081_50 with the LCN2 protein.</p>
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<p>Overlapping effects of 10 receptor–ligand complex-based pharmacophore models.</p>
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<p>The 5NKN eutectic with redocked ligand in superposition state. (<b>a</b>) Maestro: superposition state of 5NKN eutectic with redocked ligand (original eutectic ligand in red, redocked ligand in blue) (RMSD: 1.2575 Å). (<b>b</b>) Discovery Studio: superposition state of 5NKN eutectic with redocked ligand (original eutectic ligand in red, redocked ligand in blue) (RMSD: 0.76875 Å).</p>
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24 pages, 9580 KiB  
Article
Preliminary Data on Silybum marianum Metabolites: Comprehensive Characterization, Antioxidant, Antidiabetic, Antimicrobial Activities, LC-MS/MS Profiling, and Predicted ADMET Analysis
by Sabrina Lekmine, Ouided Benslama, Mohammad Shamsul Ola, Nabil Touzout, Hamza Moussa, Hichem Tahraoui, Haroun Hafsa, Jie Zhang and Abdeltif Amrane
Metabolites 2025, 15(1), 13; https://doi.org/10.3390/metabo15010013 - 3 Jan 2025
Viewed by 589
Abstract
Background/Objectives: Silybum marianum extract, obtained via microwave-enhanced extraction, was evaluated for its antioxidant, antidiabetic, and antimicrobial activities to explore its therapeutic potential. Methods: The extraction was performed using microwave-enhanced techniques, and LC-MS/MS was employed to profile the metabolites in the extract. Total phenolic [...] Read more.
Background/Objectives: Silybum marianum extract, obtained via microwave-enhanced extraction, was evaluated for its antioxidant, antidiabetic, and antimicrobial activities to explore its therapeutic potential. Methods: The extraction was performed using microwave-enhanced techniques, and LC-MS/MS was employed to profile the metabolites in the extract. Total phenolic and flavonoid contents were quantified using spectrophotometric methods. Antioxidant activity was assessed using DPPH, ABTS, CUPRAC, Phenanthroline, and FRAP assays. Enzyme inhibition assays were conducted to evaluate antidiabetic activity against α-glucosidase and α-amylase. Antimicrobial activity was determined using the disc diffusion method, and in silico ADMET and drug-likeness analyses were performed for key metabolites. Results: The extract contained 251.2 ± 1.2 mg GAE/g of total phenolics and 125.1 ± 1.6 mg QE/g of total flavonoids, with 33 metabolites identified, including phenolic acids, tannins, flavonoids, and flavolignans. Strong antioxidant activity was observed, with IC50 values of 19.2 ± 2.3 μg/mL (DPPH), 7.2 ± 1.7 μg/mL (ABTS), 22.2 ± 1.2 μg/mL (CUPRAC), 35.2 ± 1.8 μg/mL (Phenanthroline), and 24.1 ± 1.2 μg/mL (FRAP). Antidiabetic effects were significant, with IC50 values of 18.1 ± 1.7 μg/mL (α-glucosidase) and 26.5 ± 1.3 μg/mL (α-amylase). Antimicrobial activity demonstrated inhibition zones of 8.9 ± 1.1 mm (Bacillus subtilis), 12.6 ± 1.6 mm (Escherichia coli), 8.2 ± 1.2 mm (Fusarium oxysporum), and 9.2 ± 1.1 mm (Aspergillus niger). In silico analyses showed high absorption, favorable metabolism and excretion, and minimal toxicity, with no hERG channel inhibition or hepatotoxicity. Conclusions: The comprehensive results highlight the significant antioxidant, antidiabetic, and antimicrobial activities of S. marianum extract, suggesting its potential for therapeutic and preventive applications. Full article
(This article belongs to the Special Issue Metabolism of Bioactives and Natural Products)
32 pages, 9784 KiB  
Article
Discovery of Non-Peptide GLP-1 Positive Allosteric Modulators from Natural Products: Virtual Screening, Molecular Dynamics, ADMET Profiling, Repurposing, and Chemical Scaffolds Identification
by Mohamed S. Gomaa, Mansour S. Alturki, Nada Tawfeeq, Dania A. Hussein, Faheem H. Pottoo, Abdulaziz H. Al Khzem, Mohammad Sarafroz and Samar Abubshait
Pharmaceutics 2024, 16(12), 1607; https://doi.org/10.3390/pharmaceutics16121607 - 17 Dec 2024
Cited by 1 | Viewed by 724
Abstract
Background/Objectives: Glucagon-like peptide-1 (GLP-1) receptor is currently one of the most explored targets exploited for the management of diabetes and obesity, with many aspects of its mechanisms behind cardiovascular protection yet to be fully elucidated. Research dedicated towards the development of oral GLP-1 [...] Read more.
Background/Objectives: Glucagon-like peptide-1 (GLP-1) receptor is currently one of the most explored targets exploited for the management of diabetes and obesity, with many aspects of its mechanisms behind cardiovascular protection yet to be fully elucidated. Research dedicated towards the development of oral GLP-1 therapy and non-peptide ligands with broader clinical applications is crucial towards unveiling the full therapeutic capacity of this potent class of medicines. Methods: This study describes the virtual screening of a natural product database consisting of 695,133 compounds for positive GLP-1 allosteric modulation. The database, obtained from the Coconut website, was filtered according to a set of physicochemical descriptors, then was shape screened against the crystal ligand conformation. This filtered database consisting of 26,325 compounds was used for virtual screening against the GLP-1 allosteric site. Results: The results identified ten best hits with the XP score ranging from −9.6 to −7.6 and MM-GBSA scores ranging from −50.8 to −32.4 and another 58 hits from docked pose filter and a second round of XP docking and MM-GBSA calculation followed by molecular dynamics. The analysis of results identified hits from various natural products (NPs) classes, to whom attributed antidiabetic and anti-obesity effects have been previously reported. The results also pointed to β-lactam antibiotics that may be evaluated in drug repurposing studies for off-target effects. The calculated ADMET properties for those hits revealed suitable profiles for further development in terms of bioavailability and toxicity. Conclusions: The current study identified several NPs as potential GLP-1 positive allosteric modulators and revealed common structural scaffolds including peptidomimetics, lactams, coumarins, and sulfonamides with peptidomimetics being the most prominent especially in indole and coumarin cores. Full article
(This article belongs to the Special Issue Computer-Aided Development: Recent Advances and Expectations)
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<p>Filtration protocol for the Coconut natural products database.</p>
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<p>Chemical structure of GLP-1 co-crystallized ligands positive allosteric modulator used in the shape screening.</p>
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<p>Hit identification protocol.</p>
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<p>Surface representation of the overlay of the top 10 hits (magenta sticks) and the crystal ligand (red stick) in GLP-1 allosteric site (PDB ID: 6VCB). GLP-1 receptor is represented in gray surface and GLP-1 peptide in green surface.</p>
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<p>(<b>a</b>) Three-dimensional representation of the binding interactions between hit <b>1</b> and GLP-1 receptor allosteric site and GLP-1 peptide (PDB ID: 6VCB). Ligand atoms are shown as sticks (carbon atoms colored in magenta) and the key residues are shown as sticks (carbon atoms colored in green). Potential electrostatic interactions are represented as yellow dotted lines and are measured in Angstrom. (<b>b</b>) Two-dimensional ligand–protein binding interactions between hit <b>1</b> and GLP-1 receptor allosteric site and GLP-1 peptide (PDB ID: 6VCB). H bond is represented as a purple arrow and salt bridge as a blue line.</p>
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<p>(<b>a</b>) Three-dimensional representation of the binding interactions between hit <b>12</b> and GLP-1 receptor allosteric site and GLP-1 peptide (PDB ID: 6VCB). Ligand atoms are shown as sticks (carbon atoms colored in magenta) and the key residues are shown as sticks (carbon atoms colored in green). Potential electrostatic interactions are represented as yellow dotted lines and are measured in Angstrom. (<b>b</b>) Two-dimensional ligand–protein binding interactions between hit <b>12</b> and GLP-1 receptor allosteric site and GLP-1 peptide (PDB ID: 6VCB). H bond is represented as a purple arrow, salt bridge as a blue line, and π-π stacking as a green line.</p>
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<p>(<b>a</b>) Three-dimensional representation of the binding interactions between hit <b>44</b> (Ampicillin) and GLP-1 receptor allosteric site and GLP-1 peptide (PDB ID: 6VCB). Ligand atoms are shown as sticks (carbon atoms colored in magenta) and the key residues are shown as sticks (carbon atoms colored in green). Potential electrostatic interactions are represented as yellow dotted lines and are measured in Angstrom. (<b>b</b>) Two-dimensional ligand–protein binding interactions between hit <b>44</b> (Ampicillin) and GLP-1 receptor allosteric site and GLP-1 peptide (PDB ID: 6VCB). H bond is represented as a purple arrow, and salt bridge as a blue line.</p>
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<p>Root mean square deviation (RMSD) graphs for the hit compounds (<b>A</b>): compound <b>3</b> (CNP0086660.2), (<b>B</b>): compound <b>5</b> (CNP0039190.2), (<b>C</b>): compound <b>2</b> (CNP0549010.1). The green graph shows fluctuations in the protein backbone from the initial reference point while the red shows the ligand fluctuations. The RMSD profile of the ligand with respect to its initial fit to the protein binding pocket indicates that all ligands did not fluctuate beyond a 2–7 Å range.</p>
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<p>Interaction diagram of hit compound <b>3</b> (CNP0086660.2) with the GLP-1 allosteric binding pocket. (<b>A</b>) Interaction of compound <b>3</b> with residues in each trajectory frame. The depth of color indicating the higher the interaction with contact residues; (<b>B</b>) the protein–ligand contacts showing the bonding interactions fraction and the nature of the interactions; (<b>C</b>) graphical 2D illustration of compound <b>3</b> interacting with the protein residues during MD simulation.</p>
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<p>Interaction diagram of hit compound <b>5</b> (CNP0039190.2) with the GLP-1 allosteric binding pocket. (<b>A</b>) Interaction of compound <b>5</b> with residues in each trajectory frame. The depth of color indicating the higher the interaction with contact residues; (<b>B</b>) the protein–ligand contacts showing the bonding interactions fraction and the nature of the interactions; (<b>C</b>) graphical 2D illustration of compound <b>5</b> interacting with the protein residues during MD simulation.</p>
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<p>Interaction diagram of hit compound <b>2</b> (CNP0549010.1) with the GLP-1 allosteric binding pocket. (<b>A</b>) Interaction of compound <b>2</b> with residues in each trajectory frame. The depth of color indicating the higher the interaction with contact residues; (<b>B</b>) the protein–ligand contacts showing the bonding interactions fraction and the nature of the interactions; (<b>C</b>) graphical 2D illustration of compound <b>2</b> interacting with the protein residues during MD simulation.</p>
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<p>Chemical structures of identified novel scaffolds for GLP-1 positive allosteric modulation with their hit no (hits are arranged according to their XP/Docking score), Coconut ID, and XP/Docking score.</p>
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<p>Chemical structures of identified novel scaffolds for GLP-1 positive allosteric modulation with their hit no (hits are arranged according to their XP/Docking score), Coconut ID, and XP/Docking score.</p>
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<p>Chemical structures of identified novel scaffolds for GLP-1 positive allosteric modulation with their hit no (hits are arranged according to their XP/Docking score), Coconut ID, and XP/Docking score.</p>
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<p>Chemical structures of identified novel scaffolds for GLP-1 positive allosteric modulation with their hit no (hits are arranged according to their XP/Docking score), Coconut ID, and XP/Docking score.</p>
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<p>Chemical structures of identified novel scaffolds for GLP-1 positive allosteric modulation with their hit no (hits are arranged according to their XP/Docking score), Coconut ID, and XP/Docking score.</p>
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21 pages, 4969 KiB  
Article
Comprehensive In Vitro Evaluation of Antibacterial, Antioxidant, and Computational Insights into Blepharis ciliaris (L.) B. L. Burtt from Hail Mountains, Saudi Arabia
by Abdel Moniem Elhadi Sulieman, Hajo Idriss, Mamdouh Alshammari, Nujud A. M. Almuzaini, Nosyba A. Ibrahim, Mahmoud Dahab, Abdulrahman Mohammed Alhudhaibi, Hamad Mohammed Abdullah Alrushud, Zakaria Ahmed Saleh and Emad M. Abdallah
Plants 2024, 13(24), 3491; https://doi.org/10.3390/plants13243491 - 13 Dec 2024
Viewed by 845
Abstract
The arid mountainous region of Hail in Saudi Arabia has a variety of desert vegetation, some of which are conventionally used in Bedouin traditional medicine. These plants need scientific examination. This research seeks to examine Blepharis ciliaris using a thorough multi-analytical methodology that [...] Read more.
The arid mountainous region of Hail in Saudi Arabia has a variety of desert vegetation, some of which are conventionally used in Bedouin traditional medicine. These plants need scientific examination. This research seeks to examine Blepharis ciliaris using a thorough multi-analytical methodology that includes antibacterial and antioxidant assessments as well as computational modeling. GC–MS analysis of the methanolic extract revealed 17 organic compounds, including pentadecanoic acid, ethyl methyl ester (2.63%); hexadecanoic acid, methyl ester (1.00%); 9,12-octadecadienoic acid (Z,Z)-, methyl ester (2.74%); 9-octadecenoic acid, methyl ester (E) (2.78%); octadecanoic acid (5.88%); 9-tetradecenoic acid (Z) (3.22%); and undec-10-enoic acid, undec-2-n-1-yl ester (5.67%). The DPPH test evaluated antioxidant activity, revealing a notable increase with higher concentrations of the methanolic extract, achieving maximum inhibition of 81.54% at 1000 µg/mL. The methanolic extract exhibited moderate antibacterial activity, with average inhibition zones of 10.33 ± 1.53 mm, 13.33 ± 1.53 mm, 10.67 ± 1.53 mm, and 10.00 ± 2.00 mm against Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Serratia marcescens, respectively, as determined by the disk diffusion method. The minimum inhibitory concentration (MIC) values were 500 µg/mL for S. aureus and B. subtilis, whereas E. coli and S. marcescens showed susceptibility at 1000 µg/mL. Computational simulations were employed to assess the toxicity, drug-likeness, and ADMET profiles of compounds derived from Blepharis ciliaris. Thirteen bioactive compounds were assessed in silico against Staphylococcus aureus sortase A (PDB: 1T2O), Bacillus subtilis BsFabHb (PDB: 8VDB), Escherichia coli LPS assembly protein (LptD) (PDB: 4RHB), and a modeled Serratia marcescens outer-membrane protein TolC, focusing on cell wall and membrane structures. Compound 3, (+)-Ascorbic acid 2,6-dihexadecanoate, shown significant binding affinities to B. subtilis BsFabHb, E. coli LPS assembly protein, and S. marcescens TolC. Full article
(This article belongs to the Section Phytochemistry)
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<p>GC-MS chromatogram of the methanol extract of <span class="html-italic">Blepharis ciliaris</span>.</p>
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<p>Three-dimensional protein structures. (<b>A</b>): <span class="html-italic">Staphylococcus aureus</span> sortase A (PDB: 1T2O); (<b>B</b>): <span class="html-italic">Bacillus subtilis</span> BsFabHB (PDB: 8VDB); (<b>C</b>): <span class="html-italic">E. coli</span> LPS assembly protein (LptD) (PDB: 4RHB). Uses specific colors to represent the various receptor structures of helices, strands, and <span class="html-italic">E. coil</span> with medium state blue, corn flue blue, and dim gray, respectively. Green color represents helices, strands of the particular region.</p>
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<p>Modeled and structural quality assessment of <span class="html-italic">Serratia marcescens</span> outer-membrane protein. (<b>A</b>): Outer-membrane protein TolC (<b>B</b>): Ramachandran plot analysis, created using SAVES v6.1 web tool; (<b>C</b>): TolC protein ERRAT evaluation obtained from the SAVES v6.1 web tool.</p>
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<p>Predicted protein–ligand interaction. The docked compounds are shown in a stick model, colored yellow. (<b>A</b>) <span class="html-italic">S. aureus</span> sortase A docked with 9-Octadecenoic acid, methyl ester (E); (<b>B</b>) <span class="html-italic">B. subtilis</span> BsFabHb docked with 3 (+)-Ascorbic acid 2,6-dihexadecanoate; (<b>C</b>) <span class="html-italic">S. marcescens</span> outer-membrane protein TolC. docked with 3 (+)-Ascorbic acid 2,6-dihexadecanoate; (<b>D</b>) <span class="html-italic">E. coli</span> LPS assembly protein docked with 3 (+)-Ascorbic acid 2,6-dihexadecanoate. Lower graph represented ten DockThor-VS binding mode scores obtained for the predicted complexes used GraphPad Prism 5 Software.</p>
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<p>The plant sample and the collection site: (<b>A</b>) <span class="html-italic">Blepharis ciliaris</span>; (<b>B</b>) the location of collection, Ha’il area.</p>
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21 pages, 2189 KiB  
Article
In Vitro and In Silico Biological Activities Investigation of Ethyl Acetate Extract of Rubus ulmifolius Schott Leaves Collected in Algeria
by Amina Bramki, Djamila Benouchenne, Maria Michela Salvatore, Ouided Benslama, Anna Andolfi, Noureddine Rahim, Mohamed Moussaoui, Sourore Ramoul, Sirine Nessah, Ghozlane Barboucha, Chawki Bensouici, Alessio Cimmino, Jesùs Garcìa Zorrilla and Marco Masi
Plants 2024, 13(23), 3425; https://doi.org/10.3390/plants13233425 - 6 Dec 2024
Viewed by 672
Abstract
This investigation aimed to assess the in vitro and in silico biological properties of the ethyl acetate (EtOAc) extract obtained from leaves of Rubus ulmifolius Schott collected in Algeria. The phytochemical screening data disclosed that flavonoids, tannins, coumarins, saponins, and anthocyanins were abundant. [...] Read more.
This investigation aimed to assess the in vitro and in silico biological properties of the ethyl acetate (EtOAc) extract obtained from leaves of Rubus ulmifolius Schott collected in Algeria. The phytochemical screening data disclosed that flavonoids, tannins, coumarins, saponins, and anthocyanins were abundant. High levels of total phenolics, total flavonoids and flavonols (523.25 ± 3.53 µg GAE/mg, 20.41 ± 1.80, and 9.62 ± 0.51 µg QE/mg respectively) were detected. Furthermore, GC-MS analysis was performed to identify low molecular weight compounds. d-(-)-Fructofuranose, gallic acid, caffeic acid, and catechin were detected as main metabolites of the EtOAc extract. The outcomes revealed that the extract exerted a potent antioxidant apt, and ensured significant bacterial growth inhibitory capacity, where the inhibition zone diameters ranged from 20.0 ± 0.5 to 24.5 ± 0.3 mm. These outcomes were confirmed through molecular docking against key bacterial enzymes that revealed significant interactions and binding affinities. d-(-)-Fructofuranose was identified as the most polar and flexible compound. Gallic acid and caffeic acid demonstrated higher unsaturation. Caffeic acid was well absorbed in the blood–brain barrier (BBB) and human intestine. Catechin was well absorbed in CaCO3, and can act as an inhibitor of CYP1A2. These results highlight how crucial it is to keep looking into natural substances in the quest for more potent and targeted pathology therapies. Full article
(This article belongs to the Section Phytochemistry)
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<p>Chemical structures of compounds identified in the crude EtOAc extract by GC-MS.</p>
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<p>2D and 3D visualizations of the best-docked compound interaction within the active site for each studied enzyme.</p>
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<p>2D and 3D visualizations of the best-docked compound interaction within the active site for each studied enzyme.</p>
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16 pages, 2712 KiB  
Article
In Vitro Investigation of Biological and Toxic Effects of 4-Octylphenol on Human Cells
by Antonio Massimiliano Romanelli, Antonio Montefusco, Silvia Sposito, Bernardina Scafuri, Ivana Caputo and Gaetana Paolella
Int. J. Mol. Sci. 2024, 25(23), 13032; https://doi.org/10.3390/ijms252313032 - 4 Dec 2024
Viewed by 736
Abstract
Alkylphenols are byproducts of anthropogenic activities that widely contaminate waters, soils and air; among them, the most represented are 4-nonylphenol (4-NP) and 4-octylphenol (4-OP). These compounds tend to bioaccumulate in animal and plant tissues and also represent a risk to human health. Indeed, [...] Read more.
Alkylphenols are byproducts of anthropogenic activities that widely contaminate waters, soils and air; among them, the most represented are 4-nonylphenol (4-NP) and 4-octylphenol (4-OP). These compounds tend to bioaccumulate in animal and plant tissues and also represent a risk to human health. Indeed, humans are constantly exposed to alkylphenols through ingestion of contaminated water and food, inhalation and dermal absorption. In the present work, we characterized the cytotoxic ability of 4-OP towards several human cell lines, representing the potential main targets in the human body, also comparing its effect with that of 4-NP and of a mixture of both 4-OP and 4-NP in a range of concentrations between 1 and 100 μM. Viability assays demonstrated that each cell type had a peculiar sensitivity to 4-OP and that, in some cases, a combination of the two alkylphenols displayed a higher cytotoxic activity with respect to the single compound. Then, we focused our attention on a liver cell line (HepG2) in which we observed that 4-OP increased cell death and also caused interference with protective physiological cell processes, such as the unfolded protein response, autophagy and the antioxidant response. Finally, our experimental data were compared and correlated with ADMET properties originating from an in silico analysis. Altogether, our findings highlight a possible contribution of this pollutant to deregulation of the normal homeostasis in human liver cells. Full article
(This article belongs to the Special Issue Toxicity Mechanism of Emerging Pollutants)
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<p>Cell viability assay on different human cell lines treated for 24 h with 4-OP in the range of concentrations from 6.25 μM to 100 μM. Values are the means ± standard error (SE) of three independent experiments performed in triplicate. Statistical analysis was performed using the Student’s <span class="html-italic">t</span>-test. * <span class="html-italic">p</span> &lt; 0.05 vs. cells treated with the vehicle (DMSO).</p>
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<p>Comparison of the effect on cell viability of 4-OP with the effect of 4-NP and with the effect of a mixture of them (each used at half the concentration of the pure compound) in the range of concentrations from 25 μM to 100 μM. Values are the means ± SE of three independent experiments performed in triplicate. Statistical analysis was performed using the Student’s <span class="html-italic">t</span>-test. * <span class="html-italic">p</span> &lt; 0.05 as indicated.</p>
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<p>Effect of 4-OP on proliferation and apoptosis in HepG2 cells. (<b>a</b>) BrdU incorporation in cells treated for 24 h with 25 and 50 μM of 4-OP. Data are reported as the mean ± SE from three independent experiments. * <span class="html-italic">p</span> &lt; 0.05 versus vehicle-treated cells (DMSO). (<b>b</b>) Western blot of p53 in cells treated for 24 h with 4-OP. (<b>c</b>) Western blot of procaspase and cleaved caspase-3 in cells treated for 8 h with 4-OP. In (<b>b</b>,<b>c</b>), GAPDH is reported as the internal reference. (<b>d</b>) TUNEL assay in cells treated for 24 h. Apoptotic nuclei are in green, and total Hoechst-stained nuclei are in blue. Magnification 40×, with oil.</p>
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<p>Effect of 4-OP on UPR and ER stress. (<b>a</b>) Visualization on a 2.5% agarose gel of PCR products revealing unspliced and spliced forms of XBP1 in HepG2 cells treated for 4 h. THP 1 µM represents the positive control. (<b>b</b>,<b>c</b>) Western blot and densitometric analysis, respectively, of GRP78 in HepG2 cells treated for 24 h with 4-OP. Data are reported as the mean ± SE from at least three independent experiments. * <span class="html-italic">p</span> &lt; 0.05 versus vehicle-treated cells. (<b>d</b>,<b>e</b>) Western blot of GRP78 in MRC5 and Caco-2 cells, respectively, treated with 4-OP for 24 h.</p>
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<p>Effect of 4-OP on autophagic markers. (<b>a</b>,<b>b</b>) Western blot and densitometric analysis, respectively, of p62 in HepG2 cells treated for 4 h with 4-OP. (<b>c</b>,<b>d</b>) Western blot and densitometric analysis, respectively, of LC3-II in HepG2 cells treated for 24 h with 4-OP. Starvation represents the positive control. Data are reported as the mean ± SE from three independent experiments. * <span class="html-italic">p</span> &lt; 0.05 versus vehicle-treated cells.</p>
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<p>Effect of 4-OP on autophagic markers (<b>a</b>) Immunofluorescence images of HepG2 cells treated with 4-OP for 24 h and stained with anti-LC3 antibodies (red); Hoechst-stained nuclei are in blue. Magnification 40×, with oil. (<b>b</b>) Western blot showing LC3-II levels in the presence of Baf A1, 4-OP and a combination of both; HepG2 cells were treated for 4 h with 4-OP and then for a further 20 h with Baf A1 at 50 nM.</p>
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<p>Effect of 4-OP on antioxidant enzymes. (<b>a</b>) CAT assay on HepG2 cells after 18 h of treatment with 4-OP. (<b>b</b>) Representative Western blot showing CAT ad Sod levels after 18 h of treatment with 4-OP. (<b>c</b>,<b>d</b>) Densitometric analyses of CAT and SOD levels, respectively, in HepG2 cells treated for 18 h with 4-OP. (<b>e</b>) Western blot showing CAT ad SOD levels after 4 h of treatment with 4-OP. Data are reported as the mean ± SE from three independent experiments. * <span class="html-italic">p</span> &lt; 0.05 versus vehicle-treated cells.</p>
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16 pages, 10079 KiB  
Article
Predicting Pharmacokinetics of Active Constituents in Spatholobi caulis by Using Physiologically Based Pharmacokinetic Models
by Xiaoyan Liu, Ruihu Du, Tao Zhang, Yingzi Li, Ludi Li, Zheng Yang, Youbo Zhang and Qi Wang
Pharmaceuticals 2024, 17(12), 1621; https://doi.org/10.3390/ph17121621 - 3 Dec 2024
Viewed by 703
Abstract
Background/Objectives: Spatholobi Caulis (SPC) is a medicinal plant that mainly grows in China and Southeast Asian countries and commonly used in clinics; the pharmacokinetic characteristics in humans need to be determined. This study was to establish the physiologically based pharmacokinetic (PBPK) models of [...] Read more.
Background/Objectives: Spatholobi Caulis (SPC) is a medicinal plant that mainly grows in China and Southeast Asian countries and commonly used in clinics; the pharmacokinetic characteristics in humans need to be determined. This study was to establish the physiologically based pharmacokinetic (PBPK) models of multiple active constituents from SPC in rats, and predict the pharmacokinetic properties of rats with different dosages and extrapolated to humans. Methods: The parameters were collected based on our previous study and by prediction using ADMET Predictor software predict. The PBPK models for 3′-methoxydadizein (1), 8-O-methylretusin (2), daidzin (3), and isolariciresinol (4) administered orally to rats were established using GastroPlus software. These models were employed to simulate the pharmacokinetic properties in rats across various dosages, and subsequently extrapolated to humans. The calculated parameters including Cmax, Tmax, and AUC were compared with observed values. The accuracy of the PBPK models was assessed using fold-error (FE) values. Result: The FE values ranged from 1.03 to 1.52, meeting the PBPK model regulations where FE should be less than 2. The sensitivity analysis focusing on the absorption amount and AUC0→t of these four constituents in humans was also conducted. These results confirm the successful establishment of PBPK models of these four constituents from SPC in this study, and these models were applicable to predict pharmacokinetics across various doses and extrapolate across species. Conclusions: The PBPK models of four constituents can be used to predict the pharmacokinetic characteristics in humans after oral administration of SPC and provide useful data for safe and rational medication in clinical practice. Full article
(This article belongs to the Section Natural Products)
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<p>The four active constituents extracted from SPC.</p>
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<p>The concentration-time curves of four constituents from SPC in rats (p.o.). observed and simulated by PBPK models. The observed and calculated concentration-time curves of 3′-methoxydadizein (<b>A</b>), 8-<span class="html-italic">O</span>-methylretusin (<b>B</b>), daidzin (<b>C</b>), and isolariciresinol (<b>D</b>), respectively (Mean ± SD, <span class="html-italic">n</span> = 5).</p>
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<p>Correlation between observed and calculated values of four constituents in rats by gavage SPC. Correlation between observed and calculated values of 3′-methoxydadizein (<b>A</b>), 8-<span class="html-italic">O</span>-methylretusin (<b>B</b>), daidzin (<b>C</b>), and isolariciresinol (<b>D</b>), respectively. Note: The hollow circles represent the intersection of the two values; the solid line represents a linear regression.</p>
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<p>Sensitivity analysis curves of four constituents in humans. The sensitivity of the absorption amount and <span class="html-italic">AUC<sub>0</sub><sub>→t</sub></span> of 3′-methoxydadizein (<b>A</b>), 8-<span class="html-italic">O</span>-methylretusin (<b>B</b>), daidzin (<b>C</b>), and isolariciresinol (<b>D</b>) to changes in particle size of constituents, <span class="html-italic">P<sub>app</sub></span>, Solubility, <span class="html-italic">Log D</span>, <span class="html-italic">CL<sub>Liver</sub></span>, <span class="html-italic">R<sub>bp</sub></span>, and <span class="html-italic">f<sub>up</sub></span>.</p>
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<p>The concentration-time curves at different doses of four constituents in rats by p.o. simulated by PBPK models. The simulated concentration-time curves at different doses of 3′-methoxydadizein (<b>A</b>), 8-<span class="html-italic">O</span>-methylretusin (<b>B</b>), daidzin (<b>C</b>), and isolariciresinol (<b>D</b>).</p>
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<p>The multiple concentration-time curves of four constituents in rats by p.o. simulated by PBPK models. The simulated multiple concentration-time curves of 3′-methoxydadizein (<b>A</b>), 8-<span class="html-italic">O</span>-methylretusin (<b>B</b>), daidzin (<b>C</b>), and isolariciresinol (<b>D</b>).</p>
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<p>The concentration-time curves of four constituents from SPC in rats (60 g crude drug/kg body weight by p.o.). validated and simulated by PBPK models. The validated and simulated concentration-time curves at twice doses of 3′-methoxydadizein (<b>A</b>), 8-<span class="html-italic">O</span>-methylretusin (<b>B</b>), daidzin (<b>C</b>), and isolariciresinol (<b>D</b>) (Mean ± SD, <span class="html-italic">n</span> = 5).</p>
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<p>Correlation between calculated and validated values of constituents <b>1</b>–<b>4</b> in rats by gavage 60 g crude drug/kg bodyweight of SPC. Correlation between calculated and validated values of 3′-methoxydadizein (<b>A</b>), 8-<span class="html-italic">O</span>-methylretusin (<b>B</b>), daidzin (<b>C</b>), and isolariciresinol (<b>D</b>). Note: the hollow circles represent the intersection of the two values; the solid line represents a linear regression.</p>
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<p>The plasma concentration-time curves of four constituents in humans by p.o. simulated by PBPK models. The simulated concentration-time curves in humans of 3′-methoxydadizein (<b>A</b>), 8-<span class="html-italic">O</span>-methylretusin (<b>B</b>), daidzin (<b>C</b>), and isolariciresinol (<b>D</b>).</p>
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<p>The physiological pharmacokinetic processes of the four constituents of SPC in rats by oral administration.</p>
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17 pages, 4037 KiB  
Article
In Vitro Antibacterial Activity, Molecular Docking, and ADMET Analysis of Phytochemicals from Roots of Dovyalis abyssinica
by Dereilo Bekere Belitibo, Asfaw Meressa, Abiy Abebe, Temesgen Negassa, Milkyas Endale, Frehiwot Teka Assamo, Messay Wolde-Mariam, Temesgen Abdisa Ayana, Marcel Frese, Norbert Sewald and Negera Abdissa
Molecules 2024, 29(23), 5608; https://doi.org/10.3390/molecules29235608 - 27 Nov 2024
Viewed by 771
Abstract
Dovyalis abyssinica is widely used in Ethiopia for treating various human ailments, yet its pharmacological properties and chemical composition remain largely unexplored. The chromatographic separation of D. abyssinica roots extract afforded five compounds, namely tremulacin (1), cochinchiside A (2), [...] Read more.
Dovyalis abyssinica is widely used in Ethiopia for treating various human ailments, yet its pharmacological properties and chemical composition remain largely unexplored. The chromatographic separation of D. abyssinica roots extract afforded five compounds, namely tremulacin (1), cochinchiside A (2), 5-methoxydurmillone (3), catechin-7-O-α-L-rhamnopyranoside (4), and stigmasterol (5), confirmed via IR, NMR, and MS spectral data. This is the first report of these compounds from this plant, except for compounds 1 and 5. The extracts and isolated compounds were tested for antibacterial activity against S. aureus, S. epidermidis, E. faecalis, E. coli, K. pneumoniae, and P. aeruginosa strains. Methanol roots extract exhibited significant antibacterial activity (MIC 0.195 mg/mL) against E. coli and P. aeruginosa. Compounds 1 and 3 showed remarkable antibacterial activity, with compound 1 (MIC 0.625 mg/mL) exhibiting antibacterial activity against S. aureus and S. epidermidis, whereas compound 3 (MIC 0.625 mg/mL) exhibited antibacterial activity against S. epidermidis and K. pneumoniae. Molecular docking analysis revealed better binding energies for compound 1 (−8.0, −9.7, and −8.0 kJ/mol) and compound 3 (−9.0, −8.7, and −8.4 kJ/mol), compared to ciprofloxacin (−8.3, −7.5, and −6.7 kJ/mol), in regard to S. aureus pyruvate kinase, S. epidermidis FtsZ, and K. pneumoniae Topoisomerase IV, respectively. ADME analysis also revealed good antibacterial candidacy of these compounds, provided that in vivo analysis is conducted for further confirmation of the results. Full article
(This article belongs to the Section Natural Products Chemistry)
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Figure 1
<p>The structures of the isolated compounds (<b>1</b>–<b>5</b>).</p>
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<p>Key <sup>1</sup>H-<sup>1</sup>H COSY (bold line) and HMBC (curved arrow) correlations of compound <b>1</b>.</p>
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<p>Key <sup>1</sup>H-<sup>1</sup>H COSY (bold line) and HMBC (curved arrow) correlations of compound <b>2</b>.</p>
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<p>The 2D (<b>top</b>) and 3D (<b>bottom</b>) binding interactions of compound <b>1</b>, <b>3</b>, and ciprofloxacin (from <b>left</b> to <b>right</b>) against <span class="html-italic">S. aureus</span> pyruvate kinase (PDB ID: 3T07).</p>
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<p>The 2D (<b>top</b>) and 3D (<b>bottom</b>) binding interactions of compound <b>1</b>, <b>3</b>, and ciprofloxacin against <span class="html-italic">S. epidermidis</span> FtsZ (PDB ID: 4M8I).</p>
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<p>The 2D (<b>top</b>) and 3D (<b>bottom</b>) binding interactions of compound <b>1</b>, <b>3</b>, and ciprofloxacin against <span class="html-italic">K. pneumoniae</span> topoisomerase IV (ParE–ParC) in complex with DNA (PDB ID: 7LHZ).</p>
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33 pages, 8596 KiB  
Article
Design, Synthesis and Biological Exploration of Novel N-(9-Ethyl-9H-Carbazol-3-yl)Acetamide-Linked Benzofuran-1,2,4-Triazoles as Anti-SARS-CoV-2 Agents: Combined Wet/Dry Approach Targeting Main Protease (Mpro), Spike Glycoprotein and RdRp
by Ameer Fawad Zahoor, Saba Munawar, Sajjad Ahmad, Fozia Iram, Muhammad Naveed Anjum, Samreen Gul Khan, Jamila Javid, Usman Nazeer and Mashooq Ahmad Bhat
Int. J. Mol. Sci. 2024, 25(23), 12708; https://doi.org/10.3390/ijms252312708 - 26 Nov 2024
Viewed by 587
Abstract
A novel series of substituted benzofuran-tethered triazolylcarbazoles was synthesized in good to high yields (65–89%) via S-alkylation of benzofuran-based triazoles with 2-bromo-N-(9-ethyl-9H-carbazol-3-yl)acetamide. The inhibitory potency of the synthesized compounds against SARS-CoV-2 was evaluated by enacting molecular docking against [...] Read more.
A novel series of substituted benzofuran-tethered triazolylcarbazoles was synthesized in good to high yields (65–89%) via S-alkylation of benzofuran-based triazoles with 2-bromo-N-(9-ethyl-9H-carbazol-3-yl)acetamide. The inhibitory potency of the synthesized compounds against SARS-CoV-2 was evaluated by enacting molecular docking against its three pivotal proteins, namely, Mpro (main protease; PDB ID: 6LU7), the spike glycoprotein (PDB ID: 6WPT), and RdRp (RNA-dependent RNA polymerase; PDB ID: 6M71). The docking results indicated strong binding affinities between SARS-CoV-2 proteins and the synthesized compounds, which were thereby expected to obstruct the function of SARS proteins. Among the synthesized derivatives, the compounds 9e, 9h, 9i, and 9j exposited the best binding scores of −8.77, −8.76, −8.87, and −8.85 Kcal/mol against Mpro, respectively, −6.69, −6.54, −6.44, and −6.56 Kcal/mol against the spike glycoprotein, respectively, and −7.61, −8.10, −8.01, and −7.54 Kcal/mol against RdRp, respectively. Furthermore, the binding scores of 9b (−8.83 Kcal/mol) and 9c (−8.92 Kcal/mol) against 6LU7 are worth mentioning. Regarding the spike glycoprotein, 9b, 9d, and 9f expressed high binding energies of −6.43, −6.38, and −6.41 Kcal/mol, accordingly. Correspondingly, the binding affinity of 9g (−7.62 Kcal/mol) against RdRp is also noteworthy. Furthermore, the potent compounds were also subjected to ADMET analysis to evaluate their pharmacokinetic properties, suggesting that the compounds 9e, 9h, 9i, and 9j exhibited comparable values. These potent compounds may be selected as inhibitory agents and provide a pertinent context for further investigations. Full article
(This article belongs to the Section Biochemistry)
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Figure 1
<p>Literature survey-based biological effects of different moieties of benzofuran (<b>A</b>), triazole (<b>B</b>), and carbazole (<b>C</b>) scaffolds.</p>
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<p>Structures of synthesized benzofuran-based carbazole derivatives <b>9</b>(<b>a</b>–<b>j</b>).</p>
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<p>Interaction modes of compound <b>9c</b>: (<b>A</b>) Calculated surface of docked <b>9c</b> with the main protease, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9b</b>: (<b>A</b>) Calculated surface of docked <b>9b</b> with the main protease, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9j</b>: (<b>A</b>) Calculated surface of docked <b>9j</b> with the main protease, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9i</b>: (<b>A</b>) Calculated surface of docked <b>9i</b> with the main protease, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9e</b>: (<b>A</b>) Calculated surface of docked <b>9e</b> with the main protease, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9h</b>: (<b>A</b>) Calculated surface of docked <b>9h</b> with the main protease, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Two-dimensional diagram of standard drugs and their binding with the main protease (M<sup>pro</sup>).</p>
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<p>Interaction modes of compound <b>9e</b>: (<b>A</b>) Calculated surface of docked <b>9e</b> with the spike glycoprotein, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9h</b>: (<b>A</b>) Calculated surface of docked <b>9h</b> with the spike glycoprotein, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9i</b>: (<b>A</b>) Calculated surface of docked <b>9i</b> with the spike glycoprotein, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9j</b>: (<b>A</b>) Calculated surface of docked <b>9j</b> with the spike glycoprotein, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9b</b>: (<b>A</b>) Calculated surface of docked <b>9b</b> with the spike glycoprotein, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9d</b>: (<b>A</b>) Calculated surface of docked <b>9d</b> with the spike glycoprotein, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9f</b>: (<b>A</b>) Calculated surface of docked <b>9f</b> with the spike glycoprotein, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Two-dimensional diagram of standard drugs after binding with the spike glycoprotein.</p>
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<p>Interaction modes of compound <b>9h</b>: (<b>A</b>) Calculated surface of docked <b>9h</b> with the RNA-dependent RNA-polymerase, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9i</b>: (<b>A</b>) Calculated surface of docked <b>9i</b> with the RNA-dependent RNA-polymerase, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9g</b>: (<b>A</b>) Calculated surface of docked <b>9g</b> with the RNA-dependent RNA-polymerase, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9e</b>: (<b>A</b>) Calculated surface of docked <b>9e</b> with the RNA-dependent RNA-polymerase, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Interaction modes of compound <b>9j</b>: (<b>A</b>) Calculated surface of docked <b>9j</b> with the RNA-dependent RNA-polymerase, (<b>B</b>) 2D representation, (<b>C</b>) Hydrophobic interface, (<b>D</b>) Hydrogen bonding interface.</p>
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<p>Two-dimensional diagram of standard drugs after binding with the RNA-dependent RNA polymerase.</p>
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<p>Synthesis of benzofuran-based carbazole derivatives <b>9</b>(<b>a</b>–<b>j</b>).</p>
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