African plants acting on Pseudomonas aeruginosa: Cut-off points for the antipseudomonal agents from plants

Tankeo, Simplice B.; Kuete, Victor

doi:10.1016/bs.abr.2022.08.007

Cited by 2 publications

(1 citation statement)

References 115 publications

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“…The MIC values were used to generate targets, i.e., they were labeled "Highly Potent" for MIC <512 μg mL −1 and "less potent" for MIC ≥512 μg mL −1 based on a recent study of the phytochemical activity on P. aeruginosa drug-resistant strains. 42 These targets were then encoded using the "LabelEncoder" function of Scikit-Learn Python library, which encoded "highly potent" as '1' and "less potent" as '0'. Additional clustering was performed using K-Means and a Gaussian mixture on the remaining descriptors.…”

Section: ■ Introductionmentioning

confidence: 99%

Machine Learning-Guided Discovery of AcrB and MexB Efflux Pump Inhibitors

Bera,

Roy,

Joshi

et al. 2024

J. Phys. Chem. B

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Multidrug efflux pump is one of the reasons behind the antimicrobial inactivity related to infection caused by Gramnegative pathogens. The inner membrane resistance−nodulation− cell division transporter proteins, AcrB and MexB, in association with outer membrane proteins, TolC and OprM, are responsible for the extrusion of a broad range of substrates, followed by recognizing them. Although various inhibitors were proposed to stop the efflux activity of the transporter protein, none of them had been approved clinically. Our study aims to identify potent inhibitor-like molecules employing supervised classification models trained upon the molecular descriptors of previously known inhibitors. Based on the intrinsic minimum inhibitory concentration (MIC) values of the reported inhibitors, they were classified into highly potent and less potent categories. A total of 10 different classification models were built using various molecular descriptors; among them, support vector machine, Random Forest, AdaBoost, and LightGBM models appeared to deliver promising results with >80% accuracy. These top four models were implemented on a library of 5043 to obtain 8 hit molecules after the multistep filtering process. To assess their activity toward AcrB and MexB, several molecular dynamics simulations of their ligand-bound structures were performed. We also calculated the binding free-energy values and analyzed other structural properties. Mol.3488 of the unknown molecules showed higher binding affinities for both AcrB and MexB. Also, the presence of "pyridopyrimidone" and "benzothiazole" moieties in the molecules and "V"-shaped orientation of ligands inside the deep binding pocket increase the binding affinity, thereby higher inhibitory properties.

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Section: ■ Introductionmentioning

confidence: 99%

Machine Learning-Guided Discovery of AcrB and MexB Efflux Pump Inhibitors

Bera,

Roy,

Joshi

et al. 2024

J. Phys. Chem. B

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Phytochemical Composition, Antibacterial, and Antibiotic‐Resistance Modulatory Activity of Extracts of Lippia multiflora Moldenke, Terminalia mollis M. A. Lawson, and Cinchona officinalis L. Against Multidrug‐Resistant Pseudomonas aeruginosa

Mouozong,

Fankam,

Diffo

et al. 2024

Scientifica

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Pseudomonas aeruginosa is a critical‐class pathogen that shows great resistance to most conventional antibiotics. Hence, it is of utmost importance to search for novel drugs to fight infections caused by this bacterium. This study aimed to evaluate the antibacterial activity of Lippia multiflora, Terminalia mollis, and Cinchona officinalis extracts alone and in combination with antibiotics against multidrug‐resistant (MDR) P. aeruginosa. Phytochemical analysis was performed using standard qualitative and quantitative assays. The microdilution method was used to assess the antibacterial and antibiotic‐resistance modulatory activity of the extracts. The interaction between antibiotics and Cinchona officinalis leaf extract was carried out using the checkerboard broth microdilution method. Phenols and flavonoids were detected in all extracts, whereas other phytochemical classes were selectively distributed. T. mollis leaf extract demonstrated the highest phenolic content (151.59 mg GAE/g), while L. multiflora leaf (LML) extract showed the highest flavonoid content (24.51 mg QE/g). These extracts exhibited antibacterial activity, with minimum inhibitory concentrations (MICs) ranging from 128 to 2048 μg/mL. LML extract displayed the best antipseudomonal activity, with MIC of 128 μg/mL against ATCC 27853 and 256 μg/mL against some MDR isolates (PA1, PA2, and PA7). Moreover, C. officinalis leaf extract (MIC/8), although weakly active, had improved by 2 to 64‐fold the activity of imipenem, streptomycin, kanamycin, and ceftriaxone against MDR P. aeruginosa. It also showed synergy (ΣFIC ≤ 0.5) with streptomycin, ampicillin, tetracycline, and vancomycin against P. aeruginosa PA3. The overall results indicate that the tested extracts, especially those from L. multiflora and C. officinalis leaves, necessitate further exploration for the development of natural drugs to treat infections caused by MDR P. aeruginosa.

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African plants acting on Pseudomonas aeruginosa: Cut-off points for the antipseudomonal agents from plants

Cited by 2 publications

References 115 publications

Machine Learning-Guided Discovery of AcrB and MexB Efflux Pump Inhibitors

Machine Learning-Guided Discovery of AcrB and MexB Efflux Pump Inhibitors

Phytochemical Composition, Antibacterial, and Antibiotic‐Resistance Modulatory Activity of Extracts of Lippia multiflora Moldenke, Terminalia mollis M. A. Lawson, and Cinchona officinalis L. Against Multidrug‐Resistant Pseudomonas aeruginosa

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