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Efficacy and Mechanism of Traditional Medicinal Plants and Bioactive Compounds against Clinically Important Pathogens.

ABSTRACT: Traditional medicinal plants have been cultivated to treat various human illnesses and avert numerous infectious diseases. They display an extensive range of bene?cial pharmacological and health effects for humans. These plants generally synthesize a diverse range of bioactive compounds which have been established to be potent antimicrobial agents against a wide range of pathogenic organisms. Various research studies have demonstrated the antimicrobial activity of traditional plants scientifically or experimentally measured with reports on pathogenic microorganisms resistant to antimicrobials. The antimicrobial activity of medicinal plants or their bioactive compounds arising from several functional activities may be capable of inhibiting virulence factors as well as targeting microbial cells. Some bioactive compounds derived from traditional plants manifest the ability to reverse antibiotic resistance and improve synergetic action with current antibiotic agents. Therefore, the advancement of bioactive-based pharmacological agents can be an auspicious method for treating antibiotic-resistant infections. This review considers the functional and molecular roles of medicinal plants and their bioactive compounds, focusing typically on their antimicrobial activities against clinically important pathogens.

SUBMITTER: Mickymaray S

PROVIDER: S-EPMC6963422 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Efficacy and Mechanism of Traditional Medicinal Plants and Bioactive Compounds against Clinically Important Pathogens.

Mickymaray Suresh S

Antibiotics (Basel, Switzerland) 20191209 4

Traditional medicinal plants have been cultivated to treat various human illnesses and avert numerous infectious diseases. They display an extensive range of beneﬁcial pharmacological and health effects for humans. These plants generally synthesize a diverse range of bioactive compounds which have been established to be potent antimicrobial agents against a wide range of pathogenic organisms. Various research studies have demonstrated the antimicrobial activity of traditional plants scientifical ...[more]

PMID: 31835403

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Project description:BackgroundNeorautanenia mitis, Hydnora abyssinica, and Senna surattensis are medicinal plants with a variety of traditional uses. In this study, we sought to isolate the bioactive compounds responsible for some of these activities, and to uncover their other potential medicinal properties.MethodsThe DCM and ethanol extracts of the roots of N. mitis and H. abyssinica, and the leaves of S. surattensis were prepared and their phytochemical components were isolated and purified using chromatographic methods. These extracts and their pure phytochemical components were evaluated in in-vitro models for their inhibitory activities against Plasmodium falciparum, Trypanosoma brucei rhodesiense, Mycobacterium tuberculosis, α-amylase (AA), and α-glucosidase (AG).ResultsRautandiol B had significant inhibitory activities against two strains of Plasmodium falciparum showing a high safety ratio (SR) and IC50 values of 0.40 ± 0.07 μM (SR - 108) and 0.74 ± 0.29 μM (SR - 133) against TM4/8.2 and K1CB1, respectively. While (-)-2-isopentenyl-3-hydroxy-8-9-methylenedioxypterocarpan showed the highest inhibitory activity against T. brucei rhodesiense with an IC50 value of 4.87 ± 0.49 μM (SR > 5.83). All crude extracts showed inhibitory activities against AA and AG, with three of the most active phytochemical components; rautandiol A, catechin, and dolineon, having only modest activities against AG with IC50 values of 0.28 mM, 0.36 mM and 0.66 mM, respectively.ConclusionThese studies have led to the identification of lead compounds with potential for future drug development, including Rautandiol B, as a potential lead compound against Plasmodium falciparum. The relatively higher inhibitory activities of the crude extracts against AG and AA over their isolated components could be due to the synergistic effects between their phytochemical components. These crude extracts could potentially serve as alternative inhibitors of AG and AA and as therapeutics for diabetes.

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

Project description:BackgroundThere are handful hypothesis-driven ethnobotanical studies in Nepal. In this study, we tested the non-random medicinal plant selection hypothesis using national- and community-level datasets through three different types of regression: linear model with raw data, linear model with log-transformed data and negative binomial model.MethodsFor each of these model, we identified over-utilized families as those with highest positive Studentized residuals and underutilized families with highest negative Studentized residuals. The national-level data were collected from online databases and available literature while the community-level data were collected from Baitadi and Darchula districts.ResultsBoth dataset showed larger variance (national dataset mean 6.51 < variance 156.31, community dataset mean 1.16 < variance 2.38). All three types of regression were important to determine the medicinal plant species selection and use differences among the total plant families, although negative binomial regression was most useful. The negative binomial showed a positive nonlinear relationship between total plant family size and number of medicinal species per family for the national dataset (β1 = 0.0160 ± 0.0009, Z1 = 16.59, p < 0.00001, AIC1 = 1181), and with similar slope and stronger performance for the community dataset (β2 = 0.1747 ± 0.0199, Z2 = 8.76, p < 0.00001, AIC2 = 270.78). Moraceae and Euphorbiaceae were found over-utilized while Rosaceae, Cyperaceae and Caryophyllaceae were recorded as underutilized.ConclusionsAs our datasets showed larger variance, negative binomial regression was found the most useful for testing non-random medicinal plant selection hypothesis. The predictions made by non-random selection of medicinal plants hypothesis holds true for community-level studies. The identification of over-utilized families is the first step toward sustainable conservation of plant resources and it provides a baseline for pharmacological research that might be leading to drug discovery.

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Efficacy and Mechanism of Traditional Medicinal Plants and Bioactive Compounds against Clinically Important Pathogens.

Publications

Efficacy and Mechanism of Traditional Medicinal Plants and Bioactive Compounds against Clinically Important Pathogens.

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