Antimicrobial Secondary Metabolites—Extraction, Isolation, Identification, and Bioassay

Rahman, M. Mukhlesur

doi:10.1016/b978-0-12-800874-4.00023-4

Cited by 4 publications

(4 citation statements)

References 91 publications

(90 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ecuador is a country of impressive botanical abundance, where the traditional use of many plant species has attracted the curiosity of researchers of natural products. The study of medicinal plants revealed the existence of secondary metabolites, responsible for their biological activity [1,2,3,4]. Among the pharmacologically interesting natural products obtained by plants we can cite essential oils.…”

Section: Introductionmentioning

confidence: 99%

Chemical, Enantioselective, and Sensory Analysis of a Cholinesterase Inhibitor Essential Oil from Coreopsis triloba S.F. Blake (Asteraceae)

Espinosa

Bec

Larroque

et al. 2019

Plants

View full text Add to dashboard Cite

The fresh leaves of Coreopsis triloba S.F. Blake, collected at Cerro Villonaco in Loja, Ecuador, were investigated with respect to their essential oil (EO). The chemical composition was determined qualitatively through gas chromatography coupled with mass spectrometry (GC-MS) and quantitatively by gas chromatography coupled with flame ionization (GC-FID), using relative response factors (RRF) based on the enthalpy of combustion. The essential oil contained between 92.5% and 93.4% of monoterpene hydrocarbons, with (E)-β-ocimene being the main component (35.2–35.9%), followed by β-phellandrene (24.6–25.0%), α-pinene (15.3–15.9%), myrcene (10.9–11.0%), sabinene (2.2–2.4%), (Z)-β-ocimene (1.5%), and germacrene D (1.2–1.3%). The enantiomeric distribution of α-pinene, β-pinene, limonene, and germacrene D was also determined. The main components responsible for the aroma were identified through aroma extract dilution analysis (AEDA), a gas chromatography-olfactometry (GC-O) based technique, being α-pinene, β-pinene (0.6%), terpinolene (0.1%), α-copaene (0.1–0.3%), β-phellandrene, and (E)-4,8-dimethyl-1,3,7-nonatriene (0.1–0.2%) the main olfactory constituents according to the decreasing factor of dilution (FD) order. The biological tests showed IC50 inhibition values of 42.2 and 6.8 µg/mL for acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Chemical, Enantioselective, and Sensory Analysis of a Cholinesterase Inhibitor Essential Oil from Coreopsis triloba S.F. Blake (Asteraceae)

Espinosa

Bec

Larroque

et al. 2019

Plants

View full text Add to dashboard Cite

show abstract

“…Synergy is an effect of a combination of substances which is greater than would be expected by adding together their separate contributions (Williamson, 2001). There is currently much research aimed at identifying and isolating secondary metabolites of plants with antimicrobial activity (Rahman, 2014). Williamson (2001) discusses the limitations of isolated constituent research and advocates for more synergy research.…”

Section: The Synergistic Approachmentioning

confidence: 99%

“…Individual and combinations of constituents at varying dilutions are applied to the agar on paper discs or in wells cut out of the agar before incubation of plates. Zones of inhibition are measured (diameter in mm) to show antimicrobial activity (Rahman, 2014). The size of the zones of inhibition depends upon the diffusion of the active compounds into the agar.…”

Section: Literature Review: Antimicrobial Research and Plant Synergymentioning

confidence: 99%

See 1 more Smart Citation

Antimicrobial resistance and synergy in herbal medicine

Mundy

Pendry

Rahman

2016

Journal of Herbal Medicine

Self Cite

View full text Add to dashboard Cite

Antimicrobial resistance (AMR) is a serious and growing threat to human health. The development of new antibiotics is limited and slow. The tradition of synergy in herbal medicine is being used as a source of research ideas. A literature review of antimicrobial research and plant synergy published in a five year period was carried out using online databases. The in vitro findings were that most of the research reported synergy both within plants and between plants and antibiotics.Whole plant extracts and combinations of compounds were shown to be more effective antimicrobials than isolated constituents. The discussion highlights that the in vitro herbal research findings are difficult to apply to practice and aren't progressing to clinical trials.Collaborative, innovative, inter-disciplinary clinical research is recommended.

show abstract

Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network

Karim

Kanaya

Altaf‐Ul‐Amin

2022

Molecular Informatics

View full text Add to dashboard Cite

The plants produce numerous types of secondary metabolites which have pharmacological importance in drug development for different diseases. Computational methods widely use the fingerprints of the metabolites to understand different properties and similarities among metabolites and for the prediction of chemical reactions etc. In this work, we developed three different deep neural network models (DNN) to predict the antibacterial property of plant metabolites. We developed the first DNN model using the fingerprint set of metabolites as features. In the second DNN model, we searched the similarities among fingerprints using correlation and used one representative feature from each group of highly correlated fingerprints. In the third model, the fingerprints of metabolites were used to find structurally similar chemical compound clusters. Form each cluster a representative metabolite is selected and made part of the training dataset. The second model reduced the number of features where the third model achieved better classification results for test data. In both cases, we applied the simple graph clustering method to cluster the corresponding network. The correlation‐based DNN model reduced some features while retaining an almost similar performance compared to the first DNN model. The third model improves classification results for test data by capturing wider variance within training data using graph clustering method. This third model is somewhat novel approach and can be applied to build DNN models for other purposes.

show abstract

Antimicrobial Secondary Metabolites—Extraction, Isolation, Identification, and Bioassay

Cited by 4 publications

References 91 publications

Chemical, Enantioselective, and Sensory Analysis of a Cholinesterase Inhibitor Essential Oil from Coreopsis triloba S.F. Blake (Asteraceae)

Chemical, Enantioselective, and Sensory Analysis of a Cholinesterase Inhibitor Essential Oil from Coreopsis triloba S.F. Blake (Asteraceae)

Antimicrobial resistance and synergy in herbal medicine

Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network

Contact Info

Product

Resources

About