Encyclopedia
Please note this is an old version of this entry, which may differ significantly from the current revision.
Subjects:
Medicine, Research & Experimental
It is accepted that the medicinal use of complex mixtures of plant-derived bioactive compounds is more effective than purified bioactive compounds due to beneficial combination interactions. However, synergy and antagonism are very difficult to study in a meticulous fashion since most established methods were designed to reduce the complexity of mixtures and identify single bioactive compounds. The utilization of bioactive compounds from medicinal plant extracts as appropriate antimicrobials is important and needs to be facilitated by means of new metabolomics technologies to discover the most effective combinations among them. Understanding the nature of the interactions between medicinal plant-derived bioactive compounds will result in the development of new combination antimicrobial therapies.
- medicinal plants
- bioactive compounds
- antimicrobial combination effects
1. Introduction
Research in medicinal plant-derived mixtures tends either to focus on one or two bioactive compounds (secondary metabolites) or to ignore the chemical composition altogether, studying the biological effects of complex mixtures for which bioactive compounds are unknown. Moreover, research design may be complicated by the chemical complexity and variability of the medicinal plant extracts/bioactive compounds [1,2,3,4]. Recent studies to elucidate the mechanisms of action of traditionally used medicinal plant extracts have found a complex mixture of bioactive compounds that act at multiple targets [5,6,7,8].
Most of the studies presenting antimicrobial properties of medicinal plant extracts and bioactive compounds make use of unfractionated extracts that usually show weak in vitro antimicrobial activity. These studies were infrequently confirmed by means of in vivo assays. Thus, the precise mechanisms of action of the vast majority of such bioactive compounds are unknown [9].
The scientific search for medicinal plant extracts is challenging because of their huge complexity and variability. Since complex plant extracts and no single bioactive molecules are often used for medicinal purposes, understanding the interactions between the active compounds could be of great importance [10,11,12]. It was found that disease resistance is less likely to occur against a combination of bioactive compounds than against single active molecules [11,12,13]. Medicinal plants are targeting microbes through the combined action of structurally and functionally diverse active compounds. Combined effects can vary based on the target microbial species [10,14].
Medicinal plant extracts may contain hundreds or even thousands of individual bioactive compounds in varying abundances and identifying the bioactive compounds responsible for a given biological activity is a significant challenge [15]. In fact, the overall activity of medicinal plant extracts is a result of the combined action of multiple compounds with synergistic, additive, or antagonistic activity [12,16,17,18,19]. It is not acceptable to use bioactive compounds as medicines without understanding intra-extract interactions on a wider scale. In several instances, medicinal plant extract activity shows a better effect than an equivalent dose of an isolated compound [20] and cannot be predicted on what is known about individual compounds. Whether it is synergy, enhanced bioavailability, cumulative effects, or simply the additive properties requires further research.
Synergistic interactions between the compounds of individuals or mixtures or medicinal plant extracts are a vital part of their therapeutic efficacy. Therefore, medicinal plant extract synergy needs to be evaluated by rigorous analysis methods and validated in clinical trials. Moreover, the specific bioactive compounds responsible for those effects and the basic mechanisms by which they interact are still incompletely understood [21].
2. Antimicrobial Combination Effects of Medicinal Plant-Derived Mixture Compounds
The uniqueness of medicinal plants is due to their use in combinations and to the interactions between bioactive compounds. Synergy, a key factor in medicinal plant medicine, is an effect seen by a combination of compounds being greater than would have been expected by adding together their separate contributions [48]. Proving and defining synergy is difficult since complexity in methodology in identifying interaction effects exists. To do this, the testing of individual compounds and comparing the activity with an equivalent dose in the mixture is necessary. Although some studies confirm this methodology, the term “polyvalent” is used to denote an improved and cooperative effect without qualifying it [49].
It is generally agreed that the use of a combination of multiple antimicrobial agents can result in different combined effects depending on the composition and concentration of the compounds. Specifically, synergy is obtained when two antimicrobial compounds are combined and produce antibacterial activity greater than the sum of the antibacterial activity of the individual compound. An additive effect is produced by combining antimicrobials producing an antimicrobial activity that is equal to the sum of the individual compound. An antagonist effect results when the antimicrobial activity of two compounds in combination is less than the sum of the effects of the individual compounds [50,51].
Testing antimicrobial combinations for their interactive effects helps to attain the following purposes: (1) synergistic combinations could be discovered and enhance antimicrobial effects of the individual compounds; (2) it would exclude antagonistic interactions which can be harmful; and (3) it would minimize the toxicity and adverse effects of the compounds since they could be used in lower doses in the combination than when used individually [52].
3. Synergistic Interactions between Compounds and Antibiotics
Combining antibiotics with activity-enhancing plant-derived compounds is a significant strategy in the rapidly developing antimicrobial therapy options. Certain medicinal plants can inactivate antibiotic resistance mechanisms. This ability is due to synergy between medicinal plant compounds and antibiotics whose activity would be low when compounds are absent. In addition, compounds show relevant activity only when they are utilized together with an antibiotic. However, identifying which compound in an extract is responsible for the synergistic interaction is difficult. Additionally, some compounds show synergistic activity by other mechanisms, in addition to their own antimicrobial activity due to “polyvalent” effects [48,49].
3.1. Essential Oils
EOs and their bioactive compounds comprise part of the group of secondary metabolites that can interact with antibiotics. A combination of five EOs with seven antibiotics was studied; the combined effect of peppermint, cinnamon bark, and lavender EO with piperacillin and meropenem showed significant synergy against different Escherichia coli strains [72].
The combination of Origanum compactum, Chrysanthemum coronarium, Melissa officinalis, Thymus willdenowii, Boiss, and Origanum majorana, EOs with gentamycin, tobramycin, imipenem, and ticarcillin against ten Gram-positive and Gram-negative bacterial strains showed synergy in some cases, but also an antagonistic effect against different bacterial strains was found [73]. In a recent study, EOs prepared from Laurus nobilis L. and Prunus armeniaca L. species were tested for potential synergistic antibacterial and antifungal effects with three antibiotics, namely fluconazole, ciprofloxacin, and vancomycin. The EO from Laurus nobilis had the highest antimicrobial activity, with MICs ranging from 1.39 to 22.2 mg/mL for bacteria and between 2.77 and 5.55 mg/mL for yeasts. Of the 32 interactions evaluated, 23 (71.87%) exhibited total synergy, and nine (28.12%) a partial synergy. The main EOs from Laurus nobilis (eucalyptol, a-terpinyl acetate, and methyl eugenol) showed the highest synergistic effect with all the antibiotics tested with FIC index values in the range of 0.266 to 0.75 for bacteria, and between 0.258 and 0.266 for yeasts [74].
In another study, the investigation of combinations in Eucalyptus camaldulensis EOs with three conventional antibiotics (gentamycin, ciprofloxacin, and polymyxin B) exhibits synergy even in some re-sensitized multi-drug-resistant Acinetobacter baumannii strains. The detected MICs for the Eucalyptus camaldulensis EOs were in the range from 0.0005 to 0.002 mg/mL. When two Eucalyptus camaldulensis Eos were combined with ciprofloxacin, synergy was identified against two out of three tested multi-drug-resistant Acinetobacter baumannii strains with an FIC index value <0.5 [75]. Bioactive compounds of Eos, namely thymol and carvacrol, exhibited synergy with penicillin against Escherichia coli and Salmonella typhimurium. In addition, carvacrol was found to exhibit synergy in combination with both ampicillin and nitrofurantoin against Klebsiella oxytoca, with FIC index values of 0.375 and 0.15, respectively, while thymol was non-active. Carvacrol showed the highest MIC values of 2.5 mg/mL against Klebsiella oxytoca [76]. It was found that eugenol exhibited synergy with ampicillin against Streptococcus cricetid and Streptococcus gordonii and with gentamycin against Streptococcus sanguinis and Porfyromonas gingivalis. The MIC for eugenol was found to be between 0.1 and 0.8 mg/mL in combination with eugenol and ampicillin the MIC was reduced >4–8-fold in all tested bacteria producing synergy as defined by the FIC index ≤0.375–0.5 [77]. The antibacterial and streptomycin-modifying activity of the Thymus glabrescens EO was also studied. The main compounds of this EO were geraniol, geranyl acetate, and thymol. The MIC for Thymus glabrescens EO was identified to be between 2.508 and 5.0168 mg/mL. All the combinations studied between compounds and streptomycin showed mainly antagonistic interactions. Combinations between geraniol and thymol produced a dominant additive effect (FIC index 0.76 to 1.09) [78].
Phenols, alcohols, and oxygenated monoterpenes identified in some EOs are effective in biofilm destruction [79]. Rosato et al. [80] reported the synergistic effect of Cinnammonum zeylanicum, Mentha piperita, Origanum vulgare, and Thymus vulgaris EOs with gentamycin (FIC index 0.08 to 0.16), oxacillin (FIC index 0.08 to 0.23) and norfloxacin (FIC index 0.08 to 0.23) on bacterial biofilm growth of four different strains of Gram-positive bacteria. The interaction of EOs with norfloxacin was the most effective on biofilm growth in all the tested combinations.
3.2. Propolis
Propolis is a complex biological mixture. Row propolis usually contains 50–70% plant resins, 30% waxes, 10% EOs and aromatic oils, 5% pollen, and 5% other organic bioactive compounds. Bioactive compounds include flavonoids (flavanols, flavones, and flavanones), aromatic acids, terpenes, esters, aldehydes, coumarin, sterols, and fatty acids [81].
The synergy between propolis and other antibiotics can potentially prevent resistance, increase antibacterial efficacy and provide wider antibacterial activity than antibiotic monotherapy. Therefore, numerous articles were published to indicate the synergistic effect of propolis and antibiotics [82,83,84]. For instance, propolis’s ethanolic extract has a synergistic effect (FIC index ≤ 0.5) with aminoglycoside antibiotics (gentamycin, amikacin, and kanamycin), tetracycline, and fusidic acid on the inhibition of Staphylococcus aureus growth [61]. Staphylococcus aureus MIC values were between 0.128 and 0.512 mg/mL. No antagonistic interaction (FIC index > 4) was identified in this study.
3.3. Phenolic Compounds/Polyphenols
The synergistic activities of baicalein, a flavonoid isolated from the root of Saitellaria baicalensis Georgi with ampicillin or gentamycin against Gram-positive and Gram-negative oral bacteria strains, were studied. Both combinations exhibited synergistic effects (FIC index < 0.375–0.5). Saitellaria baicalensis Georgi was determined with MIC values ranging from 0.08 to 0.32 mg/mL against oral bacteria [85]. Another study evaluating the effect between amentoflavone (biflavonoid isolated from Selaginella tamariscina) and ampicillin, chloramphenicol, and cefotaxime showed that amentoflavone exhibited a synergistic interaction with antibiotics against the Gram-positive and Gram-negative bacteria studied (FIC index 0.375 to 0.5) except for Streptococcus mutants. The results showed that amentoflavone, with a MIC value of 0.004 to 0.032, had remarkable antibacterial activity [86]. In another study, the antimicrobial activity of seven phenolic compounds with six antibiotics against multidrug-resistant bacteria of the ESKAPE group was evaluated. Phenolic compounds on their own revealed little or no inhibitory effects (MIC 0.0125 to 0.4). However, thirty combinations showed antagonistic effects (FIC index > 2) and twenty-four potential synergistic effects (FIC index 1.0 to 1.5) [87]. Recent studies report that plant extracts show different antimicrobial properties against bacterial strains depending on the antibiotic resistance profile [88]. For instance, Cistus salviifolius and Punica granatum extracts were tested against 100 Staphylococcus aureus clinical isolates, which resulted in average MIC values ranging between 0.05 and 0.08 mg/mL. The extract of Cistus salviifolius has exerted greater efficacy against strains of Staphylococcus aureus resistant to beta-lactam antibiotics and this increased efficacy may be due to the existence of synergy between different classes of polyphenols. However, the extract of Punica granatum has shown greater efficacy against strains sensitive to oxacillin and quinolones [89].
3.4. Alkaloids
The compound 1-4-naphthoquinone is a natural alkaloid shown to have antibacterial activity against both Gram-positive and Gram-negative bacteria. The MIC values of 1-4-naphthoquinone range from 0.0078 to 0.125 mg/mL. 1,4-naphthoquinone exerts synergy (FIC index ≤ 0.5) with imipenem, cefotaxime, and cefuroxime against methicillin-resistant Staphylococcus aureus (MRSA), while against American-type culture collection (ATCC)-cultured MRSA, a synergistic effect was found only between 1,4-naphthoguinone and cefotaxime (FIC index = 0.5). An additive combination with imipenem (FIC index = 1.063) was produced and antagonistic action was identified between 1,4-naphthoquinone and cefuroxime (FIC index = 8.5) [90].
5. Mechanisms Underlying the Combination Effects
5.1. Mechanisms Underlying Synergistic or Antagonistic Antimicrobial Activity
5.1.1. Pharmacodynamic Synergy
Pharmacodynamic synergy results from the targeting of multiple pathways, which may include substrates, enzymes, metabolites, ion channels, ribosomes, and signal cascades [12]. It may also occur through complementary actions, where synergists in a mixture interact with multiple sites of a given pathway and can result in positive regulation of a target or in negative regulation of competing mechanisms [54].
5.1.2. Pharmacokinetic Synergy
Plant-derived compounds can increase the solubility, absorption, transport, distribution or stimulate the metabolism of bioactive constituents. In this way, the bioavailability of compounds is enhanced, resulting in increased efficacy of the extract as compared to individual compounds in isolation [54]. Compounds that improve the solubility of bioactive constituents are a significant type of synergy that is often underestimated. Modulation of compound/drug transport enhances their absorption through disruption of transport barrier, delay of barrier recovery, or reduction of excretion by inhibiting drug effects [91,92]. Modulation of distribution increases the concentration by blocking the compound/drug uptake and inhibiting the metabolic processes that convert a compound/drug into excretable forms. In addition, metabolic modulation stimulates the metabolism of drugs into active forms or inhibits the metabolism of compounds/drugs into inactive forms [54].
5.1.3. Targeting Disease Resistance Mechanisms
The bacterial resistance to beta-lactam antibiotics can be overcome by the combination of beta-lactamase inhibitors with beta-lactam antibiotics. It was reported that a dichloromethane extract of Vitellaria paradoxa C.F. Gaertn leaves the activity of ampicillin, oxacillin, and nafcillin synergized against MRSA by targeting PBP2a+/−beta-lactamase enzymes. Oleanolic acid and ursolic acid were found to be the compounds that exerted this synergy [93,94].
5.1.4. Elimination of Adversely Acting Compounds
The elimination or neutralization of adverse effects of a toxic, but bioactive compound by inactive mixture compounds comprises an additional type of synergy. This mechanism does not improve the efficacy of bioactive compounds but rather acts to minimize the adverse effects that an active compound may cause [12].
Although mechanisms by which synergy can occur in drugs are relatively well known, the mechanisms by which medicinal plant-derived compounds exhibit synergetic effects have not yet been fully clarified. Bioactive compounds act in a synergistic or antagonistic manner, and it appears that in most compounds multi-target effects predominate [95]. For example, the following mechanisms of antimicrobial interactions can produce synergy between EOs bioactive compounds: (1) inhibition of several steps in a biochemical pathway; (2) inhibition of enzymes that degrade antimicrobials; (3) interaction of antimicrobials with the cell wall; or (4) interaction with the cell wall resulting in increased uptake of other antimicrobials [96]. Moreover, antagonism is supposed to occur when (1) a combination of bacteriostatic and bactericidal antimicrobials exists; (2) antimicrobials act on the same site; or (3) antimicrobials act with each other [97].
5.2. Approaches Identifying Mechanisms of Combination Effects
Determination of the bioactive compounds related to the biological effects of complex mixtures and recognition of the interactions in which they are involved is very important. However, it is also important to identify molecular mechanisms responsible for the combined effects of complex mixtures. This can occur through the following approaches, including targeted biological assays to identify molecules that affect specific molecular targets and evaluation of changes in protein, gene, and metabolic profiles in an untargeted way [98].
5.2.1. Targeted Assays
A common method to evaluate EPI involves the use of an efflux pump substrate that fluoresces when it is contacted with cellular DNA. EPI increases the fluorescence of the substrate because of the increased cellular accumulation. This method was successfully used to discover EPIs from medicinal plant mixtures [99].
5.2.2. Untargeted Approaches
Multi-target effects, whether they are related to a single compound or multiple compounds, can be identified with indirect approaches. A search of medicinal plant compounds using molecular interaction profiles may detect the synergistic mechanisms of action. In addition, the efficacy of medicinal plant mixtures and their effect on molecular targets can be influenced by differences in genes, timing and dosage of therapy, and environment [54].
A visualization approach (“Synergy Maps”) can provide information on the mechanisms of actions by identifying relationships between individual compound characteristics and their combination effects [100]. The use of DNA and RNA microassays is another approach for searching for combination effects within complex mixtures. This approach enables the identification of genes that are regulated by synergistic or antagonistic interactions between the plant species [101]. In silico approaches predicting the mechanisms of action were developed to overcome the time- and material-consuming nature of biological testing. Experimental activity data can be utilized to discover ligand–target relationships and find the biological activities of different molecules [102]. The Functional Signature Ontology (FUSION) maps are used to link natural products to their mechanism of action. Data from measuring gene expression of a representative subset of genes can be combined into FUSION maps to link bioactive molecules to the proteins that they target in cells [103]. Another approach, the network pharmacology approach can predict the interactions between molecules and proteins in a biological system and evaluate the pharmacological effects of natural product mixtures [102].
6. Determination of Combination Effects
6.1. Collecting Biological Data
The most successful way to collect proper data for understanding combination effects in complex systems is to choose a suitable biological assay for combination testing. Indeed, the establishment of high-quality in vitro testing promises for identifying multi-target compounds in mixtures [103]. Except for carefully collecting the biological assay to study the combined effects, data relevant to the comparison between a compound combination and compounds in isolation should be gathered [11].
Potential combination effects including synergy and antagonism can occur over a wide range of concentrations. Therefore, different ratios of the samples must be tested [11]. Simple assays employing concentration-based methods cannot claim synergy without further in-depth studies because they lack the range of concentration combinations required to evaluate combination effects [104]. Time-based approaches were also applied to identify antimicrobial synergy. These methods involve sampling cultures at regular time intervals and defining synergistic, additive, and antagonistic effects by using a resulting dose–response curve [105].
6.2. Assessing Combination Effects
Different reference models that are used to identify the outcome or a given combination may cause confusion concerning the classification of synergistic or antagonistic interactions [28,106]. Several reference models as well as their biological properties are summarized below.
The combination index (CI) is a practical model used for the quantitative identification of the synergy of multi-compound combination agents acting on the same target/receptor in a fixed ratio. Synergy occurs when the CI value is <1, while additive effect occurs when the CI value is 1 and antagonism exists when the CI value is >1 [107].
The two main reference models are the Bliss independence model [108] and the Loewe additivity model [109]. The Bliss model suggests that each sample has an independent effect, while the Loewe model considers the expected effect as a sample combined with itself. If both models confirm an interaction as synergistic, that interaction should be considered strong synergy. However, if the combination is identified as synergistic by one model only, it should be considered weak synergy [27].
The isobole equation, based on the Loewe additivity principle, is widely accepted as one of the most practical models to study combination effects [110]. An isobole or isobologram is the graphical representation of the combined effects of two samples [11,29,111]. The isobologram model is designed to assess the synergistic/antagonistic interactions between two compounds acting on the same target and is less adequate to assess the complex interactions among multiple potential bioactive compounds that may act on a network target [112].
Another more recently developed model based on both Loewe and Bliss models, the zero-interaction potency model, is related to the assumption that two non-interacting samples cause minimal changes to the dose–response curves. This model potentially identifies the variety of combination effects that occur in different concentration ranges [113].
The systematic analysis/system-to-system (S2S) model was developed to address the multi-target synergistic actions of mixed chemical compounds, with a system of targeted protein/receptors. The S2S studies the multi-target mechanisms of the action of complex compound mixtures and identifies bioactive compounds, which may bind to most of the corresponding targets [114]. It is popular as a valuable tool to assess the synergy of complex medicinal plant formulations [115].
6.3. Scoring Biological Data
Most synergy analyses focus on the variations of isobologram and FIC index, which have found many applications [11]. Using measurements of the MIC, the FIC index is calculated according to the formula: FICA = MICA+B/MICA, FICB = MICB+A/MICB, FIC index = FICA + FICB. The MICA+B value is the MIC of compound A in the presence of compound B, and vice versa [116]. This definition was replaced by a general, widely accepted definition where synergistic interactions are considered to be any values ≤0.5, additive interactions range from 0.5–1.0, non-interactive effects: range from 1.0–4.0, and antagonistic interactions are considered to be any values >4.0 [11,117].
Another score, the delta-score, is visualized using an interaction landscape over all tested dosage combinations to discover any changes in combination effects along with multiple dosages and response levels [113].
The above-described approaches have not yet been applied widely to identify synergy in complex natural products. However, the FIC index is frequently used in natural product research [11].
7. Determination of Bioactive Compounds Responsible for Combination Effects
To identify bioactive compounds and to improve the efficacy of medicinal plant extract mixtures, bioactive compounds responsible for the biological identity, whether synergistic, additive, or antagonistic, should be isolated and characterized and their concentrations should be determined. The data that needs to be integrated for the efficient identification of bioactive compound combinations include chemical bioactivity data, gene expression data, targets, and pathway annotations, and gene–protein interaction networks [118]. Metabolomics (the comprehensive analytical approach for the identification and quantification of secondary metabolites in a biological system) is a significant tool for standardization and quality control in medicinal plants [119]. Metabolomics combines sophisticated analytical technologies (mass spectrometry (MS) coupled with various chromatographic separation techniques) with the application of statistical and multi-variant methods for data interpretation. Because of the large number of bioactive compounds and the large variations in abundance, there is no single method available to analyze the whole number of the chemical compounds [120].
7.1. Methods to Identify Bioactive Molecules
The most commonly used method to identify bioactive compounds is bioassay-guided fractionation. In this method, extracts are separated using different chromatographic techniques, the fractions are evaluated for biological activity and the process is repeated until bioactive compounds are identified and characterized. To avoid the isolation of known bioactive compounds, structural evaluation steps to discard samples containing known bioactive compounds should be taken through high-resolution MS, UV spectroscopy, NMR, and tandem mass spectrometry (MS/MS) molecular networking [121].
7.2. Methods to Identify Synergy
The synergy-directed fractionation, a modification of bioassay-guided fractionation, combines chromatographic separation and synergy testing in order to identify synergistic interactions between the bioactive compounds present in a mixture. Through a combination of fractions with a known bioactive compound found in the original extract and testing for combination effects, synergists that did not exhibit activity on their own could be identified [16]. This method uses MS for guided isolation of bioactive compounds, with potential synergistic interactions among extracts that could not have possessed any biological activity through conventional guided fractionation [122].
7.3. Metabolomics Methods to Identify Bioactive Compounds
Fractionation approaches focus mainly on the most easily isolated compounds in a mixture rather than those that are active [123]. Therefore, many efforts were made to identify and isolate bioactive compounds by combining the chemical and biological properties of samples under analysis. To achieve this, bioactive compounds were identified based on MS and gas chromatography (GC) and biological data. The combination of MS and GC can be an important analytical tool that separates compounds and identifies their chemical structures [124]. Nuclear magnetic resonance (NMR) spectroscopy is one of the three (the other two being GC–MS and LC–MS) principal analytical methods in metabolomics for profiling and identifying the metabolite in complex mixtures such as plant extracts [125]. NMR-based metabolomics was successfully applied for the identification of antibacterial mechanisms of action of various compounds [126] and also for the characterization of plant secondary metabolites [127]. In particular, the NMR approach coupled with multivariate data analysis identified compounds of medicinal plants contributing to the antiviral activity [128], suggesting actinobacteria and their compounds as a potential control against phytopathogenic bacteria [129] and showing the antimicrobial mechanism of organic acids on Salmonella enterica strains [130].
Multivariate statistical methods were applied to integrate biological assay data with measurements of chemical compounds, a process that is termed “biochemometrics”. Using more than one statistical model appears to overcome the problems of each model independently [131,132].
7.4. Metabolomics Methods to Identify Synergy
To establish a metabolomic profile, spectroscopic and spectrometric methods are used, such as NMR and MS, and separation methods coupled to mass spectrometric detection, such as high-performance liquid chromatography (HPLC), ultra-HPLC, GC and supercritical fluid chromatography. The selection method is influenced by the matrix and the amount of sample, and the concentration and properties of the metabolites [136]. For example, using LC–MS-based metabolomics, the synergistic activity of a colistin–sulbactam combination was found effective against multidrug-resistant Acinetobacter baumannii [137].
Flaxomics, a new metabolomics application measures the actual reaction rates (fluxes) of metabolic pathways indirectly by the shifts in metabolic levels. Therefore, the flaxone (total set fluxes) is observing the interactions between all the “-omes”, thus granting a synergistic insight [138]. In a study, the metabolomic profile of Vibrio alginolyticus and the role of its metabolism in multi-drug resistance was examined. This was carried out by detecting the metabolic differences of acetyl-CoA fluxes into and through the P-cycle and fatty acid biosynthesis [139].
In a recent study, a large discrepancy between the expected and observed activities of an extract containing the bioactive compounds of berberine and magnolol was noted. The evaluation of this discrepancy indicated the presence of antagonists within the mixture. Using chromatographic separation, the antagonists were separated from bioactive compounds, and the activity of the extract was reinstated [134]. Therefore, predictive methods which can identify bioactive compounds alone may not be capable of determining the complexity of the extract mixture and other methods, which can identify the presence of synergists or antagonists, are required.
The combination of synergy-directed fractionation with biochemometric analysis could identify synergists and additives in complex medicinal plant-derived extracts. In a study, MS was combined with a biological assay to produce selectivity ratio plots predicting potential synergistic or additive mechanisms between bioactive compounds from Hydrastis canadensis, which enhanced the antimicrobial activity of berberine against Staphylococcus aureus [22]. Using this method, bioactive compounds not previously identified with synergy-directed fractionation approaches alone were found as synergists or additives [16].
This entry is adapted from the peer-reviewed paper 10.3390/antibiotics11081014
This entry is offline, you can click here to edit this entry!
