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
[28]. 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
[29].
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
[30]. 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 [31]. 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
[32]. 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)
[33].
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
[41]. 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
[42]. 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)
[43]. Recent studies report that plant extracts show different antimicrobial properties against bacterial strains depending on the antibiotic resistance profile
[44]. 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
[45].
4. Mechanisms Underlying the Combination Effects
4.1. Mechanisms Underlying Synergistic or Antagonistic Antimicrobial Activity
4.1.1. 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
[47].
4.1.2. 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
[47]. 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
[48][49]. 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
[47].
4.1.3. 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
[50][51].
4.1.4. 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
[52]. 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
[53]. 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
[54].
4.2. 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
[55].
4.2.1. 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
[56].
4.2.2. 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
[47].
A visualization approach (“Synergy Maps”) can provide information on the mechanisms of actions by identifying relationships between individual compound characteristics and their combination effects
[57]. 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
[58]. 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
[59]. 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
[60]. 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
[59].
5. Determination of Combination Effects
5.1. 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
[60]. 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
[61]. 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
[62].
5.2. 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
[63][64]. 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
[65].
The two main reference models are the Bliss independence model
[66] and the Loewe additivity model
[67]. 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
[68].
The isobole equation, based on the Loewe additivity principle, is widely accepted as one of the most practical models to study combination effects
[69]. An isobole or isobologram is the graphical representation of the combined effects of two samples
[11][70][71]. 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
[72].
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
[73].
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
[74]. It is popular as a valuable tool to assess the synergy of complex medicinal plant formulations
[75].
5.3. 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: FIC
A = MIC
A+B/MIC
A, FIC
B = MIC
B+A/MIC
B, FIC index = FIC
A + FIC
B. The MIC
A+B value is the MIC of compound A in the presence of compound B, and vice versa
[76]. 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][77].
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
[73].
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].
6. 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
[78]. 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
[79]. 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
[80].
6.1. 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
[81].
6.2. 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
[82].
6.3. 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
[83]. 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
[84]. 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
[85]. NMR-based metabolomics was successfully applied for the identification of antibacterial mechanisms of action of various compounds
[86] and also for the characterization of plant secondary metabolites
[87]. In particular, the NMR approach coupled with multivariate data analysis identified compounds of medicinal plants contributing to the antiviral activity
[88], suggesting actinobacteria and their compounds as a potential control against phytopathogenic bacteria
[89] and showing the antimicrobial mechanism of organic acids on
Salmonella enterica strains
[90].
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
[91][92].
6.4. 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
[93]. For example, using LC–MS-based metabolomics, the synergistic activity of a colistin–sulbactam combination was found effective against multidrug-resistant
Acinetobacter baumannii [94].
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
[95]. 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
[96].
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
[97]. 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 [98]. Using this method, bioactive compounds not previously identified with synergy-directed fractionation approaches alone were found as synergists or additives
[16].