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Interaction between lichen secondary metabolites and antibiotics against clinical isolates methicillin-resistant Staphylococcus aureus strains.

ABSTRACT

The in vitro antimicrobial activities of five compounds isolated from lichens, collected in several Southern regions of Chile (including the Chilean Antarctic Territory), were evaluated alone and in combination with five therapeutically available antibiotics, using checkerboard microdilution assay against methicillin-resistant clinical isolates strains of Staphylococcus aureus. [MIC.sub.90], [MIC.sub.50], as well as [MBC.sub.50] and [MBC.sub.50], for the lichen compounds were evaluated. The [MIC.sub.90] was ranging from 32 [micro]g/ml for perlatolic acid to 128 [micro]g/ml for [alpha]- collatolic acid. [MBC.sub.90] was ranging from onefold up to twofold the [MIC.sub.90] for each compound. A synergistic action was observed in combination with gentamicin, whilst antagonism was observed for some lichen compounds in combination with levofloxacin. All combinations with erythromycin were indifferent, whilst variability was observed for clindamycin and oxacillin combinations. Data from checkerboard assay were analysed and interpreted using the fractional inhibitory concentration index and the response surface approach using the [DELTA]E model. Discrepancies were found between both methods for some combinations. These could mainly be explained by the failure of FIC approach, being too much subjective and sensitive to experimental errors. These findings suggest, however, that the natural compounds from lichens are good candidates for the individuation of novel templates for the development of new antimicrobial agents or combinations of drugs for chemotherapy.

Keywords:

Lichen secondary metabolites

MRSA

Checkerboard assay

Antimicrobial activity

Introduction

Lichens are symbiotic organisms derived by the close cellular union of a fungal (mycobiont) and an algal and/or cyanobacteria! (phytobiont) partner, comprising about 20,000 known species. They produce a variety of secondary compounds that typically arise from the fungal component secondary metabolism, many of them, exclusive of the lichen production. Chemotaxonomic studies have shown that the most unique lichen metabolites belong to the chemical classes of depsides, depsidones and dibenzofurans. The almost 800 known lichen secondary metabolites can be classified according to the classic biosynthetic pathways: the poliketidic path (monocyclic phenols, depsides, depsidones, depsones, dibenzofurans, xanthones, naphtaquinones anthraquinones, macrocyclic lactones, aliphatic acids, etc.), the mevalonic acid path (steroids, carotenoids, etc.) and the shikimic acid path (amino acid derivatives, cyclopeptides, etc.) (Huneck 1999).

Although medicinal plants have been used for centuries as sources of therapeutic agents worldwide, they cannot be classified as pure and efficient antimicrobial agents. However, in spite of the fact that plant-derived antibacterial compounds show a general low degree of activity, most plants, indeed, are successful in fighting infections (Hemaiswarya et al. 2008). Plants, in fact adopt "synergy" as their peculiar different paradigm to fight pathogenic microorganisms. Several studies have demonstrated that a number of natural products, which failed as antimicrobials, are able to dramatically increase the effectiveness of chemotherapeutic agents against Gram-negative and Gram-positive bacteria (Gibbons and Udo 2000; Tegos et al. 2002; Stavri et al. 2007; Hemaiswarya et al. 2008; Celenza et al. 2012; Segatore et al. 2012).

Antimicrobial resistance has emerged among pathogenic bacteria since the beginning of the antibiotic era. Resistance potentially extends to the entire repertoire of available therapeutic agents. Nowadays, bacteria expressing multidrug resistant (MDR), extensively drug resistant (XDR) and pandrug resistant (PDR) phenotypes are amongst the most important cause of infections in nosocomial and community settings and new drugs are urgently needed.

As a result of its intrinsic ability to overcome antibiotic chemotherapy, Staphylococcus aureus continuously expands its ecological niche. It is resistant to many adverse environmental conditions, so that MRSA strains are mainly associated with hospital acquired infections (HA-MRSA). The rate of mortality of septicemia caused by VISA raised from 30% for MRSA, to almost 80% (Hiramatsu et al. 1997; Burnie et al. 2000; Fridkin et al. 2003). Thus, the emergence of resistant S. aureus bacteria has serious consequences both in terms of therapeutic failures and impact on Health Care System.

To meet the growing challenge of S. aureus, the identification of novel targets for small molecules is one of the most important approach to face the problem (Garcia-Lara et al. 2005; Wright and Sutherland 2007; Gibbons 2008; Silver 2011).

To overcome antibiotic-mediated resistance, a valuable alternative would be the use of combination of drugs. Thus, substances that can increase susceptibility to currently licensed agents would be a very attractive and valuable option (Wagner and Ulrich-Merzenich 2009).

In this paper the authors analyse five selected compounds from Chilean lichens for their antimicrobial activity against MRSA clinical isolated strains, tested alone and in combination with five therapeutically available antibiotics. Data from checkerboard assay were interpreted by Loewe additivity-based model and Bliss independence-based model.

Materials and methods

Organisms

Twenty methicillin-resistant S. aureus strains were used in this study. The organisms were collected during a 4 years period, from 2006 to 2010, at the University Hospital "San Salvatore" of l'Aquila, Italy. They were isolated from hospitalized patients, from wounds, surgical wounds, vascular and urinary catheters, blood, respiratory tract. They were identified as MRSA organisms by Phoenix System (Becton Dickinson). The methicillin-resistantS. aureus from the American Type Culture Collection, ATCC 43300 was used as control. Four strains, namely, AQ004, AQ006, AQ007 and AQ012 clinical isolates and the reference strain ATCC 43300 were used for the drug interaction assay. All those strains were resistant to clindamycin, erythromycin, gentamicin, levofloxacin, oxacillin, with the exception of ATCC 43300 that was sensible to levofloxacin.

Antibiotics

All tested antibiotics, clindamycin (CL1), erythromycin (ERY), gentamicin (GEN), levofloxacin (LVX), oxacillin (OXA), were from SigmaAldrich (Milan, Italy).

Secondary metabolites from lichens

The lichen secondary metabolites used in this study are: a-collatolic acid (COL), epiforellic acid (EPI), lobaric acid (LOB), perlatolic acid (PER) and (+)-protolichesterinic acid (PRO), whose structures are reported in Fig. 1 and Table 1. These compounds were isolated and structurally determined as previously reported (Fiedler et al. 1986; Piovano et al. 1989). The degree of purity for the compounds was >98% as determined by thin layer chromatography (TLC) and 1H NMR analyses.

In vitro susceptibility tests

The antimicrobial susceptibility pattern of the organisms used in this study was determined in accordance with the CLSI guidelines (CLSI 2010) by microdilution test performed in a 96 microwell plates with an inoculum of 5 x [10.sup.5] CFU/ml.

Bactericidal tests were performed as previously described by Pearson et al. (1980) and Taylor et al. (1983).

Checkerboard microdilution assay

The in vitro interactions between the antibiotics and the compounds from lichens were investigated by a two-dimensional checkerboard microdilution assay, using a 96-well microtitration plates as previously described (Segatore et al. 2012; Celenza et al. 2012). Briefly, in each well of the microplate 25 [micro]l of microbial growth medium were added. An aliquot of 25 [micro]l of a fourfold concentrated antibiotic was added to column 12. Then a twofold dilution was made from column 12 to column 2. A 25 [micro]l aliquot of each drug concentration of the compound was added to rows A to G. Row H contained only the antibiotic whilst column 1 only the compound. Well H1 was the drug free well used as growth control. Finally, 50 [micro]l of a saline solution (0.9% of NaCl) containing bacteria were added to each well of the microplate in order to obtain a final inoculum of 5 x [10.sup.5] CFU/ml. The microtitre plates were incubated at 37[degrees]C for 18 h. The growth in each well was quantified spectrophotometrically at 595 nm by a microplate reader iMark, BioRad (Milan, Italy). The percentage of growth in each well was calculated as:

[OD.sub.drug combination well] - [OD.sub.background]/ [OD.sub.drug free well] - [OD.sub.background]

where the background was obtained from the microorganism-free plates, processed as the inoculated plates. The MICs for each combination of drugs were defined as the concentration of drug that reduced growth by 80% compared to that of organisms grown in the absence of drug. All experiments were performed in triplicate.

Drug interaction models

In order to assess the nature of the in vitro interactions between the lichen compounds and antibiotics against each S. aureus, the data obtained from the checkerboard assay were analysed by nonparametric models based on the Loewe additivity model (LA) and the Bliss independence (BI) theory (Greco et al. 1995).

Loewe additivity-based model

The Loewe additivity model, based on the idea that an agent cannot interact with itself, is expressed by the following equation:

1 = [D.sub.1]/[ID.sub.x.1] + [D.sub.2]/[ID.sub.x.2]

where [ID.sub.x.1] and [ID.sub.x.2] are the concentrations of the drugs to result in X% inhibition for each respective drug alone. [D.sub.1] and [D.sub.2] are concentrations of each drug in the mixture that yield X% inhibition.

The interaction index as define by Berenbaum (1977) is expressed by the equation:

I = [D.sub.1]/[ID.sub.x.1] + [D.sub.2]/[ID.sub.x.2]

When I > 1, Loewe antagonism is claimed, when I < 1, Loewe synergism is claimed.

The interaction index as define by Berenbaum, can be adapted to calculate the fractional inhibitory concentration index (FICI), that is the mathematical expression of the effect of the combination of antibacterial agents expressed as:

[DELTA]FIC = [FIC.sub.A] + [FIC.sub.B] = [MIC.sub.AB]/[MIC.sub.A] + [MIC.sub.BA]/[MIC.sub.B]

where [MIC.sub.A] and [MIC.sub.B] are the MICs of drugs A and B when acting alone and [MIC.sub.AB] and [MIC.sub.BA] are the MICs of drugs A and B when acting in combination. Among all [SIGMA]FICs calculated for each microplate, the FICI was determined as the lowest [SIGMA]FIC ([SIGMA][FIC.sub.min]) when synergy is supposed, or the highest [SIGMA]FIC ([SIGMA][FIC.sub.max]) when antagonism is evident.

Since in MIC determination, the variation in a single result places a MIC value in a three-dilution range ([+ or -]1 dilution), therefore, the reproducibility errors in MIC checkerboard assays are considerable.

For that reasons, the interpretation of FICI data should be done taking into consideration values well below or above the theoretical cut-off (1.0) defined by Berenbaum. Synergy was, therefore, defined when FICI [less than or equal to] 0.5, while antagonism was defined when FICI > 4. A FIC index between 0.5 and 4 (0.5 < FICi [less than or equal to] 4) was considered indifferent (Odds 2003).

Bliss independence-based model

In the Bliss models, the combined effects of the drugs calculated from the effect of the individual drugs, are compared with those obtained experimentally. The BI theory is described by the equation: [E.sub.i] = [E.sub.A] x [E.sub.B], where [E.sub.i] is the calculated percentage of growth based on the theoretical non-interactive combination of drug A and B, and [E.sub.A] and [E.sub.B] are the experimental percentages of growth of each drug acting alone.

The experimental dose-response surface (Fig. 2A) is subtracted from the calculated theoretical surface to reveal any significant deviation from the zero-plane. The interaction is described by the difference ([DELTA]E) between the predicted and measured percentages of growth with drugs at various concentrations ([DELTA]E = [E.sub.predicted] - [E.sub.measured]). To determine the significance of differences between the experimental and calculated additive effects, the upper and lower 95% confidence limits of the experimental data were compared to the calculated additive effects. If the lower confidence limit of a point was greater than the calculated additivity, the observed synergy was considered to be significant. Similarly, if the upper confidence limit was lower than the calculated additivity, the observed antagonism was considered to be significant (Deminie et al. 1996; Prichard and Shipman 1990; Prichard et al. 1991). The [DELTA]E values calculated on a point-by-point basis were subsequently plotted on the z axis (Fig. 2B). Points of the difference surface above zero (positive) indicate synergy, below zero (negative) antagonism. In order to summarize the interaction surface, the Bliss synergy and antagonism differences and all their combinations were added up to yield a summary measure, respectively of Bliss synergy ([SIGMA]SYN) and Bliss antagonism ([SIGMA]ANT). Interactions <100% were considered weak, interactions between 100% and 200% were considered moderate, whilst interaction >200% were considered strong (Meletiadis et al. 2005).

Results

In vitro susceptibility test and interaction of drugs

The in vitro antibacterial effects of lichen compounds is reported in Table 2. For all the 20 MRSA clinical isolates and the ATCC4300, [MIC.sub.50] and [MIC.sub.90] were calculated, as well as [MBC.sub.50] and [MBC.sub.90]. The minimum inhibitory activity required to inhibit the growth of 90% of the organisms was ranging from 32 [micro]g/ml for PER to 128 [micro]g/ml for COL. The [MBC.sub.90] measured was one- or twofold higher than the calculated [MIC.sub.90].

Amongst the compounds the most active was PER with [MIC.sub.50] and [MIC.sub.90] values 16 [micro]g/ml and 32 [micro]g/ml, respectively.

All 20 S. aureus clinical isolates and the reference strain ATCC 43300 were tested for their susceptibility to clindamycin, erythromycin, gentamicin, levofloxacin and oxacillin by microdilution test (data not shown). Amongst these strains, four (namely AQ004, AQ006, AQ007 and AQ012) and the reference strain ATCC 4330 were chosen for the interaction assay, since they were resistant to all tested antibiotics (Table 3), with the exception of the control strains which was sensible to levofloxacin. The MICs values of the lichen compounds versus the selected strains are reported in Table 4.

Clindamycin, erythromycin, gentamicin, levofloxacin and oxacillin were chosen for the drug-interaction assay, since they belong to different classes of antimicrobial agents, respectively, lincosamides, macrolides, aminoglycosides, quinolones and [beta]-lactams.

Tables 5-9 summarize the results of the broth microdilution checkerboard analysis interpreted by the F1CI and [DELTA]E methods of the five MRSA strains for the combination of lichen compounds and antibiotics. Results for each combination of antibiotics and lichen compounds are reported below.

Clindamycin

According to FIC1 interpretation, synergism was found only in S. aureus AQ006 in combination with LOB (F1C1 = 0.3125), PER (FIC1 = 0.3125) and PRO (FICI = 0.3125). Indifference (FIG > 0.5) was observed in all other combinations. [DELTA]E model interpretation for clindamycin confirmed synergism for those combinations. Moreover, most of the combinations, which FIG was approximately 0.5 and reported as indifference, were interpreted by the [DELTA]E model as synergism (Table 5).

Erythromycin

For the combination of erythromycin with lichen compounds, no synergism was found. In all combinations, FICI and [DELTA]E model were in accordance defining indifference (Table 6).

Gentamicin

For the gentamicin combination, FICI synergism was observed in all lichen compounds and strains. The lowest FICIs were observed in PRO combination, with FIC indexes ranging from 0.1563 for ATCC 43300 and AQ007, to 0.2813 for AQ012 (Table 7).

Levofloxacin

For the combination of levofloxacin with lichen compounds, no synergism was found. FIC index for each combination was reported as indifference. [DELTA]E model confirmed FICI interpretation only in the combination with EPI and PRO. For all other combinations, antagonism was reported (Table 8).

Oxacillin

According to FICI interpretation, synergism was found in only in two MRSA strains in oxacillin combination with COL (ATCC 4330, FICI = 0.5; AQ004, FICI = 0.3125), one with EPI (ATCC 4330, FICI = 0.5) and four with PRO (ATCC 43300, AQ004, AQ007 and AQ012, FICI = 0.5). Indifference (FICI > 0.5) was observed in all other combinations. [DELTA]E model interpretation for oxacillin confirmed synergism for those combinations. Moreover, most of the combinations, which FICI was approximately 0.5 and reported as indifference, were interpreted by the [DELTA]E model as synergism (Table 9).

Discussion

The aim of this paper is to investigate the potential antimicrobial activity of several selected natural compounds from lichens alone, and in combination with commercially available antibiotics.

The MIC values calculated for these compounds versus MRSA clinical isolates were comparable with those generally found in many antibiotics used in clinical therapy. Since, the MBCs calculated were close to the MICs, we hypothesize that those compounds, at least against MRSA, act as bactericidal. Synergy is ideally defined as the interaction of two or more substances, to produce a combined effect greater than the sum of their separate effects. In addition, it is important to emphasize that even the models used to analyse drug-drug interaction might, somehow, affect the interpretation of the data. There are many published methods for assessing drug interactions, most of them are summarized and compared in the review of Greco (Greco et al. 1995). For instance we used two models, the FIG model based on Loewe additivity theory and [DELTA]E model based on Bliss independence theory.

The percentage of agreement in the interpretation of the FIC index and the response surface approach was highly variable amongst the combinations. It was ranging from 0% to 100% for the levofloxacin combinations (Table 8) to 100% for the erythromycin combinations (Table 6).

The discrepancy between the results of the two methods is the natural consequence of the high variability intrinsic in the FICI model (Cappelletty and Rybak 1996; Mackay et al. 2000; Sun et al. 2008; TeDorsthorst et al. 2002). The results obtained by this method are strongly dependent on the MIC endpoints and the cutoff values used to define synergism and antagonism. For instance, most of the combinations interpreted as indifference by F1C index model, which value is more than 0.5 but less than 1, are interpreted as synergic using the [DELTA]E model. The same is found in levofloxacin combinations, in which, the interpretation of FIC index model as indifference (FICI > 1 but <4) is, for some lichen compounds combinations, interpreted by the [DELTA]E model as antagonism.

In comparison with the FIC index model, the response surface approach, as determined by the [DELTA]E model, allows an objective analysis of the experimental data which are compared with a theoretical model. Fitting of the theoretical model to the whole data surface allows the use of all available data from the checkerboard assay, thereby indicating the statistical significance of the interaction.

Interestingly, levofloxacin, in accordance to the [DELTA]E model interpretation, acts antagonistically in combination with COL, LOB and PER (Table 8). Although, generally, fluoroquinolones rarely show antagonism in combination with other antibiotics, our data seem to agree with several studies in which fluoroquinolones both Grampositive and Gram-negative bacteria, in combination with ertapenem, rifampin, fusidic acid and linezolid act antagonistically (Hosgor-Limoncu et al. 2008; Murillo et al. 2008; Neu 1991; Sahuquillo et al. 2006; Sweeney and Zurenko 2003; Uri 1993).

A few data about the biological activities of the tested compounds are reported. The cytotoxic activity of lobaric acid and protolichesterinic acid has been assessed in a previous study (Brisdelli et al. 2013), where protolichesterinic acid has been demonstrated to show antiproliferative activity on several human cancer cell lines. Its cytotoxic effect has been related to the ability to induce apoptosis through a caspase-dependent pathway in HeLa cells.

Amongst the tested compounds, only for protolichesterinic acid it is possible to hypothesize a mechanism of action. The acyl-itaconic acid derivative protolichesterinic acid resembles, from a structural stand-point, the well-known anticancerogenic C75 (Kuhajda et al. 2000). They differ from each other for the length of the acyl-moiety, which is respectively Cl 2 and C8. C75 is a novel, potent synthetic inhibitor of the beta-ketoacyl synthase moiety of the eukaryotic fatty acid synthase (FAS type 1).

Although several differences can be retrieved between FAS type 1 and FAS type II (Archea and Eubacteria), FAS systems are quite conserved. It is plausible that protolichesterinic acid, acts as inhibitor of the beta-ketoacyl-acyl carrier protein synthase III (FabH) from S. aureus.

Bacterial pathogens are increasingly becoming resistant even to the most recently approved antibiotics (Cottarel and Wierzbowski 2007). There is a crucial and urgent need for the developing of new classes of antibiotics or for revitalizing the already used ones. The emergence of MDR, XDR and PDR bacteria has serious consequences both in terms of therapeutic failures and impact on Health Care System. Thus, substances able to increase the susceptibility to currently licensed agents, would be a very attractive and valuable option. Preserving the efficacy of currently available antibiotics, therefore, remains a major goal in particular if we consider that pharmaceutical companies seem to be no more interested in developing new classes of antimicrobial agents. Furthermore, the investigation of the potential antimicrobial activity of natural molecules from the secondary metabolism of living wild organisms could provide us, as happened in the past, new templates for the synthesis of more efficient and potent antimicrobial agents.

We can conclude that the compounds used in this study show a good antimicrobial activity against multi-drug resistant MRSA clinical isolates. The combination of these compounds with clyndamicin, erythromycin, levofloxacin, oxacillin and gentamicin, is highly synergic only for gentamicin, and, on the contrary, the combination with levofloxacin is not advantageous.

We chose to analyse the checkerboard assay data with two models, the FIC index and the response surface approach. However, although the former methods is popular among bacteriologists, and historically important, it is subjective, sensitive to experimental errors and often provides approximated results and variable conclusions.

At this purpose, the response surface approach is a good alternative for determining the interaction between drugs with antimicrobial activity.

Finally, it is important to emphasize that the results obtained in in vitro studies do not necessarily correlate with clinical outcome. However, they are a first step for the individuation of novel molecules or templates for the development of new antimicrobial agents or combinations of drugs for chemotherapy.

Conflict of interest

There was no conflict of interest.

ARTICLE INFO

Article history:

Received 16 June 2014

Revised 6 October 2014

Accepted 14 December 2014

References

Berenbaum, M.C., 1977. Synergy, addicivism and antagonism in immunosuppression, a critical review. Clin. Exp. Immunol. 28,1-18.

Brisdelli, F., Perilli, M., Sellitri, D., Piovano, M., Carbarino, J.A., Nicoletti, M., Bozzi, A., Amicosante, G., Celenza, G., 2013. Cytotoxic activity and antioxidant capacity of purified lichen metabolites: an in vitro study. Phytother. Res. 27,431-437.

Burnie, J., Matthews, R., Jiman-Fatami, A., Gottardello, P., Hodgetts, S., D'arcy, S., 2000. Analysis of 42 cases of septicemia caused by an epidemic strain of methicillinresistant Staphylococcus aureus: evidence of resistance to vancomycin. Clin. Infect. Dis. 31. 684-689.

Cappelletty, D.M., Rybak, M.J., 1996. Comparison of methodologies for synergism testing of drug combinations against resistant strains of Pseudomonas aeruginosa. Antimicrob. Agents Chemother. 40, 677-683.

Celenza, G., Segatore, B., Setacci, D., Bellio, P., Brisdelli, F., Piovano, M., Garbarino, J.A., Nicoletti, M., Perilli, M., Amicosante, G., 2012. In vitro antimicrobial activity of pannarin alone and in combination with antibiotics against methicillin-resistant Staphylococcus aureus clinical Isolates. Phytomedicine 7,596-602.

Clinical and Laboratory Standards Institute. 2010. Performance Standards for Antimicrobial Susceptibility Testing. Twenty-First Informational Supplement M100-S21.

Cottarel, G., Wierzbowski, J., 2007. Combination drugs, an emerging option for antibacterial therapy. Trends Biotechnol. 25,547-555.

Deminie, C.A., Bechtold, C.M, Stock, D., Alam, M., Djang, F., Balch, A.H., Chou, T.C., Prichard, M., Colonno, R.J., Lin, P.F., 1996. Evaluation of reverse transcriptase and protease inhibitors in two-drug combinations against human immunodeficiency virus replication. Antimicrob. Agents Chemother. 40,1346-1351.

Fiedler, P., Gambaro, V., Garbarino, J.A., Quilhot, W., 1986. Studies on Chilean lichens. 13. Epiphorellic acids 1 and 2 .Two diaryl ethers from the lichen Comicularia epiphorella. Phytochemistry 25,461-465.

Fridkin, S.K., Hageman, J., McDougal, L.K., Mohammed, J., Jarvis, W.R., Perl, T.M., Tenover, F.C., V.-l. S. a. E. S. Group, 2003. Epidemiological and microbiological characterization of infections caused by Staphylococcus aureus with reduced susceptibility to vancomycin, United States. 1997-2001. Clin. Infect. Dis. 36.429-439.

Garcia-Lara, J., Masalha, M., Foster, S.J., 2005. Staphylococcus aureus: the search for novel targets. Drug. Discov. Today 10, 643-651.

Gibbons, S., 2008. Phytochemicals for bacterial resistance-strengths, weaknesses and opportunities. Planta Med. 74, 594-602.

Gibbons, S.. Udo, E.E., 2000. The effect of reserpine, a modulator of multidrug efflux pumps, on the in vitro activity of tetracycline against clinical isolates of methicillin resistant Staphylococcus aureus (MRSA) possessing the tet(K) determinant. Phytother. Res. 14,139-140.

Greco, W.R., Bravo, G., Parsons, J.C., 1995. The search for synergy: a critical review from a response surface perspective. Pharmacol. Rev. 47,331-385.

Hemaiswarya, S., Kruthiventi, A.K., Doble, M., 2008. Synergism between natural products and antibiotics against infectious diseases. Phytomedicine 15, 639-652.

Hiramatsu, K., Hanaki, H., Ino, T., Yabuta, K., Oguri, T., Tenover, F.C., 1997. Methicillin-resistant Staphylococcus aureus clinical strain with reduced vancomycin susceptibility. J. Antimicrob. Chemother. 40,135-136.

Hosgor-Limoncu, M., Ermertcan, S., Tasli, H., Yurtman, A.N., 2008. Activity of amikacin, ertapenem, ciprofloxacin and levofloxacin alone and in combination against resistant nosocomial pathogens by time-kill. West Indian Med.J. 57,106-111.

Huneck, S., 1999. The significance of lichens and their metabolites. Naturwissenschaften 86,559-570.

Kuhajda, F.P., Pizer, E.S., Li, J.N., Mani, N.S., Frehywot, C.L., Townsend, C.A., 2000. Synthesis and antitumor activity of an inhibitor of fatty acid synthase. Proc. Natl. Acad. Sci. U.S.A. 7, 3450-3454.

Mackay, M.L., Milne, K., Gould, I.M., 2000. Comparison of methods for assessing synergic antibiotic interactions. Int. J. Antimicrob. Agents 15,125-129.

Meletiadis, J., Verweij, P.E., TeDorsthorst, D.T, Meis, J.F., Mouton, J.W., 2005. Assessing in vitro combinations of antifungal drugs against yeasts and filamentous fungi: comparison of different drug interaction models. Med. Mycol. 43,133-152.

Murillo, O., Pachon, M.E., Euba, G., Verdaguer, R., Tubau, F., Cabellos, C., Cabo, J., Gudiol, F., Ariza, J., 2008. Antagonistic effect of rifampin on the efficacy of high-dose levofloxacin in staphylococcal experimental foreign-body infection. Antimicrob. Agents Chemother. 52, 3681-3686.

Neu, H.C., 1991. Synergy and antagonism of combinations with quinolones. Eur. J. Clin. Microbiol. Infect. Dis. 10, 255-261.

Odds, F.C., 2003. Synergy, antagonism and what the chequerboard puts between them. J. Antimicrob. Chemother. 52, 1.

Pearson, R.D., Steigbigel, R.T., Davis, H.T., Chapman, S.W., 1980. Method of reliable determination of minimal lethal antibiotic concentrations. Antimicrob. Agents Chemother. 18,699-708.

Piovano, M., Chamy, M.C., Garbarino, J.A., Quilhot, W., 1989. Studies on Chilean lichens. IX. Secondary metabolites from Antarctic lichens. Instituto Antarctico Chileno. Serie Cientffica INACH 39,75-89.

Prichard, M.N., Prichard, L.E., Baguley, W.A., Nassiri, M.R., Shipman, C., 1991. Three-dimensional analysis of the synergistic cytotoxicity of ganciclovir and zidovudine. Antimicrob. Agents Chemother. 35,1060-1065.

Prichard, M.N., Shipman. C., 1990. A three-dimensional model to analyze drug- drug interactions. Antiviral Res. 14,181-205.

Sahuquillo Arce, J.M., Colombo Gainza, E., Gil Brusola, A., Ortiz Estevez, R., Canton. E., Gobernado, M., 2006. In vitro activity of linezolid in combination with doxycycline, fosfomycin, levofloxacin, rifampicin and vancomycin against methicillinsusceptible Staphylococcus aureus. Rev. Esp. Quimioter. 19,252-257.

Segatore, B., Bellio, P., Setacci, D., Brisdelli, F., Piovano, M., Garbarino, J.A., Nicoletti, M.. Amicosante, G., Perilli, M., Celenza, G., 2012. In vitro interaction of usnic acid in combination with antimicrobial agents against methicillin- resistantStap/jy/ococcus aureus clinical isolates determined by FICI and [DELTA]E model methods. Phytomedicine 3-4, 341-347.

Silver, L.L., 2011. Challenges of antibacterial discovery. Clin. Microbiol. Rev. 24,71-109.

Stavri, M., Piddock, L.J., Gibbons. S., 2007. Bacterial efflux pump inhibitors from natural sources. J. Antimicrob. Chemother. 59,1247-1260.

Sun, S., Li, Y., Guo, Q., Shi, C, Yu, J., Ma, L., 2008. In vitro interactions between tacrolimus and azoles against Candida albicans determined by different methods. Antimicrob. Agents Chemother. 52,409-417.

Sweeney, M.T., Zurenko, G.E., 2003. In vitro activities of linezolid combined with other antimicrobial agents against Staphylococci. Enterococci, Pneumococci, and selected gram-negative organisms. Antimicrob. Agents Chemother. 47,1902-1906.

Taylor, P.C., Schoenknecht, F.D., Sherris, J.C., Linner, E.C., 1983. Determination of minimum bactericidal concentrations of oxacillin for Staphylococcus aureus: influence and significance of technical factors. Antimicrob. Agents Chemother. 23,142-150.

TeDorsthorst, D.T., Verweij, P.E., Meletiadis, J., Bergervoet, M., Punt, N.C., Meis, J.F., Mouton, J.W., 2002. In vitro interaction of flucytosine combined with amphotericin B or fluconazole against thirty-five yeast isolates determined by both the fractional inhibitory concentration index and the response surface approach. Antimicrob. Agents Chemother. 46,2982-2989.

Tegos, G., Stermitz, F.R., Lomovskaya, 0., Lewis, K., 2002. Multidrug pump inhibitors uncover remarkable activity of plant antimicrobials. Antimicrob. Agents Chemother. 46,3133-3141.

Uri. J.V., 1993. Antibacterial antagonism between fusidic acid and ciprofloxacin. Acta Microbiol. Hung. 40,141-149.

Wagner, H., Ulrich-Merzenich, G., 2009. Synergy research: approaching a new generation pf phytopharmaceuticals. Phytomedicine 16,97-110.

Wright, G.D., Sutherland, A.D., 2007. New strategies for combating multidrug- resistant bacteria. Trends Mol. Med. 13,260-267.

http://dx.doi.org/10.1016/j.phymed.2014.12.005

Alessia Sabatini (a), Fabrizia Brisdelli (a), Marisa Piovano (b), Marcello Nicoletti (c), Gianfranco Amicosante (a), Mariagrazia Perilli (a), Giuseppe Celenza (a),*

(a) Department of Biotechnological and Applied Clinical Sciences, University of l'Aquila, L'Aquila, Italy

(b) Department of Chemistry, Universidad Tecnica F. Santa Maria, Casilla 110 V, Valparaiso, 6, Chile

(c) Department of Enviromental Biology, University Sapienza, Rome, Italy

* Corresponding author at: Department of Biotechnological and Applied Clinical Sciences, University of l'Aquila, Via Vetoio, 1,67100 l'Aquila, Italy. Tel.: +39 0862433444.

E-mail address: celenza@>univaq.it (G. Celenza).

Table 1
Selected constituents tested for antimicrobial activity, their lichen
species and Chilean regions of collection.

Compound                  Species

[alpha]-Collatolic acid   Lecanora atra (Hudson) Acharius
Epiphorellic acid         Comicularia epiphorella (Nyl.) Du Rietz
Lobaric acid              Stereocaulon alpinum Laurer ex Funck
Perlatolic acid           Stereocaulon sp.
Protolichesterinic acid   Comicularia aculeata (Schreb.) Ach.

Compound                  Chilean geographic origin


[alpha]-Collatolic acid   Robert Island, Shetland del Sur, Antarctica
Epiphorellic acid         Conguillio National Park, Region de la
                            Araucania
Lobaric acid              Ardley Cove, King George island, Shetland
                            del Sur, Antarctica
Perlatolic acid           Parque Nacional Puyehue, Region de Los
                            Lagos, Chile
Protolichesterinic acid   Ardley Cove, King George Island, Shetland
                            del Sur, Antarctica

Compound                  Reference

[alpha]-Collatolic acid   Sweeney and Zurenko (2003)
Epiphorellic acid         Gibbons (2008)
Lobaric acid              Sweeney and Zurenko (2003)
Perlatolic acid           Not published
Protolichesterinic acid   Sweeney and Zurenko (2003)

Table 2
MIC and MBC calculated for all S. aureus strains.

Compound   MIC ([micro]g/ml)

           Range    [MIC.sub.50]   [MIC.sub.90]

COL        32-128   128            128
EPI         8-32     32             32
LOB        32-128    32             64
PER         4-64     16             32
PRO         4-64     32             64

Compound   MBC ([micro]g/ml)

           Range     [MIC.sub.50]   [MIC.sub.90]

COL        128-512   256            512
EPI         16-128    64            128
LOB         32-256    64            128
PER         16-64     32             64
PRO         32-128    64            128

Table 3
MIC of antibiotics for the strains of S. aureus used in the
checkerboard assay.

Strain      Median MIC ([micro]g/ml) (range)

            Antibiotic

            CLI           ERY               GEN

ATCC43300   8192          (512-1024)1024    (64-256)256
AQ004       8192          (512-1024)1024    (16-32)32
AQ006       (64-128)128   512               (128-256)128
AQ007       (32-64)32     (16-64)64         128
AQ012       16,384        (1024-2048)1024   (32-64)64

Strain      Median MIC ([micro]g/ml) (range)

            Antibiotic

            LVX                           OXA

ATCC43300   [less than or equal to] 0.5   (8-16)8
AQ004       (16-64)16                     (128-256)128
AQ006       (8-32)8                       2048
AQ007       (16-64)16                     (256-512)256
AQ012       32                            (256-512)512

Table 4
MIC of lichen compounds for the strains of S. aureus used in the
checkerboard assay.

Strain      Median MIC ([micro]g/ml) (range)

            Compound

            COL            EP1          LOB

ATCC43300   (16-32) 32     (8-16) 8     32
AQ004       (32-64) 32     (8-16) 16    64
AQ006       (32-128) 128   (16-32) 16   (64-128) 64
AQ007       (64-128) 128   (16-32) 32   64
AQ012       (32-128) 64    (8-32) 16    (64-128) 64

Strain      Median MIC ([micro]g/ml) (range)

            Compound

            PER          PRO

ATCC43300   (8-32) 32    (8-16) 16
AQ004       (32-64) 32   (4-16) 16
AQ006       (8-32) 16    (32-64) 32
AQ007       (4-32) 16    32
AQ012       (8-32) 16    (16-32) 32

Table 5
In vitro interaction between clindamycin and secondary metabolites
from lichens as determined by non-parametric FIC1 and the [DELTA]E
model (a).

Compound   Strain      FICI

                       Median (range)           INT

COL        ATCC43300   (1.0625-1.125) 1.25      IND
           AQ004       (1.0625-1.125) 1.125     IND
           AQ006       (0.625-0.75) 0.625       IND
           AQ007       1                        IND
           AQ012       (1.0625-1.125) 1.125     IND

EPI        ATCC43300   (0.5156-0.5313) 0.5156   IND
           AQ004       (0.5625-0.625) 0.625     IND
           AQ006       (0.5313-05625) 0.5313    IND
           AQ007       (0.5313-0.5625) 0.5625   IND
           AQ012       (1.0313-1.0625) 1.0625   IND

LOB        ATCC43300   (1.0625-1.125) 1.0625    IND
           AQ004       (0.5020-0.0539) 0.502    IND
           AQ006       (0.2813-0.3125) 0.3125   SYN
           AQ007       (0.625-0.75) 0.75        IND
           AQ012       (1.0625-1.125) 1.125     IND

PER        ATCC43300   (1.0039-1.0078) 1.0039   IND
           AQ004       (0.5-0.501) 0.501        IND
           AQ006       (0.2813-0.3125) 0.3125   SYN
           AQ007       (0.5313-0.625) 0.625     IND
           AQ012       (0.5-0.5002) 0.5002      IND

PRO        ATCC43300   (0.5078-0.5313) 0.5313   IND
           AQ004       (0.5-0.501) 0.501        IND
           AQ006       (0.3125-0.375) 0.3125    SYN
           AQ007       (0.5313-0.5625) 0.5625   IND
           AQ012       (1.0625-1.125) 1.0625    IND

Compound   Strain      [DELTA]E model (b)

                       [SIGMA]SYN (n)   [SIGMA]ANT (n)   INT

COL        ATCC43300     62.7 (28)      -30.7 (30)       IND
           AQ004         42.7 (29)      -38.1 (35)       IND
           AQ006         98.1 (26)      -96.8 (43)       IND
           AQ007         87.5 (18)      -67.6 (20)       IND
           AQ012         43.1 (12)      -89.3 (18)       IND

EPI        ATCC43300   1285.9 (30)      -85.9 (8)        SYN
           AQ004        119.4 (20)      -82.5 (11)       SYN
           AQ006        179.0 (23)      -46.0 (7)        SYN
           AQ007        780.4 (41)      -43.2 (9)        SYN
           AQ012         73.1 (15)      -91.5 (25)       IND

LOB        ATCC43300     69.8 (36)      -92.2 (11)       IND
           AQ004        199.6 (51)      -40.0 (13)       SYN
           AQ006        401.2 (23)      -65.4 (34)       SYN
           AQ007         82.3 (16)      -65.7 (14)       IND
           AQ012         83.4 (15)      -94.0 (19)       IND

PER        ATCC43300     22.8 (31)      -99.6 (27)       IND
           AQ004        164.1 (46)      -50.1 (25)       SYN
           AQ006       1057.2 (41)      -52.3 (24)       SYN
           AQ007         88.9 (44)      -41 (26)         IND
           AQ012        692,5 (30)      -71.4 (25)       SYN

PRO        ATCC43300    149.2 (50)      -4.5 (1)         SYN
           AQ004        154.6 (56)      -32.6 (6)        SYN
           AQ006       1340.3 (38)      -65.6 (25)       SYN
           AQ007        795.1 (40)       95.1 (14)       SYN
           AQ012         75.2 (17)      -79.9 (16)       IND

(a) INT, interpretation; IND, indifference; SYN, synergy; ANT,
antagonism. Synergy was defined as an FICI of <0.5, antagonism was
defined as an FICI of >4, and indifference was defined as an FICI
>0.5 and <4.

(b) n, number of drug combinations (among the 77 drug combinations
for each strain) with statistically significant synergy or
antagonism.

Table 6
In vitro interaction between erythromycin and secondary metabolites
from lichens as determined by nonparametric FIC1 and the [DELTA]E
model (a).

Compound   Strain      FICI

                       Median (range)          INT

COL        ATCC43300   (0.5625-0.625) 0.625    IND
           AQ004       (0.5625-0.625) 0.625    IND
           AQ006       (0.5625-0.625) 0.5625   IND
           AQ007       (0.625-0.75) 0.75       IND
           AQ012       (0.625-1) 0.75          IND

EP1        ATCC43300   (0.625-075) 0.75        IND
           AQ004       (0.5625-0.625) 0.625    IND
           AQ006       (0.625-0.75) 0.75       IND
           AQ007       (0.625-0.75) 0.75       IND
           AQ012       (1.25-1.5) 1.25         IND

LOB        ATCC43300   (1.25-1.5) 1.25         IND
           AQ004       (0.625-0.75) 0.75       IND
           AQ006       (0.5625-0.625) 0.625    IND
           AQ007       1                       IND
           AQ012       (1.25-1.5) 1.25         IND

PER        ATCC43300   (1.25-1.5) 1.5          IND
           AQ004       (0.75-1) 0.75           IND
           AQ006       (0.5625-0.625) 0.625    IND
           AQ007       (0.625-0.75) 0.75       IND
           AQ012       (1.0625-1.125) 1.125    IND

PRO        ATCC43300   (1.25-1.5) 1.25         IND
           AQ004       (0.625-075) 0.75        IND
           AQ006       (0.5625-0.625) 0.625    IND
           AQ007       (0.625-0.75) 0.75       IND
           AQ012       (0.625-0.75) 0.75       IND

Compound   Strain      [DELTA]E model (b)

                       [SIGMA]SYN (n)   [SIGMA]ANT(n)   INT

COL        ATCC43300   64.5 (9)         -99.2 (9)       IND
           AQ004       99.8 (13)        -83.6 (11)      IND
           AQ006       23.8 (4)         -96.8 (19)      IND
           AQ007       13.2 (2)         -86.9 (2)       IND
           AQ012       13.1 (2)         -94.8 (18)      IND

EP1        ATCC43300   68.3 (11)        -97.7 (22)      IND
           AQ004       56.5 (9)         -94.6 (21)      IND
           AQ006       45.7 (7)         -90.4 (21)      IND
           AQ007       46.3 (6)         -92.8 (17)      IND
           AQ012       20.7 (5)         -84.6 (15)      IND

LOB        ATCC43300   29.6 (5)         -28.4 (5)       IND
           AQ004       13.5 (3)         -68.1 (13)      IND
           AQ006       42.0 (7)         -89.3 (19)      IND
           AQ007       41.7 (6)         -13.1 (2)       IND
           AQ012       11.4 (2)         -18.6 (4)       IND

PER        ATCC43300   17.5 (3)         -66.9 (11)      IND
           AQ004       80.4 (21)        -36.1 (7)       IND
           AQ006       49.8 (9)         -63.6 (13)      IND
           AQ007       14.8 (5)         -33.9 (7)       IND
           AQ012       58.7 (10)        -74.0 (15)      IND

PRO        ATCC43300   74.0 (13)        -44.4 (9)       IND
           AQ004       93.2 (20)        -98.0 (25)      IND
           AQ006       99.8 (21)        -99.2 (26)      IND
           AQ007       88.6 (17)        -29.9 (8)       IND
           AQ012       38.4 (7)         -58.1 (10)      IND

(a) INT, interpretation; IND, indifference; SYN, synergy; ANT,
antagonism. Synergy was defined as an FICI of [less than or equal to]
0.5, antagonism was defined as an FICI of >4, and indifference was
defined as an F1C1 >0.5 and [less than or equal to] 4.

(b) n, number of drug combinations (among the 77 drug combinations
for each strain) with statistically significant synergy or
antagonism.

Table 7
In vitro interaction between gentamicin and secondary metabolites
from lichens as determined by non-parametric FIC1 and the [DELTA]E
model (a).

Compound   Strain      FICI

                       Median (range)           INT

COL        ATCC43300   (0.375-0.5) 0.5          SYN
           AQ004       (0.1875-0.25) 0.1875     SYN
           AQ006       (0.1875-0.250) 0.25      SYN
           AQ007       (0.375-0.5) 0.375        SYN
           AQ012       0.5                      SYN

EPI        ATCC43300   0.3125                   SYN
           AQ004       (0.375-0.5) 0.5          SYN
           AQ006       (0.25-0.2578) 0.25       SYN
           AQ007       (0.25-0.2578) 0.25       SYN
           AQ012       (0.3125-0.375) 0.375     SYN

LOB        ATCC43300   (0.3125-0.375) 0.375     SYN
           AQ004       0.375                    SYN
           AQ006       (0.5-0.5002) 0.5         SYN
           AQ007       (0.5-0.501) 0.5          SYN
           AQ012       (0.5-0.502) 0.5          SYN

PER        ATCC43300   (0.2813-0.375) 0.375     SYN
           AQ004       (0.3125-0.375) 0.375     SYN
           AQ006       (0.375-0.5) 0.375        SYN
           AQ007       (0.375-0.5) 0.375        SYN
           AQ012       (0.375-0.5) 0.5          SYN

PRO        ATCC43300   (0.1563-0.1875) 0.1563   SYN
           AQ004       (0.1875-0.25) 0.1875     SYN
           AQ006       (0.25-0.2578) 0.25       SYN
           AQ007       (0.1563-0.1875) 0.1563   SYN
           AQ012       (0.2813-0.25) 0.2813     SYN

Compound   Strain      [DELTA]E model (b)

                       [SIGMA]SYN (n)   [SIGMA]ANT (n)   INT

COL        ATCC43300   1182.1 (44)      -66.4 (13)       SYN
           AQ004       1819.2 (52)      -95.2 (20)       SYN
           AQ006        843.0 (37)      -58.1 (12)       SYN
           AQ007       1145.0 (47)      -63.7 (12)       SYN
           AQ012        473.0 (37)      -91.8 (15)       SYN

EPI        ATCC43300    929.8 (29)      -76.4 (16)       SYN
           AQ004       2192.0 (53)      -26.0 (3)        SYN
           AQ006       1631.1 (46)      -97.6 (18)       SYN
           AQ007       1237.8 (45)      -99.3 (21)       SYN
           AQ012        564.4 (30)      -94.1 (10)       SYN

LOB        ATCC43300    659.3 (21)      -89.4 (15)       SYN
           AQ004       2639.7 (51)      -66.5 (13)       SYN
           AQ006        734.2 (43)      -91.9 (15)       SYN
           AQ007        345.2 (20)      -68.3 (12)       SYN
           AQ012        335.0 (23)      -76.2 (5)        SYN

PER        ATCC43300    360.9 (21)      -57.1 (10)       SYN
           AQ004        534.7 (32)      -88.5 (15)       SYN
           AQ006       1058.5 (37)      -74.1 (16)       SYN
           AQ007        598.7 (32)      -81.2 (16)       SYN
           AQ012        595.7 (35)      -67.0 (12)       SYN

PRO        ATCC43300   1657.2 (44)      -87.4 (19)       SYN
           AQ004       3312.7 (55)      -58.6 (6)        SYN
           AQ006       1173.4 (48)      -44.7 (9)        SYN
           AQ007       2875.3 (54)      -35.1 (5)        SYN
           AQ012        664.0 (35)      -97.3 (8)        SYN

(a) INT, interpretation; IND, indifference; SYN, synergy; ANT,
antagonism. Synergy was defined as an FICI of [less than or equal to]
0.5, antagonism was defined as an FICI of >4, and indifference was
defined as an FICI >0.5 and [less than or equal to] 4.

(b) n, number of drug combinations (among the 77 drug combinations
for each strain) with statistically significant synergy or
antagonism.

Table 8
In vitro interaction between levofloxacin and secondary metabolites
from lichens as determined by nonparametric FIC1 and the [DELTA]E
model (a).

Compound   Strain      FICI

                       Median (range)       INT

COL        ATCC43300   ND
           AQ004       (1.5-2) 1.5          IND
           AQ006       1.5                  IND
           AQ007       2                    IND
           AQ012       (1.125-1.5) 1.5      IND

EPI        ATCC43300   ND
           AQ004       (1.0625-1.5) 1.5     IND
           AQ006       (1.0625-1.25) 1.25   IND
           AQ007       1.5                  IND
           AQ012       1.5                  IND

LOB        ATCC43300   ND
           AQ004       2.5                  IND
           AQ006       2.5                  IND
           AQ007       (2-2.5) 2.5          IND
           AQ012       2.25                 IND

PER        ATCC43300   ND
           AQ004       2.5                  IND
           AQ006       2.5                  IND
           AQ007       2.5                  IND
           AQ012       2.25                 IND

PRO        ATCC43300   ND
           AQ004       (2-2.125) 2          IND
           AQ006       (1.25-1.5) 1.5       IND
           AQ007       (1-1.5) 1.5          IND
           AQ012       1                    IND

Compound   Strain      [DELTA]E model (b)

                       [SIGMA]SYN (n)   [SIGMA]ANT (n)   INT

COL        ATCC43300   ND               ND
           AQ004       25.3 (5)          -635.4 (59)     ANT
           AQ006       24.9 (3)          -459.8 (45)     ANT
           AQ007        5.3 (2)         -1601.8 (60)     ANT
           AQ012       29.5 (6)          -502.0 (52)     ANT

EPI        ATCC43300   ND               ND
           AQ004       72.3 (15)          -45.1 (9)      IND
           AQ006       98.1 (21)          -62.3 (12)     IND
           AQ007       56.8 (12)          -90.9 (17)     IND
           AQ012       64.3 (14)          -87.9 (16)     IND

LOB        ATCC43300   ND               ND
           AQ004       99.7 (19)         -485.4 (54)     ANT
           AQ006       98.6 (20)         -507.2 (41)     ANT
           AQ007       0 (0)             -998.5 (61)     ANT
           AQ012       92.9 (12)         -643.4 (40)     ANT

PER        ATCC43300   ND               ND
           AQ004       43 (8)            -971.7 (36)     ANT
           AQ006       14.5 (4)          -483.0 (39)     ANT
           AQ007       57.4 (20)         -408.1 (28)     ANT
           AQ012       75.1 (27)         -272.8 (25)     ANT

PRO        ATCC43300   ND               ND
           AQ004       82.1 (19)          -96.0 (10)     IND
           AQ006       57.2 (10)          -98.3 (15)     IND
           AQ007       38.3 (5)           -78.1 (39)     IND
           AQ012       97.1 (19)          -68.0 (12)     IND

(a) INT, interpretation; IND, indifference; SYN, synergy; ANT.
antagonism. Synergy was defined as an FICI of [less than or equal
to] 0.5, antagonism was defined as an FICI of >4, and indifference
was defined as an FICI >0.5 and [less than or equal to] 4. ND.
not detected.

(b) n, number of drug combinations (among the 77 drug combinations
for each strain) with statistically significant synergy or
antagonism.

Table 9
In vitro interaction between oxacillin and secondary metabolites
from lichens as determined by nonparametric FICI and the [DELTA]E
model (a).

Compound   Strain      FICI

                       Median (range)           INT

COL        ATCC43300   (0.5-0.5002) 0.5         SYN
           AQ004       (0.3125-0.375) 0.3125    SYN
           AQ006       (0.5313-0.5625) 0.5625   IND
           AQ007       (0.625-0.75) 0.75        IND
           AQ012       (0.625-0.75) 0.75        IND

EPI        ATCC43300   (0.5-0.501) 0.5          SYN
           AQ004       (0.5625-0.6250) 0.625    IND
           AQ006       0.625                    IND
           AQ007       (0.5078-0.5156) 0.5078   IND
           AQ012       (0.5078-0.5156) 0.5078   IND

LOB        ATCC43300   (1-1.0039) 1             IND
           AQ004       (1-1.002) 1.002          IND
           AQ006       (0.5313-0.5625) 0.5625   IND
           AQ007       (0.5625-0.625) 0.625     IND
           AQ012       (0.5625-0.625) 0.625     IND

PER        ATCC43300   0.75                     IND
           AQ004       1                        IND
           AQ006       (1.0039-1.25) 1.0039     IND
           AQ007       1                        IND
           AQ012       (0.625-0.75) 0.75        IND

PRO        ATCC43300   (0.5-0.5002) 0.5         SYN
           AQ004       (0.5-0.5002) 0.5         SYN
           AQ006       (0.5039-0.5078) 0.5039   IND
           AQ007       (0.5-0.502) 0.5          SYN
           AQ012       (0.5-0.5002) 0.5         SYN

Compound   Strain      [DELTA]E model (b)

                       [SIGMA]SYN (n)   [SIGMA]ANT (n)   INT

COL        ATCC43300    645.8 (25)      -99.2 (9)        SYN
           AQ004       1075.3 (42)      -83.6 (6)        SYN
           AQ006        238.0 (19)      -48.9 (9)        SYN
           AQ007        132.7 (11)      -86.9 (19)       SYN
           AQ012        131.1 (13)      -94.8 (22)       SYN

EPI        ATCC43300    665.7 (30)      -88.0 (17)       SYN
           AQ004        495.5 (20)      -99.1 (22)       SYN
           AQ006        819.0 (43)      -62.0 (19)       SYN
           AQ007       1129.6 (34)      -82.4 (12)       SYN
           AQ012        804.1 (32)      -47.3 (11)       SYN

LOB        ATCC43300     92.5 (18)      -77.6 (14)       IND
           AQ004         89.1 (15)      -85.0 (12)       IND
           AQ006        274.3 (27)      -94.1 (13)       SYN
           AQ007         99.2 (17)      -67.2 (8)        IND
           AQ012         87.2 (13)      -80.5 (10)       IND

PER        ATCC43300     97.1 (23)      -75.2 (12)       IND
           AQ004         89.2 (16)      -67.3 (12)       IND
           AQ006         79.8 (18)      -98.9 (21)       IND
           AQ007         89.8 (17)      -54.6 (9)        IND
           AQ012         63.7 (9)       -92.2 (11)       IND

PRO        ATCC43300    426.6 (18)      -31.5 (6)        SYN
           AQ004        406.8 (37)      -74.9 (15)       SYN
           AQ006        380.2 (31)      -99.2 (10)       SYN
           AQ007        819.8 (27)      -76.7 (15)       SYN
           AQ012       1047.5 (35)      -23.4 (4)        SYN

(a) INT, interpretation; IND, indifference; SYN, synergy; ANT,
antagonism. Synergy was defined as an FICI of [less than or equal to]
0.5, antagonism was defined as an FICI of >4, and indifference was
defined as an FICI >0.5 and [less than or equal to] 4.

(b) n, number of drug combinations (among the 77 drug combinations
for each strain) with statistically significant synergy or
antagonism.
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Author:Bellio, Pierangelo; Segatore, Bernardetta; Mancini, Alisia; Di Pietro, Letizia; Bottoni, Carlo; Saba
Publication:Phytomedicine: International Journal of Phytotherapy & Phytopharmacology
Article Type:Report
Date:Feb 15, 2015
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