*Highlights (for review)

Highlights

x The biological activity of selected essential oil components against strains of E. coli and S. aureus are presented. x Structure-activity relationships are derived from minimum inhibitory concentration data and propose important molecular descriptors in essential oil biological activity. x Molecular topography of the components, electrostatic nature and ligand efficiency of the components are highlighted as principal descriptors

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1 Title: Structure-Activity Modelling of Essential Oils, their Components, and Key Molecular 1 2 2 Parameters and Descriptors. 3 4 5 3 6 7 8 4 Lucy Owena, Katie Lairda and Philippe B. Wilsonb. 9 10

11 5 12 13 a 14 6 Infectious Disease Research Group, The School of Pharmacy, De Montfort University, 15 16 7 Leicester, UK. 17 18 19 8 bThe School of Pharmacy, De Montfort University, Leicester, UK. 20 21 22 9 23 24 25 10 Corresponding Author: Philippe B. Wilson, The School of Pharmacy, De Montfort University, 26 27 11 Leicester, UK, [email protected]. 28 29 30 12 31 32 33 13 34 35 36 14 37 38 39 15 40 41 42 16 43 44 45 17 46 47 48 18 49 50 51 19 52 53 54 20 55 56 21 57 58 59 22 60 61 62 63 64 65 23 Abstract 1 2 3 24 Many essential oil components are known to possess broad spectrum antimicrobial activity, including 4 5 25 against antibiotic resistant bacteria. These compounds may be a useful source of new and novel 6 7 26 antimicrobials. However, there is limited research on the structure-activity relationship (SAR) of 8 9 27 essential oil compounds, which is important for target identification and lead optimization. This study 10 11 12 28 aimed to elucidate SARs of essential oil components from experimental and literature sources. 13 14 29 Minimum Inhibitory Concentrations (MICs) of essential oil components were determined against 15 16 30 Escherichia coli and Staphylococcus aureus using a microdilution method and then compared to 17 18 31 those in published in literature. Of 12 essential oil components tested, carvacrol and cuminaldehyde 19 20 21 32 were most potent with MICs of 1.98 and 2.10 mM, respectively. The activity of 21 compounds 22 23 33 obtained from the literature, MICs ranged from 0.004 mM for limonene to 36.18 mM for α-terpineol. 24 25 34 A 3D qualitative SAR model was generated from MICs using FORGE software by consideration of 26 27 35 electrostatic and steric parameters. An r2 value of 0.807 for training and cross-validation sets was 28 29 30 36 achieved with the model developed. Ligand efficiency was found to correlate well to the observed

31 2 32 37 activity (r =0.792), while strongly negative electrostatic regions were present in potent molecules. 33 34 38 These descriptors may be useful for target identification ofessential oils or their major components in 35 36 39 antimicrobial/drug development . 37 38 39 40 Keywords: Essential oil, antimicrobial, structure-activity relationship, Escherichia coli, 40 41 41 Staphylococcus aureus. 42 43 44 42 45 46 47 43 1. Introduction 48 49 50 44 Antimicrobial resistance has greatly increased in recent years and is now considered a global public 51 52 45 health threat [1]. Novel antimicrobials are needed to continue treating antibiotic resistant infections, 53 54 55 46 yet the production of new antibiotics has stalled [2]. Natural products are a reservoir of structurally 56 57 47 diverse compounds, so may be a source for the development of novel antimicrobial agents. Essential 58 59 48 Oils (EOs) have been the subject of scientific interest over recent decades, with extensive screening 60 61 62 63 64 65 49 indicating that many of these plant extracts and their isolated components possess antimicrobial 1 2 50 activity [3]. 3 4 5 51 EOs are aromatic, oily plant extracts derived by steam distillation. They are complex mixture of 6 7 52 volatile, low molecular weight organic compounds [4]. Terpenes and their oxygenated derivatives, 8 9 53 terpenoids, are the most common EO compounds [5], while phenylpropanoids and benzenoid 10 11 12 54 compounds are less abundant [6]. Numerous EO components inhibit an array of clinically relevant 13 14 55 pathogenic bacteria, including antibiotic resistant isolates, suggesting they may be candidates for the 15 16 56 development of new antimicrobials. For example, carvacrol, thymol and menthol inhibited 11 17 18 57 important foodborne pathogens at MICs ranging 0.02-4.0 μg/mL [7]. The EO compounds 1,8-cineole, 19 20 21 58 carvacrol, terpinen-4-ol, eugenol and cinnamaldehyde inhibited Staphylococcus aureus and 22 23 59 Methicillin Resistant S. aureus (MRSA) at Minimum Inhibitory Concentrations (MICs) ranging 0.006 24 25 60 to 1.6% and Enterococcus faecalis at MICs ranging 0.012 to >3.2% [8]. Wang et al. [9] reported that 26 27 61 hinokitiol inhibited MRSA and Escherichia coli at 60 and 40 μg/ml, respectively. Orhan et al. [10] 28 29 30 62 showed that thirty-five EO components inhibited 11 isolates of Klebsiella pneumoniae, including 31 32 63 extended-spectrum beta-lactamase producing strains, at MICs ranging 8-64 μg/mL. 33 34 35 64 The mechanism by which EO components exert their antimicrobial effect is incompletely understood. 36 37 65 Much of the published research has concluded that EO components change the structure and function 38 39 66 of bacterial cell membranes; it has been proposed that the hydrophobic nature of these compounds 40 41 67 allows them to partition in the membrane [11]. This non-specific mechanism of action is assumed to 42 43 44 68 bypass many antibiotic resistance mechanisms and inhibit antibiotic resistant isolates. Moreover, it 45 46 69 has been hypothesised that the risk of antibiotic resistance developing is lower than other antibiotics 47 48 70 [12], as per other membrane-targeting antimicrobials such as cationic antimicrobial peptides [13]. EO 49 50 71 components could be an attractive class of compounds for the development of new antimicrobial 51 52 53 72 therapies. 54 55 56 73 Understanding the Structure-Activity Relationships (SAR) of antimicrobial agents is important in 57 58 74 antimicrobial development to identify the most potent compounds and allow optimization of lead 59 60 75 compounds, however there are limited published studies on the SAR of EO components as 61 62 63 64 65 76 antimicrobial agents. The hydrophobicity of EO components, measured by octanal/water partitioning 1 2 77 coefficient (log P) has been correlated with their antimicrobial activity [14,15]. For example, Ben 3 4 78 Arfa et al. [15] reported that carvacrol (log P = 3.52) was more antimicrobial than eugenol (logP = 5 6 79 2.73). The positive correlation of hydrophobicity and antimicrobial activity relates to a greater affinity 7 8 9 80 for partitioning in the bacterial cell membrane, this correlation does not hold true at logP greater than 10 11 81 4, indicating that there are other important structural characteristics [15,16] 12 13 14 82 Several published papers have emphasised the role of a phenolic group in activity, for example, 15 16 83 Andrade-Ochoa et al. [14] reported that thymol, which possesses a phenolic group had a significantly 17 18 84 lower MIC than menthol, which has the equivalent structure but is alicyclic. The importance of a 19 20 21 85 hydroxyl moiety alongside a phenolic group was demonstrated by comparing with aromatic 22 23 86 compounds with alkyl substituents [14,16,17]. It is hypothesised that the delocalised electron system 24 25 87 of the phenolic moiety facilitates proton exchange through the hydroxyl group, which dissipates 26 27 88 proton motive force [16]. 28 29 30 89 This study aims to construct a 3D QSAR model of EO component activity from experimental and 31 32 90 literature sources based on electrostatic and steric descriptors to inform future antimicrobial drug 33 34 35 91 target identification. 36 37 38 92 39 40 41 93 42 43 44 94 2. Materials and Methods 45 46 95 47 48 49 96 2.1. EO Components 50 51 52 97 Authentic standards of (-)-β-pinene (99%), carvacrol (98%), cuminaldehyde (98%), linalool (97%), p- 53 54 55 98 cymene (99%), thymol (≥98.5%), β-caryophyllene (≥80%) and γ-terpinene (97%) were obtained from 56 57 99 Sigma Aldrich (Gillingham, UK). 58 59 60 100 2.2. Microorganisms 61 62 63 64 65 101 Escherichia coli NCTC 8003 and Staphylococcus aureus NCTC 12981 were cultured using nutrient 1 2 102 broth and agar (Oxoid, Basingstoke, UK) and grown aerobically at 37˚C for 24 hours. 3 4 5 103 2.3. Minimum Inhibitory Concentrations 6 7 8 104 The MIC of EO components were determined using a broth microdilution method adapted from the 9 10 105 International Organization for Standardisation (ISO) 20776-1 antibiotic susceptibility test [18]. Serial 11 12 106 two-fold dilutions of EO components to yield final concentrations ranging 8-0.01% (v/v) in nutrient 13 14 15 107 broth supplemented with 10% dimethyl sulfoxide (Fisher Scientific, Loughborough, UK) were 16 17 108 prepared in polystyrene 96-well plates (Scientific Laboratory Supplies, Wilford, UK). An equal 18 19 109 volume (75 μl) of bacterial suspension was added to each well to yield a final well concentration of 20 21 110 5x105 colony forming units (CFU)/ml. Controls were antimicrobial free and inoculum free wells. 22 23 24 111 Bacterial growth was determined by measuring optical density (595 nm) of samples using a 25 26 112 Spectramax Plus 384 microplate reader and Softmax Pro version 6.4 software (Molecular Devices, 27 28 113 Sunnyvale, USA) immediately after inoculation and after 24 hr. incubation at 37°C. Experiments were 29 30 114 repeated three times and replicated twice (n=6). 31 32 33 115 2.4. Literature Review 34 35 36 116 Pubmed searches for recently published papers in peer-reviewed journals containing MIC data for a 37 38 117 range of EO components against E. coli and S. aureus that had not been tested in this study. MICs 39 40 41 118 were converted to milimolar (mM) concentrations to enable comparison between studies. Where units 42 43 119 were volumetric, densities at 25˚C according to Sigma Aldrich were used for unit conversion. 44 45 46 120 2.5. Computational Methods 47 48 49 121 Structure activity relationships (SAR) and quantitative analogues (QSAR) have long been employed 50 51 122 to understand the links between molecular descriptors and biological activity. Indeed, the FORGE 52 53 123 program from the Cresset Suite of molecular modelling software used in this study is the latest in a 54 55 124 wide toolkit of structural techniques implemented to harness the relationships between activity and 56 57 58 125 molecular structure. 59 60 61 62 63 64 65 126 The FORGE 3D QSAR protocols follow traditional workflow for statistical method development. 1 2 127 Molecules begin by forming alignments with a reference or reference set, where their structures are 3 4 128 compared to a molecule with known favourable biological activity data. These molecules are then 5 6 129 visually inspected to ensure that alignments performed computationally are correct, before being 7 8 9 130 passed onto training and test sets. These sets can originate as a single grouping of molecules 10 11 131 containing activity data, however a partitioning is favourable in order to use the test set molecules as 12 13 132 questioning parameters for the model developed from the training set. The training set molecules are 14 15 133 then superimposed onto a formulated grid and a 3D QSAR model generated from the training set. The 16 17 18 134 training and test set are then scored against the model and the statistical data is reported. The model is 19 20 135 then available to visualise, and can be used for future scoring of newly-designed molecules, potential 21 22 136 targets, or entire databases. 23 24 25 137 Within the context of this study, a 3D QSAR model has been generated from cumulated experimental 26 27 138 data and data from literature sources. Biological activities in terms of MICs against E. coli and S. 28 29 30 139 aureus have been collated and used as activity model predictors for the QSAR framework. Data from 31 32 140 both strains were cumulated in order to obtain more generalised, unified relationships between EO 33 34 141 components and their effect on both Gram-positive and Gram-negative bacterial strains and their 35 36 142 growth. This enables us to consider drug targets with the broadest possible spectrum for biological 37 38 143 activity and bacterial growth inhibition. 39 40 41 144 Once molecules were aligned to the carvacrol reference (lowest MIC), a conformational hunt was 42 43 44 145 initiated in order to consider the training and test set molecules based on collective torsions and 45 46 146 rotational degrees of freedom. The sets were aligned by substructure to carvacrol before being subject 47 48 147 to field point analysis. Contrary to traditional QSAR methods, FORGE incorporates field points, as 49 50 148 opposed to 3D-lattice intersections with probes. The advantage of considering molecular parameters 51 52 53 149 in terms of field points, is that far fewer sample points are required than in lattice or grid-based 54 55 150 techniques. These sample points are physically chosen by the software, rather than statistically 56 57 151 derived from the model, and are therefore gauge invariant. The use of sample points provide a reliable 58 59 152 and robust model for alignment and development of a QSAR model. 60 61 62 63 64 65 153 Field QSAR electrostatic parameters are generated from the set of aligned molecules and their 1 2 154 respective field points. The electrostatic field points are extracted from these and a sphere-exclusion 3 4 155 algorithm applied to space out the sample of field point positions, deriving a unified model for the 5 6 156 aligned molecules. The sphere exclusion algorithm selects field points with the greatest magnitudes 7 8 9 157 and negates field points within a 1Å region of each of these. The procedure is repeated for all field 10 11 158 points until a representative set of field points covering the entire molecular surface is produced. 12 13 159 Finally, for each of the molecules in the dataset, descriptor vectors are generated at each location at 14 15 160 which the electrostatic field is measured. These electrostatic descriptors and the descriptor vectors are 16 17 18 161 generated based on the positions of positive and negative field points for each molecule. 19 20 21 162 Steric effects are described similarly using data for heavy atoms as opposed to electrostatics. Previous 22 23 163 studies have described evidence that for 3D QSAR techniques, Lennard-Jones/Morse potentials do not 24 25 164 legitimately provide information to supplement simple Van der Waals volume descriptors. As such, a 26 27 165 binary indicator variable is employed to discern probe positions either inside or outside Van der 28 29 30 166 Waals radii for heavy atoms in the aligned molecules. Following this, the earlier sphere exclusion 31 32 167 algorithm is applied in an analogous manner to that in the electrostatics field point derivation, 33 34 168 producing an appropriate and representative set of steric field points. 35 36 37 169 Descriptors having been generated, a matrix of molecular electrostatic and volume vectors is formed. 38 39 170 This matrix contains two substructures based on the electrostatic and volume vectors, respectively. In 40 41 171 order to scale the descriptors, the SIMPLS algorithm is employed. Indeed, issues with the statistical 42 43 44 172 validity of the model would be raised if one of the descriptor blocks exhibited a larger variance than 45 46 173 the other. This would lead to a tendency for the regression to favour the aforementioned descriptors 47 48 174 over the descriptor set with lower variance, regardless of the relationships to biological activity. 49 50 175 Descriptors are therefore rescaled before running the partial least squares (PLS) analysis. Once 51 52 53 176 scaling has taken place, the standard deviation of each block of descriptors becomes equal. 54 55 56 177 The SIMPLS algorithm as a regression model allows correlated descriptors to be considered. The 57 58 178 latent variables are extracted as orthogonal components, or linear combinations of the descriptors 59 60 179 considered in the first instance. In order to amplify the covariance between descriptors and biological 61 62 63 64 65 180 activity, the latent variables are chosen selectively, and an equation is formulated predicting activity 1 2 181 as a linear combination of the latent variables. Implementing cross-validation through leave one out 3 4 182 and leave many out techniques defines the best possible number of latent variables. This will by 5 6 183 nature also correspond to the highest value of q2 and predictive ability of the model. 7 8 9 184 From the 27 molecules with known biological activity (MIC), the dataset was partitioned to 20% test 10 11 12 185 and 80% training set through a combined activity/random selection algorithm in order to robustly test 13 14 186 the QSAR model. Carvacrol as the most potent in the training set with MIC =1.98 mM, was used as 15 16 187 the reference and to align the training set to the compound. 17 18 19 188 20 21 22 189 3. Results, Statistics and Discussion 23 24 25 190 Carvacrol was the most antimicrobial agent tested in this study, with an MIC of 1.98 mM against both 26 27 191 E. coli and S. aureus (Table 1). Cuminaldehyde was also strongly antimicrobial against E. coli but 28 29 192 weakly antimicrobial against S. aureus, with MICs of 2.10 and 33.60 mM, respectively. Linalool, p- 30 31 32 193 Cymene and γ-terpinene were weakly antimicrobial with MICs ranging 7.98-250.46 mM. MICs of 12 33 34 194 of 21 EO components obtained from literature sources were considerably lower than that of carvacrol, 35 36 195 ranging 0.004-0.59 mM, where limonene possessed the greatest reported antimicrobial activity (Table 37 38 196 2). Differences in MICs observed may be attributed to susceptibility of bacterial isolates used between 39 40 41 197 studies, which can vary significantly, or methodological differences as inoculum size, inoculum 42 43 198 preparation method, growth medium and incubation time, which are known to influence MIC values 44 45 199 [18]. 46 47 48 200 The FORGE SIMPLS algorithm was used as a partial least squares regression method. The training 49 50 201 set of 27 compounds was split into 20% test set, and 80% training set (5 and 22 compounds 51 52 202 respectively). Stratification was established through activity. The 3D field QSAR model was 53 54 55 203 calculated with the following parameters: the maximum number of PLS components was 50, with 50 56 57 204 Y scrambles to use; molecular weighting power was quadratically derived. 58 59 60 61 62 63 64 65 205 For the training and cross-validation sets, a good r2 value of 0.807 was achieved with the model 1 2 206 developed. A generally good distribution is observed (Fig 1a) with the training CV contributing to the 3 4 207 few outlying points (Fig 1b). Indeed, particular molecular descriptors have been found to contribute 5 6 208 significantly to the activities observed (Fig. 2). Ligand efficiency, calculated as a measure of average 7

8 2 9 209 binding energy per atom, is found to correlate well to the observed activity, with an r value of 0.792. 10 11 210 The ligand efficiency was defined as 1.4(-logIC50)N where IC50 represents half the minimum 12 13 211 inhibitory concentration, and N the number of non-hydrogen atoms. 14 15 16 212 This suggests that for future studies of EOs and their components, the ligand efficiency of the EO 17 18 213 constituents could form an integral part of target identification. Moreover, it is possible to analyse the 19 20 21 214 electrostatic origin of inhibition from activity cliff summaries. These poses are generated based on the 22 23 215 electrostatic origin of activity, and highlight areas of positive and negative charge in the species 24 25 216 considered (Figure 3). 26 27 28 217 Similar distributions of positive and negative charge are located in the highly potent carvacrol and 29 30 218 menthol constituents. These are in parallel to the weakly inhibitory terpinen-4-ol and 1,8-cineole EO 31 32 219 components, which demonstrate low biological activity, however, both lack the region of strong 33 34 35 220 negative electrostatics present in the highly potent molecules of carvacrol and menthol. 36 37 38 221 An important concept in terms of activity cliffs and summaries is that of disparity and associated 39 40 222 matrices. The activity miner employed here uses a combination of 3D and 2D molecular similarity 41 42 223 parameters in order to find regions or structure activity relationships which are particularly influential 43 44 224 in a chemical structure or moiety. The disparity between a pair of molecules can be defined as the 45 46 47 225 difference in their activity as a quotient of their distance. Disparity effectively measured the difference 48 49 226 in activity based on substructure. A pair of molecules with a high disparity index indicates that the 50 51 227 molecular structures are likely similar, however the activities differ significantly. This helps to 52 53 228 identify structural features inherently important in the biological activity of a set of molecules. 54 55 56 229 The disparity matrix (Table 3) highlights certain structural features incorporated in the compounds 57 58 230 tested, which affect the biological activity in situ. Indeed, compounds such as α-pinene and 1,8- 59 60 61 62 63 64 65 231 cineole possess little similarity bar the presence of cyclic alkyl, contributing to steric bulk of the 1 2 232 compound. Moreover, this feature is also highlighted in carvacrol and menthol, both of these also 3 4 233 featuring prominently in the pair matching of the disparity matrix. 5 6 7 234 8 9 10 235 4. Conclusions 11 12 13 236 The QSAR study on antimicrobial effects of essential oil components provides some promising 14 15 237 targets for identification and future bacterial screening . Indeed, the steric effects of some EO 16 17 18 238 components have been shown as a structural commonality between inhibitory molecules, with the 19 20 239 electrostatic properties and topography correlating well with the lowest MICs. Carvacrol and menthol 21 22 240 were found to have the lowest MICs against E. coli and S. aureus, with highly correlated electrostatic 23 24 241 topographies. Most notably, the ligand efficiency, defined as a function of the MIC and the number of 25 26 242 heavy (non-hydrogen atoms), has been noted to correlate well with EO components exhibiting high 27 28 29 243 antimicrobial activity. This is a promising finding which will help lead target identification for future 30 31 244 studies of essential oils, their components, and the use of natural products in antimicrobial treatments. 32 33 34 245 35 36 37 246 Acknowledgements: This research did not receive any specific grant from funding agencies in the 38 39 247 public, commercial, or not-for-profit sectors. 40 41 42 248 Conflicts of interest: none. 43 44 45 249 46 47 48 250 References 49 50 51 251 [1] Laxminarayan, R., Matsoso, P., Pant, S., Brower, C., Røttingen, J.-A., Klugman, K., Davies, S., 52 53 252 2016. Access to effective antimicrobials: a worldwide challenge. 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Antimicrobial activity, 33 34 323 cytotoxicity and chemical analysis of lemongrass essential oil (Cymbopogon flexuosus) and pure 35 36 324 citral. Appl. Microbiol. Biotechnol. 100, 9619–9627. 37 38 39 325 40 41 326 42 43 327 44 45 46 328 47 48 49 329 50 51 52 330 53 54 55 331 56 57 58 332 59 60 61 62 63 64 65 Figure 1

a

b

Figure 1(a) and (b). Three-component EO 3D-QSAR model – predicted against experimental activity of compounds in the training and test sets.

Figure 2

Figure 2. Ligand efficiency relation to activity observed, showing good agreement with the

QSAR model developed.

Figure 3

Figure 3. Comparison of electrostatic field points for highly potent (carvacrol and menthol) and

weakly inhibitory (1,8-cineole and terpinen-4-ol) EO components. Activity cliffs in the form of

positive (red) charge and negative (blue) charge areas are shown.

Table 1

Table 1: MIC (mM) of EO components against E. coli and S. aureus (n=6).

MIC (mM) EO component E. coli S. aureus

Carvacrol 1.98 1.98

Cuminaldehyde 2.10 33.60

Linalool 57.05 114.10

p-Cymene 15.97 7.98

γ-Terpinene 250.46 62.61

Table 2

Table 2: MIC (mM) of EO components against E. coli and S. aureus from the published literature

sources.

MIC (mM) EO Component Reference E. coli S. aureus

1,8-cineole 18.15 18.15 [20]

Allyl isothiocyanate 1.58 1.58 [21]

α-pinene 0.06 0.04 [7]

α-Terpineol 36.18 42.20 [22]

α-thujone 0.39 0.39 [23]

β-Caryophyllene 0.29 0.29 [23]

β-pinene 0.06 0.04 [7]

β-Thujone 0.04 0.39 [23]

Camphor 0.05 0.39 [7,23]

Carvone 6.38 50.19 [24,25]

Cinnamaldehyde 0.59 0.95 [8,21]

Citral 1.75 [26]

Citronellol 8.95 4.48 [20]

Eugenol 6.50 [8]

Geraniol 9.08 4.54 [20] Hinokitiol 0.24 [22]

Limonene 0.004 0.44 [23]

Linalyl acetate 0.05 0.31 [7,23]

Menthol 0.01 0.01 [7]

Terpinen-4-ol 0.04 12.10 [8,23]

Thymol 9.32 4.60 [20]

Table 3

Table 3. Disparity Matrix for EO components based on activity and structural information. Green

indicates a high disparity, yellow little disparity, and red, significant disparity.

W M Q O U N H G D V A R B C X P K Y T E S F L J I

-1.9 -2.5 -2.2 -0.9 -4.4 -3.2 -5.4 -1.3 -2.4 -6.8 -3.9 1.7 2.9 1.4 2.6 1.9 8.4 -3 A 0 0 3 0 0 0 0 11.8 -2.4 -3.1 -2.6 -1.6 -5.3 -4.1 -6.6 -3.8 -2.1 -2.8 -8.1 -5.7 1.3 2.4 2.4 -5 B 0 2 0 0 0 0 0 0 -13.6 -11.8 -3.9 -4.8 -3.5 -1.2 -7.5 -5.8 -8.4 -2.4 -3.2 -5.3 -5.3 -9.4 -3.5 -3.2 -8.9 -8.4 -4 -7 C 0 0 0 0 0 13.6 -1.2 -1.7 -1.6 -4.2 -2.2 -5.1 -1.9 -8.7 -4.1 2.3 4.5 2.3 3.5 3.8 2.4 3.2 5.7 D 0 0 0 0 0 0 0 -2.3 -2.3 -4.2 3.6 1.2 6.3 3.4 3.9 2.4 2.5 1.4 4.6 0.9 3.5 4.1 4.1 8.9 3.9 0 0 0 0 0 0 5 E -2.7 -3.4 -2.8 -1.8 -5.3 -3.8 -5.9 -0.9 -3.1 -0.9 -2.2 -8.1 -4.1 -3.2 -1.9 0.8 2.1 1.5 1.5 3.2 -3 0 0 0 0 F -2.3 -2.6 -2.3 -1.1 -4.9 -3.6 -5.1 -2.6 -1.5 -2.5 -6.6 -3.5 -2.4 1.3 2.5 1.8 1.9 3.5 G 1 0 0 0 0 0 0 7.1 2.7 3.4 3.7 8.1 6.3 1.2 3.2 1.6 8.6 2.6 6.5 4.4 8.2 4.4 6.6 8.1 4.2 8.7 9.4 8.1 6.8 H 5 7 0 -2.8 -1.2 -4.4 -0.9 2.6 4.8 3.8 1.3 1.8 3.5 2.5 5.3 2.8 2.4 -1 -3 0 0 0 3 0 0 0 3 0 I -3.8 -4.3 -3.7 -2.6 -5.9 -4.4 -6.5 -2.1 -3.5 -1.8 -3.2 -3.5 -8.2 -1.9 -1.5 -4.6 -3.8 -2.4 0.8 -3 0 0 0 0 0 J -1.1 -1.4 -3.1 -1.5 -3.3 -1.2 -4.4 -1.4 2.3 3.8 2.3 3.2 0.9 1.1 3.2 1.5 2.2 5.3 2.1 1.3 -1 K 0 0 0 0 -1.6 -1.9 -0.8 -2.5 -4.2 -1.1 -1.8 -6.5 -2.5 1.3 3.5 1.1 1.9 1.8 0.9 3.2 -2 -4 -2 0 0 0 0 0 0 L -1.9 -2.6 2.6 2.6 3.5 1.6 1.2 3.5 2.6 3.1 1.9 3.8 M -2 0 0 0 1 5 0 0 2 0 0 7 3 -1.9 -2.6 -2.1 -2.4 -4.4 -1.6 -0.9 -1.3 -8.6 -2.4 1.6 2.9 1.4 2.1 0.9 2.4 -1 -4 N 2 0 0 0 0 0 0 -1.6 5.1 1.9 1.7 1.9 3.2 6.5 5.7 6.4 1.7 4.4 4.2 3.3 6.5 5.1 5.9 2.3 5.1 8.4 6.6 5.4 O 0 0 2 3 -1.7 -1.7 -3.2 3.7 1.5 4.7 3.7 4.3 2.4 2.5 1.5 4.4 1.2 3.6 3.8 2.2 5.8 4.1 3.2 P 0 0 0 0 0 0 -1.2 1.8 1.5 1.8 2.9 6.5 5.7 5.5 1.7 1.9 3.1 5.9 2.8 4.9 5.3 2.3 4.2 7.5 5.3 4.4 Q 5 0 0 4 4 -4.3 -4.6 -3.3 -3.1 -1.1 -5.5 -4.3 -6.4 -3.5 -1.9 -3.2 -3.8 -1.8 -1.5 -3.9 -3.5 -2.6 -2 -7 -2 R 0 0 0 0 0 -3.6 -3.6 -2.8 -2.3 -5.7 -3.7 -5.7 -1.4 -2.6 -1.1 -2.3 -6.3 -3.4 -2.3 -1.4 1.6 1.1 1.2 -3 -1 0 0 0 0 0 S -0.9 -3.6 -4.7 -1.6 -6.5 -4.7 -6.5 -2.9 -3.5 -3.8 -0.8 -4.8 -8.1 -2.5 -2.1 -6.3 -4.5 -2.4 -2.9 -4 -3 -5 0 0 0 T -1.4 -1.7 -1.2 -2.9 -1.5 -3.2 -1.2 2.3 2.3 3.1 0.8 2.6 1.1 1.8 3.5 1.6 0.9 -1 -5 U 0 3 1 0 0 0 -1.8 -1.9 -3.7 3.2 1.2 2.8 3.3 2.1 1.9 3.7 2.3 2.8 1.6 2.6 2.2 V 0 0 0 4 0 0 1 0 0 4 -1.5 -1.7 -3.4 3.7 1.7 4.7 3.6 4.6 2.6 1.4 4.3 2.6 3.4 1.7 4.8 3.1 2.5 W 0 0 0 0 0 2 1 0 -1.8 -1.9 -2.7 3.4 1.4 3.6 3.6 4.3 1.9 1.6 1.1 3.8 2.3 2.7 1.2 3.9 2.4 1.9 X 0 0 0 0 0 0 0 -3.4 -3.7 -3.2 -2.3 -3.7 -5.1 -1.6 -2.6 -1.3 -2.3 -2.6 -7.1 -1.3 -0.8 -3.6 -2.3 -1.3 -1.7 0.9 -5 0 0 0 0 0 Y

Table 4

1 Table 4. Molecular identities used to describe compounds in Table 3.

ID Compound

A Thymol

B p-Cymene

C γ-Terpinene

D Carvacrol

E Hinokitiol

F Terpinen-4-ol

G Eugenol

H menthol

I Cinnamaldehyde

J α-Terpineol

K Allyl isothiocyanate

L Geraniol

M limonene

N Citronellol

O α-pinene

P β-caryophyllene

Q β-pinene

R Carvone

S 1,8-cineole

T Linalool

U Citral

V α-thujone

W linalyl acetate

X Camphor

Y Cuminaldehyde 2