In Vitro Evaluation of the Potentiating Effects of Plant-Derived Molecules on Conventional Biocides

Summary

This protocol provides a systematic framework to assess how phytochemicals can enhance the bactericidal effects of traditional biocides using bactericidal activity modeling and combinatorial interaction assays , which aim to identify synergistic mixtures that improve microbial control and reduce overall biocide usage.

Abstract

Developing innovative and efficient approaches to enhance the antimicrobial activity of conventional biocides is a promising strategy for improving microbial control for healthcare disinfection purposes. In this context, we describe a detailed protocol for evaluating the potentiating effects of plant-derived phytochemicals on synthetic biocides. These plant-based products are attractive candidates as biocide adjuvants due to their structural diversity, bioactivity, and often lower toxicity. The workflow here described allows for the determination of minimum bactericidal concentrations (MBCs) and the systematic generation of both dose-response and time-response curves for each product tested individually (biocides and phytochemicals) and for their dual or triple combinations. The resulting data allow for advanced mathematical modelling, including determining the fractional bactericidal concentration index (FBCI) and assessing the interactions between the tested products regarding synergistic, additive, or antagonistic effects using the software Combenefit. In that sense, it is possible to identify promising natural products as biocide enhancers, optimize antimicrobial efficacy, and design more sustainable and safer disinfection strategies that may reduce the concentrations required of synthetic biocides. This improves microbial control and minimizes environmental and public health risks associated with excessive biocide use.

Introduction

Healthcare-associated infections (HAIs) are among the most critical and worrisome public health challenges worldwide¹. Alarmingly, they are strongly associated with the spread of multidrug-resistant (MDR) microorganisms and the persistence of bacterial bioburden on hospital surfaces, often due to inappropriate or ineffective disinfection practices^2,3. Moreover, the cross-contamination between patients, healthcare staff, and medical equipment further amplifies this issue^4,5. In Europe, for instance, it has been estimated that nearly 80,000 hospitalized patients suffer from at least one HAI on any given day, accounting for approximately 16 million additional hospital days each year². The COVID-19 pandemic has further complicated this issue, contributing to a surge in MDR bacterial infections due to the high usage of antimicrobial agents/disinfectants and increased hospitalizations^6,7. The rise of antibiotic-resistant bacteria, including extended-spectrum β-lactamase (ESBL)-producing Escherichia coli and methicillin-resistant Staphylococcus aureus (MRSA), has led to a growing health and economic burden worldwide. In fact, these strains underscore the urgency of developing innovative solutions for infection prevention and control^1,8,9.

With that in mind, biocides became crucial tools to control the spread of resistant bacteria and bacterial infections, being extensively applied to disinfect hospital surfaces, sterilize equipment, treat water systems, and prevent contamination of medical devices^10,11. Unlike antibiotics, which act on specific microbial targets¹², biocides disrupt multiple cellular structures simultaneously, including membranes, enzymes, ribosomal RNA, and metabolic pathways^13,14,15. This broad-spectrum action was initially thought to reduce the risk of resistance. However, evidence shows that microorganisms can adapt to biocide exposure, leading to tolerance, persistence, and even cross-resistance to antibiotics^10,16. For example, exposure of Pseudomonas aeruginosa to benzalkonium chloride selected for variants resistant to ciprofloxacin and novobiocin through the overexpression of efflux pumps¹⁷. Similarly, triclosan has been shown to activate efflux pumps in Stenotrophomonas maltophilia, while chlorhexidine exposure has been associated with increased antibiotic resistance in S. aureus¹⁸.

Furthermore, quaternary ammonium compounds (QACs), chlorhexidine, diamidines, acridines, and triclosan have been implicated as possible causes for the selection and persistence of bacterial resistance to several antibiotics^19,20. Resistance genes such as qacA/B and smr are known to confer tolerance to QACs, and clinical failures such as bacteremia linked to catheters stored in contaminated QAC solutions and intrinsic microbial contamination of iodophors have also been reported^16,17,21. These findings highlight the urgent need for optimized biocide use, supported by mechanistic studies and rational disinfection strategies.

Biofilms, which are high-cell-density and well-structured bacterial communities embedded in an extracellular matrix of polysaccharides, proteins, and nucleic acids, pose an additional and significant obstacle to infection control^22,23. They exhibit remarkable tolerance to antibiotics and biocides, contributing to the persistence of infections in patients. Moreover, biofilms are also often associated with the persistent contamination of medical devices, surgical instruments, and hospital surfaces²⁴. Despite their clinical importance, biofilms have only been recognized as major contributors to chronic infections and antimicrobial resistance in recent decades. Their resilience is such that even aggressive disinfecting agents often fail to eradicate them, leaving behind persister cells capable of reseeding infection^20,25,26. Therefore, finding effective antibiofilm strategies is crucial to controlling HAIs and reducing the public health and economic burden of MDR pathogens^27,28.

As a promising solution, phytochemicals, which are secondary metabolites produced and extracted from plants, represent a largely untapped resource for novel antimicrobial and antibiofilm agents²⁹. However, no phytochemical is used as an antibiotic, due to its modest antimicrobial activity compared to conventional antibiotics. Phytochemicals are routinely classified as antimicrobials based on susceptibility tests that produce the minimum inhibitory concentration (MIC) in the range of 100 to 1000 µg/mL, which are values much higher than those of conventional antibiotics^30,31. Importantly, phytochemicals have already demonstrated antibiofilm activity when used alone or as potentiators of conventional biocides. For example, quercetin may inhibit alginate production, leading to decreased adhesion during biofilm development^32,33,34. Emodin inhibited the development of biofilms by P. aeruginosa, E. coli, and S. aureus through the decrease of expression of key genes involved in biofilm formation³⁵. Combining phytochemicals with conventionally used yet increasingly ineffective biocides, as resistance-modifying agents, represents a promising strategy to enhance antimicrobial efficacy³⁶. Such synergistic interactions can interfere with microbial resistance mechanisms, lowering the minimum effective concentrations of biocides required to achieve satisfactory disinfection³⁷. This approach optimizes the biocidal performance, reduces selective pressure for resistance development, and reduces the ecological and toxicological impacts associated with excessive biocide application^15,20. Phytochemicals are structurally diverse^38,39,40, widely available^38,39,41, inexpensive^38,39,42, and often less toxic to humans and the environment than synthetic biocides^38,39. They are natural products that are often derived from renewable plant sources and, in some cases, have been reported to exhibit lower environmental persistence and reduced toxicity profiles compared with certain synthetic biocides^43,44,45. While their cost, availability, and toxicity profiles vary depending on the compound and extraction process, several phytochemicals have demonstrated favorable safety and environmental characteristics compared with certain conventional biocides^46,47.

This protocol aims to evaluate the potential of phytochemicals, individually and in combination with conventional biocides, for healthcare disinfection. In brief, the protocol evaluates biocide-phytochemical interactions across broad concentration ranges. Biocides were initially tested across 0.1–500 mg/L and phytochemicals across 50–10,000 mg/L to determine minimum bactericidal concentrations (MBCs) (Table 1). For dual combinations, sub-bactericidal concentrations were selected as follows: benzalkonium chloride (BAC) at 0.1, 0.5, and 0.75 mg/L; peracetic acid (PAA) at 0.1, 0.3, and 0.5 mg/L; salicylic acid (SAL) at 50, 100, and 250 mg/L; and eugenol (EUG) at 100, 250, and 750 mg/L (Table 1). Triple combinations were conducted using BAC (0.75 mg/L) or PAA (0.5 mg/L) combined with SAL (100 or 250 mg/L) and EUG (250 or 750 mg/L) (Table 1). Time-response assays were performed over 1–90 min, followed by a 24 h bacterial regrowth assessment. Antimicrobial interactions are quantified using the fractional bactericidal concentration index (FBCI) and analyzed with Combenefit software, while disinfection kinetics are modeled using the Chick-Watson and Weibull models to characterize concentration dependence and inactivation dynamics.

The innovation lies in the rational design of stable dual and triple interactions between phytochemicals and biocides, optimizing the disinfection activity while minimizing toxicity and environmental impact due to a decreased use of conventional biocides. Mathematical approaches such as calculating the FBCI and synergy evaluation were also used to quantify phytochemical-biocide interactions in microbial control. While FBCI offers a categorical interpretation of interaction (synergy, antagonism, or indifference) at specific concentration combinations, Combenefit enables a more comprehensive, response-surface-based evaluation of interaction patterns across a broader concentration range. This allows visualization and quantification of concentration-dependent interaction effects that may not be captured by single-point indices^48,49. Moreover, disinfection kinetics are modeled using the Chick-Watson (for dose-response) and Weibull (for time-response) models⁵⁰. These models provide kinetic modeling of microbial inactivation, offering quantitative parameters describing disinfection rates and survival curve behavior⁵⁰. They help characterize whether killing follows log-linear or non-linear dynamics and allow more mechanistic interpretation of treatment effects, which extends beyond the descriptive of FBCI^51,52.

This protocol ensures the systematic identification of promising phytochemical-biocide combinations and the development of an efficient, sustainable strategy for healthcare disinfection. Unlike classical MIC or checkerboard assays that provide static endpoint measurements, this protocol integrates dynamic bactericidal modeling and interaction analyses to capture the magnitude and kinetics of potentiation. While classical checkerboard assays provide valuable information on inhibitory interactions based on MIC endpoints, they are limited to growth measurements and single interaction indices. The protocol described here advances beyond this framework by quantifying bactericidal activity rather than growth inhibition, assessing post-treatment regrowth to evaluate sustained antimicrobial effects, evaluating both dual and triple biocide-phytochemical(s) combinations, modeling disinfection kinetics through Chick-Watson and Weibull approaches, and applying concentration-response surface analysis using Combenefit. This multidimensional strategy enables a more robust and predictive assessment of biocide-phytochemical potentiation.

Protocol

NOTE: The methodology proposed was adapted from the European Standard EN 1276⁵³.

1. Culture preparation

1. Cryopreserve the bacterial strains tested, Escherichia coli CECT 434 and Staphylococcus aureus CECT 976, by storing them in cryovials with glycerol 30% (v/v) at -80 °C.
2. For recovery, culture bacteria in plate count agar (PCA) plates and allow them to grow, for 24 h at 25 °C, by incubating the plates.
3. Prepare overnight-grown cultures of the strains under study by inoculating one bacterial colony from a streaked plate in tryptic soy broth (TSB) to reach the exponential growth phase (according to the calibration curve obtained from next steps), and incubating on a shaking incubator at 100 rpm and 25 °C.
4. Using a spectrophotometer, obtain a calibration curve of the colony-forming units (CFU)/mL as a function of the optical density at 600 nm (OD₆₀₀) for each bacterial strain under the previously mentioned culturing conditions.
   1. Grow an overnight bacterial culture in TSB at 25 °C with agitation (100 rpm).
   2. Prepare 10-fold serial dilutions of the culture in sterile saline solution (0.85% w/v NaCl).
   3. Measure the OD₆₀₀ of each dilution using a spectrophotometer.
   4. Plate appropriate dilutions on PCA. Incubate plates at 25 °C for 24 h.
   5. Select plates presenting between 10 and 100 colonies and determine the corresponding CFU/mL values.
   6. Plot CFU/mL values against the corresponding OD₆₀₀ readings within the linear range (0.04–0.8).
   7. Perform at least two independent assays with at least two replicates. Perform linear regression analysis to obtain the calibration equation.
   8. Use the resulting equation to estimate bacterial concentrations in subsequent experiments.
5. Centrifuge the inocula at 3772 x g for 10 min, and wash once with phosphate buffer saline (PBS) (8 g/L of NaCl, 0.2 g/L of KCl, 1.44 g/L of Na₂HPO₄, and 0.24 g/L of KH₂PO₄).
6. Using a spectrophotometer, adjust the OD₆₀₀ of the inocula in PBS to 1 x 10⁸ colony-forming units (CFU)/mL.

2. Molecule solutions preparation and controls

1. Consider the selection, study, and preparation of tested molecules as follows: use two biocides –BAC and PAA (15% (w/v) – and two phytochemicals –SAL and EUG 99%.
   NOTE: BAC was chosen in this study because it is a widely used QAC, effective against bacteria through membrane disruption^54,55. Peracetic acid is a broad-spectrum oxidising disinfectant increasingly used for medical instrument sterilisation^56,57. Salicylic acid is a phenolic compound of interest because it exhibits antibacterial and antibiofilm activity and influences bacterial virulence and antibiotic-resistance mechanisms, suggesting potential roles in disinfection and microbial control^58,59. Eugenol is a natural terpenoid/phenolic aromatic compound with recognized antimicrobial properties that can act as a safer, eco-friendly alternative to synthetic disinfectants^60,61.
2. Prepare BAC and PAA freshly in ultrapure water.
3. Dissolve EUG and SAL were dissolved in dimethyl sulfoxide (DMSO), ensuring that the cells were never exposed to a concentration of DMSO higher than 5%.
4. Prepare the concentrations tested of BAC, PAA, EUG, and SAL, according to Table 1.
5. Prepare the tested concentrations in 15 mL centrifuge tubes by adjusting the volumes of stock solution and cell suspension to obtain the desired final molecule concentration and cell density.
6. For all subsequent assays, include two negative controls: (i) bacteria without treatment and (ii) bacteria exposed to 5% DMSO (as this is the maximum concentration of DMSO the cells are being exposed to throughout the assays).

3. Bacterial exposure to biocide or phytochemical

1. Add 1 mL of cell suspension to 1 mL of ultrapure water and wait 2 min.
2. Then, add 8 mL of biocide or phytochemical solution (in ultrapure water or 5% DMSO) and incubate on a shaking incubator at 100 rpm and 25 °C, for 30 min.
3. Mix the compounds with the cells by vortexing.

4. Neutralization, serial dilution, and plating

1. Perform the neutralization of each biocide or phytochemical at each concentration tested using the dilution-neutralisation method and the universal neutralizer: 30 g/L of polysorbate 80, 30 g/L of saponin, 1 g/L of L-histidine, 3 g/L of lecithin, and 5 g/L of sodium thiosulphate in 0.0025 M phosphate buffer.
2. Add 1 mL of the sample being neutralized to 8 mL of universal neutralizer and 1 mL of ultrapure water⁵³.
3. Perform ten-fold serial dilution by adding 100 μL of a specific dilution to 900 μL of sterile saline solution to make the subsequent dilution (repeat this from the dilution 10^-1 to the dilution 10^-7), and determine the surviving cells on the neutralized samples by plating 10 μL of each dilution onto PCA, according to the drop plate method⁶².
4. Incubate the plates at 25 °C without agitation for 24 h for cell counts.
5. CFU were visually counted for plates containing more than 10 and less than 100 colonies, and the concentration at which a complete reduction in culturability occurs is recorded as the MBC. According to Equation (1), calculate the CFU/mL, where n is the number of CFU counted, SV is the plated sample volume (0.01 mL), and Dil is the dilution where the CFU were counted.
   CFU/mL = n/(SV Dil) (1)
6. Assess the logarithmic reduction, log reduction, according to Equation (2), where X₀ and X are the CFU/mL for the untreated bacteria (control) and treated bacteria, respectively.
   log reduction = log (X₀) - log (X) = log (X₀/X) (2)

5. Dual and triple combinations assessment

1. Apply the methodology described above to assess the activity of all the possible dual and triple combinations of one biocide and one phytochemical (for dual combinations) or two phytochemicals (for triple combinations) to search for possible potentiation/synergistic effects.
2. Consider the following choosing of concentrations: for the dual combinations, the concentrations tested for BAC were 0.1, 0.5, and 0.75 mg/L; for PAA were 0.1, 0.3, and 0.5 mg/L; for SAL were 50, 100, and 250 mg/L; and for EUG were 100, 250, and 750 mg/L (Table 1). Regarding the triple combination, the concentration tested for BAC was 0.75 mg/L; for PAA was 0.5 mg/L; for SAL were 100 and 250 mg/L; and for EUG were 250 and 750 mg/L (Table 1).
3. Select the concentrations, choosing the ones assessed on the dose-response assay for individual molecules that resulted in non-bactericidal effects.

6. Bacterial regrowth assessment

1. To assess bacterial regrowth ability, allow the microbial cells to grow in liquid medium for more 24 h, at 25 °C and 100 rpm, after treatment and neutralization.
2. Perform ten-fold serial dilution, plating, incubation of the PCA plates, and quantification of the CFU/mL using the same techniques and conditions.

7. Interpretation of combinatorial results – Fractional Bactericidal Concentration Index (FBCI)

1. Interpret the results from combinatorial analysis as follows:
   1. For dual combinations, FBCI = FBC(A) + FBC(B), where the fractional bactericidal concentration of A, FBC(A), is the ratio between the MBC of A in dual combination and the MBC of A alone (MBC_A ^combination/MBC_A ^alone), and FBC(B) is the ratio between the MBC of B in dual combination and the MBC of B alone (MBC_B ^combination/MBC_B ^alone) (Equation 3).
   2. For triple combinations, FBCI = FBC(A) + FBC(B) + FBC(C), where FBC(A) is the ratio between the MBC of A in triple combination and the MBC of A alone (MBC_A ^combination/MBC_A ^alone), FBC(B) is the ratio between the MBC of B in triple combination and the MBC of B alone (MBC_B ^combination/MBC_B ^alone), and FBC(C) is the ratio of the MBC of C in triple combination and the MBC of C alone (MBC_C ^combination/MBC_C ^alone) (Equation 4).
   3. Applying the classification proposed by Fernandes et al.⁵⁰, FBCI ≤ 0.5 corresponds to synergism, 0.5 < FBCI ≤ 1 indicates additivity, 1 < FBCI ≤ 4 is classified as indifference, and FBCI > 4.00 is considered antagonism.
      FBC(A) + FBC(B) = (MBC ^{dual combination}/MBC_A ^alone) + (MBC_B ^{dual combination}/MBC_B ^alone) (3)
      FBC(A) + FBC(B) + FBC(C) = (MBC_A ^{triple combination}/MBC_A ^alone) + (MBC_B ^{triple combination}/MBC_B ^alone) + (MBC_C ^{triple combination}/MBC_C ^alone) (4)

8. Procedure for time-response assay

1. Prepare the overnight cultures of E. coli CECT 434 and S. aureus CECT 976 according to the previously described growth conditions (section 1).
2. Add 1 mL of cell suspension to 1 mL of ultrapure water and wait 2 min. At this point, add 8 mL of biocidal/phytochemical solution (in ultrapure water or 5% DMSO) to reach the desired concentrations for each molecule: 1/4 x MBC and MBC, and for the bacterial suspension (by adjusting the OD), at 100 rpm and 25 °C. Mix the molecules with the cells by vortexing.
3. Evaluate the following exposure times: 1, 3, 5, 15, 30, 45, 60, and 90 min.
4. As described in section 4, evaluate the antimicrobial activity by calculating the log (CFU/mL) reduction as log (X/X₀).
5. Perform the biocide or phytochemical neutralization for each pair of concentration/time tested using the dilution-neutralization method and the universal neutralizer, the serial dilution and plating, as described before in section 4.

9. Analyzing combinatorial data with the software Combenefit

1. Open the provided file 'REPLICATE_TEMPLATE.xls', at https://sourceforge.net/projects/combenefit/files/ , to ensure the proper structure and formatting.
2. Organize your data into a matrix, according to the Supplemental Table 1, where:
   1. Increase the concentrations of product A from top to bottom (from the lowest to the highest numbered rows). Include at least three different concentrations for product A.
   2. Increase the concentrations of product B from left to right (from the lowest to the highest numbered columns): Include at least three different concentrations for product B.
   3. Represent the top row (0 mg/L product A) single-agent product B.
   4. Represent the first column (0 mg/L product B) single-agent product A.
   5. Represent the (0,0) cell as is the untreated control.
   6. Normalize, for each case, the values used, using Equation (5), where the treat_0h is the log (CFU/mL) for a specific treatment, immediately after exposure (0h); treat_24h is the log (CFU/mL) for a specific treatment, 24h after the exposure (regrowth); control_0h is the log (CFU/mL) for the corresponding control (only cells or cells exposed to 5% DMSO), immediately after exposure (0 h); and control_24h is the log (CFU/mL) for the corresponding control (only cells or cells exposed to 5% DMSO), 24h after the exposure (regrowth).
   7. Performe, in all cases, CFU enumeration after 24 h incubation of plated samples on PCA.
      (treat_0h - treat_24h)/(control_0h - control_24h) 100 (5)
      NOTE: Include controls/untreated conditions which define 100% viability and a positive control which defines maximum inhibition (optional); Use numeric values only, no formulas; Use the same number of rows and columns across replicates; Use '.' for decimals.
3. Save file as .xls or .xlsx.
4. Create an Excel file for each replicate, as described before and as in Supplemental Table 1, and add all the files in the same folder.
5. Open the software Combenefit (Cancer Research UK Cambridge Institute, Cambridge, version 2.021, available at https://sourceforge.net/projects/combenefit/).
6. Upload that folder to the Combenefit.
   1. Open the software Combenefit
   2. Press Select project folder and choose the correct folder.
   3. Selecting the reference model: Loewe additivity that assumes the exact mechanism for both products; Bliss independence that assumes independent action, or HAS, which is a conservative model (compares the combination to the best single agent). The best practice consists of running at least two models and choosing the best one.
   4. In this case, the Bliss model was selected, given the mode of action of the tested molecules.
   5. Select the desired graphical outputs, knowing you must select at least one more for the Synergy distribution.
   6. Press Run analysis (Supplemental Figure 1). Tip: In the Choose Save during analysis window, select Everything to save all resulting graphical outputs in the original folder.

10. Applying Chick-Watson and Weibull Models to dose- and time-response data

1. Prepare Your Data by formatting the dataset in Excel or CSV before importing: For dose-response, Column A corresponds to the dose (concentration, mg/L) and Column B corresponds to the log (X/X₀); For the time-response data, Column A corresponds to the time (minutes) and Column B corresponds to the log (X/X₀).
2. Save your file in `.csv` or copy-paste directly into Prism.
3. Open GraphPad Prism (version used: 9.0 for Windows 11; La Jolla, California, USA).
4. In New table and graph, select XY, and in Data table, select Enter or import data into a new table (Supplemental Figure 2).
5. In Options, for X, select Number, for Y select Enter replicate values in side-by-side subcolumns, and press Create (Supplemental Figure 2).
6. Enter dose/time data in X and log survival data in Y.
7. Go to Analyze, Nonlinear regression (curve fit), select the datasets you want to analyze, and press OK (Supplemental Figure 2).
8. Select New and then Create new equation.
9. In Equation, for Equation Type, choose Explicit Equation: Y = a function of X and parameters; for the Name, write Chick-Watson Model/Weibull Model; and in Definition write Y = -a*x^(n)*30 Chick-Watson Model (Supplemental Figure 3) and Y = -1/(2.303)*(x/a)^(b) for Weibull Model (Supplemental Figure 4).
   NOTE: The Chick-Watson model is expressed by Equation 6, where X₀ and X are the CFU/mL for the untreated bacteria (control) and treated bacteria, C is the concentration (dose), k_dis is the inactivation rate coefficient, C is the concentration (dose), and n is the concentration exponent, and t is the exposure time (in this case, 30 min)⁵⁰. The Chick-Watson model was applied to observe the dose-response and to characterize the relationship between disinfectant concentration and microbial reduction.
   log (X/X₀) = -k_dis · Cⁿ · t (6)
   NOTE: The Weibull model is expressed by Equation 7, where X₀ and X are the CFU/mL for the untreated bacteria (control) and treated bacteria, t is the time, α is the scale parameter (characteristic time), and β is the shape parameter⁵⁰. The Weibull model was applied to observe the time-response and to characterize the shape of survival curves. The scale parameter represents the characteristic time of inactivation, while the shape parameter describes deviations from log-linear behavior (β = 1 log-linear; β < 1 tailing; β > 1 shoulder).
   log (X/X₀) = -1/2.303 (t/α)^β (7)
10. In Roles for Initial Values, choose 0 as the Initial Value for every parameter and (Initial Value, to be fit) as the Rule (Supplemental Figure 5).
11. Keep the rest of the settings as the default and press OK.
12. Run the fit and inspect the parameters estimated.

11. Biological replication

1. Perform, for all the assays, at least two independent experiments with at least two replicates.

Results

This study evaluated the bactericidal activity of all four compounds tested — BAC, PAA, SAL, and EUG — against E. coli CECT 434 and S. aureus CECT 976. As shown in Figure 1 and Figure 2, BAC, PAA, SAL, and EUG exhibited an apparent concentration-dependent effect on culturability. Table 2 presents the MBC values for the tested molecules after exposure and 24h after exposure (regrowth capacity assessment). For BAC, no CFUs were o...

Discussion

The protocol presented in this study provides an integrated framework for assessing the bactericidal activity of two commonly used biocides – BAC and PAA – and two promising phytochemicals – EUG and SAL – individually and in combination (dual and triple combinations), against E. coli and S. aureus. Several steps within this protocol are particularly critical for ensuring reproducibility and accurate interpretation of antimicrobial interactions. These include the standardization of t...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Benzalkonium Chloride	Merck, Spain	8.14363.0100	Biocide
Centrifuged Mega Star 600R	VWR, USA	VWR Mega Star 600R	-
Combenefit - Version 2.021	Cancer Research UK Cambridge Institute, Cambridge, United kingdom	-	Software for synergism assessment
Cuvettes	VWR, Germany	VWRI634-0678	1.5 mL cuvettes for optical density adjustment
Destilatted water	-	-	For media preparition
Dimethyl Sulphoxide (DMSO)	VWR, France	23500.322	Organic solvent
Disposable inoculation loops	VWR, Italy	VWRI612-9357	10 μL inoculation loops
Eppendorf tubes	VWR, China	VWRI525-1164	1.5 mL eppendorf safe-lock tubes
Escherichia coli	Spanish Type Culture Collection (CECT)	434	Reference strain used in this study
Ethanol	VWR, France	83813.36	For desisfection of work plece, material, and hands
Eugenol 99%	Sigma-Aldrich, USA	163333	Phytochemical
Falcon tubes	VWR, USA	VWRI525-1085	15 mL falcon tubes
Falcon tubes	VWR, Belgium	VWRI525-1109	50 mL falcon tubes
GraphPad Prism 9.0 for Windows 11	La Jolla, California, USA	-	Software for mathematical and statistical analysis
Lecithin	Alfa Aesar, Germany	Q04A010	-
L-histidine	Merck, USA	1.04351.0025	-
Orbital Incubator	VWR, USA	VWR 5000I Incubating shaker 230V	-
Peracetic acid 15%	AppliChem, Germany	143495-1211	Biocide
Petri dishes	VWR, Italy	VWRI391-0582	-
Pipette 1 - 10 mL	VWR, USA		-
Pipette 100 - 1000 μL	VWR, USA		-
Pipette tips 100 - 1000 μL	Frilabo, Portugal	1-202-50-0	-
Pippette 20 - 200 μL	VWR, USA		-
Pippette tips 1 - 10 mL	VWR, Belgium	vwri613-7094	-
Pippette tips 2 - 200 μL	VWR, Mexico	vwri613-6418	-
Plate Count Agar	VWR, Belgium	84608-0500	Isolation and cultivation of microorganisms
Polysorbate 80	VWR, USA	20E2756726	-
Potassium Chloride - KCl	VWR, Belgium	11D110008	-
Potassium Dihydrogen Phosphate - KH2PO4	VWR, Germany	26931.263	-
Sacilicic acid	Sigma-Aldrich, USA	84210-500G	Phytochemical
Saponin	VWR, Germany	21A124103	-
Sodium Chloride - NaCl	VWR, Belgium	27788.297	-
Sodium Hydrogen Phosphate - Na2HPO4	VWR, Belgium	28026.26	-
Sodium Thiosulphate - Na2S2O3	VWR, USA	18D2056589	-
Spectrophotometer	VWR, USA	UV-1600PC Spectrophotometer	Optical density adjustment
Staphylococcus aureus	Spanish Type Culture Collection (CECT)	976	Reference strain used in this study
Tryptic Soy Broth	Merck, Germany	1.05459.0500	Isolation and cultivation of microorganisms
Ultrapure water	-	-	For sample preparation