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Establishing the Epidemiological Cut-Off Value (ECOFF) for Cefquinome Against Staphylococcus aureus Using Standardised MIC Data.

Ahmad I, Ahmed S, Faizan M, Nawaz SA, Naz S, Israr M, Khan RU, Perillo A, Alqahtani HA. Published July 1, 2026 CC-BY

Staphylococcus aureus is a major pathogen responsible for a wide range of infections in both animals and humans, and the increasing emergence of antimicrobial resistance highlights the need for effective surveillance tools. Cefquinome, a fourth-generation cephalosporin, is widely used in veterinary medicine for the treatment of infections caused by Gram-positive and Gram-negative bacteria, including S. aureus. In this study, 110 S. aureus strains were isolated from cattle and subjected to minimum inhibitory concentration (MIC) determination using standardised agar dilution and microdilution methods. MIC values were obtained following 24 h incubation in 96-well plates. The MIC distribution was analysed using goodness-of-fit testing and non-linear least squares regression to establish the wild-type cut-off value (COWT). The MIC range for cefquinome against S. aureus was 0.03-2 µg/mL, and the epidemiological cut-off value (ECV) was determined to be 2 µg/mL, encompassing 99.1% of the wild-type population. An MIC value of 0.5 µg/mL covered 95% of the distribution, indicating its potential relevance for resistance monitoring. These findings provide a scientific basis for cefquinome susceptibility interpretation in cattle and support antimicrobial resistance surveillance. Further studies are warranted to develop population pharmacokinetic models for cefquinome in calves to optimise its therapeutic application.

Introduction

Antimicrobial resistance (AMR) continues to escalate across major veterinary pathogens, posing a significant threat to animal health, food safety, and treatment efficacy. Increasing resistance has been documented inStaphylococcus aureus,Streptococcusspp., andEnterococcus faecalisisolated from dairy animals and livestock products (Nadi et al.2024; Dongjing et al.2024; Li et al.2025; Bessembayeva et al.2024). Similar trends have been reported in companion animals, wherePseudomonasspp. associated with otitis exhibit growing multidrug resistance (Mohammed et al.2024). Methicillin‐resistantS. aureus(MRSA) has also been frequently detected in bovine mastitis, highlighting the expanding range of resistant strains in cattle populations (Abd El‐Razik et al.2025).

In parallel, reports from diverse agricultural systems indicate increasing resistance in additional bacterial species, includingStreptococcusspp. from yaks (Li et al.2025) and multiple multidrug‐resistant pathogens associated with mastitis and wound infections (Ahsan et al.2024; Rasool et al.2024). Research focusing on pathogen isolation, MIC‐based susceptibility testing, and the evaluation of novel antimicrobial agents – such as selenium nanoparticles, silver oxide nanoparticles, plant extracts, and biologically derived compounds – continues to demonstrate the complexity of resistance mechanisms in veterinary pathogens (Ibrahim et al.2024; Insumran et al.2024; Kunu et al.2025; Li et al.2025; Ahsan et al.2024). Beneficial microbes also show promise as alternative control options, as evidenced by the antagonistic activity ofBacillusspp. andStreptomycesmetabolites against pathogenic bacteria (Fitriadi et al.2024; Ryandini et al.2024).

The ability of pathogens to form biofilms further complicates treatment outcomes. Biofilm formation byS. aureusandSalmonella typhimuriumisolated from poultry houses contributes to enhanced persistence and antimicrobial tolerance (Laban et al.2025). Similarly,Staphylococcusisolates from bovine mastitis exhibit high biofilm‐forming ability (Bessembayeva et al.2024), while emerging evidence shows biofilm‐associated resistance in other veterinary pathogens such as uropathogenicE. coli(Gizinger et al.2024). Innovative approaches, including bacteriophage‐based strategies, have shown potential for disrupting biofilms and limiting pathogen survival (Kinanti et al.2024).

Given the rising prevalence of AMR and biofilm‐associated tolerance, the establishment of standardised antimicrobial susceptibility criteria is critical for improving therapeutic decision‐making.Staphylococcus aureus, a major pathogen causing mastitis in cattle, also poses risks to human health, being implicated in bacteremia, skin infections, soft tissue infections, and endocarditis (Park and Ronholm2021; Tong et al.2015; van der Vaart et al.2022). Antimicrobial susceptibility testing plays an essential role in determining treatment effectiveness (Zaffiri et al.2012). However, for many veterinary pathogens – includingS. aureusin cattle – susceptibility breakpoints remain insufficiently defined (Rey et al.2014; Kahlmeter and Turnidge2023).

Breakpoint determination relies on key scientific components: MIC distributions, resistant strain selection, PK/PD relationships, and characterisation of wild‐type isolates. Statistical tools proposed by Meletiadis et al. (2012) and Kronvall (2010) are commonly applied, and breakpoints are categorised into wild‐type, clinical, and PK/PD types (Turnidge and Paterson2007). When dosage regimen data are lacking, Monte Carlo simulations incorporating PK/PD indices and MIC distributions can be used to predict optimal clinical breakpoints (CLSI2009; Aarestrup and Skov2010). Although plasma drug levels are frequently employed for PK/PD modelling, drug concentrations at the infection site may more accurately reflect therapeutic outcomes (Barbour et al.2010).

The objective of this study was to determine the minimum inhibitory concentrations (MICs) of cefquinome against 110Staphylococcus aureusstrains isolated from cattle and, based on these data, to establish the epidemiological cut‐off values (ECOFFs) through detailed MIC distribution analysis and identification of the wild‐type population. By generating these foundational parameters, the study aims to support the development of standardised antimicrobial susceptibility breakpoints for cefquinome, thereby improving the accuracy and reliability of therapeutic decision‐making in veterinary medicine.

Materials and Methods

A total of 111 clinicalStaphylococcus aureusisolates were included in the present study. The isolates were obtained from cattle (Bos taurus) presenting clinical infections, and were recovered from milk samples collected from animals showing signs of mastitis at commercial dairy farms located in Peshawar, Pakistan during 2023. Following isolation, all strains were preserved in glycerol stocks at −70°C until further analysis. Prior to each experiment, the isolates were freshly subcultured on Chrom agar and Mueller–Hinton (MH) agar and incubated at 37°C for 24 h to ensure optimal growth and purity. Molecular confirmation of all isolates was performed using polymerase chain reaction (PCR) targeting theS. aureus‐specific nuc gene, as previously described by Karimzadeh and Ghassab (2022). The primers sequences are given below:Nuc F GCGATTGATGGTGATACGGTT 3Nuc R AGCCAAGCCTTGACGAACTAAAGC 3

Growth Curve Determination

Before MIC determination, the growth curves were checked in MH broth by counting the bacteria and also by spectrophotometer at wave length of 600. We also checked the growth stages of bacteria in both the shaker and incubator. From this study the different growth stages of bacteria were confirmed. The spectrophotometer and counting method were used. The samples were taken over 24 h to check the OD values and bacterial count (CFU/mL) from both samples, which were placed in a shaker incubator and a static incubator. This will helpful for MIC determination.

Minimum Inhibitory Concentration

Minimum inhibitory concentration was performed using the agar dilution method according to the CLSI M07‐A9 standard. A single bacterial colony was cultured in broth at 37°C, and the concentration was adjusted using a spectrophotometer to achieve a McFarland standard of 0.5. Bacterial cells in the logarithmic or lag phase were used for minimum inhibitory concentration (MIC) determination.

After inoculation, the agar plates were incubated at 37°C for 24 h. A 96‐well plate was prepared with a serial dilution of drug concentrations ranging from 0.01 µg/mL to 8 µg/mL. To ensure the reliability of the MIC results,Staphylococcus aureus(ATCC 12598) was used as quality control (QC) strain.

Definition of WT Distribution

The WT distribution included isolates without any acquired genotypic resistance mechanisms in a bacterium‐antibiotic combination (Turnidge et al.2006; Kahlmeter and Turnidge2023). And the COWTor ECV was the higher cut‐off of the distribution. Ideally, at least 95% WT isolates should be encompassed in the ECV.

2.3.1 ECV

The ECV was calculated by following Turnidge method described (Turnidge et al.2006). The WT distribution was conducted by normality test with sigma stat software v.3.5. Furthermore, to fit log2‐transformed MICs, nonlinear regression was used with Graphpad prism v.5.01. Finally, a WT distribution cut‐off was set by employed NORDIST and NORMINV functions.

Results

Confirmation of Bacteria

All 110 isolates ofStaphylococcus aureuswere confirmed using a combination of selective culture media, CHROMagar, and PCR. Initially, isolates were grown on selective media, where they exhibited characteristic colony morphology indicative ofS. aureus. Further confirmation was performed using CHROMagar, where the isolates produced the expected colour reactions, reinforcing their identification.

To ensure molecular confirmation, PCR analysis was conducted usingnucgene specific primers, targeting specificS. aureus nucgenes. Gel electrophoresis results demonstrated the presence of the expected amplicon size of around 279 bp in all selected representative isolates (not shown). This multi‐step confirmation approach validated the identity ofS. aureusisolates with high accuracy.

Growth Curve ofStaphylococcus aureus

The growth curve ofStaphylococcus aureuswas analysed in Mueller–Hinton (MH) broth using both bacterial counting and spectrophotometric measurements at a wavelength of 600 nm. Growth stages were monitored under two different conditions: in a shaker and a static incubator, as shown in Table1.

Table: Growth curve ofStaphylococcus aureusunder static and shaker incubator conditions measured by optical density (OD600) and colony‐forming units (CFU/mL).

The study successfully confirmed the different growth phases ofS. aureus, including lag, exponential, stationary, and decline phases. The combination of spectrophotometric readings and viable cell counts provided a reliable assessment of bacterial growth dynamics. This information is crucial for determining the optimal bacterial phase for minimum inhibitory concentration (MIC) testing. The bacterial concentrations were more than 106in shaker even after 2 h in shaker incubator; however the concentration of bacteria reached to 106within 3–4 h in static incubation. Meanwhile the OD value were checked and compared with counting method. The bacterial concentration at 106showed the OD value of 0.3.

Minimum Inhibitory Concentration

The minimum inhibitory concentration (MIC) for the reference strainStaphylococcus aureus(ATCC 29213) was determined to be 0.25 µg/mL. This value falls within the acceptable quality control (QC) range as recommended by the CLSI M31‐A3 guidelines, confirming the accuracy and reliability of the MIC testing method. The MIC values of different strains ofStaphylococcus auerusare shown in Table2. The inoculum preparation for MIC in broth culture was used. Inoculums density was further used by checking through spectrophotometer or Mc Farland value or 0.5. Usually 0.5 Mc Farland turbidity standard equal to colony count of 1–2 × 108CFU/mL, which will further 100‐fold diluted to make it 106CFU/mL, were used for MIC determination by micro dilution method.

Table: MIC distribution ofStaphylococcus aureusstrains.

WT MIC Distribution and ECV

The distribution of wild type MIC for cefquinome againstS. aureusranged from 0.03 to 2 µg/mL. The cumulative log2MIC results validated normal distribution which was confirmed through normality test (p= 0.200), even though there was continuous decrease of MIC at 0.5 µg/mL. Non‐linear regression as shown in Table3was used to obtain the optimum MIC range which is around 0.06–2 µg/mL. We used NORMINV function as shown in Table4, to further adjust the optimum MIC range to 0.03–2 µg/mL. Similarly, NORMDIST function was used to calculate the probability of the higher and lower than high cut‐off value (0.18%) and low cut‐off value (0.07%), respectively. Therefore, as shown in Table4, the ECV was expressed as 2 µg/mL and incorporated 99.1% of the wild type isolates.

Table: Optimal non‐linear least squares regression for pooled MICs (µg/mL) data.

Table: The WT MIC range and ECV for cefquinome andStaphylococcus aureus.

Discussion

In this study, we determined the minimum inhibitory concentration (MIC) ofStaphylococcus aureusstrains isolated from cattle. Antimicrobial susceptibility patterns can vary across different regions and may be influenced by factors such as sampling time, environmental conditions, and local antimicrobial usage (Espinel‐Ingroff et al.2010; Smith et al.2024). Broader microbial responses to changing environmental and management conditions have also been reported to influence pathogen dynamics and resistance trends in livestock systems (Anuoluwa et al.2024). A total of 110S. aureusstrains were collected and analysed to assess their growth characteristics, species confirmation, and antimicrobial susceptibility, with a specific focus on establishing the epidemiological cut‐off value (ECV) for cefquinome.

Cefquinome is a fourth‐generation cephalosporin used for the treatment of severe bacterial infections, particularly when susceptibility testing indicates resistance to other antimicrobials (Ahmad et al.2015). Monitoring resistance to critically important antimicrobials remains essential for safeguarding their therapeutic efficacy in veterinary medicine, particularly in the context of livestock disease transmission (Sarwar et al.2025). The present study provides a comprehensive evaluation of the MIC distribution of cefquinome againstS. aureus, contributing to a better understanding of resistance monitoring and epidemiological trends.

The growth curve analysis ofS. aureusin Mueller–Hinton (MH) broth, assessed using both bacterial counting and spectrophotometric measurement at 600 nm, successfully delineated different bacterial growth stages under both shaking and static incubation conditions. Similar approaches integrating morphological, biochemical, and growth kinetics analyses have been shown to be reliable for characterising bacterial growth behaviour under controlled laboratory conditions (Guo et al.2024; Xue et al.2024)

The consistency between these methods confirms their reliability in assessing bacterial growth dynamics, which is crucial for optimising MIC determination. Standardising growth conditions is essential for ensuring reproducibility in antimicrobial susceptibility testing.

Molecular confirmation of all 110S. aureusisolates through PCR further validated the bacterial identity, ensuring that onlyS. aureusisolates were included in the MIC determination. Molecular epidemiological approaches have been widely applied to enhance the accuracy of pathogen identification and resistance surveillance in bacterial populations (Dolhan et al.2010), as also demonstrated in recent molecular epidemiology studies addressing infectious agents in animal and human health contexts (Ben Hadj Hassine et al.2024).

The MIC determination for the reference strainS. aureusATCC 29213 yielded a value of 0.25 µg/mL, which falls within the quality control (QC) ranges recommended by the CLSI M31‐A3 guidelines, confirming the reliability of the MIC testing protocol used in this study. The wild‐type (WT) MIC distribution for cefquinome againstS. aureusranged from 0.03 to 2 µg/mL, with a decline observed at an MIC of 0.5 µg/mL. The cumulative log2 MIC data followed a well‐defined normal distribution, as confirmed by the normality test (p= 0.200). Identifying this distribution pattern is critical for establishing ECVs, as it helps distinguish wild‐type bacterial populations from potentially resistant subpopulations (Turnidge et al.2006; Feßler et al.2023).

Using non‐linear regression, the optimal MIC range was initially determined as 0.06–2 µg/mL and was subsequently corrected to 0.03–2 µg/mL using the NORMINV function. Probability calculations performed using the NORMDIST function indicated that the probability of values exceeding the high cut‐off was 0.18%, while the probability of values below the low cut‐off was 0.07%. Based on these findings, the epidemiological cut‐off value (ECV) for cefquinome againstS. aureuswas established at 2 µg/mL, encompassing 99.1% of the wild‐type isolates.

The determination of an ECV is essential for distinguishing susceptible bacterial populations from those potentially harbouring acquired resistance mechanisms (Kahlmeter and Turnidge2023; Gunnar Kahlmeter and John Turnidge2024; Woo et al.2022). The established ECV of 2 µg/mL serves as a valuable reference for future surveillance studies and antimicrobial resistance detection efforts. The well‐defined normal MIC distribution suggests minimal influence of acquired resistance within the studied population. However, continuous surveillance is recommended to monitor potential shifts in MIC distributions over time, which could indicate emerging resistance trends, particularly in the context of evolving livestock disease ecology (Sarwar et al.2025).

Conclusion

In conclusion, this study provides a robust epidemiological framework for assessing cefquinome susceptibility inS. aureus. The integration of growth curve analysis, molecular confirmation, and statistical modelling ensures the reliability of the established ECV. These findings are expected to support antimicrobial stewardship efforts by improving resistance monitoring and guiding clinical decision‐making regarding cefquinome use againstS. aureusinfections.

Author Contributions

Shabana Naz: writing – review and editing, writing – original draft.Muhammad Faizan: data curation.Muhammad Israr: funding acquisition.I:Shahab Ahmad Nawaz: software.Ijaz Ahmad: conceptualisation, methodology, validation, visualisation, software, investigation.Shakoor Ahmed: writing – original draft.Antonella Perillo: writing – review and editing.Rifat Ullah Khan: formal analysis.Muhammad Israr: author.Huda A. Alqahtani: Funding acquisition; Analysis

Ethics Statement

The study was approved by the Ethical Committee of Faculty of Animal Husbandry & Veterinary Sciences, The University of Agriculture, Peshawar, Pakistan (Approval No. 12/FAH&VS/2021

Conflicts of Interest

No potential conflict of interest was reported by authors

AI

Artificial intelligence (ChatGPT 5) has partially been used for English language.

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Republished from the open web under CC-BY. Authors: Ahmad I, Ahmed S, Faizan M, Nawaz SA, Naz S, Israr M, Khan RU, Perillo A, Alqahtani HA. Read the original.

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