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. 2026 Jun 19;12(4):e70995. doi: 10.1002/vms3.70995

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

Ijaz Ahmad 1, Shakoor Ahmed 1, Muhammad Faizan 1, Shahab Ahmad Nawaz 1, Shabana Naz 2,✉, Muhammad Israr 3, Rifat Ullah Khan 1,✉, Antonella Perillo 4, Huda A Alqahtani 5
PMCID: PMC13281152  PMID: 42319167

ABSTRACT

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.

Keywords: COWT and COPD , Staphylococcus aureus, susceptibility breakpoints

A total of 110 Staphylococcus aureus isolates from cattle were analysed to determine the epidemiological cut‐off value (ECOFF) for cefquinome. Minimum inhibitory concentrations (MICs) were determined using standardised agar dilution and broth microdilution methods following 24 h incubation. MIC distributions were evaluated using goodness‐of‐fit analysis and non‐linear least squares regression to define the wild‐type population. The MIC values ranged from 0.03 to 2 µg/mL, with 99.1% of isolates classified as wild type at an ECOFF of 2 µg/mL. An MIC of 0.5 µg/mL encompassed 95% of the population, indicating its relevance for resistance monitoring. These findings support standardised susceptibility interpretation of cefquinome in cattle and provide a framework for antimicrobial resistance surveillance and future PK/PD optimisation.

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1. 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 in Staphylococcus aureus, Streptococcus spp., and Enterococcus faecalis isolated 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, where Pseudomonas spp. associated with otitis exhibit growing multidrug resistance (Mohammed et al. 2024). Methicillin‐resistant S. 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, including Streptococcus spp. 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 of Bacillus spp. and Streptomyces metabolites against pathogenic bacteria (Fitriadi et al. 2024; Ryandini et al. 2024).

The ability of pathogens to form biofilms further complicates treatment outcomes. Biofilm formation by S. aureus and Salmonella typhimurium isolated from poultry houses contributes to enhanced persistence and antimicrobial tolerance (Laban et al. 2025). Similarly, Staphylococcus isolates 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 uropathogenic E. 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 Ronholm 2021; 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 – including S. aureus in cattle – susceptibility breakpoints remain insufficiently defined (Rey et al. 2014; Kahlmeter and Turnidge 2023).

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 Paterson 2007). When dosage regimen data are lacking, Monte Carlo simulations incorporating PK/PD indices and MIC distributions can be used to predict optimal clinical breakpoints (CLSI 2009; Aarestrup and Skov 2010). 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 110 Staphylococcus aureus strains 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.

2. Materials and Methods

A total of 111 clinical Staphylococcus aureus isolates 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 the S. aureus‐specific nuc gene, as previously described by Karimzadeh and Ghassab (2022). The primers sequences are given below:

  • Nuc F GCGATTGATGGTGATACGGTT 3

  • Nuc R AGCCAAGCCTTGACGAACTAAAGC 3

2.1. 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.

2.2. 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.

2.3. 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 Turnidge 2023). And the COWT or 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.

3. Results

3.1. Confirmation of Bacteria

All 110 isolates of Staphylococcus aureus were 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 of S. 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 using nuc gene specific primers, targeting specific S. aureus nuc genes. 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 of S. aureus isolates with high accuracy.

3.2. Growth Curve of Staphylococcus aureus

The growth curve of Staphylococcus aureus was 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 Table 1.

TABLE 1.

Growth curve of Staphylococcus aureus under static and shaker incubator conditions measured by optical density (OD600) and colony‐forming units (CFU/mL).

A. Optical density (OD600)
Time (h) Static incubator Shaker incubator
5 0.10 0.20
10 0.25 1.20
12 0.30 1.50
15 0.35 1.70
24 0.50 1.80
B. Colony forming units (CFU/mL)
5 1.0 × 105 5.0 × 105
10 5.0 × 106 5.0 × 107
12 1.0 × 107 8.0 × 107
15 1.0 × 107 1.0 × 108
24 1.0 × 107 1.0 × 108
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The study successfully confirmed the different growth phases of S. 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 106 in shaker even after 2 h in shaker incubator; however the concentration of bacteria reached to 106 within 3–4 h in static incubation. Meanwhile the OD value were checked and compared with counting method. The bacterial concentration at 106 showed the OD value of 0.3.

3.3. Minimum Inhibitory Concentration

The minimum inhibitory concentration (MIC) for the reference strain Staphylococcus 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 of Staphylococcus auerus are shown in Table 2. 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 × 108 CFU/mL, which will further 100‐fold diluted to make it 106 CFU/mL, were used for MIC determination by micro dilution method.

TABLE 2.

MIC distribution of Staphylococcus aureus strains.

MIC (µg/mL) Bacterial strains
0.015 0
0.03125 1
0.0625 3
0.125 6
0.25 30
0.5 63
1 6
2 1
4 0
8 0
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3.4. WT MIC Distribution and ECV

The distribution of wild type MIC for cefquinome against S. aureus ranged from 0.03 to 2 µg/mL. The cumulative log2 MIC 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 Table 3 was used to obtain the optimum MIC range which is around 0.06–2 µg/mL. We used NORMINV function as shown in Table 4, 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 Table 4, the ECV was expressed as 2 µg/mL and incorporated 99.1% of the wild type isolates.

TABLE 3.

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

Subset fitted Number of isolates Mean MIC (log2) Standard deviation (log2)
True Est. Diff. ASE Est./ASE 95% CI a Est. ASE Est./ASE 95% CI Est. ASE Est./ASE 95% CI a
≦0.5 103 462 359 371.9 1.2 −1139, 2062 0.257 1.297 0.2 −5.323, 5.837 1.651 0.403 4.1 −0.000, 3.385
≦1 109 111 2 5.318 20.9 94, 128 −1.812 0.090 −20.1 −2.098, −1.527 0.625 0.128 4.9 0.217, 1.034
≦2 110 111 1 3.206 34.6 102, 119 −1.817 0.069 −26.3 −2.008, −1.625 0.617 0.102 6.0 0.334, 0.901
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Note: Est., non‐linear regression estimate of value; Diff., estimate of N minus true N; ASE, asymptotic standard error; Est./ASE, estimate divided by asymptotic standard error.

a

95% CI of estimate of value.

TABLE 4.

The WT MIC range and ECV for cefquinome and Staphylococcus aureus.

MC range (µg/mL) Optimum MIC range (µg/mL) ECV (µg/mL) ECV (%)
0.03–2 0.06–2 2 99.1
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4. Discussion

In this study, we determined the minimum inhibitory concentration (MIC) of Staphylococcus aureus strains 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 110 S. aureus strains 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 against S. aureus, contributing to a better understanding of resistance monitoring and epidemiological trends.

The growth curve analysis of S. aureus in 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 110 S. aureus isolates through PCR further validated the bacterial identity, ensuring that only S. aureus isolates 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 strain S. aureus ATCC 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 against S. aureus ranged 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 against S. aureus was 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 Turnidge 2023; Gunnar Kahlmeter and John Turnidge 2024; 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).

5. Conclusion

In conclusion, this study provides a robust epidemiological framework for assessing cefquinome susceptibility in S. 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 against S. aureus infections.

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.

Acknowledgement

This research was funded by Ongoing Research Funding (ORF‐2026‐1450), King Saud University, Riyadh, Saudi Arabia

Contributor Information

Shabana Naz, Email: drshabanananz@gcuf.edu.pk.

Rifat Ullah Khan, Email: rukhan@aup.edu.pk.

Data Availability Statement

The relevant data are provided in the paper. The data of the current experiment can be obtained from corresponding author when needed.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The relevant data are provided in the paper. The data of the current experiment can be obtained from corresponding author when needed.

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