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Probabilistic MIC modelling for AMR risk assessment.

Alba Puente Baños1, Constantine-Richard Stefanou2, Leonardos Stathas2

  • 1Department of Food Hygiene and Technology Universidad de León León Spain.

EFSA Journal. European Food Safety Authority
|December 17, 2025
PubMed
Summary
This summary is machine-generated.

Probabilistic modeling of antibiotic resistance in foodborne pathogens offers a more accurate risk assessment than traditional methods. This approach accounts for individual bacterial cell variability, improving strategies against antimicrobial resistance (AMR) in the food chain.

Keywords:
AMRListeria monocytogenesQMRARTEcooked hamscMIC

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Area of Science:

  • Food safety and microbiology
  • Quantitative risk assessment
  • Antimicrobial resistance (AMR)

Background:

  • Traditional minimum inhibitory concentration (MIC) testing lacks accuracy due to inter-individual bacterial variability.
  • Antimicrobial resistance (AMR) in foodborne pathogens poses a significant threat to public health.
  • Probabilistic MIC modeling offers a more realistic approach to understanding AMR dynamics.

Purpose of the Study:

  • To develop a probabilistic quantitative microbiological risk assessment (QMRA) for an antimicrobial-resistant foodborne pathogen.
  • To incorporate the variability of individual bacterial cell MICs into the QMRA.
  • To enhance AMR risk estimation in food products.

Main Methods:

  • Collected data on Listeria monocytogenes in ready-to-eat (RTE) cooked ham.
  • Conducted MIC assessment experiments for ampicillin using the plate-count agar method.
  • Employed Monte Carlo analysis and the R programming language to construct the QMRA model.

Main Results:

  • Generated 1000 simulated doses of L. monocytogenes in cooked ham servings.
  • Determined the maximum and 95th percentile of single-cell MIC values for ampicillin per simulated dose.
  • Performed a sensitivity analysis to evaluate model robustness.

Conclusions:

  • Probabilistic MIC modeling provides a more accurate QMRA for AMR in foodborne pathogens.
  • This approach can inform targeted strategies to combat AMR in the food chain.
  • The study highlights the importance of considering bacterial variability in risk assessments.