Probabilistic MIC modelling for AMR risk assessment

This EU-FORA fellowship project developed a more realistic assessment of antimicrobial resistance in the food chain by integrating probabilistic minimum inhibitory concentration (MIC) modelling into a quantitative microbiological risk assessment (QMRA). Focusing on Listeria monocytogenes in ready-to-eat cooked ham, the study moved beyond traditional deterministic MIC values by explicitly accounting for cell-to-cell variability in antimicrobial response. Using laboratory-based ampicillin MIC measurements, growth modelling, and Monte Carlo simulations implemented in R, the QMRA generated 1,000 simulated consumer doses at the point of consumption, each linked to distributions of single-cell MIC values (including maximum and 95th percentiles). The results, supported by sensitivity analysis, demonstrate that incorporating probabilistic MICs yields a more nuanced and realistic estimation of AMR-related risks associated with foodborne pathogens.