Time Kill Curve PD Modelling Experiments Are Affected by Trailing MIC Endpoints: Refinement of MIC Determination for S. pseudintermedius

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Abstract
Trailing endpoints are a recognised challenge in broth microdilution MIC testing, particularly for bacteriostatic agents such as trimethoprim (TMP) and sulphonamides. In this study, we applied a pharmacodynamically guided refinement to determine the MIC of the combination against Staphylococcus pseudintermedius ; we did not aim at redefining clinical susceptibility, but at refining MIC determination to better guide pharmacodynamic study design. By providing more reliable thresholds for growth suppression, this approach supports optimisation of PD modelling and may ultimately inform translational applications, such as dose prediction and reducing misclassification in PD contexts. Visual MICs were compared to those derived from log 10 changes in CFU/mL over 24 h, using pharmacodynamic thresholds of +2.3 log 10 (growth from standard inoculum of 5 × 10 5 to ~10 8 CFU/mL, corresponding to visible growth MIC) and 0 log 10 change (stationary concentration). Across 10 clinical isolates, visual MICs often underestimated the concentration required to suppress growth by 2–4 fold (more than one dilution step), particularly for sulphonamides. TMP‐sulphonamide combinations at a 1:19 ratio showed reduced trailing and closer agreement between visual and count‐based MICs, reflecting enhanced bactericidal activity. Time‐kill curve experiments anchored on the log 10 count‐based MIC provided a well‐distributed range of PD responses, capturing both suppression and killing more accurately than curves centred on visual MICs. This method supports more rational selection of concentrations for PD studies and may be especially valuable for slow‐acting or ratio‐sensitive combinations, and has translational value for sulphonamides, such as sulfamethoxazole, used in both human and veterinary medicine.
Funding Information
  • Medical Research Council (MR/X03173X/1)
  • Joint Programming Initiative on Antimicrobial Resistance (JPIAMR2022‐007)

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