Uncertainty undermines the validity of antimicrobial pharmacodynamics

Antimicrobial therapy is informed by quantitative models of drug disposition and action. These models utilize experimental and observational evidence, subject to uncertainties, to support drug selection and dosage regimen optimization, and interpret antimicrobial resistance data. The framework includes multiple components, which characterize mechanisms contributing to therapeutic outcome. The components must be combined in a logical sequence to generate predictions, so propagation of uncertainty is a critical consideration. Quantitative evaluation of this uncertainty has received apparently little attention. This essay argues for the importance of uncertainty quantification in antimicrobial pharmacology. The impact of parameter uncertainties and measurement errors on the validity of pharmacokinetic-pharmacodynamic modelling of antimicrobials is described. Major components of the modelling workflow are assessed, and uncertainties characterized. The influence of major design and statistical analysis decisions at each step is emphasized. Finally, using detailed simulations, the impact of these sources of uncertainty on outcomes including clinical breakpoints and dose individualization is illustrated. Measurement of antimicrobial potency as the minimum inhibitory concentration contributes approximately twofold error, which is important for individual dose determination. Interpretation of PK/PD parameters is generally conducted dichotomously as thresholds, which are empirically determined, and subject to error. Parameter uncertainties in the exposure-response relationship are potentially substantial, and contribute apparently major uncertainty to predictions at both population and individual levels. The importance of uncertainty in pharmacokinetics appears context-sensitive. Applications including dose optimization or susceptibility breakpoints appear overly confident, and point estimation from these models may be an unreliable basis for decision making. These observations highlight the importance of uncertainty quantification for rigorous antimicrobial pharmacology.