Pharmacodynamic variability beyond that explained by MICs.

Area of Science:

• Pharmacology
• Microbiology
• Computational Biology

Background:

• Monte Carlo simulations (MCS) are vital for assessing drug regimen efficacy and target attainment probabilities.
• Traditional MCS models incorporate pharmacokinetic (PK) and minimum inhibitory concentration (MIC) variability but often neglect interstrain pharmacodynamic (PD) variability.
• Understanding PD variability is essential for accurate prediction of treatment outcomes.

Purpose of the Study:

• To develop a population PK/PD model for doripenem incorporating interstrain PD variability.
• To evaluate the impact of interstrain PD variability on the accuracy of MCS in predicting treatment success.
• To quantify the variability in doripenem's PD parameters (EC50, Emax, H) across different bacterial strains.

Main Methods:

• A population PK/PD model was constructed using murine thigh infection data from 20 bacterial strains.
• PK data were analyzed using a linear two-compartment model.
• PD was modeled using Hill-type equations, and simulations were performed to calculate %fT>MIC targets.

Main Results:

• The population PK/PD model accurately described doripenem's PD across strains (r(2) > 0.98).
• Substantive interstrain variability was observed in EC50 (CV% = 29.2%) and H (CV% = 46.1%), with modest variability in Emax (CV% = 19.7%).
• This variability resulted in a ~50% coefficient of variation in %fT>MIC targets required for stasis to a 2-log10 reduction in bacterial burden.

Conclusions:

• Interstrain PD variability significantly impacts MCS predictions of drug efficacy.
• MCS models that consider only MIC variability may underestimate treatment failures.
• Accurate assessment of drug regimens requires incorporating both PK and interstrain PD variability.