Pharmacodynamic variability beyond that explained by MICs

Abstract

Monte Carlo simulations (MCS) present a powerful tool to evaluate candidate regimens by determining the probability of target attainment. Although these assessments have traditionally incorporated variability in pharmacokinetic (PK) parameters and MICs, consideration of interstrain pharmacodynamic (PD) variability has been neglected. A population PK/PD model was developed for doripenem using murine thigh infection data based on 20 bacterial strains. PK data were fit to a linear two-compartment model with first-order input and elimination processes and an absorption lag time from a separate site (r(2) > 0.96). PK parameters were utilized to simulate free-drug profiles for various regimens in PD studies, from which the percentage of the dosing interval for which free-drug concentrations exceed the MIC of the targeted strain (%fT>MIC) was calculated. Doripenem PD was excellently described with Hill-type models (r(2) > 0.98); significant differences between mean PD estimates determined using a two-stage approach versus population analyses were not observed (P > 0.05); however, the variance in 50% effective concentration (EC50) and maximum effect (Emax) among strains was much greater using the two-stage approach. Even using the population approach, interstrain variability in EC50 (coefficient of variation expressed as a percentage [CV%] = 29.2%) and H (CV% = 46.1%) parameters was substantive, while the variability in Emax (CV% = 19.7%) was modest. This resulted in extensive variability in the range of %fT>MIC targets associated with stasis to those associated with a 2-log10 reduction in bacterial burden (CV% ∼ 50%). It appears that MCS, based on the assumption that PD variability is due to MIC alone, underestimates variability and may consequently underestimate treatment failures.

Fig 1

Two-compartment model used to model the PK characteristics of doripenem in mice. X(c), X(p), and X(a) represent the amounts of drug (mg) in the central, peripheral, and absorptive sites, respectively; k_a is the first-order absorption rate constant (h⁻¹); T(lag) is the absorption lag time (h); Vc and Vp are the volumes of distribution of the central and peripheral compartments, respectively (liters/kg); Cl_d is the distributional clearance between the central and peripheral compartments (liters/h/kg); and Cl_t is the total clearance from the central compartment (liters/h/kg).

Fig 2

Plot of the observed doripenem concentrations (individual symbols) versus the derived PK model following administration of 150 (solid line), 37.5 (dashed line), or 9.38 (dot-dashed line) mg/kg in mice (r² = 0.977, 0.961, and 0.968, respectively).

Fig 3

Plots of the separate Hill models derived using population analyses for each of the 20 bacterial strains (lines) overlaid on the raw data points (individual symbols), illustrating the variability between bacterial species. Different colors correspond to the six different bacterial species included in the present investigation, namely, E. cloacae (black), E. coli (orange), K. pneumoniae (blue), P. aeruginosa (purple), S. aureus (red), and S. pneumoniae (green).