Moxifloxacin dosing in post-bariatric surgery patients

Abstract

Introduction: Given the ever increasing number of obese patients and obesity related bypass surgery, dosing recommendations in the post-bypass population are needed. Using a population pharmacokinetic (PK) analysis and PK-pharmacodynamic (PD) simulations, we investigated whether adequate moxifloxacin concentrations are achieved in this population.

Methods: In this modelling and simulation study we used data from a trial on moxifloxacin PK. In this trial, volunteers who had previously undergone bariatric surgery (at least 6 months prior to inclusion), received two doses (intravenous and oral) of 400 mg moxifloxacin administered on two occasions.

Results: In contrast to other papers, we found that moxifloxacin PK were best described by a three compartmental model using lean body mass (LBM) as a predictor for moxifloxacin clearance. Furthermore, we showed that the probability of target attainment for bacterial eradication against a hypothetical Streptococcus pneumoniae infection is compromised in patients with higher LBM, especially when targeting microorganisms with minimum inhibitory concentrations (MICs) of 0.5 mg l(-1) or higher (probability of target attainment (PTA) approaching zero). When considering the targets for suppression of bacterial resistance formation, even at MIC values as low as 0.25 mg l(-1) , standard moxifloxacin dosing does not attain adequate levels in this population. Furthermore, for patients with a LBM of 78 kg or higher, the probability of hitting this target approaches zero.

Conclusions: Throughout our PK-PD simulation study, it became apparent that, whenever optimal bacterial resistance suppression is deemed necessary, the standard moxifloxacin dosing will not be sufficient. Furthermore, our study emphasizes the need for a LBM based individualized dosing of moxifloxacin in this patient population.

Keywords: NONMEM; PK-PD; bariatric surgery; moxifloxacin; pharmacokinetics.

Figure 1

Observed moxifloxacin plasma concentration–time profiles after oral dosing (A) and after a 1 h i.v. infusion (B) The LLOQ of the assay is depicted by the dashed line

Figure 2

Overview of the structural model building: V_c: volume of distribution for the central compartment; CL: vlearance from central compartment; V_p and V_p2: volume of distribution for the first and second peripheral compartment; Q_p and Q_p2: inter-compartmental clearance from the central compartment to the first and second peripheral compartment; θ_lag: absorption lag time; θ_MTT: mean transit time; θ_n: number of transit compartments; σ_Add: residual error variance explained by the additive error term

Figure 3

Goodness of fit plots for our final PK model, insets show the first 3 h post-dosing. The black line represents a LOESS smoother

Figure 4

Observed moxifloxacin plasma concentrations after a standard 400 mg oral dose (open circles). The model simulated median moxifloxacin plasma concentration (solid line) along with the 5% and 95% percentile (dashed lines) from the simulations are shown to assess the model's goodness of fit

Figure 5

Visual predictive check comparing the distribution of the 100 model simulated AUC_72h values (histogram on top) against the observed AUC_72h values (open circles) after a standard 400 mg oral dose of moxifloxacin

Figure 6

AUIC values calculated as the ratio of the model simulated moxifloxacin AUC_24h values after a standard 400 mg oral dose vs. the theoretical Streptococcus pneumoniae MIC values. Simultaneously the AUIC cut-off values for bacterial eradication as well as suppression of bacterial resistance formation, as proposed in the literature, are shown. (long dashes: AUIC = 100; short dashed lines: AUIC = 200)

Figure 7

AUIC values calculated as the ratio of the model simulated moxifloxacin AUC_24h values for subjects with a LBM of 42 kg (open squares) and 78 kg (solid squares) after a standard 400 mg oral dose vs. the theoretical Streptococcus pneumoniae MIC values. Simultaneously the AUIC cut-off values for bacterial eradication as well as suppression of bacterial resistance formation, as proposed in the literature, are shown. (long dashes: AUIC = 100; short dashed lines: AUIC = 200) Overall, AUIC values for the high LBM group are 40.5% lower than those for the low LBM group