Joint modeling of moxifloxacin pharmacokinetics and fecal microbiota disruption in healthy volunteers
Charles Burdet
INSERM & Paris Diderot University, UMR 1137, Paris, France; AP-HP, Bichat Hospital, Paris, France
Objectives: Metagenomic analysis provides a detailed picture of the intestinal microbiota. Among the various factors that shape the microbiota, antibiotic administration has a major impact in disrupting its composition [1]. Animal data suggest that the impact of antimicrobials can be predicted from fecal antibiotic exposure [2]. We developed a joint model of plasma and fecal pharmacokinetics (PK) of moxifloxacin (MOX), a fluoroquinolone antibiotic, after oral administration in humans, and of MOX effect on bacterial richness observed within the intestinal microbiota.
Methods: Twenty two healthy volunteers were included in a randomized clinical trial (sponsor Da Volterra) among which 14 received MOX (400 mg orally OAD) from D1 to D5. MOX plasma concentrations were assayed at D1 and D5. Fecal samples were obtained before treatment and up to D37 for measurements of free MOX concentrations and microbiota analysis by 16S rRNA gene profiling. Bacterial richness was evaluated using the number of operational taxonomic units (OTUs). Nonlinear mixed-effects modeling was used to analyze the plasma PK of MOX and its fecal excretion, and to evaluate the effect of fecal concentration on bacterial richness. Analysis was performed using the SAEM algorithm in the Monolix software (Lixoft, France) [3]. Model selection was performed by visual inspection of goodness of fit plots and the Bayesian Information Criteria.
Results: MOX plasma concentrations were best described by a 2-compartments model with transit compartments for absorption and linear elimination. Fecal concentrations were modeled using a transit compartment between plasma and feces, with reabsorption from the fecal compartment to the central compartment. The effect of MOX on the number of OTUs was best described by a turn-over model, with an Emax model where fecal MOX concentration increased the loss rate. Goodness-of-fit of this model was satisfactory.
Conclusions: We developed the first joint model of the co-evolution of individual plasma and fecal exposure to an antibiotic, and of bacterial richness observed in the intestinal microbiota. The analysis of other microbiota metrics such as the α- or β-diversity is necessary to refine MOX effects on the intestinal microbiota.
References:
[1] Dethlefsen, L. & Relman, D. A. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc Natl Acad Sci U A 108 Suppl 1, 4554–4561 (2011).
[2] Nguyen TT, Guedj J, Chachaty E, de Gunzburg J, Andremont A, Mentre F. Mathematical modeling of bacterial kinetics to predict the impact of antibiotic colonic exposure and treatment duration on the amount of resistant enterobacteria excreted. PLoS Comput Biol 2014; 10(9): e1003840.
[3] Kuhn E, Lavielle M. Maximum likelihood estimation in nonlinear mixed effects models. Comput Statist Data Anal 2005; 49: 1020-38.
