Population Pharmacokinetics of Mefloquine and its Major Metabolite in Healthy Volunteers.
C. Woloch (1), H. Marouani (1), D. Parzy (2), Iliadis A (1)
(1) Dpt. of Pharmacokinetics, UMR-MD3, University of Méditerranée, Marseilles, France (2) Service de Santé des Armées, IMTSSA, UMR-MD3, Marseilles, France.
Objectives: Mefloquine has a pivotal position as an important first-line anti-malarial drug and needs to be used to prevent resistance. The aim of the study is to propose a population pharmacokinetic (PK) model for mefloquine and its major metabolite in healthy volunteers during prophylaxis for malaria infections and to investigate the mefloquine prophylactic efficacy achievement .
Methods: Data came from 118 French soldiers based in Djibouti. Subjects received an oral weekly maintenance dose of 250 mg mefloquine over 4 months. Sparse plasma concentration profiles for both compounds were available (4 per subjects). The sampling protocol included plasma trough concentration before the next drug intake at week 2, 5, 14 and a peak plasma concentration 6 hours after the sixth weekly dose. Kinetics profile of mefloquine and of its metabolite were analysed simultaneously using the population approach implemented in NONMEM VI (FOCE Interaction) and Monolix 3.1.
Results: The final model consisted of two compartments. One compartment with first order absorption and elimination rates best described mefloquine concentration; an additional compartment described the PKs of drug metabolite with first order metabolism and elimination rates. Inter-individual variability was described by exponential terms and residual variability by a proportional error model. The population PK parameters describing the model were for mefloquine, absorption rate ka, total clearance CL, distribution volume V1 and for mefloquine metabolite, total clearance CLm, distribution volume V2 and metabolism ratio R. A sensitivity analysis resulted in introducing the same distribution volume for both compounds (V1=V2), a fixed mefloquine absorption rate ka at 6 d-1 and a same inter-individual variability for CLm and R. Typical values (inter-individual variability expressed in percent) for CL, V1, CLm and R were respectively 32.8 L/d (28%), 535 L (41%), 85 L/d and 1.85 (24%). Residual variability for mefloquine and its metabolite were 29% and 36% respectively. No covariates were included in the model. The results are similar for both approaches (NONMEM vs. Monolix) and they are in adequacy with previous report for mefloquine .
Conclusions: The model adequately described the PK of mefloquine and its major metabolite. It would be used next to investigate mefloquine accumulation and its impact on the relationships between adverse events-compliance-resistance during chemoprophylaxis.
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