PATIENTS AND METHODS: Mefloquine pharmacokinetics was assessed in 24 healthy adults and 43 patients with Plasmodium falciparum malaria administered mefloquine in combination with artesunate. Population pharmacokinetic modelling was conducted using NONMEM.
RESULTS: A two-compartment model with a single transit compartment and first-order elimination from the central compartment most adequately described mefloquine concentration-time data. The model incorporated population parameter variability for clearance (CL/F), central volume of distribution (VC/F) and absorption rate constant (KA) and identified, in addition to body weight, malaria infection as a covariate for VC/F (but not CL/F). Monte Carlo simulations predict that falciparum malaria infection is associated with a shorter elimination half-life (407 versus 566 h) and T>MIC (766 versus 893 h).
CONCLUSIONS: This is the first known population pharmacokinetic study to show falciparum malaria to influence mefloquine disposition. Protein binding, anaemia and other factors may contribute to differences between healthy individuals and patients. As VC/F is related to the earlier portion of the concentration-time profiles, which occurs during acute malaria, and CL/F is more related to the terminal phase during convalescence after treatment, this may explain why malaria was found to be a covariate for VC/F but not CL/F.
METHODS: Published population pharmacokinetic models and the Australasian Neonatal Medicines Formulary were used to simulate antimicrobial concentration-time profiles in a virtual neonate population. Laboratory quality assurance data were used to quantify analytical variation in antimicrobial measurement methods used in clinical practice. Guideline-informed dosing recommendations based on drug concentrations were applied to compare the impact of analytical variation and nonanalytical factors on antimicrobial dosing.
RESULTS: Analytical variation caused differences in subsequent guideline-informed dosing recommendations in 9.3-12.1% (amikacin), 16.2-19.0% (tobramycin), 12.2-45.8% (gentamicin), and 9.6-19.5% (vancomycin) of neonates. For vancomycin, inaccuracies in drug administration time (45.6%), use of non-trough concentrations (44.7%), within-subject biological variation (38.2%), and dosing errors (27.5%) were predicted to result in more dosing discrepancies than analytical variation (12.5%). Using current analytical performance specifications, tolerated dosing discrepancies would be up to 14.8% (aminoglycosides) and 23.7% (vancomycin).
CONCLUSIONS: Although analytical variation can influence neonatal antimicrobial dosing recommendations, nonanalytical factors are more influential. These result in substantial variation in subsequent dosing of antimicrobials, risking inadvertent under- or overexposure. Harmonization of measurement methods and improved patient management systems may reduce the impact of analytical and nonanalytical factors on neonatal antimicrobial dosing.