title: Population pharmacokinetics of micafungin in pediatric patients and implications for antifungal dosing.
abstract: The echinocandins potentially have an important role in treatment of infections caused by Candida spp. and Aspergillus spp. in immunocompromised children. However, there are no population pharmacokinetic models of the echinocandins for pediatric patients. The safety and descriptive pharmacokinetics of micafungin in children were recently reported. However, a population pharmacokinetic model in children is needed in order to accurately determine the dosage of micafungin that produces an equivalent magnitude of drug exposure to that observed in adults. In order to explore the effect of weight on micafungin pharmacokinetics, a standard two-compartment pharmacokinetic model, a linear model, and an allometric power model were developed. For all three models, the fit to the data was excellent, with comparable measures of precision and bias. However, the superior log-likelihood value of the allometric power model suggested that it best reflected the data and was therefore chosen for a more detailed analysis of the magnitude and pattern of drug exposure which develop following the administration of micafungin. The allometric power model suggested that clearance in smaller children is higher than that predicted on the basis of weight alone. Consequently, a degree of dosage increase is required in smaller children to ensure comparable levels of drug exposure to those observed in larger children and adults. The allometric power model developed in this study enables identification of pediatric dosage regimens of micafungin which, based upon Monte Carlo simulations, result in equivalent drug exposures to those observed in adults, for which antifungal efficacy has been established.
authors.affiliation: Pediatric Oncology Branch, NCI/NIH, CRC Room 1-5750, Bethesda, MD 20892-1100, USA.
authors.forename: William W
authors.initials: WW
authors.lastname: Hope
authors.forename: Nita L
authors.initials: NL
authors.lastname: Seibel
authors.forename: Cindy L
authors.initials: CL
authors.lastname: Schwartz
authors.forename: Antonio
authors.initials: A
authors.lastname: Arrieta
authors.forename: Patricia
authors.initials: P
authors.lastname: Flynn
authors.forename: Aziza
authors.initials: A
authors.lastname: Shad
authors.forename: Edythe
authors.initials: E
authors.lastname: Albano
authors.forename: James J
authors.initials: JJ
authors.lastname: Keirns
authors.forename: Donald N
authors.initials: DN
authors.lastname: Buell
authors.forename: Tawanda
authors.initials: T
authors.lastname: Gumbo
authors.forename: George L
authors.initials: GL
authors.lastname: Drusano
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Walsh
mesh_terms: D000293:Adolescent; D000465:Algorithms; D000935:Antifungal Agents / Q000008:administration & dosage / Q000009:adverse effects / Q000493:pharmacokinetics*; D019540:Area Under Curve; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D054714:Echinocandins / Q000008:administration & dosage / Q000009:adverse effects / Q000493:pharmacokinetics*; D005260:Female; D005544:Forecasting; D006801:Humans; D016014:Linear Models; D055666:Lipopeptides; D008074:Lipoproteins / Q000008:administration & dosage / Q000009:adverse effects / Q000493:pharmacokinetics*; D008297:Male; D000077551:Micafungin; D015233:Models, Statistical; D009010:Monte Carlo Method; D011153:Population
chemical_list: D000935:Antifungal Agents; D054714:Echinocandins; D055666:Lipopeptides; D008074:Lipoproteins; D000077551:Micafungin