title: Population-based analysis of methadone distribution and metabolism using an age-dependent physiologically based pharmacokinetic model.
abstract: Limited pharmacokinetic (PK) and pharmacodynamic (PD) data are available to use in methadone dosing recommendations in pediatric patients for either opioid abstinence or analgesia. Considering the extreme inter-individual variability of absorption and metabolism of methadone, population-based PK would be useful to provide insight into the relationship between dose, blood concentrations, and clinical effects of methadone. To address this need, an age-dependent physiologically based pharmacokinetic (PBPK) model has been constructed to systematically study methadone metabolism and PK. The model will facilitate the design of cost-effective studies that will evaluate methadone PK and PD relationships, and may be useful to guide methadone dosing in children. The PBPK model, which includes whole-body multi-organ distribution, plasma protein binding, metabolism, and clearance, is parameterized based on a database of pediatric PK parameters and data collected from clinical experiments. The model is further tailored and verified based on PK data from individual adults, then scaled appropriately to apply to children aged 0-24 months. Based on measured variability in CYP3A enzyme expression levels and plasma orosomucoid (ORM2) concentrations, a Monte-Carlo-based simulation of methadone kinetics in a pediatric population was performed. The simulation predicts extreme variability in plasma concentrations and clearance kinetics for methadone in the pediatric population, based on standard dosing protocols. In addition, it is shown that when doses are designed for individuals based on prior protein expression information, inter-individual variability in methadone kinetics may be greatly reduced.
authors.affiliation: Department of Physiology, Medical College of Wisconsin, Biotechnology and Bioengineering Center, Milwaukee, WI 53226, USA.
authors.forename: Feng
authors.initials: F
authors.lastname: Yang
authors.forename: Xianping
authors.initials: X
authors.lastname: Tong
authors.forename: D Gail
authors.initials: DG
authors.lastname: McCarver
authors.forename: Ronald N
authors.initials: RN
authors.lastname: Hines
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Beard
mesh_terms: D000328:Adult; D000367:Age Factors; D000701:Analgesics, Opioid / Q000008:administration & dosage / Q000493:pharmacokinetics; D019540:Area Under Curve; D001682:Biological Availability; D002675:Child, Preschool; D003198:Computer Simulation; D006801:Humans; D006863:Hydrogen-Ion Concentration; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008691:Methadone / Q000737:chemistry / Q000493:pharmacokinetics* / Q000652:urine; D008875:Middle Aged; D008954:Models, Biological; D009010:Monte Carlo Method; D011485:Protein Binding; D012044:Regression Analysis; D013237:Stereoisomerism; D014018:Tissue Distribution
chemical_list: D000701:Analgesics, Opioid; D008691:Methadone