PT  - JOURNAL ARTICLE
AU  - Weize Huang
AU  - Mariko Nakano
AU  - Jennifer E Sager
AU  - Isabelle Ragueneau-Majlessi
AU  - Nina Isoherranen
TI  - Physiologically Based Pharmacokinetic (Pbpk) Model of the Cyp2d6 Probe Atomoxetine: Extrapolation to Special Populations and Drug-Drug Interactions
AID  - 10.1124/dmd.117.076455
DP  - 2017 Jan 01
TA  - Drug Metabolism and Disposition
PG  - dmd.117.076455
4099  - http://dmd.aspetjournals.org/content/early/2017/08/31/dmd.117.076455.short
4100  - http://dmd.aspetjournals.org/content/early/2017/08/31/dmd.117.076455.full
AB  - Physiologically based pharmacokinetic (PBPK) modeling of drug disposition and drug-drug interactions has become a key component of drug development. PBPK modeling has also been considered as an approach to predict drug disposition in special populations. However, whether models developed and validated in healthy populations can be extrapolated to special populations is not well established. The goal of this study was to determine whether a drug specific PBPK model validated using healthy populations could be used to predict drug disposition in specific populations and in organ impairment. A full PBPK model of atomoxetine was developed using a training set of PK data from CYP2D6 genotyped individuals. The model was validated using drug-specific acceptance criteria and a test set of 14 healthy subject PK studies. Population PBPK models were then challenged by simulating the effects of ethnicity, drug-drug interactions, pediatrics and renal and hepatic impairment on atomoxetine PK. Atomoxetine disposition was successfully predicted in 100% of healthy subject studies, 88% of studies in Asians, 79% of drug-drug interaction (DDI) studies, and 100% of pediatric studies. However, atomoxetine AUC was overpredicted by 3-4 fold in end stage renal disease and hepatic impairment. The results show that validated PBPK models can be extrapolated to different ethnicities, DDIs, and pediatrics but not to renal and hepatic impairment patients, likely due to incomplete understanding of the physiological changes in these conditions. These results show that systematic modeling efforts can be used to further refine population models to improve the predictive value in this area.