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Population pharmacokinetic analysis for risperidone using highly sparse sampling measurements from the CATIE study
Author(s) -
Feng Yan,
Pollock Bruce G.,
Coley Kim,
Marder Stephen,
Miller Del,
Kirshner Margaret,
Aravagiri Manickam,
Schneider Lon,
Bies Robert R.
Publication year - 2008
Publication title -
british journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.216
H-Index - 146
eISSN - 1365-2125
pISSN - 0306-5251
DOI - 10.1111/j.1365-2125.2008.03276.x
Subject(s) - risperidone , cyp2d6 , paliperidone palmitate , nonmem , population , pharmacology , pharmacokinetics , medicine , concomitant , antipsychotic , psychiatry , schizophrenia (object oriented programming) , cytochrome p450 , environmental health , metabolism
WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Risperidone metabolism is affected by blocking CYP2D6 and CYP3A4 (in CYP2D6 poor metabolizers) metabolizing enzymes. • Age affects risperidone disposition and renal function affects elimination of 9‐hydroxy‐risperidone (primary active metabolite). WHAT THIS STUDY ADDS • The detection of a systematic shift in estimated apparent clearance in the African‐American population (it is not clear if there are biological or sociological contributors), and a shift in the clearance rate of risperidone based on concomitant administration of paroxetine, manifested as a change in assignment to a different metabolizer subpopulation group that may be primarily related to CYP2D6 metabolizer status. • The study shows an age‐related decrement in 9‐hydroxy‐risperidone clearance across a wide range of ages. • Information on the nature of the pharmacokinetic variability with risperidone when used in a typical clinical patient population. • There are significant differences in the absolute values as well as the assignment to metabolizer status across race and concomitant paroxetine administration. AIMS To characterize pharmacokinetic (PK) variability of risperidone and 9‐OH risperidone using sparse sampling and to evaluate the effect of covariates on PK parameters. METHODS PK analysis used plasma samples collected from the Clinical Antipsychotic Trials of Intervention Effectiveness. A nonlinear mixed‐effects model was developed using nonmem to describe simultaneously the risperidone and 9‐OH risperidone concentration–time profile. Covariate effects on risperidone and 9‐OH risperidone PK parameters were assessed, including age, weight, sex, smoking status, race and concomitant medications. RESULTS PK samples comprised 1236 risperidone and 1236 9‐OH risperidone concentrations from 490 subjects that were available for analysis. Ages ranged from 18 to 93 years. Population PK submodels for both risperidone and 9‐OH risperidone with first‐order absorption were selected to describe the concentration–time profile of risperidone and 9‐OH risperidone. A mixture model was incorporated with risperidone clearance (CL) separately estimated for three subpopulations [poor metabolizer (PM), extensive metabolizer (EM) and intermediate metabolizer (IM)]. Age significantly affected 9‐OH risperidone clearance. Population parameter estimates for CL in PM, IM and EM were 12.9, 36 and 65.4 l h −1 and parameter estimates for risperidone half‐life in PM, IM and EM were 25, 8.5 and 4.7 h, respectively. CONCLUSIONS A one‐compartment mixture model with first‐order absorption adequately described the risperidone and 9‐OH risperidone concentrations. Age was identified as a significant covariate on 9‐OH risperidone clearance in this study.