Alzheimer Disease: Bies RR

Bigos KL, Pollock BG, Coley KC, Miller del D, Marder SR, Aravagiri M, Kirshner MA, Schneider LS, Bies RR. · Department of Pharmaceutical Sciences, University of Pittsburgh, School of Pharmacy, 805 Salk Hall, 3501 Terrace St, Pittsburgh, PA 15261, USA. · J Clin Pharmacol. · Pubmed #18199892 No free full text.

Abstract: Response to antipsychotics is highly variable, which may be due in part to differences in drug exposure. The goal of this study was to evaluate the magnitude and variability of concentration exposure of olanzapine. Patients with Alzheimer's disease (n = 117) and schizophrenia (n = 406) were treated with olanzapine as part of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE). Combined, these patients (n = 523) provided 1527 plasma samples for determination of olanzapine concentrations. Nonlinear mixed-effects modeling was used to determine the population pharmacokinetics of olanzapine, and patient-specific covariates were evaluated as potential contributors to variability in drug exposure. The population mean olanzapine clearance and volume of distribution were 16.1 L/h and 2150 L, respectively. Elimination of olanzapine varied nearly 10-fold (range, 6.66-67.96 L/h). Smoking status, sex, and race accounted for 26%, 12%, and 7% of the variability, respectively (P < .0001). Smokers cleared olanzapine 55% faster than non/past smokers (P < .0001). Men cleared olanzapine 38% faster than women (P < .0001). Patients who identified themselves as black or African American cleared olanzapine 26% faster than other races (P < .0001). Differences in olanzapine exposure due to sex, race, and smoking may account for some of the variability in response to olanzapine.

Feng Y, Pollock BG, Coley K, Marder S, Miller D, Kirshner M, Aravagiri M, Schneider L, Bies RR. · Strategic Modeling and Simulation Group, Bristol-Myers Squibb Co., Princeton, NJ, USA. · Br J Clin Pharmacol. · Pubmed #18771484 No free full text.

Abstract: 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.