Frederik Vandenberghe1, Monia Guidi2,3, Eva Choong1, Armin von Gunten4, Philippe Conus5, Chantal Csajka2,3, Chin B Eap6,7. 1. Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Hospital of Cery, Lausanne University Hospital, 1008, Prilly, Switzerland. 2. Division of Clinical Pharmacology, Department of Laboratories, Lausanne University Hospital, Lausanne, Switzerland. 3. School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland. 4. Service of Old Age Psychiatry, Department of Psychiatry, Lausanne University Hospital, 1008, Prilly, Switzerland. 5. Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital, 1008, Prilly, Switzerland. 6. Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Hospital of Cery, Lausanne University Hospital, 1008, Prilly, Switzerland. chin.eap@chuv.ch. 7. School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland. chin.eap@chuv.ch.
Abstract
BACKGROUND: High interindividual variability in plasma concentrations of risperidone and its active metabolite, 9-hydroxyrisperidone, may lead to suboptimal drug concentration. OBJECTIVE: Using a population pharmacokinetic approach, we aimed to characterize the genetic and non-genetic sources of variability affecting risperidone and 9-hydroxyrisperidone pharmacokinetics, and relate them to common side effects. METHODS: Overall, 150 psychiatric patients (178 observations) treated with risperidone were genotyped for common polymorphisms in NR1/2, POR, PPARα, ABCB1, CYP2D6 and CYP3A genes. Plasma risperidone and 9-hydroxyrisperidone were measured, and clinical data and common clinical chemistry parameters were collected. Drug and metabolite concentrations were analyzed using non-linear mixed effect modeling (NONMEM(®)). Correlations between trough concentrations of the active moiety (risperidone plus 9-hydroxyrisperidone) and common side effects were assessed using logistic regression and linear mixed modeling. RESULTS: The cytochrome P450 (CYP) 2D6 phenotype explained 52% of interindividual variability in risperidone pharmacokinetics. The area under the concentration-time curve (AUC) of the active moiety was found to be 28% higher in CYP2D6 poor metabolizers compared with intermediate, extensive and ultrarapid metabolizers. No other genetic markers were found to significantly affect risperidone concentrations. 9-hydroxyrisperidone elimination was decreased by 26% with doubling of age. A correlation between trough predicted concentration of the active moiety and neurologic symptoms was found (p = 0.03), suggesting that a concentration >40 ng/mL should be targeted only in cases of insufficient, or absence of, response. CONCLUSIONS: Genetic polymorphisms of CYP2D6 play an important role in risperidone, 9-hydroxyrisperidone and active moiety plasma concentration variability, which were associated with common side effects. These results highlight the importance of a personalized dosage adjustment during risperidone treatment.
BACKGROUND: High interindividual variability in plasma concentrations of risperidone and its active metabolite, 9-hydroxyrisperidone, may lead to suboptimal drug concentration. OBJECTIVE: Using a population pharmacokinetic approach, we aimed to characterize the genetic and non-genetic sources of variability affecting risperidone and 9-hydroxyrisperidone pharmacokinetics, and relate them to common side effects. METHODS: Overall, 150 psychiatricpatients (178 observations) treated with risperidone were genotyped for common polymorphisms in NR1/2, POR, PPARα, ABCB1, CYP2D6 and CYP3A genes. Plasma risperidone and 9-hydroxyrisperidone were measured, and clinical data and common clinical chemistry parameters were collected. Drug and metabolite concentrations were analyzed using non-linear mixed effect modeling (NONMEM(®)). Correlations between trough concentrations of the active moiety (risperidone plus 9-hydroxyrisperidone) and common side effects were assessed using logistic regression and linear mixed modeling. RESULTS: The cytochrome P450 (CYP) 2D6 phenotype explained 52% of interindividual variability in risperidone pharmacokinetics. The area under the concentration-time curve (AUC) of the active moiety was found to be 28% higher in CYP2D6 poor metabolizers compared with intermediate, extensive and ultrarapid metabolizers. No other genetic markers were found to significantly affect risperidone concentrations. 9-hydroxyrisperidone elimination was decreased by 26% with doubling of age. A correlation between trough predicted concentration of the active moiety and neurologic symptoms was found (p = 0.03), suggesting that a concentration >40 ng/mL should be targeted only in cases of insufficient, or absence of, response. CONCLUSIONS: Genetic polymorphisms of CYP2D6 play an important role in risperidone, 9-hydroxyrisperidone and active moiety plasma concentration variability, which were associated with common side effects. These results highlight the importance of a personalized dosage adjustment during risperidone treatment.
Authors: Rikus Knegtering; Pepijn Baselmans; Stynke Castelein; Fokko Bosker; Richard Bruggeman; Robert J van den Bosch Journal: Am J Psychiatry Date: 2005-05 Impact factor: 18.112
Authors: Kathrin Klein; Maria Thomas; Stefan Winter; Andreas K Nussler; Mikko Niemi; Matthias Schwab; Ulrich M Zanger Journal: Clin Pharmacol Ther Date: 2012-06 Impact factor: 6.875
Authors: Wagner F Gattaz; João Alberto de Oliveira Campos; Acioly L T Lacerda; Elaine Henna; Sandra Inês Ruschel; Rodrigo A Bressan; Irismar Reis de Oliveira; Fábio Lopes Rocha; Hamilton M Grabowski; Ernindo Sacomani; Mario R Louzã; João Quevedo; Hélio Elkis; Dirceu Zorzetto Filho; Cintia de Azevedo-Marques Périco; Fábio Lorea Lawson; José Carlos Appolinário Journal: Curr Med Res Opin Date: 2013-12-16 Impact factor: 2.580
Authors: Tessa A M Mulder; Ruben A G van Eerden; Mirjam de With; Laure Elens; Dennis A Hesselink; Maja Matic; Sander Bins; Ron H J Mathijssen; Ron H N van Schaik Journal: Front Genet Date: 2021-07-08 Impact factor: 4.599
Authors: Paula Soria-Chacartegui; Gonzalo Villapalos-García; Pablo Zubiaur; Francisco Abad-Santos; Dora Koller Journal: Front Pharmacol Date: 2021-07-14 Impact factor: 5.810