Paroxetine

Population pharmacokinetic analysis in late-life depression using sparse concentration sampling

Yan Feng, Bruce G. Pollock, Robert E. Ferrell, Mark A. Kimak, Charles F. Reynolds, Robert Bies

Research output: Contribution to journalArticle

41 Citations (Scopus)

Abstract

Aim: To develop a population pharmacokinetic (PK) model using sparse sampling of long-term treatment with paroxetine in elderly depressed subjects, incorporating CYP2D6 genotype as well as other covariates. Methods: Elderly subjects (age ≥70 years) with nonpsychotic, nonbipolar major depressive disorder from the inpatient and outpatient clinic were treated with paroxetine in a 5-year clinical trial investigating 'Maintenance Therapies in Late-Life Depression' (MTLD-2). Plasma concentrations were collected during regular visits. CYP2D6 genotype was determined using polymerase chain reaction (PCR) for each individual. A nonlinear mixed-effects model was developed with NONMEM® for these subjects who received 10-40 mg day-1 of paroxetine during treatment. One- and two-compartment models with linear and nonlinear elimination (Michaelis-Menten) were evaluated. PK parameters as well as interindividual and residual variability were estimated. The effects of age, weight, sex, race and CYP2D6 genotypes on the pharmacokinetics of paroxetine were evaluated. Results: One hundred and seventy-one subjects with a mean age of 77 years (range 69-95) and a mean weight of 72.0 kg (range 32.9-137.0) were enrolled in the MTLD-2 clinical trial. A total of 1970 paroxetine concentrations were available for population PK analyses. Approximately 10 samples were taken per subject. A two-compartment nonlinear PK model with additive and proportional error provided the best base model for description of the data. Weight and CYP2D6 polymorphisms were found to have a significant effect on maximal velocity (Vm), whereas sex had an effect on volume of distribution of the central compartment. The Vm estimates in each of the CYP2D6 phenotypic groups were: 125 μg h-1 in poor metabolizer (n = 1), 182 μg h-1 in intermediate metabolizers (n = 28), 454 μg h-1 in extensive metabolizers (n = 36) and 3670 μg h-1 in ultra-rapid metabolizers (n = 5). Conclusions: The population PK model adequately described paroxetine data in this elderly depressed population. The data indicate that female and male subjects with different CYP2D6 polymorphisms have different elimination rates and therefore may need to be dosed differently based on metabolizer genotype.

Original languageEnglish (US)
Pages (from-to)558-569
Number of pages12
JournalBritish Journal of Clinical Pharmacology
Volume61
Issue number5
DOIs
StatePublished - May 2006
Externally publishedYes

Fingerprint

Cytochrome P-450 CYP2D6
Paroxetine
Pharmacokinetics
Depression
Genotype
Population
Weights and Measures
Clinical Trials
Nonlinear Dynamics
Major Depressive Disorder
Ambulatory Care Facilities
Inpatients
Linear Models
Therapeutics
Polymerase Chain Reaction

Keywords

  • CYP2D6 polymorphism
  • Geriatrics
  • NONMEM
  • Paroxetine
  • Pharmacokinetics

ASJC Scopus subject areas

  • Pharmacology (medical)
  • Pharmacology, Toxicology and Pharmaceutics(all)

Cite this

Paroxetine : Population pharmacokinetic analysis in late-life depression using sparse concentration sampling. / Feng, Yan; Pollock, Bruce G.; Ferrell, Robert E.; Kimak, Mark A.; Reynolds, Charles F.; Bies, Robert.

In: British Journal of Clinical Pharmacology, Vol. 61, No. 5, 05.2006, p. 558-569.

Research output: Contribution to journalArticle

Feng, Yan ; Pollock, Bruce G. ; Ferrell, Robert E. ; Kimak, Mark A. ; Reynolds, Charles F. ; Bies, Robert. / Paroxetine : Population pharmacokinetic analysis in late-life depression using sparse concentration sampling. In: British Journal of Clinical Pharmacology. 2006 ; Vol. 61, No. 5. pp. 558-569.
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AU - Pollock, Bruce G.

AU - Ferrell, Robert E.

AU - Kimak, Mark A.

AU - Reynolds, Charles F.

AU - Bies, Robert

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N2 - Aim: To develop a population pharmacokinetic (PK) model using sparse sampling of long-term treatment with paroxetine in elderly depressed subjects, incorporating CYP2D6 genotype as well as other covariates. Methods: Elderly subjects (age ≥70 years) with nonpsychotic, nonbipolar major depressive disorder from the inpatient and outpatient clinic were treated with paroxetine in a 5-year clinical trial investigating 'Maintenance Therapies in Late-Life Depression' (MTLD-2). Plasma concentrations were collected during regular visits. CYP2D6 genotype was determined using polymerase chain reaction (PCR) for each individual. A nonlinear mixed-effects model was developed with NONMEM® for these subjects who received 10-40 mg day-1 of paroxetine during treatment. One- and two-compartment models with linear and nonlinear elimination (Michaelis-Menten) were evaluated. PK parameters as well as interindividual and residual variability were estimated. The effects of age, weight, sex, race and CYP2D6 genotypes on the pharmacokinetics of paroxetine were evaluated. Results: One hundred and seventy-one subjects with a mean age of 77 years (range 69-95) and a mean weight of 72.0 kg (range 32.9-137.0) were enrolled in the MTLD-2 clinical trial. A total of 1970 paroxetine concentrations were available for population PK analyses. Approximately 10 samples were taken per subject. A two-compartment nonlinear PK model with additive and proportional error provided the best base model for description of the data. Weight and CYP2D6 polymorphisms were found to have a significant effect on maximal velocity (Vm), whereas sex had an effect on volume of distribution of the central compartment. The Vm estimates in each of the CYP2D6 phenotypic groups were: 125 μg h-1 in poor metabolizer (n = 1), 182 μg h-1 in intermediate metabolizers (n = 28), 454 μg h-1 in extensive metabolizers (n = 36) and 3670 μg h-1 in ultra-rapid metabolizers (n = 5). Conclusions: The population PK model adequately described paroxetine data in this elderly depressed population. The data indicate that female and male subjects with different CYP2D6 polymorphisms have different elimination rates and therefore may need to be dosed differently based on metabolizer genotype.

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