Optimal sampling of antipsychotic medicines

A pharmacometric approach for clinical practice

Vidya Perera, Robert Bies, Gary Mo, Michael J. Dolton, Vaughan J. Carr, Andrew J. McLachlan, Richard O. Day, Thomas M. Polasek, Alan Forrest

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

AIM To determine optimal sampling strategies to allow the calculation of clinical pharmacokinetic parameters for selected antipsychotic medicines using a pharmacometric approach.

METHODS This study utilized previous population pharmacokinetic parameters of the antipsychotic medicines aripiprazole, clozapine, olanzapine, perphenazine, quetiapine, risperidone (including 9-OH risperidone) and ziprasidone. D-optimality was utilized to identify time points which accurately predicted the pharmacokinetic parameters (and expected error) of each drug at steady-state. A standard two stage population approach (STS) with MAP-Bayesian estimation was used to compare area under the concentration-time curves (AUC) generated from sparse optimal time points and rich extensive data. Monte Carlo Simulation (MCS) was used to simulate 1000 patients with population variability in pharmacokinetic parameters. Forward stepwise regression analysis was used to determine the most predictive time points of the AUC for each drug at steady-state.

RESULTS Three optimal sampling times were identified for each antipsychotic medicine. For aripiprazole, clozapine, olanzapine, perphenazine, risperidone, 9-OH risperidone, quetiapine and ziprasidone the CV% of the apparent clearance using optimal sampling strategies were 19.5, 8.6, 9.5, 13.5, 12.9, 10.0, 16.0 and 10.7, respectively. Using the MCS and linear regression approach to predict AUC, the recommended sampling windows were 16.5-17.5 h, 10-11 h, 23-24 h, 19-20 h, 16.5-17.5 h, 22.5-23.5 h, 5-6 h and 5.5-6.5 h, respectively.

CONCLUSION This analysis provides important sampling information for future population pharmacokinetic studies and clinical studies investigating the pharmacokinetics of antipsychotic medicines.

Original languageEnglish
Pages (from-to)800-814
Number of pages15
JournalBritish Journal of Clinical Pharmacology
Volume78
Issue number4
DOIs
StatePublished - Apr 29 2014

Fingerprint

Antipsychotic Agents
Pharmacokinetics
olanzapine
Perphenazine
Risperidone
Clozapine
Population
Pharmaceutical Preparations
Linear Models
Regression Analysis
Medicine

Keywords

  • Antipsychotic medicines
  • Optimal sampling
  • Pharmacometrics
  • Population pharmacokinetics
  • Therapeutic drug monitoring

ASJC Scopus subject areas

  • Pharmacology (medical)
  • Pharmacology

Cite this

Optimal sampling of antipsychotic medicines : A pharmacometric approach for clinical practice. / Perera, Vidya; Bies, Robert; Mo, Gary; Dolton, Michael J.; Carr, Vaughan J.; McLachlan, Andrew J.; Day, Richard O.; Polasek, Thomas M.; Forrest, Alan.

In: British Journal of Clinical Pharmacology, Vol. 78, No. 4, 29.04.2014, p. 800-814.

Research output: Contribution to journalArticle

Perera, V, Bies, R, Mo, G, Dolton, MJ, Carr, VJ, McLachlan, AJ, Day, RO, Polasek, TM & Forrest, A 2014, 'Optimal sampling of antipsychotic medicines: A pharmacometric approach for clinical practice', British Journal of Clinical Pharmacology, vol. 78, no. 4, pp. 800-814. https://doi.org/10.1111/bcp.12410
Perera, Vidya ; Bies, Robert ; Mo, Gary ; Dolton, Michael J. ; Carr, Vaughan J. ; McLachlan, Andrew J. ; Day, Richard O. ; Polasek, Thomas M. ; Forrest, Alan. / Optimal sampling of antipsychotic medicines : A pharmacometric approach for clinical practice. In: British Journal of Clinical Pharmacology. 2014 ; Vol. 78, No. 4. pp. 800-814.
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abstract = "AIM To determine optimal sampling strategies to allow the calculation of clinical pharmacokinetic parameters for selected antipsychotic medicines using a pharmacometric approach.METHODS This study utilized previous population pharmacokinetic parameters of the antipsychotic medicines aripiprazole, clozapine, olanzapine, perphenazine, quetiapine, risperidone (including 9-OH risperidone) and ziprasidone. D-optimality was utilized to identify time points which accurately predicted the pharmacokinetic parameters (and expected error) of each drug at steady-state. A standard two stage population approach (STS) with MAP-Bayesian estimation was used to compare area under the concentration-time curves (AUC) generated from sparse optimal time points and rich extensive data. Monte Carlo Simulation (MCS) was used to simulate 1000 patients with population variability in pharmacokinetic parameters. Forward stepwise regression analysis was used to determine the most predictive time points of the AUC for each drug at steady-state.RESULTS Three optimal sampling times were identified for each antipsychotic medicine. For aripiprazole, clozapine, olanzapine, perphenazine, risperidone, 9-OH risperidone, quetiapine and ziprasidone the CV{\%} of the apparent clearance using optimal sampling strategies were 19.5, 8.6, 9.5, 13.5, 12.9, 10.0, 16.0 and 10.7, respectively. Using the MCS and linear regression approach to predict AUC, the recommended sampling windows were 16.5-17.5 h, 10-11 h, 23-24 h, 19-20 h, 16.5-17.5 h, 22.5-23.5 h, 5-6 h and 5.5-6.5 h, respectively.CONCLUSION This analysis provides important sampling information for future population pharmacokinetic studies and clinical studies investigating the pharmacokinetics of antipsychotic medicines.",
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N2 - AIM To determine optimal sampling strategies to allow the calculation of clinical pharmacokinetic parameters for selected antipsychotic medicines using a pharmacometric approach.METHODS This study utilized previous population pharmacokinetic parameters of the antipsychotic medicines aripiprazole, clozapine, olanzapine, perphenazine, quetiapine, risperidone (including 9-OH risperidone) and ziprasidone. D-optimality was utilized to identify time points which accurately predicted the pharmacokinetic parameters (and expected error) of each drug at steady-state. A standard two stage population approach (STS) with MAP-Bayesian estimation was used to compare area under the concentration-time curves (AUC) generated from sparse optimal time points and rich extensive data. Monte Carlo Simulation (MCS) was used to simulate 1000 patients with population variability in pharmacokinetic parameters. Forward stepwise regression analysis was used to determine the most predictive time points of the AUC for each drug at steady-state.RESULTS Three optimal sampling times were identified for each antipsychotic medicine. For aripiprazole, clozapine, olanzapine, perphenazine, risperidone, 9-OH risperidone, quetiapine and ziprasidone the CV% of the apparent clearance using optimal sampling strategies were 19.5, 8.6, 9.5, 13.5, 12.9, 10.0, 16.0 and 10.7, respectively. Using the MCS and linear regression approach to predict AUC, the recommended sampling windows were 16.5-17.5 h, 10-11 h, 23-24 h, 19-20 h, 16.5-17.5 h, 22.5-23.5 h, 5-6 h and 5.5-6.5 h, respectively.CONCLUSION This analysis provides important sampling information for future population pharmacokinetic studies and clinical studies investigating the pharmacokinetics of antipsychotic medicines.

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