ArticleBritish journal of clinical pharmacology2014
Optimal sampling of antipsychotic medicines: a pharmacometric approach for clinical practice.
Article in British journal of clinical pharmacology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Bayesian optimization of tacrolimus exposure in stable kidney transplant patients.Pharmacotherapy · 2023Trial
- Acute Quetiapine Intoxication: Relationship Between Ingested Dose, Serum Concentration and Clinical Presentation-Structured Literature Review and Analysis.Journal of xenobiotics · 2024Review
- Optimizing the design of a pharmacokinetic trial to evaluate the dosing scheme of a novel tuberculosis drug in children living with or without HIV.CPT: pharmacometrics & systems pharmacology · 2024Article
- Population pharmacokinetic model and limited sampling strategy for clozapine using plasma and dried blood spot samples.Therapeutic advances in psychopharmacology · 2022Article
- Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning.F1000Research · 2018Article
- A novel approach for estimating ingested dose associated with paracetamol overdose.British journal of clinical pharmacology · 2016Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo determine optimal sampling strategies to allow the calculation of clinical pharmacokinetic parameters for selected antipsychotic medicines using a pharmacometric approach.
methodsThis 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.
resultsThree 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.
conclusionThis analysis provides important sampling information for future population pharmacokinetic studies and clinical studies investigating the pharmacokinetics of antipsychotic medicines.
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