ArticleBritish journal of clinical pharmacology2022
MAP Bayesian modelling combining striatal dopamine receptor occupancy and plasma concentrations to optimize antipsychotic dose regimens in individual patients.
Article in British journal of clinical pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Effect of olanzapine exposure on relapse and brain structure in patients with major depressive disorder with psychotic features.Molecular psychiatry · 2024Trial
- Population Pharmacokinetics of Risperidone and Paliperidone in Schizophrenia: A Systematic Review.Pharmaceuticals (Basel, Switzerland) · 2025Review
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Authors and funding
12 authors.
Funding
Abstract
aimsDevelop a robust and user-friendly software tool for the prediction of dopamine D
methodsPreviously developed population pharmacokinetic models for olanzapine and risperidone were combined with a pharmacodynamic model for D2 RO and implemented in the R programming language. Maximum a posteriori Bayesian estimation was used to provide predictions of plasma concentration and RO based on sparse concentration sampling. These predictions were then compared to observed plasma concentration and RO.
resultsThe average (standard deviation) response times of the tools, defined as the time required for the application to predict parameter values and display the output, were 2.8 (3.1) and 5.3 (4.3) seconds for olanzapine and risperidone, respectively. The mean error (95% confidence interval) and root mean squared error (95% confidence interval) of predicted vs. observed concentrations were 3.73 ng/mL (-2.42-9.87) and 10.816 ng/mL (6.71-14.93) for olanzapine, and 0.46 ng/mL (-4.56-5.47) and 6.68 ng/mL (3.57-9.78) for risperidone and its active metabolite (9-OH risperidone). Mean error and root mean squared error of RO were -1.47% (-4.65-1.69) and 5.80% (3.89-7.72) for olanzapine and -0.91% (-7.68-5.85) and 8.87% (4.56-13.17) for risperidone.
conclusionOur monitoring software predicts concentration-time profiles and the corresponding D
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