MAP Bayesian modelling combining striatal dopamine receptor occupancy and plasma concentrations to optimize antipsychotic dose regimens in individual patients.

Aims: Develop a robust and user-friendly software tool for the prediction of dopamine D receptor occupancy (RO) in patients with schizophrenia treated with either olanzapine or risperidone, in order to facilitate clinician exploration of the impact of treatment strategies on RO using sparse plasma concentration measurements.

Methods: Previously 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.

Results: The 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.

Conclusion: Our monitoring software predicts concentration-time profiles and the corresponding D RO from sparsely sampled concentration measurements in an accessible and accurate form.