Mohamed Ismail1, Thomas Straubinger1, Hiroyuki Uchida2, Ariel Graff-Guerrero3,4,5,6, Shinichiro Nakajima2, Takefumi Suzuki7, Fernando Caravaggio3,5, Philip Gerretsen3,5, David Mamo8, Benoit H Mulsant4,5,6, Bruce G Pollock4,5,6, Robert Bies1,9. 1. Department of Pharmaceutical Sciences, University at Buffalo, Buffalo, NY, USA. 2. Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan. 3. Multimodal Imaging Group in Geriatrics - Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, Canada. 4. Geriatric Mental Health Division, Centre for Addiction and Mental Health, Toronto, Canada. 5. Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada. 6. Campbell Research Institute, Centre for Addiction and Mental Health, Toronto, Canada. 7. Department of Neuropsychiatry, University of Yamanashi Faculty of Medicine, Yamanashi, Japan. 8. Departments of Psychiatry & Gerontology, University of Malta, Msida, Malta. 9. Institute for Computational and Data Sciences, University at Buffalo, Buffalo, NY, USA.
Abstract
AIMS: Develop a robust and user-friendly software tool for the prediction of dopamine D2 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 D2 RO from sparsely sampled concentration measurements in an accessible and accurate form.
AIMS: Develop a robust and user-friendly software tool for the prediction of dopamine D2 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 D2 RO from sparsely sampled concentration measurements in an accessible and accurate form.
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Authors: Ariel Graff-Guerrero; Tarek K Rajji; Benoit H Mulsant; Shinichiro Nakajima; Fernando Caravaggio; Takefumi Suzuki; Hiroyuki Uchida; Philip Gerretsen; Wanna Mar; Bruce G Pollock; David C Mamo Journal: JAMA Psychiatry Date: 2015-09 Impact factor: 21.596
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