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Using Bayesian Modeling to Optimize Antipsychotic Dosage in Clinical Practice
  • +9
  • Mohamed Ismail,
  • Thomas Straubinger,
  • Hiroyuki Uchida,
  • Ariel Graff-Guerrero,
  • Shinichiro Nakajima,
  • Takefumi Suzuki,
  • Fernando Caravaggio,
  • Philip Gerretsen,
  • David Mamo,
  • Benoit Mulsant,
  • Bruce Pollock,
  • Robert Bies
Mohamed Ismail
University at Buffalo - The State University of New York
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Thomas Straubinger
University at Buffalo - The State University of New York
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Hiroyuki Uchida
Keio University School of Medicine Graduate School of Medicine Department of Neuropsychiatry
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Ariel Graff-Guerrero
Centre for Addiction and Mental Health
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Shinichiro Nakajima
Keio University School of Medicine Graduate School of Medicine Department of Neuropsychiatry
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Takefumi Suzuki
University of Yamanashi Faculty of Medicine Graduate School of Medicine
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Fernando Caravaggio
Centre for Addiction and Mental Health
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Philip Gerretsen
Centre for Addiction and Mental Health
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David Mamo
University of Malta
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Benoit Mulsant
Centre for Addiction and Mental Health
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Bruce Pollock
Centre for Addiction and Mental Health
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Robert Bies
University at Buffalo - The State University of New York
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Abstract

Aim A robust and user-friendly software tool was developed for the prediction of dopamine D2 receptor occupancy (RO) in patients with schizophrenia treated with either olanzapine or risperidone. This tool can facilitate clinician exploration of the impact of treatment strategies on RO using sparse plasma concentration measurements. Methods Previously developed population pharmacokinetic (PPK) models for olanzapine and risperidone were combined with a PD model for D2 receptor occupancy (RO) and implemented in the R programming language. MAP Bayesian estimation was used to provide predictions of plasma concentration and receptor occupancy and based on sparse PK measurements. Results The average (standard deviation) response times of the tools 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 (RMSE, 95% CI) of predicted versus observed concentrations were 3.73 ng/mL (-2.42 – 9.87) and 10.816 (6.71 – 14.93) for olanzapine, and 0.46 ng/mL (-4.56 – 5.47) and 6.68 (3.57 – 9.78) for risperidone and its active metabolite (9-OH risperidone). Mean error and RMSE 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 Treatment of schizophrenia with antipsychotics offers unique challenges and requires careful monitoring to establish the optimal dosing regimen. Our monitoring software predicts RO in a reliable and accurate form.

Peer review status:IN REVISION

14 Apr 2021Submitted to British Journal of Clinical Pharmacology
15 Apr 2021Assigned to Editor
15 Apr 2021Submission Checks Completed
18 May 2021Reviewer(s) Assigned
06 Jun 2021Review(s) Completed, Editorial Evaluation Pending
20 Jun 2021Editorial Decision: Revise Major