Thomas M Polasek1,2, Geoffrey T Tucker3, Michael J Sorich1,4, Michael D Wiese5, Titus Mohan6, Amin Rostami-Hodjegan7,8, Porntipa Korprasertthaworn9, Vidya Perera10, Andrew Rowland1,4. 1. Department of Clinical Pharmacology, Flinders University, Adelaide, SA, Australia. 2. d3 Medicine, A Certara Company, Melbourne, VIC, Australia. 3. Medicine and Biomedical Sciences (Emeritus), University of Sheffield, Sheffield, UK. 4. Flinders Centre for Innovation in Cancer, Flinders University, Adelaide, SA, Australia. 5. School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, SA, Australia. 6. Department of Psychiatry, Flinders Medical Centre, Adelaide, SA, Australia. 7. Certara, Blades Enterprise Centre, Sheffield, UK. 8. Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK. 9. Department of Pharmacology, Faculty of Science, Mahidol University, Bangkok, Thailand. 10. Clinical Pharmacology and Pharmacometrics, Early Clinical and Translational Research, Bristol Myers Squibb, Princeton, NJ, USA.
Abstract
AIM: The aim of the present study was to predict olanzapine (OLZ) exposure in individual patients using physiologically based pharmacokinetic modelling and simulation (PBPK M&S). METHODS: A 'bottom-up' PBPK model for OLZ was constructed in Simcyp® (V14.1) and validated against pharmacokinetic studies and data from therapeutic drug monitoring (TDM). The physiological, demographic and genetic attributes of the 'healthy volunteer population' file in Simcyp® were then individualized to create 'virtual twins' of 14 patients. The predicted systemic exposure of OLZ in virtual twins was compared with measured concentration in corresponding patients. Predicted exposures were used to calculate a hypothetical decrease in exposure variability after OLZ dose adjustment. RESULTS: The pharmacokinetic parameters of OLZ from single-dose studies were accurately predicted in healthy Caucasians [mean-fold errors (MFEs) ranged from 0.68 to 1.14], healthy Chinese (MFEs 0.82 to 1.18) and geriatric Caucasians (MFEs 0.55 to 1.30). Cumulative frequency plots of trough OLZ concentration were comparable between the virtual population and patients in a TDM database. After creating virtual twins in Simcyp®, the R2 values for predicted vs. observed trough OLZ concentrations were 0.833 for the full cohort of 14 patients and 0.884 for the 7 patients who had additional cytochrome P450 2C8 genotyping. The variability in OLZ exposure following hypothetical dose adjustment guided by PBPK M&S was twofold lower compared with a fixed-dose regimen - coefficient of variation values were 0.18 and 0.37, respectively. CONCLUSIONS: Olanzapine exposure in individual patients was predicted using PBPK M&S. Repurposing of available PBPK M&S platforms is an option for model-informed precision dosing and requires further study to examine clinical potential.
AIM: The aim of the present study was to predict olanzapine (OLZ) exposure in individual patients using physiologically based pharmacokinetic modelling and simulation (PBPK M&S). METHODS: A 'bottom-up' PBPK model for OLZ was constructed in Simcyp® (V14.1) and validated against pharmacokinetic studies and data from therapeutic drug monitoring (TDM). The physiological, demographic and genetic attributes of the 'healthy volunteer population' file in Simcyp® were then individualized to create 'virtual twins' of 14 patients. The predicted systemic exposure of OLZ in virtual twins was compared with measured concentration in corresponding patients. Predicted exposures were used to calculate a hypothetical decrease in exposure variability after OLZ dose adjustment. RESULTS: The pharmacokinetic parameters of OLZ from single-dose studies were accurately predicted in healthy Caucasians [mean-fold errors (MFEs) ranged from 0.68 to 1.14], healthy Chinese (MFEs 0.82 to 1.18) and geriatric Caucasians (MFEs 0.55 to 1.30). Cumulative frequency plots of trough OLZ concentration were comparable between the virtual population and patients in a TDM database. After creating virtual twins in Simcyp®, the R2 values for predicted vs. observed trough OLZ concentrations were 0.833 for the full cohort of 14 patients and 0.884 for the 7 patients who had additional cytochrome P450 2C8 genotyping. The variability in OLZ exposure following hypothetical dose adjustment guided by PBPK M&S was twofold lower compared with a fixed-dose regimen - coefficient of variation values were 0.18 and 0.37, respectively. CONCLUSIONS:Olanzapine exposure in individual patients was predicted using PBPK M&S. Repurposing of available PBPK M&S platforms is an option for model-informed precision dosing and requires further study to examine clinical potential.
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