Prediction of olanzapine exposure in individual patients using physiologically-based pharmacokinetic modelling and simulation

ABSTRACT

AIM

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 individualised 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 versus observed trough OLZ concentrations were 0.833 for the full cohort of 14 patients and 0.884 for the 7 patients who had additional CYP2C8 genotyping. The variability in OLZ exposure following hypothetical dose-adjustment guided by PBPK M&S was 2-fold lower compared to a fixed-dose regimen – CV values were 0.18 and 0.37, respectively.

CONCLUSIONS

Olanzapine exposure in individual patients was predicted using PBPK M&S. Re-purposing 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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