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

Thomas Polasek, Geoffrey Tucker, Michael Sorich, Michael Wiese, Titus Mohan, Amin Rostami-Hodjegan, Porntipa Korprasertthaworn, Vidya Perera, Andrew Rowland

    Research output: Contribution to journalArticlepeer-review

    54 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)462-476
    Number of pages15
    JournalBritish Journal of Clinical Pharmacology
    Volume84
    Issue number3
    DOIs
    Publication statusPublished - Mar 2018

    Keywords

    • dose prediction
    • olanzapine
    • PBPK
    • personalized medicine
    • physiologically based pharmacokinetics

    Fingerprint

    Dive into the research topics of 'Prediction of olanzapine exposure in individual patients using physiologically-based pharmacokinetic modelling and simulation'. Together they form a unique fingerprint.

    Cite this