Drug-drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: Application to midazolam and a phase i compound. Part 1: Comparison of uniresponse and multiresponse designs using PopDes

Marylore Chenel, François Bouzom, Leon Aarons, Kayode Ogungbenro

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Purpose To determine the optimal sampling time design of a drug-drug interaction (DDI) study for the estimation of apparent clearances (CL/F) of two co-administered drugs (SX, a phase I compound, potentially a CYP3A4 inhibitor, and MDZ, a reference CYP3A4 substrate) without any in vivo data using physiologically based pharmacokinetic (PBPK) predictions, population PK modelling and multiresponse optimal design. Methods PBPK models were developed with AcslXtreme using only in vitro data to simulate PK profiles of both drugs when they were co-administered. Then, using simulated data, population PK models were developed with NONMEM and optimal sampling times were determined by optimizing the determinant of the population Fisher information matrix with PopDes using either two uniresponse designs (UD) or a multiresponse design (MD) with joint sampling times for both drugs. Finally, the D-optimal sampling time designs were evaluated by simulation and re-estimation with NONMEM by computing the relative root mean squared error (RMSE) and empirical relative standard errors (RSE) of CL/F. Results There were four and five optimal sampling times (=nine different sampling times) in the UDs for SX and MDZ, respectively, whereas there were only five sampling times in the MD. Whatever design and compound, CL/F was well estimated (RSE <20% for MDZ and
    Original languageEnglish
    Pages (from-to)635-659
    Number of pages24
    JournalJournal of pharmacokinetics and pharmacodynamics
    Volume35
    Issue number6
    DOIs
    Publication statusPublished - Dec 2008

    Keywords

    • Drug-drug interaction
    • Midazolam
    • Multiresponse
    • Non-linear mixed effects models
    • Optimal experimental design
    • Physiologically based pharmacokinetic models

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