Physiologically-based pharmacokinetic modeling as an approach to evaluate the effect of covariates and drug-drug interactions on variability in epidermal growth factor receptor kinase inhibitor exposure

Madele Van Dyk, Andrew Rowland

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)


    Background: Epidermal growth factor receptor (EGFR) kinase inhibitors (KIs) have shown promising results in the treatment of non-small cell lung cancer (NSCLC). However, inter-individual variability in exposure results in a large proportion of patients experience either a lack of efficacy due to sub-therapeutic dosing or toxicity as a result of excessive dosing. Physiologically-based pharmacokinetic (PBPK) modeling, is a mechanistic "bottom up" approach, whereby the concentration-time profile for a drug in a patient cohort is simulated based on the physiochemical and in vitro kinetics of the drug and the physiological characteristics of the patient cohort. In this study, PBPK profiles for afatinib, erlotinib and gefitinib were developed and validated using data from healthy volunteer trials. The capacity of the profiles to account for the impact of covariates such as age, gender, and ethnicity, and the impact of co-administration with strong cytochromes P450 (CYP) 3A4 inhibitors and inducers on in vivo clearance was assessed. Methods: The rate of microsomal KI metabolism was quantified in the presence and absence of CYP and uridine diphosphate (UDP)-glucuronosyltransferase (UGT) cofactors as well as a selective CYP3A4 inhibitor (CYP3Cide). Microsomal clearance was assessed on the basis of the substrate depletion at an initial KI concentration of 1 μM over the course of a 3-hour incubation. CYP and UGT clearances were calculated based on the depletion half-life for incubations performed in the presence of the associated cofactors. CYP3A4 mediated clearance was assessed by subtracting the clearance in the presence of CYP3Cide from total CYP clearance. PBPK profiles for each compound were created based on reported physicochemical and distribution characteristics and in vitro microsomal CLint data. All simulations were conducted utilising the Simcyp Simulatorâ® (version 15.1) and the advanced dissolution, absorption and metabolism (ADAM) sub-model was used in conjunction with the whole body "full-PBPK" model for profile development. Results: The EGFR KI compound profiles were validated by comparing simulated pharmacokinetic parameters [area under the curve (AUC), Cmax and tmax] describing exposure with those observed in clinical studies that were not used in the development of the compound profiles. With the exception of the AUC ratio describing the impact of induction on erlotinib exposure (0.64), the ratio of observed to simulated parameters describing exposure, or parameter ratios describing the impact of induction on exposure were contained within the range 0.8 to 1.2. Conclusions: Robust mechanistic models with the capacity to describe EGFR KI exposure and the impact of covariates on exposure were developed and validated. These models may be applied to inform the impact of different dosing regimens on EGFR KI exposure, the potential impact of poor compliance on EGFR KI efficacy, the need to perform bridging studies when introducing EGFR KIs to new international markets, and the potential impact of DDIs on EFGR KI exposure.

    Original languageEnglish
    Pages (from-to)S1600-S1612
    Number of pages13
    JournalTranslational Cancer Research
    Issue number10
    Publication statusPublished - 1 Dec 2017

    Bibliographical note

    Published under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). All articles published open access will be immediately and permanently free for all to read, download, copy and distribute as defined by the applied license. Copyright © 2009 - 2020 AME Publishing Company.


    • Drug-drug interactions (DDIs)
    • Epidermal growth factor receptor (EGFR) kinase inhibitors
    • In vitro substrate depletion
    • Physiologically-based pharmacokinetic (PBPK) modeling


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