Objective: Mitotane is used for the treatment of adrenocortical carcinoma. High oral daily doses of typically 1–6g are required to attain therapeutic concentrations. The drug has a narrow therapeutic index and patient management is difficult because of a high volume of distribution, very long elimination half-life and drug interaction through induction of metabolizing enzymes. The present evaluation aimed at the development of a population pharmacokinetic model of mitotane to facilitate therapeutic drug monitoring (TDM). Methods: Appropriate dosing information, plasma concentrations (1137 data points) and covariates were available from TDM of 76 adrenocortical carcinoma patients treated with mitotane. Using nonlinear mixed-effects modeling, a simple structural model was first developed, with subsequent introduction of metabolic autoinduction. Covariate data were analyzed to improve overall model predictability. Simulations were performed to assess the attainment of therapeutic concentrations with clinical dosing schedules. Results: A one-compartment pharmacokinetic model with first order absorption was found suitable to describe the data, with an estimated central volume of distribution of 6086L related to a high interindividual variability of 81.5%. Increase in clearance of mitotane during treatment could be modeled by a linear enzyme autoinduction process. BMI was found to have an influence upon disposition kinetics of mitotane. Model simulations favor a high-dose regimen to rapidly attain therapeutic concentrations, with the first TDM suggested on day 16 of treatment to avoid systemic toxicity. Conclusion: The proposed model describes mitotane pharmacokinetics and can be used to facilitate therapy by predicting plasma concentrations.
CITATION STYLE
Arshad, U., Taubert, M., Kurlbaum, M., Frechen, S., Herterich, S., Megerle, F., … Kroiss, M. (2018). Enzyme autoinduction by mitotane supported by population pharmacokinetic modeling in a large cohort of adrenocortical carcinoma patients. European Journal of Endocrinology, 179(5), 287–297. https://doi.org/10.1530/EJE-18-0342
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