American Chemical Society
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Evaluation of Interindividual Human Variation in Bioactivation and DNA Adduct Formation of Estragole in Liver Predicted by Physiologically Based Kinetic/Dynamic and Monte Carlo Modeling

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journal contribution
posted on 2016-03-07, 00:00 authored by Ans Punt, Alicia Paini, Albertus Spenkelink, Gabriele Scholz, Benoit Schilter, Peter J. van Bladeren, Ivonne M. C. M. Rietjens
Estragole is a known hepatocarcinogen in rodents at high doses following metabolic conversion to the DNA-reactive metabolite 1′-sulfooxyestragole. The aim of the present study was to model possible levels of DNA adduct formation in (individual) humans upon exposure to estragole. This was done by extending a previously defined PBK model for estragole in humans to include (i) new data on interindividual variation in the kinetics for the major PBK model parameters influencing the formation of 1′-sulfooxyestragole, (ii) an equation describing the relationship between 1′-sulfooxyestragole and DNA adduct formation, (iii) Monte Carlo modeling to simulate interindividual human variation in DNA adduct formation in the population, and (iv) a comparison of the predictions made to human data on DNA adduct formation for the related alkenylbenzene methyleugenol. Adequate model predictions could be made, with the predicted DNA adduct levels at the estimated daily intake of estragole of 0.01 mg/kg bw ranging between 1.6 and 8.8 adducts in 108 nucleotides (nts) (50th and 99th percentiles, respectively). This is somewhat lower than values reported in the literature for the related alkenylbenzene methyleugenol in surgical human liver samples. The predicted levels seem to be below DNA adduct levels that are linked with tumor formation by alkenylbenzenes in rodents, which were estimated to amount to 188–500 adducts per 108 nts at the BMD10 values of estragole and methyleugenol. Although this does not seem to point to a significant health concern for human dietary exposure, drawing firm conclusions may have to await further validation of the model’s predictions.