Pharmacokinetic Modelling of 5-FU Production from Capecitabine—A Population Study in 40 Adult Patients with Metastatic Cancer
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Aims: To model the biotransformation steps of 5-FU production from capecitabine and identify patient characteristics that may influence the drug disposition. Methods: Blood samples and demographic data were collected from two phase I studies in which adult patients received oral capecitabine for various malignancies. Capecitabine, 5′-deoxy-5-fluorocytidine (5′-DFCR), 5′-deoxy-5-fluorouridine (5′-DFUR) and 5-fluorouracile (5-FU) concentration-time data were analysed via a population approach using NONMEM. Results: Forty patients and 75 pharmacokinetic time-courses were available for analysis. Capecitabine pharmacokinetics was ascribed to a one compartment model from which 5′-DFCR, 5′-DFUR and 5-FU were sequentially produced. Capecitabine oral absorption was characterized by a rapid first order input (K a =2.1 ± 0.3 hr−1) with a lag time (0.28 ± 0.11 hr), but related inter-occasion (IOV) and inter-subject (ISV) variabilities for these parameters, 167% and 110%, indicated that this oral absorption was highly variable. The capecitabine CL (CL10 = 218± 18 L/hr, ISV = 18%) and 5′-DFUR elimination rate constant (K 34 = 5.3 ± 2.0 hr−1, ISV = 25%) were influenced by total bilirubin (BILT). The elimination rate constant of plasma 5-FU (K40) was 66 ± 24 hr−1 (ISV = 34%).The final pharmacokinetic model was validated using 2000 bootstrap runs and provided non-parametric statistics of the parameters (median, 2.5th and 97.5th percentiles). Conclusions: This study supported the possibility of modelling a complex sequential metabolic pathway which produces pharmacologicaly active compounds from a prodrug. Only BILT significantly influenced the pharmacokinetics but this effect was not considered as relevant for dosing adjustment.
Keywordscapecitabine 5-FU population pharmacokinetics drug metabolism anticancer drugs
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- Reigner, B., Blesch, K., Weidekamm, E. 2001Clinical pharmacokinetics of capecitabineClin. Pharmacokin.4085104Google Scholar
- Reigner, B., Verweij, J., Dirix, L., Cassidy, J., Twelves, C., Allman, D, WeideKamm, E., Roos, B., Banken, L., Utoh, M., Osterwalder, B. 1998Effect of food on the pharmacokinetics of capecitabine and its metabolites following oral administration in cancer patientsClin. Cancer Res.4941948PubMedGoogle Scholar
- Beal, S.L., Sheiner, L.B. 1998NONMEM User’s Guide; NONMEM project groupUniversity of CaliforniaSan Francisco, CAGoogle Scholar
- Ihaka, R., Gentleman, R.R. 1996A language for data analysis and graphicsJ. Comput. Graphic Stat.5299314Google Scholar
- Poole, C., Gardiner, J., Twelves, C., Johnston, P., Harper, P., Cassidy, J, Monkhouse, J., Banken, L., Weidekamm, E., Reigner, B. 2002Effect of renal impairment on the pharmacokinetics and tolerability of capecitabine (Xeloda) in cancer patientsCancer Chemother Pharmacol.49225234CrossRefPubMedGoogle Scholar
- Reigner, B., Watanabe, T., Schuller, J., Lucraft, H., Sasaki, Y., Bridgewater, J, Saeki, T., McAleer, J., Kuranami, M., Poole, C., Kimura, M, Monkhouse, J., Yorulmaz, C., Weidekamm, E., Grange, S. 2003Pharmacokinetics of capecitabine (Xeloda) in Japanese and Caucasian patients with breast cancerCancer Chemother Pharmacol.52193201CrossRefPubMedGoogle Scholar