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We represent a community with a shared interest in data analysis using the population approach.


2005
   Pamplona, Spain

Population pharmacokinetic modeling of total and unbound docetaxel plasma concentrations in cancer patients with poor liver function

Andrew Hooker (1), A. J. Ten Tije (2), M. A. Carducci (3), H. Gelderblom (4), F. W. Dawkins (5), W. P. McGuire (6), J. Verweij (2), Mats O. Karlsson (1) and S. D. Baker (3).

(1) Division of Pharmacokinetics and Drug Therapy, Dept. of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden; (2) Medical Oncology, Erasmus University Medical Center, Rotterdam, the Netherlands; (3) The Sidney Kimmel Comprehensive Cancer Center (SKCCC), Johns Hopkins University, Baltimore, MD, USA; (4) Leiden University Medical Center, Leiden, the Netherlands; (5) Howard University Cancer Center, Washington, DC, USA; (6) Franklin Square Hospital Center, Baltimore, Maryland, USA.

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Objectives: Docetaxel is a chemotherapy agent approved for treatment of breast cancers, non-small cell lung cancers and androgen-independent prostate cancers.  Unfortunately docetaxel has a large interindividual variability in total CL and patients with poor liver function exhibit even more variability [1].  Because a reduction in predicted clearance of just 25% is associated with a 150% increased risk of febrile neutropenia, patients with poor liver function are often not treated with docetaxel.  However, recent studies have suggested that liver function tests and CYP3A activity could be used as covariates in a population PK model to reduce the predicted CL variability in poor liver function patients [2, 3].   

Methods: PK data was collected during cycle 1 of therapy for 71 cancer patients.  21 of these patients had poor liver function.  Total and unbound concentration measurements, liver function measurements, CYP3A activity measurements (using the erythromycin breath test - EBT) and other standard covariate measurements (including AAG) were made.  We initially developed separate models for unbound concentration measurements separately for the poor and normal liver function groups.  However, due to the complexity of the model (4-compartment) and the relatively low number of poor liver function patients, we decided to combine the two groups so that the normal liver function patients’ data could inform the model on parameters that were similar between the two groups.  Thereafter, we developed a binding model and a correlated residual error structure, to simultaneously analyze total and unbound docetaxel concentrations. Finally, covariates were added to the model.

Results:  The data in this experiment was collected for a considerably longer time than previous investigations and we found that a four-compartment model fit the data better than the three-compartment model published previously on docetaxel [4].  For the good liver function group, CL includes the EBT measurements as a covariate. For the poor liver function group, covariates include EBT AAG and sex. The model significantly reduces the unexplained variability in unbound clearance estimates for both liver function groups. The unbound CL variability of the poor liver function group is lower than for the good liver function group (40% CV vs. 15% CV).

Conclusions: Our final model reduces the variability of CL in the poor liver function group to 15% CV, and should be useful in developing dosing strategies for these patients once a PD model is developed.

References:
[1]  S.D. Baker et al.  Relationship of systemic exposure to unbound docetaxel and neutropenia. Clin Pharmacol Ther. 77(1):43-53, 2005. 
[2] S.D. Baker et al. Evaluation of CYP3A activity as a predictive covariate for docetaxel clearance. J Clin Oncol. 2004 ASCO Annual Meeting Proceedings (Post-Meeting Edition). Vol 22, No 14S (July 15 Supplement), Ab No 2006, 2004.
[3] E.C. Dees et al.  Role of cytochrome P450 phenotyping in cancer treatment.
J Clin Oncol. 23(6):1053-5, 2005.
[4] R. Bruno et al. Population pharmacokinetics and pharmacokinetic-pharmacodynamic relationships for docetaxel. Invest New Drugs. 19(2):163-9, 2001.



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