A population PK-PD model of unbound Docetaxel in patients with liver impairment and identification of potential covariates for initial dosage adjustment

Objectives: Docetaxel is a commonly used anti-cancer drug. Due to higher incidence of severe neutropenia in patients with impaired liver function it is rarely given to these patients. A PK model including covariates on clearance (BSA, AAG, LF and ERMBT) have been developed for total and unbound docetaxel in normal and liver impaired patients [1]. However, it is unknown if a patient with liver impairment has the same concentration-toxicity relationship as a patient with normal liver function. The aim of the study is to develop a PKPD model describing the docetaxel-induced neutropenia in patients with normal and impaired liver function and to explore patient factors that may explain differences in toxicity.

Methods: In the study, 77 cancer patients were treated with docetaxel (75, 50 or 40 mg/m2 depending on liver function). Neutrophils were recorded on day 0, 7, 14 and 21 during one cycle of treatment. Individual concentration-time profiles were generated using the published population PK model for unbound docetaxel [1]. A semi-mechanistic model of myelosuppression [2], with some modification [3], was used to describe the neutrophil time-course, e.g. the drug-related effect was described by a sigmoid Emax model. A step-wise covariate analysis was performed. The covariates evaluated were patient demographics, α1-acid glycoprotein (AAG), haemoglobin and liver function variables: liver function group (LFG) and ERMBT (erythromycin breath test). The patients were divided in to LFG according to their AST, ALT, AP and bilirubin levels, where group 1 (n=54) had normal liver function and group 2 (n=11), 3A (n=7) and 3B (n=5) had increasing severity of liver impairment [1]. The final PKPD model with covariates was used for simulating proportion of patients with grade 3 and 4 neutropenia for each LFG given dose adjustments based on different sets of covariates.

Results: In the first step LFG was evaluated. Patients with impaired LF (2, 3A, 3B) were found to have a higher baseline (p<0.05) and EC50 (p<0.01) compared to patients with normal liver function. In the second step the remaining covariates were investigated. Patients with high levels of AAG were found to have a lower Emax and a higher Baseline than patients with low AAG levels, which confirms earlier findings [4]. “Men were allowed to have a higher Baseline than women as this covariate was well estimated and supported by the literature [4] even though it was not statistically significant. Predictions based on the model showed that the PD covariates gave rise to more variability in neutropenia than the PK covariates. However for the liver impaired patients a dose adjustment based on the covariates on clearance considerably reduced the proportions of grade 3 and 4 neutropenia.

Conclusions: The integrated PKPD model describing the time-course of neutropenia showed that when the difference in PK has been considered, patients with impaired liver function are less sensitive to docetaxel than patients with normal liver function. The model described the data well and showed good simulation properties. The model may be useful for finding a suitable dose that allows also patients with liver impairment to receive docetaxel.

References:

  1. Hooker, A et al. Population Pharmacokinetic Model for Docetaxel in Patients with Varying Degrees of Liver Function: Incorporating Cytochrome P450 3A Activity Measurements. Clin Pharmacol Ther. 2008, Jan 8
  2. Friberg, LE et al. Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. J. Clin. Oncol. 2002, 20:4713-4721
  3. Quartino, AL et al. An Extended Semi-Physiological Myelosuppression Model following Docetaxel administration with Improved Simulation Properties. ACOP 2008 http://tucson2008.go-acop.org/pdfs/15_quartino.pdf
  4. Kloft, C et al. Population pharmacokinetic-pharmacodynamic model for neutropenia with patient subgroup identification: comparison across anticancer drugs. Clin Cancer Res 2006 15;12(18):5481-90