Mechanism-based pharmacokinetic/pharmacodynamic model for troxacitabine-induced neutropenia in cancer patients,10.1007/s00280-010-1393-y,Cancer Chemot

Mechanism-based pharmacokinetic/pharmacodynamic model for troxacitabine-induced neutropenia in cancer patients (Citations: 2)


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Purposes The objective of this study was to develop a mechanism-based population pharmacokinetic/pharmacodynamic (PK/PD) model in describing troxacitabine-induced neutropenia in patients with cancer. Methods A total of 727 PK/PD samples from 31 patients with cancer were included in the analysis. A mechanism-based population PD model was developed to describe neutropenia and the final model consisted of (1) a drug-sensitive uncommitted progenitor cell compartment (2) three transit compartments, and (3) a circulating neutrophil compartment with feedback mechanism. The troxacitabine affected the proliferation of sensitive progenitor cells through an inhibitory E max model. The model parameters were estimated using the MCPEM algorithm that was implemented in a parallel computing platform consisting of a single computer equipped with a quad-core INTEL central processor unit. Results and conclusions The mechanism-based PK/PD model developed using parallelized MCPEM method adequately describes the complex relationship between the exposure and absolute neutrophil counts in troxacitabine-treated patients with cancer. The simulation results suggested that the less frequent dosing schedule of troxacitabine used currently in clinical studies was associated with less incidence of neutropenia compared to more frequent dosing schedule.

Journal: Cancer Chemotherapy and Pharmacology - CANCER CHEMOTHER PHARMACOL , vol. 67, no. 5, pp. 985-994, 2011

DOI: 10.1007/s00280-010-1393-y

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Keywords (7)

Mechanism-based pharmacokinetic/pharmacodynamic model for troxacitabine-induced neutropenia in cancer patients,10.1007/s00280-010-1393-y,Cancer Chemot

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Citations (2)

Development of a New Pre and Post-Processing Tool (SADAPT-TRAN) for Nonlinear Mixed-Effects Modeling in S-ADAPT

Performance and Robustness of the Monte Carlo Importance Sampling Algorithm Using Parallelized S-ADAPT for Basic and Complex Mechanistic Models


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