PURPOSE: Paclitaxel and carboplatin are frequently used in advanced ovarian cancer following cytoreductive surgery. Threshold models have been used to predict paclitaxel pharmacokinetic-pharmacodynamics, whereas the time above paclitaxel plasma concentration of 0.05 to 0.2 micromol/L (t(C > 0.05-0.2)) predicts neutropenia. The objective of this study was to build a population pharmacokinetic-pharmacodynamic model of paclitaxel/carboplatin in ovarian cancer patients. EXPERIMENTAL DESIGN: One hundred thirty-nine ovarian cancer patients received paclitaxel (175 mg/m(2)) over 3 h followed by carboplatin area under the concentration-time curve 5 mg/mL*min over 30 min. Plasma concentration-time data were measured, and data were processed using nonlinear mixed-effect modeling. Semiphysiologic models with linear or sigmoidal maximum response and threshold models were adapted to the data. RESULTS: One hundred five patients had complete pharmacokinetic and toxicity data. In 34 patients with measurable disease, objective response rate was 76%. Neutrophil and thrombocyte counts were adequately described by an inhibitory linear response model. Paclitaxel t(C > 0.05) was significantly higher in patients with a complete (91.8 h) or partial (76.3 h) response compared with patients with progressive disease (31.5 h; P = 0.02 and 0.05, respectively). Patients with paclitaxel t(C > 0.05) > 61.4 h (mean value) had a longer time to disease progression compared with patients with paclitaxel t(C > 0.05) < 61.4 h (89.0 versus 61.9 weeks; P = 0.05). Paclitaxel t(C > 0.05) was a good predictor for severe neutropenia (P = 0.01), whereas carboplatin exposure (C(max) and area under the concentration-time curve) was the best predictor for thrombocytopenia (P < 10(-4)). CONCLUSIONS: In this group of patients, paclitaxel t(C > 0.05) is a good predictive marker for severe neutropenia and clinical outcome, whereas carboplatin exposure is a good predictive marker for thrombocytopenia.
PURPOSE:Paclitaxel and carboplatin are frequently used in advanced ovarian cancer following cytoreductive surgery. Threshold models have been used to predict paclitaxel pharmacokinetic-pharmacodynamics, whereas the time above paclitaxel plasma concentration of 0.05 to 0.2 micromol/L (t(C > 0.05-0.2)) predicts neutropenia. The objective of this study was to build a population pharmacokinetic-pharmacodynamic model of paclitaxel/carboplatin in ovarian cancerpatients. EXPERIMENTAL DESIGN: One hundred thirty-nine ovarian cancerpatients received paclitaxel (175 mg/m(2)) over 3 h followed by carboplatin area under the concentration-time curve 5 mg/mL*min over 30 min. Plasma concentration-time data were measured, and data were processed using nonlinear mixed-effect modeling. Semiphysiologic models with linear or sigmoidal maximum response and threshold models were adapted to the data. RESULTS: One hundred five patients had complete pharmacokinetic and toxicity data. In 34 patients with measurable disease, objective response rate was 76%. Neutrophil and thrombocyte counts were adequately described by an inhibitory linear response model. Paclitaxel t(C > 0.05) was significantly higher in patients with a complete (91.8 h) or partial (76.3 h) response compared with patients with progressive disease (31.5 h; P = 0.02 and 0.05, respectively). Patients with paclitaxel t(C > 0.05) > 61.4 h (mean value) had a longer time to disease progression compared with patients with paclitaxel t(C > 0.05) < 61.4 h (89.0 versus 61.9 weeks; P = 0.05). Paclitaxel t(C > 0.05) was a good predictor for severe neutropenia (P = 0.01), whereas carboplatin exposure (C(max) and area under the concentration-time curve) was the best predictor for thrombocytopenia (P < 10(-4)). CONCLUSIONS: In this group of patients, paclitaxel t(C > 0.05) is a good predictive marker for severe neutropenia and clinical outcome, whereas carboplatin exposure is a good predictive marker for thrombocytopenia.
Authors: Marie-Rose B S Crombag; Stijn L W Koolen; Sophie Wijngaard; Markus Joerger; Thomas P C Dorlo; Nielka P van Erp; Ron H J Mathijssen; Jos H Beijnen; Alwin D R Huitema Journal: Pharm Res Date: 2019-10-15 Impact factor: 4.200
Authors: Daniel L Hertz; Kelley M Kidwell; Kiran Vangipuram; Feng Li; Manjunath P Pai; Monika Burness; Jennifer J Griggs; Anne F Schott; Catherine Van Poznak; Daniel F Hayes; Ellen M Lavoie Smith; N Lynn Henry Journal: Clin Cancer Res Date: 2018-04-27 Impact factor: 12.531
Authors: Markus Joerger; Stefanie Kraff; Alwin D R Huitema; Gary Feiss; Berta Moritz; Jan H M Schellens; Jos H Beijnen; Ulrich Jaehde Journal: Clin Pharmacokinet Date: 2012-09-01 Impact factor: 6.447
Authors: Mario González-Sales; Belén Valenzuela; Carlos Pérez-Ruixo; Carlos Fernández Teruel; Bernardo Miguel-Lillo; Arturo Soto-Matos; Juan Jose Pérez-Ruixo Journal: Clin Pharmacokinet Date: 2012-11 Impact factor: 6.447