AIM: Rituximab has dramatically improved the survival of patients with non-Hodgkin's lymphomas (NHL), but the dosing regimen currently used should be optimized. However, the concentration-effect relationship of rituximab has never been described by pharmacokinetic-pharmacodynamic (PK-PD) modelling, precluding the simulation of new dosing regimens. The aim of this study was to develop a PK-PD model of rituximab in relapsed/resistant follicular NHL (FL). METHODS: A model describing the relationship between rituximab concentrations and progression-free survival (PFS) was developed using data extracted from the pivotal study, which evaluated 151 relapsed/resistant FL patients. The influence of FCGR3A genetic polymorphism on the efficacy of rituximab was quantified using data from 87 relapsed/resistant FL patients. The predictive performance of the model was analysed using two independent datasets: a study that evaluated rituximab combined with chemotherapy [rituximab, cyclophosphamide, vincristine, adriamycin and prednisone (R-CHOP)] in 334 relapsed/resistant FL patients and a study that evaluated rituximab monotherapy in 47 asymptomatic FL patients with known FCGR3A genotype. RESULTS: For R-CHOP, observed and model-predicted PFS (90% confidence interval) at 24 months were 0.50 and 0.48 (0.40, 0.56), respectively, for the observation arm, and 0.62 and 0.59 (0.50, 0.65), respectively, for the rituximab maintenance arm. For rituximab monotherapy, observed and predicted PFS at 24 months were 0.67 and 0.63, respectively, for FCGR3A-V/V patients, and 0.41 and 0.36 (0.25, 0.49), respectively, for FCGR3A-F carriers. CONCLUSIONS: Our model provides a satisfactory prediction of PFS at 24 months. It can be used to simulate new dosing regimens of rituximab in populations of FL patients and should improve the design of future clinical trials.
AIM: Rituximab has dramatically improved the survival of patients with non-Hodgkin's lymphomas (NHL), but the dosing regimen currently used should be optimized. However, the concentration-effect relationship of rituximab has never been described by pharmacokinetic-pharmacodynamic (PK-PD) modelling, precluding the simulation of new dosing regimens. The aim of this study was to develop a PK-PD model of rituximab in relapsed/resistant follicular NHL (FL). METHODS: A model describing the relationship between rituximab concentrations and progression-free survival (PFS) was developed using data extracted from the pivotal study, which evaluated 151 relapsed/resistant FL patients. The influence of FCGR3A genetic polymorphism on the efficacy of rituximab was quantified using data from 87 relapsed/resistant FL patients. The predictive performance of the model was analysed using two independent datasets: a study that evaluated rituximab combined with chemotherapy [rituximab, cyclophosphamide, vincristine, adriamycin and prednisone (R-CHOP)] in 334 relapsed/resistant FL patients and a study that evaluated rituximab monotherapy in 47 asymptomatic FL patients with known FCGR3A genotype. RESULTS: For R-CHOP, observed and model-predicted PFS (90% confidence interval) at 24 months were 0.50 and 0.48 (0.40, 0.56), respectively, for the observation arm, and 0.62 and 0.59 (0.50, 0.65), respectively, for the rituximab maintenance arm. For rituximab monotherapy, observed and predicted PFS at 24 months were 0.67 and 0.63, respectively, for FCGR3A-V/V patients, and 0.41 and 0.36 (0.25, 0.49), respectively, for FCGR3A-F carriers. CONCLUSIONS: Our model provides a satisfactory prediction of PFS at 24 months. It can be used to simulate new dosing regimens of rituximab in populations of FL patients and should improve the design of future clinical trials.
Authors: B D Cheson; S J Horning; B Coiffier; M A Shipp; R I Fisher; J M Connors; T A Lister; J Vose; A Grillo-López; A Hagenbeek; F Cabanillas; D Klippensten; W Hiddemann; R Castellino; N L Harris; J O Armitage; W Carter; R Hoppe; G P Canellos Journal: J Clin Oncol Date: 1999-04 Impact factor: 44.544
Authors: J Mangel; R Buckstein; K Imrie; D Spaner; E Franssen; P Pavlin; A Boudreau; N Pennell; D Combs; N L Berinstein Journal: Ann Oncol Date: 2003-05 Impact factor: 32.976
Authors: T Igarashi; Y Kobayashi; M Ogura; T Kinoshita; T Ohtsu; Y Sasaki; Y Morishima; T Murate; M Kasai; N Uike; M Taniwaki; Y Kano; K Ohnishi; Y Matsuno; S Nakamura; S Mori; Y Ohashi; K Tobinai Journal: Ann Oncol Date: 2002-06 Impact factor: 32.976
Authors: Michele Ghielmini; Shu-Fang Hsu Schmitz; Sergio B Cogliatti; Gabriella Pichert; Jörg Hummerjohann; Ursula Waltzer; Martin F Fey; Daniel C Betticher; Giovanni Martinelli; Fedro Peccatori; Urs Hess; Emanuele Zucca; Roger Stupp; Tibor Kovacsovics; Claudine Helg; Andreas Lohri; Mario Bargetzi; Daniel Vorobiof; Thomas Cerny Journal: Blood Date: 2004-02-19 Impact factor: 22.113