Development of a drug-disease simulation model for rituximab in follicular non-Hodgkin's lymphoma.
OBJECTIVE
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.
Ternant
David
D
Université François Rabelais Tours, GICC, Tours, France.
Hénin
Emilie
E
Cartron
Guillaume
G
Tod
Michel
M
Paintaud
Gilles
G
Girard
Pascal
P
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000911:Antibodies, Monoclonal / Q000008:administration & dosage / Q000493:pharmacokinetics* / Q000494:pharmacology; D058846:Antibodies, Monoclonal, Murine-Derived; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000008:administration & dosage / Q000493:pharmacokinetics* / Q000494:pharmacology; D003520:Cyclophosphamide / Q000008:administration & dosage; D018572:Disease-Free Survival; D004317:Doxorubicin; D006801:Humans; D008224:Lymphoma, Follicular / Q000188:drug therapy*; D008228:Lymphoma, Non-Hodgkin / Q000188:drug therapy*; D008875:Middle Aged; D008962:Models, Theoretical; D011241:Prednisone / Q000008:administration & dosage; D016032:Randomized Controlled Trials as Topic; D017452:Receptors, IgG; D000069283:Rituximab; D014750:Vincristine / Q000008:administration & dosage; D055815:Young Adult
D000911:Antibodies, Monoclonal; D058846:Antibodies, Monoclonal, Murine-Derived; C496124:FCGR3A protein, human; D017452:Receptors, IgG; D000069283:Rituximab; D014750:Vincristine; D004317:Doxorubicin; D003520:Cyclophosphamide; D011241:Prednisone