Amina Bensalem1, Guillaume Cartron2,3, Ulrich Specks4, Emmanuel Gyan5, Gilles Paintaud1,6, Denis Mulleman1,7, David Ternant1,6.
1Université de Tours, EA 7501 GICC, Tours, France, 2CNRS UMR 5235, Université de Montpellier, Montpellier, France, 3Hematology, CHRU Montpellier, Montpellier France, 4Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA, 5Department of Hematology and Cell Therapy, Clinical Investigations Center INSERM U1415, CHU Tours, Tours, France, 6CHRU de Tours, Department of Medical Pharmacology, Tours, France, 7CHRU de Tours, Department of Rheumatology, Tours, France.
Objectives:
The pharmacokinetics of rituximab was described in 17 previous studies using nonlinear mixed-effects modeling: two in chronic lymphocytic leukemia (CLL), six in diffuse large B-cell lymphoma (DLBCL), one in follicular lymphoma (FL), three in rheumatoid arthritis (RA), and one in anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV). However, pharmacokinetic profiles were very variable between diseases. The objectives of this study were to investigate the pharmacokinetics of rituximab in these diseases in an integrated pharmacokinetic model to quantify the differences in pharmacokinetic parameters between diseases.
Methods:
Pharmacokinetic data were available from 118 CLL patients [1], 119 DLBCL patients, 14 FL patients [2], 90 RA patients [3, 4], and 92 AAV patients [5]. Patients with CLL were randomly assigned to receive 6 FCR (fludarabine, cyclophosphamide, rituximab) cycles every 28 days, with rituximab IV doses of 375 mg/m2 in cycle 1 and 500 mg/m2 in cycles 2-6 (standard FCR) or a prephase of intensified rituximab course (500 mg on day 0, and 2000 mg on days 1, 8, and 15) followed by 6 FCR cycles every 28 days with 500 mg/m2 rituximab IV (dense FCR). Patients with DLBCL had received IV of 375 mg/m2 of rituximab every 2 weeks for 4 cycles. Patients with RA had received 2 IV of 1000 mg of rituximab 2 weeks apart. Patients with VAA had received 4 weekly IV of 375 mg/m2 of rituximab. Pharmacokinetic data were described using two compartment models as mainly reported in literature [6] with first-order elimination only, or coupled with target-mediated elimination description: Michaelis-Menten, irreversible binding target-mediated drug disposition model were tested. Diseases and mass tumor volume (MTV) measured in DLBCL and FL patients were tested as categorical and quantitative covariate, respectively. RA was taken as the reference disease. Nonlinear mixed-effect modelling was applied using Monolix Suite® 2019R2.
Results:
A total of 3764 rituximab concentrations from 433 patients were assessed in this study. These concentrations were best described using a two-compartment model with both linear and irreversible binding target-mediated elimination. The population pharmacokinetic parameter estimates (interindividual standard deviation) were central volume of distribution V = 3.4 L (0.34), first-order elimination and transfer rate constants were k10 = 0.10 day-1 (0.34), k12 = 0.12 day-1 (0.44), k21 = 0.09 day-1 (-), initial latent variable amount B0 = 3920 nmol, first-order target elimination rate constant kout = 2.0 10-5 (day-1) and second-order target-mediated elimination rate constant was kdeg = 2.3 10-4 nmol-1 day−1 (1.14). Compared to other categories, V was multiplied by 1.6 and divided by 2 in DLBCL and VAA, respectively; k10 was divided by 2 and 1.2 in CLL and VAA, respectively; B0 was multiplied by 27, 10 and 12 in CLL, DLBCL and FL, respectively. In both DLBCL and FL patients, B0 increased with MTV (allometric coefficient = 0.50).
Conclusions:
This is the first description of rituximab pharmacokinetics in patients with several cancer and non-cancer diseases. We showed that rituximab pharmacokinetics is strongly altered by the underlying disease. Notably, cancer diseases are associated with higher antigen amounts which lead to decreased rituximab exposure compared to inflammatory diseases.
References:
[1] Tout M, Gagez AL, Lepretre S, Gouilleux-Gruart V, Azzopardi N, Delmer A, et al. Influence of FCGR3A-158V/F Genotype and Baseline CD20 Antigen Count on Target-Mediated Elimination of Rituximab in Patients with Chronic Lymphocytic Leukemia: A Study of FILO Group. Clin Pharmacokinet. 2017 Jun;56(6):635-47.
[2] Tout M, Casasnovas O, Meignan M, Lamy T, Morschhauser F, Salles G, et al. Rituximab exposure is influenced by baseline metabolic tumor volume and predicts outcome of DLBCL patients: a Lymphoma Study Association report. Blood. 2017 May 11;129(19):2616-23.
[3] Lioger B, Edupuganti SR, Mulleman D, Passot C, Desvignes C, Bejan-Angoulvant T, et al. Antigenic burden and serum IgG concentrations influence rituximab pharmacokinetics in rheumatoid arthritis patients. Br J Clin Pharmacol. 2017 Aug;83(8):1773-81.
[4] Bensalem A, Mulleman D, Thibault G, Azzopardi N, Goupille P, Paintaud G, et al. CD4+ count-dependent concentration-effect relationship of rituximab in rheumatoid arthritis. Br J Clin Pharmacol. 2019 Aug 27.
[5] Bensalem A, Mulleman D, Paintaud G, Azzopardi N, Gouilleux-Gruart V, Cornec D, et al. Non-Linear Rituximab Pharmacokinetics and Complex Relationship between Rituximab Concentrations and Anti-Neutrophil Cytoplasmic Antibodies (ANCA) in ANCA-Associated Vasculitis: The RAVE Trial Revisited. Clin Pharmacokinet. 2019 Oct 5.
[6] Ternant D, Azzopardi N, Raoul W, Bejan-Angoulvant T, Paintaud G. Influence of Antigen Mass on the Pharmacokinetics of Therapeutic Antibodies in Humans. Clin Pharmacokinet. 2019 Feb;58(2):169-87.
Reference: PAGE () Abstr 9559 [www.page-meeting.org/?abstract=9559]
Poster: Clinical Applications