Amina Bensalem (1,2), Guillaume Cartron (3), Ulrich Specks (4), Denis Mulleman (1,5), Emmanuel Gyan (6), Olivier Casasnovas (7), Thierry Lamy (8), Stéphane Leprêtre (9), Gilles Paintaud (10), David Ternant(10).
(1) Université de Tours, EA 7501 GICC, Tours, France, (2) R&D, Ceva Santé Animale, Libourne, France, (3) CNRS UMR 5235, Université de Montpellier, Montpellier, France; Department of Hematology, CHRU Montpellier, Montpellier, France, (4) Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA, (5) CHRU de Tours, Department of Rheumatology, Tours, France, (6) Department of Hematology and Cell Therapy, Clinical Investigations Center INSERM U1415, CHU Tours, Tours, France, (7) Department of Clinical Hematology, CHU Dijon, Dijon, France; INSERM Lipids, Nutrition, Cancer (LNC) UMR 866, Dijon, France, (8) Department of Clinical Hematology, CHU Rennes, U917, Rennes 1 University, Rennes, France, (9) Department of Hematology, Henri Becquerel Center, Rouen, France, (10) Université de Tours, EA 4245 T2I, Tours, France; Department of Medical Pharmacology, CHRU de Tours, Tours, France.
Objectives: The pharmacokinetics of rituximab was described in 20 previous studies using nonlinear mixed-effects modeling in chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), rheumatoid arthritis (RA), and anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV). Moreover, the amount of target antigen was reported to influence rituximab pharmacokinetics in several publications. However, pharmacokinetic modeling approaches and results were very variable between diseases and publications. The objectives of this study were to investigate the influence of antigen amount on rituximab pharmacokinetic variability in these diseases using an integrated model.
Methods: Pharmacokinetic data were available from 118 CLL patients [1], 119 DLBCL patients, 14 FL patients [2, 3], 90 RA patients [4, 5], and 92 AAV patients [6]. A total of 3835 rituximab concentrations were used: 1942 from CLL, 953 from DLBCL, 66 from FL, 398 from RA and, 476 from AAV patients. Patients with CLL were randomly assigned to receive IV rituximab at doses of 375 mg/m2 in cycle 1 and 500 mg/m2 in cycles 2-6 with 6 FCR (fludarabine, cyclophosphamide, rituximab) cycles every 28 days (standard FCR), or a prephase of intensified rituximab course (500 mg IV on day 0, and 2000 mg IV on days 1, 8, and 15) followed by 6 FCR cycles every 28 days with IV rituximab at doses of 500 mg/m2 (dense FCR). Patients with DLBCL and FL had received IV rituximab at doses of 375 mg/m2 every 14 days for 4 cycles (standard) with supplemental IV rituximab at days 1 and 4 after the first IV for some of them (dense). Patients with RA had received 2 IV rituximab at doses of 1000 mg every 14 days. Patients with AAV had received 4 weekly IV rituximab at doses of 375 mg/m2. Sampling strategies were different among studies; in all studies (except for RAVE, AAV patients), blood samples were collected to measure serum rituximab concentrations before and two hours after each rituximab IV, and some additional samples were taken between two weeks and 18 months after the last IV. For RAVE study, blood samples were collected before the first IV, week 2, months 1, 2, 4, 6, 9, 18, and every 6 months until the second rituximab cycle, if any. Pharmacokinetic data were described using two compartment models as mainly reported in literature [7]. Description of target antigen elimination was attempted using Michaelis-Menten, clearance exponentially decreasing with time, and irreversible binding target-mediated drug disposition models. Diseases and mass tumor volume (MTV) measured in DLBCL and FL patients were tested covariates if relevant. RA was taken as the reference disease. Nonlinear mixed-effect modelling was used using Monolix Suite® 2019R2.
Results: Rituximab concentrations were best described using a two-compartment model with both linear and irreversible binding target-mediated elimination. Central volume of distribution was 1.7 fold higher in DLBCL compared to RA, FL, CLL, and it was 1.8 fold higher in RA, FL, CLL compared to AAV. First-order elimination rate constant was 1.8 and 1.3 fold higher in RA, DLBCL, FL compared to CLL and AAV. Baseline latent antigen level (L0) was 54, 20 and 29 fold higher in CLL, DLBCL and FL, respectively compared to RA and AAV. In lymphomas, L0 was increased with baseline metabolic tumor volume (TMTV0, p=6.10-7). In CLL, second-order target-mediated elimination rate constant (kdeg) significantly increased with baseline CD20 count on circulating B cells (CD20cir, p=0.0081).
Conclusions: This is the first study that investigated the influence of underlying disease (inflammatory diseases and neoplasias) on the variability of rituximab pharmacokinetics using integrated semi-mechanistic model. This influence was associated target antigen amount. Notably, neoplasias 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] Ternant D, Monjanel H, Venel Y, Prunier-Aesch C, Arbion F, Colombat P, et al. Nonlinear pharmacokinetics of rituximab in non-Hodgkin lymphomas: A pilot study. Br J Clin Pharmacol. 2019 May 21.
[4] 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.
[5] 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.
[6] 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.
[7] 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 29 (2021) Abstr 9626 [www.page-meeting.org/?abstract=9626]
Poster: Drug/Disease Modelling - Other Topics