Camille Riff (1,2), Caroline Dartigeas (3), Gilles Paintaud (1,2), David Ternant (1,2)
(1) EA GICC, Université de Tours, Tours, France (2) Laboratoire de Pharmacologie-Toxicologie, CHRU de Tours, Tours, France, (3) Hématologie et Thérapie Cellulaire, CHRU Tours, Tours, France
Introduction: Rituximab is a chimeric human/mouse immunoglobulin G1 (IgG1) monoclonal antibody that binds specifically to the CD20 antigen present on the surface of normal and neoplastic B-lymphocytes and results in B-cell depletion. The pharmacokinetics (PK) of rituximab is characterized by a large variability, which could affect the clinical response to rituximab. In elderly patients, the influence of target-antigen burden (lymphocyte turnover) is expected to be different compared to younger patients [1]. A randomized, open-label, multicenter phase 3 trial was conducted to evaluate rituximab maintenance following an induction with rituximab in elderly patients with chronic lymphocytic leukemia (CLL). Binet staging system was used to classify CLL and to guide the initiation of treatment. This study enrolled treatment-naïve and fit patients aged 65 years or older with an active Binet stage B or C chronic lymphocytic leukemia requiring treatment.
Objectives: The objective of this analysis was to develop a population pharmacokinetic (PopPK) model for rituximab administered in elderly patients with CLL and to assess relationships between Pop PK parameters and potential individual factors of variability.
Methods: Included patients (CLL 2007 SA) [2] received induction treatment consisting of four monthly courses of full FCR (fludarabine, cyclophosphamide, rituximab) with two additional rituximab doses on day 14 of cycles 1 and 2. Rituximab was administered at a dose of 375 mg/m2 intravenously on day 0 of cycle 1 and subsequently at 500 mg/m2. Through and peak rituximab concentrations were determined before and after each rituximab infusion. Additional pharmacokinetic samples were collected one week after the first infusion and at the end of the induction phase during response assessment. The potential covariates collected included demographics characteristics (weight, age, sex, body mass index and body surface area), biological factors (baseline serum albumin concentration and lymphocyte count), and Binet stage. The population pharmacokinetic model was implemented using Monolix software version 2018R2 (Lixoft®, Antony, France).
Results: A total of 591 rituximab concentrations from 69 patients (25 women, age ranging from 64 to 86 years, weight from 39 to 121 kg) were available. The semi-mechanistic model including two compartments with linear and nonlinear target-mediated elimination. A significant correlation (r = 0.81) was found between linear clearance and the central volume of distribution. Age significantly influenced the central and the peripheral volume of distribution while Binet stage significantly influenced the production rate constant of target antigen. Lymphocyte count had no influence on rituximab PK. The mean PK parameter estimates (interindividual standard deviation) were linear clearance CL = 0.32 L/d (80.9%) central volume of distribution V1 = 5.41 L (57.5%) intercompartment clearance Q = 1.03 L/d, peripheral volume of distribution V2 = 7.73 L (70.8%), zero-order production rate constant of target antigen kin = 0.001 nmol/d (50.9%), first-order rate constant of rituximab-independent death latent target antigen kout = 1.60 x 10-4 d-1,mean maturation time MMT = 19.7 d and rituximab target-mediated elimination rate constant kdeg = 6.70 nmol-1.d-1(89.9%), respectively.
Conclusions and perspectives: A popPK model was developed and validated for rituximab in elderly patients with chronic lymphocytic leukemia. This is the first description of the rituximab PK in this population showing the nonlinear elimination of rituximab. The next step of this study will be to quantify the impact of baseline lymphocyte counts on concentration-response relationship, and prognosis in elderly patients with CLL treated with standard doses of rituximab, and to propose an optimize regimen for rituximab treatment in this population.
[1] Tout M, Clin Pharmacokinet. 2017 Jun;56(6)
[2] Dartigeas C, Lancet Haematol 2018 Feb;5(2)
Reference: PAGE 28 (2019) Abstr 9174 [www.page-meeting.org/?abstract=9174]
Poster: Drug/Disease Modelling - Other Topics