Mira Tout (1), Guillaume Cartron (2), Olivier Casasnovas (3), Michel Meignan (4), Thierry Lamy (5), Gilles Paintaud (1,6), David Ternant (1,6)
(1) CNRS UMR 7292 GICC, Université François Rabelais, Tours, France, (2) Département d’Hématologie, CHRU de Montpellier, Montpellier, France, (3) Département d’Hématologie, CHU de Dijon, Dijon, France, (4) Département de Médecine Nucléaire, Hôpital Henri Mondor, Créteil, France, (5) Service d'Hématologie Clinique, CHU, Université de Rennes, UMR 917, Rennes, France, (6) Laboratoire de Pharmacologie-Toxicologie, CHRU de Tours, Tours, France
Objectives: Rituximab is a chimeric anti-CD20 monoclonal antibody that has profoundly improved the treatment of B-cell malignancies. High variability in clinical response to rituximab is partly explained by pharmacokinetic (PK) variability [1, 2]. An inverse correlation between rituximab concentrations and tumor burden was observed in the pivotal study [3]. Here we aimed to describe rituximab PK and concentration-effect relationship and to quantify the impact of metabolic tumor volume (MTV0) [4] on rituximab PK parameters in patients with diffuse large B-cell lymphoma (DLBCL).
Methods: Data were available from 108 DLBCL patients who received rituximab 375 mg/m2 IV infusions every 2 weeks for 4 cycles. PK analyses were performed using non-linear mixed-effects modeling implemented in Monolix® 4.3.2. MTV0 was assessed with positron emission tomography (PET) at baseline. The tested covariates consisted of age, gender, body weight, height, BSA, MTV0, and baseline leucocytes and lymphocytes levels. Logistic regression was applied to evaluate AUC, MTV0 and other variables as predictors of response according to PET after cycle 4. Cut-off values associated with clinical response were determined by ROC curve analysis.
Results: A 2-compartment model with combined residual error was shown to adequately describe rituximab pharmacokinetics. The final PK model estimations of typical (interindividual standard deviation) clearance (CL), central (V1) and peripheral (V2) distribution volumes were 0.0232 L/h (48.2%), 3.96 L (28.7%) and 5.32 L (27.4%), respectively. V1 and V2 significantly increased by 2- and 9-fold between extreme MTV0 values of 0.8 and 4340 cm3, respectively. The increase in MTV0 was associated with lower exposure (R2 = 0.51, p < 0.0001) and a longer elimination half-life (R2 = 0.58, p < 0.0001). A high AUC in cycle 1 (AUC1 > 9667.31 mg.h/L) was significantly associated with a better clinical response (p < 0.001). Simulations suggest that patients with high MTV0 values may benefit of higher rituximab doses.
Conclusions: This study is the first to describe the tumor volume effect on rituximab pharmacokinetics in DLBCL patients using a population approach. An increase in MTV0 leaded to a decrease in rituximab exposure. A better clinical response was observed for higher exposure. This work may allow optimizing rituximab dose according to metabolic tumor volume.
References:
[1] Gordan LN, Grow WB, Pusateri A, Douglas V, Mendenhall NP, Lynch JW. Phase II trial of individualized rituximab dosing for patients with CD20-positive lymphoproliferative disorders. J Clin Oncol. 2005;23:1096-1102.
[2] Igarashi T, Kobayashi Y, Ogura M, et al. Factors affecting toxicity, response and progression-free survival in relapsed patients with indolent B-cell lymphoma and mantle cell lymphoma treated with rituximab: a Japanese phase II study. Ann Oncol. 2002;13:928-943.
[3] Berinstein NL, Grillo-López A, White CA, et al. Association of serum Rituximab (IDEC-C2B8) concentration and anti-tumor response in the treatment of recurrent low-grade or follicular non-Hodgkin’s lymphoma. Ann Oncol. 1998;9:995-1001.
[4] Meignan M, Sasanelli M, Casasnovas RO, et al. Metabolic tumour volumes measured at staging in lymphoma: methodological evaluation on phantom experiments and patients. Eur J Nucl Med Mol Imaging 2014;41:1113-1122.
Reference: PAGE 24 (2015) Abstr 3418 [www.page-meeting.org/?abstract=3418]
Poster: Drug/Disease modeling - Oncology