Skip to main content
SpringerLink
Log in

The Influence of Underlying Disease on Rituximab Pharmacokinetics May be Explained by Target-Mediated Drug Disposition

  • Original Research Article
  • Published:
Clinical Pharmacokinetics Aims and scope Submit manuscript

Abstract

Background and Objectives

Rituximab is an anti-CD20 monoclonal antibody approved in several diseases, including chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), rheumatoid arthritis (RA), and anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV). The influence of underlying disease on rituximab pharmacokinetics has never been investigated for several cancer and non-cancer diseases simultaneously. This study aimed at assessing this influence using an integrated semi-mechanistic model accounting for target-mediated elimination of rituximab.

Methods

Rituximab concentration–time data from five studies previously published in patients with CLL, DLBCL, FL, RA, and AAV were described using a two-compartment model with irreversible binding of rituximab to its target antigen. Both underlying disease and target antigen measurements were assessed as covariates.

Results

Central volume of distribution was [95% confidence interval] 1.7-fold [1.6–1.9] higher in DLBCL than in RA, FL, and CLL, and it was 1.8-fold [1.6–2.1] higher in RA, FL, and CLL than in AAV. First-order elimination rate constants were 1.8-fold [1.7–2.0] and 1.3-fold [1.2–1.5] higher in RA, DLBCL, and FL than in CLL and AAV, respectively. Baseline latent antigen level (L0) was 54-fold [30–94], 20-fold [11–36], and 29-fold [14–64] higher in CLL, DLBCL, and FL, respectively, than in RA and AAV. In lymphoma, L0 increased with baseline total metabolic tumor volume (p = 6.10−7). In CLL, the second-order target-mediated elimination rate constant (kdeg) increased with baseline CD20 count on circulating B cells (CD20cir, p = 0.0081).

Conclusions

Our results show for the first time that rituximab pharmacokinetics is strongly influenced by underlying disease and disease activity. Notably, neoplasms are associated with higher antigen amounts that result in decreased exposure to rituximab compared to inflammatory diseases. Our model might be used to estimate unbound target amounts in upcoming studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Bensalem A, Ternant D. Pharmacokinetic variability of therapeutic antibodies in humans: a comprehensive review of population pharmacokinetic modeling publications. Clin Pharmacokinet. 2020;59(7):857–74.

    Article  CAS  Google Scholar 

  2. Jing Li ML, Charoin J-E, Frey N, Kheoh T, Ren S, Woo M, et al. Rituximab exhibits a long half-life based on a population pharmacokinetic analysis in non-Hodgkin’s lymphoma (NHL) patients. Blood. 2007;110(11):2371.

    Article  Google Scholar 

  3. Rozman S, Grabnar I, Novakovic S, Mrhar A, Jezersek NB. Population pharmacokinetics of rituximab in patients with diffuse large B-cell lymphoma and association with clinical outcome. Br J Clin Pharmacol. 2017;83(8):1782–90.

    Article  CAS  Google Scholar 

  4. 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;56(6):635–47.

    Article  CAS  Google Scholar 

  5. 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;129(19):2616–23.

    Article  CAS  Google Scholar 

  6. Regazzi MB, Iacona I, Avanzini MA, Arcaini L, Merlini G, Perfetti V, et al. Pharmacokinetic behavior of rituximab: a study of different schedules of administration for heterogeneous clinical settings. Ther Drug Monit. 2005;27(6):785–92.

    Article  CAS  Google Scholar 

  7. Muller C, Murawski N, Wiesen MH, Held G, Poeschel V, Zeynalova S, et al. The role of sex and weight on rituximab clearance and serum elimination half-life in elderly patients with DLBCL. Blood. 2012;119(14):3276–84.

    Article  Google Scholar 

  8. Candelaria M, Gonzalez D, Fernandez Gomez FJ, Paravisini A, Del Campo GA, Perez L, et al. Comparative assessment of pharmacokinetics, and pharmacodynamics between RTXM83, a rituximab biosimilar, and rituximab in diffuse large B-cell lymphoma patients: a population PK model approach. Cancer Chemother Pharmacol. 2018;81(3):515–27.

    Article  CAS  Google Scholar 

  9. Blasco H, Chatelut E, de Bretagne IB, Congy-Jolivet N, Le Guellec C. Pharmacokinetics of rituximab associated with CHOP chemotherapy in B-cell non-Hodgkin lymphoma. Fundam Clin Pharmacol. 2009;23(5):601–8.

    Article  CAS  Google Scholar 

  10. Li J, Zhi J, Wenger M, Valente N, Dmoszynska A, Robak T, et al. Population pharmacokinetics of rituximab in patients with chronic lymphocytic leukemia. J Clin Pharmacol. 2012;52(12):1918–26.

    Article  CAS  Google Scholar 

  11. Assouline S, Buccheri V, Delmer A, Gaidano G, McIntyre C, Brewster M, et al. Pharmacokinetics and safety of subcutaneous rituximab plus fludarabine and cyclophosphamide for patients with chronic lymphocytic leukaemia. Br J Clin Pharmacol. 2015;80(5):1001–9.

    Article  CAS  Google Scholar 

  12. 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;83(8):1773–81.

    Article  CAS  Google Scholar 

  13. Ng CM, Bruno R, Combs D, Davies B. Population pharmacokinetics of rituximab (anti-CD20 monoclonal antibody) in rheumatoid arthritis patients during a phase II clinical trial. J Clin Pharmacol. 2005;45(7):792–801.

    Article  CAS  Google Scholar 

  14. Puisset F, White-Koning M, Kamar N, Huart A, Haberer F, Blasco H, et al. Population pharmacokinetics of rituximab with or without plasmapheresis in kidney patients with antibody-mediated disease. Br J Clin Pharmacol. 2013;76(5):734–40.

    Article  CAS  Google Scholar 

  15. Gota V, Karanam A, Rath S, Yadav A, Tembhare P, Subramanian P, et al. Population pharmacokinetics of Reditux, a biosimilar rituximab, in diffuse large B-cell lymphoma. Cancer Chemother Pharmacol. 2016;78(2):353–9.

    Article  CAS  Google Scholar 

  16. 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;85(9):2002–10.

    Article  CAS  Google Scholar 

  17. Lavezzi SM, de Jong J, Neyens M, Cramer P, Demirkan F, Fraser G, et al. Systemic exposure of rituximab increased by ibrutinib: pharmacokinetic results and modeling based on the HELIOS trial. Pharm Res. 2019;36(7):93.

    Article  Google Scholar 

  18. 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;85(12):2747–58.

    Article  CAS  Google Scholar 

  19. 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. 2020;59(4):519–30.

    Article  CAS  Google Scholar 

  20. Fogueri U, Cheungapasitporn W, Bourne D, Fervenza FC, Joy MS. Rituximab exhibits altered pharmacokinetics in patients with membranous nephropathy. Ann Pharmacother. 2019;53(4):357–63.

    Article  CAS  Google Scholar 

  21. Li J, Levi M, Charoin J, Frey N, Kheoh T, Ren S, et al. Rituximab exhibits a long half-life based on a population pharmacokinetic analysis in non-Hodgkin’s lymphoma (NHL) patients. Blood. 2007;110(11):2371.

    Article  Google Scholar 

  22. Liu S, Huang H, Chen RX, Wang Z, Guan YP, Peng C, et al. Low initial trough concentration of rituximab is associated with unsatisfactory response of first-line R-CHOP treatment in patients with follicular lymphoma with grade 1/2. Acta Pharmacol Sin. 2021;42:641–7.

    Article  CAS  Google Scholar 

  23. 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;58(2):169–87.

    Article  CAS  Google Scholar 

  24. Passot C, Mulleman D, Bejan-Angoulvant T, Aubourg A, Willot S, Lecomte T, et al. The underlying inflammatory chronic disease influences infliximab pharmacokinetics. MAbs. 2016;8(7):1407–16.

    Article  CAS  Google Scholar 

  25. Specks UMP, Hoffman GS, Langford CA, Spiera R, Seo P, et al. Design of the rituximab in ANCA-associated vasculitis (RAVE) trial. Open Arthritis J. 2011;4:1–18.

    Article  CAS  Google Scholar 

  26. Stone JH, Merkel PA, Spiera R, Seo P, Langford CA, Hoffman GS, et al. Rituximab versus cyclophosphamide for ANCA-associated vasculitis. N Engl J Med. 2010;363(3):221–32.

    Article  CAS  Google Scholar 

  27. Blasco H, Lalmanach G, Godat E, Maurel MC, Canepa S, Belghazi M, et al. Evaluation of a peptide ELISA for the detection of rituximab in serum. J Immunol Methods. 2007;325(1–2):127–39.

    Article  CAS  Google Scholar 

  28. Mills JR, Cornec D, Dasari S, Ladwig PM, Hummel AM, Cheu M, et al. Using mass spectrometry to quantify rituximab and perform individualized immunoglobulin phenotyping in ANCA-associated vasculitis. Anal Chem. 2016;88(12):6317–25.

    Article  CAS  Google Scholar 

  29. Pearson TC, Guthrie DL, Simpson J, Chinn S, Barosi G, Ferrant A, et al. Interpretation of measured red cell mass and plasma volume in adults: expert panel on Radionuclides of the International Council for Standardization in Haematology. Br J Haematol. 1995;89(4):748–56.

    Article  CAS  Google Scholar 

  30. Melet J, Mulleman D, Goupille P, Ribourtout B, Watier H, Thibault G. Rituximab-induced T cell depletion in patients with rheumatoid arthritis: association with clinical response. Arthritis Rheum. 2013;65(11):2783–90.

    Article  CAS  Google Scholar 

  31. Cornec D, Kabat BF, Mills JR, Cheu M, Hummel AM, Schroeder DR, et al. Pharmacokinetics of rituximab and clinical outcomes in patients with anti-neutrophil cytoplasmic antibody associated vasculitis. Rheumatology (Oxford). 2018;57(4):639–50.

    Article  CAS  Google Scholar 

  32. Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 20. Eur J Nucl Med Mol Imaging. 2015;42(2):328–54.

    Article  CAS  Google Scholar 

  33. Meignan M, Sasanelli M, Casasnovas RO, Luminari S, Fioroni F, Coriani C, 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(6):1113–22.

    Article  CAS  Google Scholar 

  34. Savic RM, Karlsson MO. Importance of shrinkage in empirical bayes estimates for diagnostics: problems and solutions. AAPS J. 2009;11(3):558–69.

    Article  Google Scholar 

  35. Brendel K, Comets E, Laffont C, Mentre F. Evaluation of different tests based on observations for external model evaluation of population analyses. J Pharmacokinet Pharmacodyn. 2010;37(1):49–65.

    Article  Google Scholar 

  36. RStudio Team. RStudio: integrated development for R. 2015. Boston: RStudio, Inc. http://www.rstudio.com/. Accessed 13 Oct 2021.

  37. Marc Lavielle. mlxR: simulation of longitudinal data. R package version 4.1.0. 2017. https://CRAN.R-project.org/package=mlxR. Accessed 13 Oct 2021.

  38. Dartigeas C, Van Den Neste E, Leger J, Maisonneuve H, Berthou C, Dilhuydy MS, et al. Rituximab maintenance versus observation following abbreviated induction with chemoimmunotherapy in elderly patients with previously untreated chronic lymphocytic leukaemia (CLL 2007 SA): an open-label, randomised phase 3 study. Lancet Haematol. 2018;5(2):e82-94.

    Article  Google Scholar 

  39. Dai HI, Vugmeyster Y, Mangal N. Characterizing exposure-response relationship for therapeutic monoclonal antibodies in immuno-oncology and beyond: challenges, perspectives, and prospects. Clin Pharmacol Ther. 2020;108(6):1156–70.

    Article  CAS  Google Scholar 

  40. Le Louedec F, Alix-Panabieres C, Lafont T, Allal BC, Garrel R, Digue L, et al. Cetuximab pharmacokinetic/pharmacodynamics relationships in advanced head and neck carcinoma patients. Br J Clin Pharmacol. 2019;85(6):1357–66.

    Article  Google Scholar 

  41. Le Louedec F, Chatelut E. Correlation between bevacizumab exposure and survival does not necessarily imply causality. Oncologist. 2020;25(12):e2022.

    Article  Google Scholar 

  42. Lamy T, Damaj G, Soubeyran P, Gyan E, Cartron G, Bouabdallah K, et al. R-CHOP 14 with or without radiotherapy in nonbulky limited-stage diffuse large B-cell lymphoma. Blood. 2018;131(2):174–81.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Université de Tours, EA 7501 GICC, Tours, France

    Amina Bensalem & Denis Mulleman

  2. CNRS UMR 5235, Université de Montpellier, Montpellier, France

    Guillaume Cartron

  3. Department of Hematology, CHRU Montpellier, Montpellier, France

    Guillaume Cartron

  4. Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA

    Ulrich Specks & Divi Cornec

  5. Department of Rheumatology, CHRU de Tours, Tours, France

    Denis Mulleman

  6. Department of Hematology and Cell Therapy, Clinical Investigations Center INSERM U1415, CHU Tours, Tours, France

    Emmanuel Gyan

  7. Rheumatology Department, Brest University Hospital, and INSERM U1227, Brest, France

    Divi Cornec

  8. Université de Tours, EA 4245 T2I, Tours, France

    Celine Desvignes, Gilles Paintaud & David Ternant

  9. Service de Pharmacologie Médicale, CHU Bretonneau, 2 Boulevard Tonnellé, 37044, Tours, France

    Celine Desvignes, Gilles Paintaud & David Ternant

  10. Department of Clinical Hematology, CHU Dijon, Dijon, France

    Olivier Casasnovas

  11. INSERM Lipids, Nutrition, Cancer (LNC) UMR 866, Dijon, France

    Olivier Casasnovas

  12. Department of Clinical Hematology, CHU Rennes, U917, Rennes, France

    Thierry Lamy

  13. Department of Hematology, Henri Becquerel Center, Rouen, France

    Stéphane Leprêtre

Authors
  1. Amina Bensalem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guillaume Cartron
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ulrich Specks
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Denis Mulleman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Emmanuel Gyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Divi Cornec
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Celine Desvignes
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Olivier Casasnovas
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Thierry Lamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Stéphane Leprêtre
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Gilles Paintaud
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. David Ternant
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Ternant.

Ethics declarations

Funding

This study was made using rituximab pharmacokinetic data belonging to the Lymphoma Study Association (France), French Innovative Leukemia Organization (FILO, France), Mayo Clinic (Rochester, MO, USA), and Tours University Hospital (Tours, France). The study in patients with diffuse large B-cell lymphoma and follicular lymphoma was funded by GELA and GOELAMS groups and F. Hoffman-La Roche Ltd (Basel, Switzerland). The study in chronic lymphocytic leukemia was funded by FILO Group and Roche SAS (Neuilly, France). The study comparing rituximab and cyclophosphamide in anti-neutrophil cytoplasmic autoantibody-associated vasculitis (RAVE) was supported by a grant from the National Institute of Allergy and Infectious Diseases to the Immune Tolerance Network (Grant N01-AI-15416; Protocol No. ITN021AI). Genentech, Inc. and Biogen IDEC, Inc. provided study medications and partial funding for this trial. Measurements of rituximab serum concentrations in patients with chronic lymphocytic leukemia, diffuse large B-cell lymphoma, follicular lymphoma, and rheumatoid arthritis were carried out by CePiBAc platform. CePiBAc is cofinanced by the European Union. Europe is committed to the région Centre Val-de-Loire with the European Regional Development Fund. CePiBac was partly supported by the French Higher Education and Research Ministry under the program ‘Investissements d’avenir’ Grant Agreement: LabEx MAbImprove ANR-10-LABX-53-01. Genentech, Inc. measured rituximab concentrations by the enzyme-linked immunosorbent assay for the RAVE trial.

Conflict of interest

Guillaume Cartron received consultancy fees from Roche and Celgene, honoraria from Roche, Celgene, Jansen, Gilead, and Sanofi, and travel arrangements from Jansen, Gilead, and Sanofi. Emmanuel Gyan received research funding from Roche, is a coordinating investigator of a clinical trial supported by Roche, and received honoraria for scientific meetings organized by Roche. Olivier Casasnovas has acted as a consultant for and received honoraria from Roche, Gilead Sciences, Bristol Myers Squibb, Takeda, MSD, and AbbVie and has received research funding from Roche and Gilead. Gilles Paintaud reports grants received by his research team from Novartis, Roche Pharma, Genzyme, MSD, Chugai, and Pfizer, outside of the submitted work. Denis Mulleman reports grants from Abbvie and Nordic Pharma and consultancy fees for MSD, Novartis, UCB, and Pfizer. David Ternant reports lecture fees for Sanofi, Novartis, Amgen, and Boehringer Ingelheim. Amina Bensalem, Ulrich Specks, Divi Cornec, Céline Desvignes, Thierry Lamy, and Stéphane Leprêtre have nothing to declare.

Ethics approval

All studies were approved by the local ethics committee.

Consent to participate

Written informed consent was obtained from all patients.

Consent for publication

Not Applicable.

Availability of data and material

Data and material are available upon request to the corresponding author.

Code availability

The code is available upon request to the corresponding author.

Author contributions

Amina Bensalem analyzed and interpreted the data and wrote the manuscript. Guillaume Cartron designed the clinical study in patients with diffuse large B-cell lymphoma and follicular lymphoma, and contributed to materials/patients, was the principal investigator of the clinical study in patients with chronic lymphocytic leukemia, and reviewed the manuscript. Ulrich Specks was responsible for the design and coordination of the anti-neutrophil cytoplasmic antibody-associated vasculitis cohort, acquired the data, and reviewed the manuscript. Emmanuel Gyan contributed to materials/patients in the clinical study in patients with diffuse large B-cell lymphoma and follicular lymphoma, and reviewed the manuscript. Gilles Paintaud designed the study in patients with chronic lymphocytic leukemia, diffuse large B-cell lymphoma, and follicular lymphoma, participated in data interpretation, and reviewed the manuscript. Denis Mulleman was responsible for the design and coordination of the rheumatoid arthritis cohort, acquired the data, participated in data interpretation, and reviewed the manuscript. David Ternant designed the study in patients with diffuse large B-cell lymphoma, follicular lymphoma, and rheumatoid arthritis, supervised data analysis and interpretation, participated in the writing of the manuscript, and reviewed the manuscript.

Supplementary Information

Below is the link to the electronic supplementary material.

40262_2021_1081_MOESM1_ESM.tiff

Supplementary file1 Supplemental figure 1. Diagnostic plots of the final model. The plots show population model-predicted (PRED) and individual model-predicted (IPRED) versus observed data (DV); population weighted residuals (PWRES) and individual weighted residuals (IWRES) versus PRED and IPRED, respectively; visual predictive checks, circles are data, solid lines are low, median and up empirical percentile of simulated data, and shaded areas are 10%, 50%, and 90% prediction intervals; distribution frequency of normalized prediction distribution errors (NPDEs) versus Gaussian (Gauss) law (TIFF 347 KB)

40262_2021_1081_MOESM2_ESM.tiff

Supplementary file2 Supplemental figure 2. Simulations of rituximab concentrations and target antigen amount profiles in CLL, DLBCL, FL, RA and AAV patients, using the final pharmacokinetic model. Original dosing regimens (top), with dense and standard arm for CLL and DLBCL, respectively, and same dosing regimen (1000 mg at days 0 and 14) (bottom) were used. The central curve are the median concentrations and target antigen amount profiles, and the shadows are 90% prediction intervals (TIFF 741 KB)

Supplementary file3 (DOCX 16 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bensalem, A., Cartron, G., Specks, U. et al. The Influence of Underlying Disease on Rituximab Pharmacokinetics May be Explained by Target-Mediated Drug Disposition. Clin Pharmacokinet 61, 423–437 (2022). https://doi.org/10.1007/s40262-021-01081-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40262-021-01081-3

Search

Navigation