Skip to main content
SpringerLink
Log in

Pharmacodynamic-Mediated Drug Disposition (PDMDD) Model of Daratumumab Monotherapy in Patients with Multiple Myeloma

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

Abstract

Background and Objective

We aimed to quantify the daratumumab concentration- and CD38 dynamics-dependent pharmacokinetics using a pharmacodynamic mediated disposition model (PDMDD) in patients with multiple myeloma (MMY) following daratumumab IV or SC monotherapy. Daratumumab, a human IgG monoclonal antibody targeting CD38 with a direct on-tumor and immunomodulatory mechanism of action, has been approved to treat patients with multiple myeloma (MM).

Methods

In total, 7788 daratumumab plasma samples from 850 patients with diagnosis of MMY were used. The serum concentration-time data of daratumumab were analysed using nonlinear mixed-effects modeling with NONMEM®. The PDMDD with quasi steady-state approximation (QSS) was compared to the previously developed Michaelis-Menten (MM) approximation with respect to the parameter estimates, the goodness-of-fit plots and prediction-corrected visual predictive check, as well as model-based simulations. The effect of patients’ covariates on daratumumab pharmacokinetics was also investigated.

Results

The QSS approximation characterized the concentration- and CD38 dynamics-dependency of daratumumab pharmacokinetics within the doses ranging from 0.1 to 24 mg/kg after IV administration and 1200 and 1800 mg after SC administration in patients with MMY, mechanistically describing the binding of daratumumab and CD38, the internalization of the daratumumab-CD38 complex and the CD38 turnover. Compared to the previously developed MM approximation, the MM approximation with the non-constant total target and dose-correction provided substantial improvement of the model fit, but it was still not as good as the QSS approximation. The effect of the previously identified covariates as well as the newly identified covariate (baseline M protein) on daratumumab pharmacokinetics was confirmed, but the magnitude of the effect was deemed not clinically relevant.

Conclusions

Accounting for the CD38 turnover and its binding capacity to daratumumab, the QSS approximation provided a mechanistic interpretation of daratumumab PK parameters and consequently well described the concentration- and CD38 dynamics-dependency of daratumumab pharmacokinetics.

Clinical studies included in the analysis were registered with the NCT number below at http://www.ClinicalTrials.gov

MMY1002 (ClinicalTrials.gov: NCT02116569), MMY1003 (NCT02852837), MMY1004 (NCT02519452), MMY1008 (NCT03242889), GEN501 (NCT00574288), MMY2002 (NCT01985126), MMY3012 (NCT03277105).

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. Cowan AJ, Green DJ, Kwok M, Lee S, Coffey DG, Holmberg LA, et al. Diagnosis and management of multiple myeloma: a review. JAMA. 2022;327(5):464–77. https://doi.org/10.1001/JAMA.2022.0003.

    Article  CAS  PubMed  Google Scholar 

  2. National Cancer Institute. Myeloma—Cancer Stat Facts. https://seer.cancer.gov/statfacts/html/mulmy.html. Accessed 6 Apr 2022.

  3. Myeloma incidence statistics|Cancer Research UK. https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/myeloma/incidence#heading-Two. Accessed 6 Apr 2022.

  4. Darzalex Assessment report-International non-proprietary name: daratumumab Procedure No. EMEA/H/C/004077/0000. https://www.ema.europa.eu/en/documents/assessment-report/darzalex-epar-public-assessment-report_en.pdf. Accessed 6 Apr 2022.

  5. Overdijk MB, Jansen JHM, Nederend M, Lammerts van Bueren JJ, Groen RWJ, Parren PWHI, et al. The therapeutic CD38 monoclonal antibody daratumumab induces programmed cell death via Fcγ receptor-mediated cross-linking. J Immunol. 2016;197(3):807–813. https://doi.org/10.4049/JIMMUNOL.1501351.

  6. Overdijk MB, Verploegen S, Bögels M, van Egmond M, Lammerts Van Bueren JJ, Mutis T, et al. Antibody-mediated phagocytosis contributes to the anti-tumor activity of the therapeutic antibody daratumumab in lymphoma and multiple myeloma. MAbs. 2015;7(2):311–320. https://doi.org/10.1080/19420862.2015.1007813.

  7. Lammerts van Bueren J, Jakobs D, Kaldenhoven N, Roza M, Hiddingh S, Meesters J, et al. Direct in vitro comparison of daratumumab with surrogate analogs of cd38 antibodies MOR03087, SAR650984 and Ab79. Blood. 2014;124(21):3474–3474. https://doi.org/10.1182/BLOOD.V124.21.3474.3474.

  8. de Weers M, Tai YT, van der Veer MS, Bakker JM, Vink T, Jacobs DCH, et al. Daratumumab, a novel therapeutic human CD38 monoclonal antibody, induces killing of multiple myeloma and other hematological tumors. J Immunol. 2011;186(3):1840–8. https://doi.org/10.4049/JIMMUNOL.1003032.

    Article  PubMed  Google Scholar 

  9. Adams HC, Stevenaert F, Krejcik J, van der Borght K, Smets T, Bald J, et al. High-parameter mass cytometry evaluation of relapsed/refractory multiple myeloma patients treated with daratumumab demonstrates immune modulation as a novel mechanism of action. Cytometry A. 2019;95(3):279–89. https://doi.org/10.1002/cyto.a.23693.

    Article  CAS  PubMed  Google Scholar 

  10. Chiu C, Casneuf T, Axel A, Lysaght A, Bald J, Khokhar NZ, et al. Daratumumab in combination with lenalidomide plus dexamethasone induces clonality increase and T-cell expansion: results from a phase 3 randomized study (POLLUX). Blood. 2016;128(22):4531–4531. https://doi.org/10.1182/BLOOD.V128.22.4531.4531.

    Article  Google Scholar 

  11. Krejcik J, Casneuf T, Nijhof IS, Verbist B, Bald J, Plesner T, et al. Daratumumab depletes CD38+ immune regulatory cells, promotes T-cell expansion, and skews T-cell repertoire in multiple myeloma. Blood. 2016;128(3):384–94. https://doi.org/10.1182/BLOOD-2015-12-687749.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ghose J, Viola D, Terrazas C, Caserta E, Troadec E, Khalife J, et al. Daratumumab induces CD38 internalization and impairs myeloma cell adhesion. OncoImmunology. 2018. https://doi.org/10.1080/2162402X.2018.1486948/SUPPL_FILE/KONI_A_1486948_SM4493.DOCX

  13. Mager DE, Jusko WJ. General pharmacokinetic model for drugs exhibiting target-mediated drug disposition. J Pharmacokinet Pharmacodyn. 2001;28(6):507–32. https://doi.org/10.1023/A:1014414520282.

    Article  CAS  PubMed  Google Scholar 

  14. Mager DE. Target-mediated drug disposition and dynamics. Biochem Pharmacol. 2006;72(1):1–10. https://doi.org/10.1016/J.BCP.2005.12.041.

    Article  CAS  PubMed  Google Scholar 

  15. Xu XS, Yan X, Puchalski T, Lonial S, Lokhorst HM, Voorhees PM, et al. Clinical implications of complex pharmacokinetics for daratumumab dose regimen in patients with relapsed/refractory multiple myeloma. Clin Pharmacol Ther. 2017;101(6):721. https://doi.org/10.1002/CPT.577.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Yan X, Clemens PL, Puchalski T, Lonial S, Lokhorst HM, Orlowski RZ, et al. Target-mediated drug disposition of daratumumab following intravenous infusion in relapsed or refractory multiple myeloma after prior proteasome inhibitors and immunomodulatory drugs: a population pharmacokinetic analysis. Blood. 2015;126(23):4222–4222. https://doi.org/10.1182/BLOOD.V126.23.4222.4222.

    Article  Google Scholar 

  17. Luo M, Usmani SZ, Mateos MV, Nahi H, Chari A, San-Miguel J, et al. Exposure-response and population pharmacokinetic analyses of a novel subcutaneous formulation of daratumumab administered to multiple myeloma patients. J Clin Pharmacol. 2021;61(5):614–27. https://doi.org/10.1002/JCPH.1771.

    Article  CAS  PubMed  Google Scholar 

  18. Wang YMC, Krzyzanski W, Doshi S, Xiao JJ, Pérez-Ruixo JJ, Chow AT. Pharmacodynamics-mediated drug disposition (PDMDD) and precursor pool lifespan model for single dose of romiplostim in healthy subjects. AAPS J. 2010;12(4):729–40. https://doi.org/10.1208/S12248-010-9234-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Mager DE, Krzyzanski W. Quasi-equilibrium pharmacokinetic model for drugs exhibiting target-mediated drug disposition. Pharm Res. 2005;22(10):1589–96. https://doi.org/10.1007/S11095-005-6650-0/TABLES/2.

    Article  CAS  PubMed  Google Scholar 

  20. Gibiansky L, Gibiansky E, Kakkar T, Ma P. Approximations of the target-mediated drug disposition model and identifiability of model parameters. J Pharmacokinet Pharmacodyn. 2008;35(5):573–91. https://doi.org/10.1007/S10928-008-9102-8.

    Article  CAS  PubMed  Google Scholar 

  21. Sutjandra L, Rodriguez RD, Doshi S, Ma M, Peterson MC, Jang GR, et al. Population pharmacokinetic meta-analysis of denosumab in healthy subjects and postmenopausal women with osteopenia or osteoporosis. Clin Pharmacokinet. 2011;50(12):793–807. https://doi.org/10.2165/11594240-000000000-00000/FIGURES/5.

    Article  CAS  PubMed  Google Scholar 

  22. Hayashi N, Tsukamoto Y, Sallas WM, Lowe PJ. A mechanism-based binding model for the population pharmacokinetics and pharmacodynamics of omalizumab. Br J Clin Pharmacol. 2007;63(5):548. https://doi.org/10.1111/J.1365-2125.2006.02803.X.

    Article  CAS  PubMed  Google Scholar 

  23. Yan X, Mager DE, Krzyzanski W. Selection between Michaelis-Menten and target-mediated drug disposition pharmacokinetic models. J Pharmacokinet Pharmacodyn. 2010;37(1):25. https://doi.org/10.1007/S10928-009-9142-8.

    Article  CAS  PubMed  Google Scholar 

  24. Yan X, Perez-Ruixo JJ, Krzyzanski W. Dose correction for a Michaelis-Menten approximation of a target-mediated drug disposition model with a multiple intravenous dosing regimens. AAPS J. 2020;22(2):1–15. https://doi.org/10.1208/S12248-019-0410-2/FIGURES/7.

    Article  Google Scholar 

  25. U.S. Department of Health and Human Services. US Food and Drug Administration; Center for Drug Evaluation and Research (CDER); Center for Veterinary Medicine (CVM). Bioanalytical Method Validation Guidance for Industry Biopharmaceutics Bioanalytical Method Validation Guidance for Industry Biopharmaceutics Contains Nonbinding Recommendations 2018. https://www.fda.gov/files/drugs/published/Bioanalytical-Method-Validation-Guidance-for-Industry.pdf. Accessed 15 Apr 2022.

  26. European Medicines Agency. Guideline on bioanalytical method validation 2011. https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-bioanalytical-method-validation_en.pdf. Accessed 15 Apr 2022.

  27. Beal SS, Sheiner LB, Boeckmann A, Bauer RJ. NONMEM user’s guides (1989–2009). Icon Development Solutions, Ellicott City, MD 2009.

  28. The R Foundation for Statistical Computing. R: Regulatory compliance and validation issues a guidance document for the use of R in regulated clinical trial environments. Documentation 2013: 24.

  29. Pascal Girard. Data transformation and Parameter Transformations in NONMEM. http://docplayer.net/47490595-Data-transformation-and-parameter-transformations-in-nonmem-pascal-girard-pharsight-corp-acknowledgements.html. Accessed 15 Apr 2022.

  30. Mandema JW, Verotta D, Sheiner LB. Building population pharmacokinetic/pharmacodynamic models. I. Models for covariate effects. J Pharmacokinet Biopharm. 1992;20(5):511–28. https://doi.org/10.1007/BF01061469.

    Article  CAS  PubMed  Google Scholar 

  31. Bergstrand M, Hooker AC, Wallin JE, Karlsson MO. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J. 2011;13(2):143–51. https://doi.org/10.1208/S12248-011-9255-Z.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the patients, investigators, and their medical, nursing, and laboratory staff who participated in the clinical study included in the present work. We would also like to acknowledge the advice from Yan Xu, Man (Melody) Luo, and Honghui Zhou during the data analysis.

Author information

Authors and Affiliations

  1. Clinical Pharmacology and Pharmacometrics, Janssen Research & Development LLC, Beerse, Belgium

    Xia Li, Anne-Gaëlle Dosne, Carlos Pérez Ruixo & Juan Jose Perez Ruixo

Authors
  1. Xia Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anne-Gaëlle Dosne
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carlos Pérez Ruixo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juan Jose Perez Ruixo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xia Li.

Ethics declarations

Funding

The clinical studies were supported by research funding from Janssen Research & Development, and the analyses presented here were supported by Janssen Research & Development.

Conflicts of interest

Xia Li, Anne-Gaëlle Dosne, Carlos Pérez Ruixo, Juan Jose Perez Ruixo are employees of Janssen Research & Development and shareholders of Johnson & Johnson.

Availability of data and material

The datasets generated and/or analyzed during the current study belong to Janssen Research & Development and are not publicly available.

Ethical approval

All studies were conducted in accordance with principles for human experimentation as defined in the 1964 Declaration of Helsinki and were approved by the Human Investigational Review Board of each study center and by the Competent Authority of each country.

Consent to participate

Informed consent was obtained from each patient before enrollment in the studies after being advised of the potential risks and benefits of the study, as well as the investigational nature of the study.

Author contributions

Xia Li performed the population pharmacokinetic modelling exercise, executed the model-based simulations, and took the lead in writing the manuscript. Juan Jose Perez Ruixo, Anne-Gaëlle Dosne, Carlos Pérez Ruixo and Xia Li decided the clinical studies to be included in the analysis, contributed to the scientific content (including data analysis and interpretation of the results), and reviewed the draft versions of the manuscript. All authors provided approval of the final version that was submitted for publication.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1009 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Dosne, AG., Pérez Ruixo, C. et al. Pharmacodynamic-Mediated Drug Disposition (PDMDD) Model of Daratumumab Monotherapy in Patients with Multiple Myeloma. Clin Pharmacokinet 62, 761–777 (2023). https://doi.org/10.1007/s40262-023-01232-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40262-023-01232-8

Search

Navigation