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Population Pharmacokinetic Analysis of Denosumab in Patients with Bone Metastases from Solid Tumours

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Abstract

Background and Objective

Denosumab (XGEVA®; AMG 162) is a fully human IgG2 monoclonal antibody, which binds to the receptor activator of nuclear factor K-B ligand (RANKL) and prevents terminal differentiation, activation and survival of osteoclasts. We aimed to characterize the population pharmacokinetics of denosumab in patients with advanced solid tumours and bone metastases.

Methods

A total of 14 228 free serum concentrations of denosumab from 1076 subjects (495 healthy subjects and 581 advanced cancer patients with solid tumours and bone metastases) included in 14 clinical studies were pooled. Denosumab was administered as either single intravenous (n= 36), single subcutaneous (n= 490) or multiple subcutaneous doses (n = 550) ranging from 30 to 180 mg (or from 0.01 to 3 mg/kg) and was given every 4 or 12 weeks for up to 3 years. An open two-compartment pharmacokinetic model with first-order absorption, linear distribution to a peripheral compartment, linear clearance and quasi-steady-state approximation of the target-mediated drug disposition was used to describe denosumab pharmacokinetics, using NONMEM Version 7.1.0 software. The influence of covariates (body weight, age, race, tumour type) was investigated using the full model approach. Model evaluation was performed through visual predictive checks. Model-based simulations were conducted to explore the role of covariates on denosumab serum concentrations and inferred RANKL occupancy.

Results

After subcutaneous administration, the dose-independent bioavailability and mean absorption half-life of denosumab were estimated to be 61% and 2.7 days, respectively. The central volume of distribution and linear clearance were 2.62L/66kg and 3.25mL/h/66kg, respectively. Clearance and volume parameters were proportional to body weight. Assuming 1:1 denosumab-RANKL binding, the baseline RANKL level, quasi-steady-state constant and RANKL degradation rate were inferred to be 4.46 nmol/L, 208ng/mL and 0.00116 h-1, respectively. Between-subject variability in model parameters was moderate. Following 120 mg dosing every 4 weeks, the inferred RANKL occupancy at steady state exceeded 97% during the entire dosing interval in more than 95% of subjects, regardless of the patient covariates.

Conclusions

The integration of pharmacokinetic data from 14 clinical studies demonstrated denosumab RANKL-mediated pharmacokinetics. Pharmacokinetics-based dosage adjustments on the basis of body weight, age, race and tumour type are not necessary in patients with bone metastases from solid tumours.

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References

  1. Coleman RE. Clinical features of metastatic bone disease and risk of skeletal morbidity. Clin Cancer Res 2006; 12: 6243s–9s

    Article  PubMed  Google Scholar 

  2. Coleman RE. Skeletal complications of malignancy. Cancer 1997; 80: 1588–94

    Article  PubMed  CAS  Google Scholar 

  3. Coleman RE. Metastatic bone disease: clinical features, pathophysiology and treatment strategies. Cancer Treat Rev 2001; 27: 165–76

    Article  PubMed  CAS  Google Scholar 

  4. Abrahm JL, Banffy MB, Harris MB. Spinal cord compression in patients with advanced metastatic cancer: “all I care about is walking and living my life”. JAMA 2008; 299: 937–46

    Article  PubMed  CAS  Google Scholar 

  5. Anderson DM, Maraskovsky E, Billingsley WL, et al. A homologue of the TNF receptor and its ligand enhance T-cell growth and dendritic-cell function. Nature 1997; 390: 175–9

    Article  PubMed  CAS  Google Scholar 

  6. Burgess TL, Qian Y, Kaufman S, et al. The ligand for osteoprotegerin (OPGL) directly activates mature osteoclasts. J Cell Biol 1999; 145: 527–38

    Article  PubMed  CAS  Google Scholar 

  7. Lacey DL, Timms E, Tan HL, et al. Osteoprotegerin ligand is a cytokine that regulates osteoclast differentiation and activation. Cell 1998; 93: 165–76

    Article  PubMed  CAS  Google Scholar 

  8. Yasuda H, Shima N, Nakagawa N, et al. Osteoclast differentiation factor is a ligand for osteoprotegerin/osteoclastogenesis-inhibitory factor and is identical to TRANCE/RANKL. Proc Natl Acad Sci U S A 1998; 95: 3597–602

    Article  PubMed  CAS  Google Scholar 

  9. Elliott R, Kostenuik PJ, Chen C, et al. Denosumab is a selective inhibitor of human receptor activator of NF-KB ligand (RANKL) that blocks osteoclast formation and function [abstract no. P149]. Osteoporos Int 2007; 18: S54

    Google Scholar 

  10. Simonet WS, Lacey DL, Dunstan CR, et al. Osteoprotegerin: a novel secreted protein involved in the regulation of bone density. Cell 1997; 89: 309–19

    Article  PubMed  CAS  Google Scholar 

  11. Akatsu T, Murakami T, Nishikawa M, et al. Osteoclastogenesis inhibitory factor suppresses osteoclast survival by interfering in the interaction of stromal cells with osteoclast. Biochem Biophys Res Commun 1998; 250: 229–34

    Article  PubMed  CAS  Google Scholar 

  12. Fizazi K, Lipton A, Mariette X, et al. Randomized phase II trial of denosumab in patients with bone metastases from prostate cancer, breast cancer, or other neoplasms after intravenous bisphosphonates. J Clin Oncol 2009; 27: 1564–71

    Article  PubMed  CAS  Google Scholar 

  13. Lipton A, Steger GG, Figueroa J, et al. Extended efficacy and safety of denosumab in breast cancer patients with bone metastases not receiving prior bisphosphonate therapy. Clin Cancer Res 2008; 14: 6690–6

    Article  PubMed  CAS  Google Scholar 

  14. Body JJ, Facon T, Coleman RE, et al. A study of the biological receptor activator of nuclear factor-kappaB ligand inhibitor, denosumab, in patients with multiple myeloma or bone metastases from breast cancer. Clin Cancer Res 2006; 12: 1221–8

    Article  PubMed  CAS  Google Scholar 

  15. Yonemori K, Fujiwara Y, Minami H, et al. Phase 1 trial of denosumab safety, pharmacokinetics, and pharmacodynamics in Japanese women with breast cancer-related bone metastases. Cancer Sci 2008; 99: 1237–42

    Article  PubMed  CAS  Google Scholar 

  16. Stopeck AT, Lipton A, Body JJ, et al. Denosumab compared with zoledronic acid for the treatment of bone metastases in patients with advanced breast cancer: a randomized, double-blind study. J Clin Oncol 2010; 28: 5132–9

    Article  PubMed  CAS  Google Scholar 

  17. Henry DH, Costa L, Goldwasser F, et al. Randomized, double-blind study of denosumab versus zoledronic acid in the treatment of bone metastases in patients with advanced cancer (excluding breast and prostate cancer) or multiple myeloma. J Clin Oncol 2011; 29: 1125–32

    Article  PubMed  CAS  Google Scholar 

  18. Fizazi K, Carducci M, Smith M, et al. Denosumab versus zoledronic acid for treatment of bone metastases in men with castration-resistant prostate cancer: a randomised, double-blind study. Lancet 2011; 377: 813–22

    Article  PubMed  CAS  Google Scholar 

  19. Sutjandra L, Rodriguez RD, Doshi S, et al. Population pharmacokinetics meta-analysis of denosumab in healthy subjects and postmenopausal women with osteopenia or osteoporosis. Clin Pharmacokinet 2011; 50: 793–807

    Article  PubMed  CAS  Google Scholar 

  20. Bekker PJ, Holloway DL, Rasmussen AS, et al. A single-dose placebo-controlled study of AMG 162, a fully human monoclonal antibody to RANKL, in postmenopausal women. J Bone Miner Res 2004; 19: 1059–66

    Article  PubMed  CAS  Google Scholar 

  21. Thomas D, Henshaw R, Skubitz K, et al. Denosumab in patients with giant-cell tumour of bone: an open-label, phase 2 study. Lancet Oncol 2010; 11: 275–80

    Article  PubMed  CAS  Google Scholar 

  22. Mager DE, Jusko WJ. General pharmacokinetic model for drugs exhibiting target-mediated drug disposition. J Pharmacokinet Pharmacodyn 2001; 28: 507–32

    Article  PubMed  CAS  Google Scholar 

  23. Nakashima T, Kobayashi Y, Yamasaki S, et al. Protein expression and functional difference of membrane-bound and soluble receptor activator of NF-kappaB ligand: modulation of the expression by osteotropic factors and cytokines. Biochem Biophys Res Commun 2000; 275: 768–75

    Article  PubMed  CAS  Google Scholar 

  24. Mager DE, Krzyzanski W. Quasi-equilibrium pharmacokinetic model for drugs exhibiting target-mediated drug disposition. Pharm Res 2005; 22: 1589–96

    Article  PubMed  CAS  Google Scholar 

  25. Gibiansky L, Gibiansky E, Kakkar T, et al. Approximations of the target-mediated drug disposition model and identifiability of model parameters. J Pharmacokinet Pharmacodyn 2008; 35: 573–91

    Article  PubMed  CAS  Google Scholar 

  26. Ma P. Theoretical considerations of target-mediated drug disposition models: simplifications and approximations. Pharm Res 2012; 29: 866–82

    Article  PubMed  CAS  Google Scholar 

  27. Girard P. Data transformation and parameter transformations in NONMEM. Eleventh Meeting, Population Approach Group in Europe; 2002 Jun 6–7; Paris [online]. Available from URL: http://www.page-meeting.org/page/page2002/PascalGirardPage2002.pdf [Accessed 2012 Feb 15]

  28. Mahmood I. Clinical pharmacology of therapeutic proteins. Rockville (MD): Pine House Publishers, 2006

    Google Scholar 

  29. Kuester K, Kloft C. Pharmacokinetics of monoclonal antibodies. In: Meibohm B, editor. Pharmacokinetics and pharmacodynamics of biotech drugs: principles and case studies in drug development. Weinheim: Wiley-VCH Verlag GmbH & Co. KGaA, 2006: 45–91

    Chapter  Google Scholar 

  30. Wang W, Wang EQ, Balthasar JP. Monoclonal antibody pharmacokinetics and pharmacodynamics. Clin Pharmacol Ther 2008; 84: 548–58

    Article  PubMed  CAS  Google Scholar 

  31. Lobo ED, Hansen RJ, Balthasar JP. Antibody pharmacokinetics and pharmacodynamics. J Pharm Sci 2004; 93: 2645–68

    Article  PubMed  CAS  Google Scholar 

  32. Block G, Bone HG, Fang L, et al. A single dose study of denosumab in patients with various degrees of renal impairment [abstract no. 57]. J Amer Kidney Dis 2010; 55(4): B46

    Article  Google Scholar 

  33. Gastonguay MR. Full covariate models as an alternative to methods relying on statistical significance for inferences about covariate effects: a review of methodology and 42 case studies. Twentieth Meeting, Population Approach Group in Europe; 2011 Jun 7–10; Athens [online]. Available from URL: http://www.page-meeting.org/pdf_assets/1694-GastonguayPAGE2011.pdf [Accessed 2012 Feb 16]

  34. Yano Y, Beal SL, Sheiner LB. Evaluating pharmacokinetic/pharmacodynamic models using the posterior predictive check. J Pharmacokinet Pharmacodyn 2001; 28: 171–92

    Article  PubMed  CAS  Google Scholar 

  35. Pérez-Ruixo JJ, Piotrovskij V, Zhang S, et al. Population pharmacokinetics of tipifarnib in healthy subjects and adult cancer patients. Br J Clin Pharmacol 2006; 62: 81–96

    Article  PubMed  Google Scholar 

  36. Frame B, Koup J, Miller R, et al. Population pharmacokinetics of clinafloxacin in healthy volunteers and patients with infections: experience with heterogeneous pharmacokinetic data. Clin Pharmacokinet 2001; 40: 307–15

    Article  PubMed  CAS  Google Scholar 

  37. Dirks NL, Meibohm B. Population pharmacokinetics of therapeutic monoclonal antibodies. Clin Pharmacokinet 2010; 49: 633–59

    Article  PubMed  CAS  Google Scholar 

  38. Olsson-Gisleskog P, Jacqmin P, Pérez-Ruixo JJ. Population pharmacokinetics meta-analysis of recombinant human erythropoietin in healthy subjects. Clin Pharmacokinet 2007; 46: 159–73

    Article  PubMed  CAS  Google Scholar 

  39. Agoram BM, Martin SW, van der Graaf PH. The role of mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modelling in translational research of biologics. Drug Discov Today 2007; 12: 1018–24

    Article  PubMed  CAS  Google Scholar 

  40. Roskos LK, Davis CG, Schwab GM. The clinical pharmacology of therapeutic monoclonal antibodies. Drug Dev Res 2004; 61: 108–20

    Article  CAS  Google Scholar 

  41. Kostenuik PJ, Nguyen HQ, McCabe J, et al. Denosumab, a fully human monoclonal antibody to RANKL, inhibits bone resorption and increases BMD in knock-in mice that express chimeric (murine/human) RANKL. J Bone Miner Res 2009; 24: 182–95

    Article  PubMed  CAS  Google Scholar 

  42. Hofbauer LC, Schoppet M, Schuller P, et al. Effects of oral contraceptives on circulating osteoprotegerin and soluble RANK ligand serum levels in healthy young women. Clin Endocrinol (Oxf) 2004; 60: 214–9

    Article  CAS  Google Scholar 

  43. Liu JM, Zhao HY, Ning G, et al. Relationships between the changes of serum levels of OPG and RANKL with age, menopause, bone biochemical markers and bone mineral density in Chinese women aged 20–75. Calcif Tissue Int 2005; 76: 1–6

    Article  PubMed  CAS  Google Scholar 

  44. Findlay D, Chehade M, Tsangari H, et al. Circulating RANKL is inversely related to RANKL mRNA levels in bone in osteoarthritic males. Arthritis Res Ther 2008; 10: R2 [online]. Available from URL: http://arthritis-research.com/content/pdf/ar2348.pdf [Accessed 2011 Oct 6]

    Article  PubMed  Google Scholar 

  45. Marathe A, Peterson MC, Mager DE. Integrated cellular bone homeostasis model for denosumab pharmacodynamics in multiple myeloma patients. J Pharmacol Exp Ther 2008; 326: 555–62

    Article  PubMed  CAS  Google Scholar 

  46. Coleman RE, Major P, Lipton A, et al. Predictive value of bone resorption and formation markers in cancer patients with bone metastases receiving the bisphosphonate zoledronic acid. J Clin Oncol 2005; 23: 4925–35

    Article  PubMed  CAS  Google Scholar 

  47. Hayashi N, Tsukamoto Y, Sallas WM, et al. A mechanism-based binding model for the population pharmacokinetics and pharmacodynamics of omalizumab. Br J Clin Pharmacol 2007; 63: 548–61

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

Part of the content of this manuscript was presented at a poster podium session at the third American Conference on Pharmacometrics, San Diego, CA, USA, from 3 to 6 April 2011 (Gibiansky L, Sutjandra L, Doshi S, Zheng J, Sohn W, Peterson M, Jang G, Chow A, Pérez-Ruixo JJ. Population Pharmacokinetic Analysis of Denosumab in Advanced Cancer Patients with Solid Tumors).

The authors thank the thousands of patients, investigators, and medical, nursing and laboratory staff who participated in the clinical studies that were included in the present analysis; Mark Ma for coordinating the bioanalytical analysis for denosumab plasma concentrations; and Belén Valenzuela for the editorial comments provided during the preparation of the manuscript.

This study was sponsored by Amgen Inc., which was involved in the study design; the data collection, analysis, interpretation; the writing of the manuscript, and the decision to submit the manuscript for publication. Leonid Gibiansky was a consultant for Amgen Inc. and received consultation fees for contributing to the current analysis. Liviawati Sutjandra, Sameer Doshi, Jenny Zheng, Winnie Sohn, Graham Jang, Andrew Chow and Juan José Pérez-Ruixo were employees of Amgen Inc. and owned stock in Amgen Inc. at the time when the analysis was conducted. Mark Peterson is a former employee of Amgen Inc.

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Correspondence to Juan José Pérez-Ruixo.

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Gibiansky, L., Sutjandra, L., Doshi, S. et al. Population Pharmacokinetic Analysis of Denosumab in Patients with Bone Metastases from Solid Tumours. Clin Pharmacokinet 51, 247–260 (2012). https://doi.org/10.2165/11598090-000000000-00000

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