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Using early biomarker data to predict long-term bone mineral density: application of semi-mechanistic bone cycle model on denosumab data

Journal of Pharmacokinetics and Pharmacodynamics volume 42, pages 333–347 (2015)Cite this article

Abstract

Osteoporosis is a chronic skeletal disease characterized by low bone strength resulting in increased fracture risk. New treatments for osteoporosis are still an unmet medical need because current available treatments have various limitations. Bone mineral density (BMD) is an important endpoint for evaluating new osteoporosis treatments; however, the BMD response is often slower and less profound than that of bone turnover markers (BTMs). If the relationship between BTMs and BMD can be quantified, the BMD response can be predicted by the changes in BTM after a single dose; therefore, a decision based on BMD changes can be informed early. We have applied a bone cycle model to a phase 2 denosumab dose-ranging study in osteopenic women to quantitatively link serum denosumab pharmacokinetics, BTMs, and lumbar spine (LS) BMD. The data from two phase 3 denosumab studies in patients with low bone mass, FREEDOM and DEFEND, were used for external validation. Both internal and external visual predictive checks demonstrated that the model was capable of predicting LS BMD at the denosumab regimen of 60 mg every 6 months. It has been demonstrated that the model, in combination with the changes in BTMs observed from a single-dose study in men, is capable of predicting long-term BMD outcomes (e.g., LS BMD response in men after 1 year of treatment) in different populations. We propose that this model can be used to inform drug development decisions for osteoporosis treatment early via evaluating LS BMD response when BTM data become available in early trials.

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Acknowledgments

The authors wish to thank the investigators of the studies included in this manuscript, and Andrew T. Chow and Ed Lee for their continuous scientific support during the completion of these analyses. We thank Geoff Smith, PhD, and Lisa A. Humphries, PhD, of Amgen Inc. for their assistance with the editing and formatting of this manuscript. This study was funded by Amgen Inc.

Conflict of interest

Amgen Inc. sponsored this study and was involved in the study design, data collection, analysis, interpretation, writing of the manuscript, and the decision to submit the manuscript for publication. Jenny Zheng was employed by Amgen Inc. and had Amgen Inc. stock and/or stock options at the time of manuscript preparation; Jenny Zheng is currently employed by Pfizer; Erno van Schaick and Philippe Jacqmin consult for Amgen Inc.; Juan Jose Perez Ruixo and Liviawati Sutjandra Wu are employed by Amgen Inc. and have Amgen Inc. stock and/or stock options.

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    Affiliations

    1. Pharmacokinetics and Drug Metabolism Division, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA, 91320, USA

      Jenny Zheng, Liviawati Sutjandra Wu & Juan Jose Perez Ruixo

    2. SGS Exprimo NV, Generaal de Wittelaan 19A b5, 2800, Mechelen, Belgium

      Erno van Schaick & Philippe Jacqmin

    3. Pharmacokinetics and Drug Metabolism Division, Amgen Inc., Picayo 3, Puzol, 46530, Valencia, Spain

      Juan Jose Perez Ruixo

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    1. Jenny Zheng
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    Correspondence to Juan Jose Perez Ruixo.

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    Jenny Zheng and Erno van Schaick contributed equally to this work

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    Zheng, J., van Schaick, E., Wu, L.S. et al. Using early biomarker data to predict long-term bone mineral density: application of semi-mechanistic bone cycle model on denosumab data. J Pharmacokinet Pharmacodyn 42, 333–347 (2015). https://doi.org/10.1007/s10928-015-9422-4

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    • DOI: https://doi.org/10.1007/s10928-015-9422-4

    Keywords

    • Osteoporosis
    • Pharmacokinetics
    • Pharmacodynamics
    • Bone remodeling
    • Denosumab