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      Application of a Systems Pharmacology-Based Placebo Population Model to Analyze Long-Term Data of Postmenopausal Osteoporosis


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          Osteoporosis is a progressive bone disease characterized by decreased bone mass resulting in increased fracture risk. The objective of this investigation was to test whether a recently developed disease systems analysis model for osteoporosis could describe disease progression in a placebo-treated population from the Early Postmenopausal Intervention Cohort (EPIC) study. First, we qualified the model using a subset from the placebo arm of the EPIC study of 222 women who had similar demographic characteristics as the 149 women from the placebo arm of the original population. Second, we applied the model to all 470 women. Bone mineral density (BMD) dynamics were changed to an indirect response model to describe lumbar spine and total hip BMD in this second population. This updated disease systems analysis placebo model describes the dynamics of all biomarkers in the corresponding datasets to a very good approximation; a good description of an individual placebo response will be valuable for evaluating treatments for osteoporosis.

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          Bone is a dynamic tissue that is constantly renewed. The cell populations that participate in this process--the osteoblasts and osteoclasts--are derived from different progenitor pools that are under distinct molecular control mechanisms. Together, these cells form temporary anatomical structures, called basic multicellular units, that execute bone remodeling. A number of stimuli affect bone turnover, including hormones, cytokines, and mechanical stimuli. All of these factors affect the amount and quality of the tissue produced. Mechanical loading is a particularly potent stimulus for bone cells, which improves bone strength and inhibits bone loss with age. Like other materials, bone accumulates damage from loading, but, unlike engineering materials, bone is capable of self-repair. The molecular mechanisms by which bone adapts to loading and repairs damage are starting to become clear. Many of these processes have implications for bone health, disease, and the feasibility of living in weightless environments (e.g., spaceflight).
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            We propose a mathematical model explaining the interactions between osteoblasts and osteoclasts, two cell types specialized in the maintenance of the bone integrity. Bone is a dynamic, living tissue whose structure and shape continuously evolves during life. It has the ability to change architecture by removal of old bone and replacement with newly formed bone in a localized process called remodeling. The model described here is based on the idea that the relative proportions of immature and mature osteoblasts control the degree of osteoclastic activity. In addition, osteoclasts control osteoblasts differentially depending on their stage of differentiation. Despite the tremendous complexity of the bone regulatory system and its fragmentary understanding, we obtain surprisingly good correlations between the model simulations and the experimental observations extracted from the literature. The model results corroborate all behaviors of the bone remodeling system that we have simulated, including the tight coupling between osteoblasts and osteoclasts, the catabolic effect induced by continuous administration of PTH, the catabolic action of RANKL, as well as its reversal by soluble antagonist OPG. The model is also able to simulate metabolic bone diseases such as estrogen deficiency, vitamin D deficiency, senescence and glucocorticoid excess. Conversely, possible routes for therapeutic interventions are tested and evaluated. Our model confirms that anti-resorptive therapies are unable to partially restore bone loss, whereas bone formation therapies yield better results. The model enables us to determine and evaluate potential therapies based on their efficacy. In particular, the model predicts that combinations of anti-resorptive and anabolic therapies provide significant benefits compared with monotherapy, especially for certain type of skeletal disease. Finally, the model clearly indicates that increasing the size of the pool of preosteoblasts is an essential ingredient for the therapeutic manipulation of bone formation. This model was conceived as the first step in a bone turnover modeling platform. These initial modeling results are extremely encouraging and lead us to proceed with additional explorations into bone turnover and skeletal remodeling.
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              A physiologically based mathematical model of integrated calcium homeostasis and bone remodeling.

              Bone biology is physiologically complex and intimately linked to calcium homeostasis. The literature provides a wealth of qualitative and/or quantitative descriptions of cellular mechanisms, bone dynamics, associated organ dynamics, related disease sequela, and results of therapeutic interventions. We present a physiologically based mathematical model of integrated calcium homeostasis and bone biology constructed from literature data. The model includes relevant cellular aspects with major controlling mechanisms for bone remodeling and calcium homeostasis and appropriately describes a broad range of clinical and therapeutic conditions. These include changes in plasma parathyroid hormone (PTH), calcitriol, calcium and phosphate (PO4), and bone-remodeling markers as manifested by hypoparathyroidism and hyperparathyroidism, renal insufficiency, daily PTH 1-34 administration, and receptor activator of NF-kappaB ligand (RANKL) inhibition. This model highlights the utility of systems approaches to physiologic modeling in the bone field. The presented bone and calcium homeostasis model provides an integrated mathematical construct to conduct hypothesis testing of influential system aspects, to visualize elements of this complex endocrine system, and to continue to build upon iteratively with the results of ongoing scientific research. Copyright (c) 2009 Elsevier Inc. All rights reserved.

                Author and article information

                CPT Pharmacometrics Syst Pharmacol
                CPT Pharmacometrics Syst Pharmacol
                CPT: Pharmacometrics & Systems Pharmacology
                John Wiley & Sons, Ltd (Chichester, UK )
                September 2015
                22 August 2015
                : 4
                : 9
                : 516-526
                [1 ]Department of Medical Informatics, Erasmus Medical Centre Rotterdam, The Netherlands
                [2 ]Leiden Academic Centre for Drug Research, Division of Pharmacology Leiden, The Netherlands
                [3 ]Merck Sharp & Dohme Corp. Whitehouse Station, New Jersey, USA
                [4 ]Department of Epidemiology, Erasmus Medical Centre Rotterdam, The Netherlands
                [5 ]Drug Safety Unit, The Health Care Inspectorate The Hague, The Netherlands
                [6 ]Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics (LAP&P) Leiden, The Netherlands
                Author notes
                Correspondence: J Berkhout ( jan.berkhout@ 123456erasmusmc.nl )
                © 2015 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

                This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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