Literature DB >> 26123920

A semi-mechanistic model of bone mineral density and bone turnover based on a circular model of bone remodeling.

Erno van Schaick1, Jenny Zheng, Juan Jose Perez Ruixo, Ronald Gieschke, Philippe Jacqmin.   

Abstract

Development of novel therapies for bone diseases can benefit from mathematical models that predict drug effect on bone remodeling biomarkers. Therefore, a bone cycle model (BCM) was developed that takes into consideration the concept of the basic multicellular unit and the dynamic equilibrium of bone remodeling. The model is a closed form cyclical model with four compartments representing resorption, formation, primary mineralization, and secondary mineralization. Equations describing the time course of bone turnover biomarkers were developed using the flow rate of bone cycle units (BCU) between the compartments or the amount of BCU in each compartment. A disease progression model representing bone loss in osteoporosis, a vitamin D and calcium supplementation (placebo) model, and a drug model for antiresorptive treatments were added to the model. Initial model parameter values were derived from published bone turnover data. The BCM accurately described biomarker-time profiles in postmenopausal women receiving either placebo or bisphosphonate treatment. The slow continual increase in bone mineral density (BMD) observed after 1 year of treatment was accurately described when changes in bone turnover were combined with increases in mineralization. For this purpose, the secondary mineralization compartment was replaced by three catenary chain compartments representing increasing mineral content. The refined BCM satisfactorily predicted biomarker profiles after long-term (10-year) bisphosphonate treatment. Furthermore, the model successfully described individual bone turnover markers and BMD results following treatment with denosumab in postmenopausal women. Analyses with this model could be used to optimize dosing regimens and to predict effects of novel osteoporotic treatments.

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Year:  2015        PMID: 26123920     DOI: 10.1007/s10928-015-9423-3

Source DB:  PubMed          Journal:  J Pharmacokinet Pharmacodyn        ISSN: 1567-567X            Impact factor:   2.745


  49 in total

1.  Computer simulation of trabecular remodeling using a simplified structural model.

Authors:  S Tayyar; P S Weinhold; R A Butler; J C Woodard; L D Zardiackas; K R St John; J M Bledsoe; J A Gilbert
Journal:  Bone       Date:  1999-12       Impact factor: 4.398

2.  Long-term predictions of the therapeutic equivalence of daily and less than daily alendronate dosing.

Authors:  C J Hernandez; G S Beaupré; R Marcus; D R Carter
Journal:  J Bone Miner Res       Date:  2002-09       Impact factor: 6.741

3.  Modeling the interactions between osteoblast and osteoclast activities in bone remodeling.

Authors:  Vincent Lemaire; Frank L Tobin; Larry D Greller; Carolyn R Cho; Larry J Suva
Journal:  J Theor Biol       Date:  2004-08-07       Impact factor: 2.691

4.  Modelling response time profiles in the absence of drug concentrations: definition and performance evaluation of the K-PD model.

Authors:  P Jacqmin; E Snoeck; E A van Schaick; R Gieschke; P Pillai; J-L Steimer; P Girard
Journal:  J Pharmacokinet Pharmacodyn       Date:  2006-10-19       Impact factor: 2.745

5.  Placebo-controlled trials in osteoporosis--proceeding with caution.

Authors:  Clifford J Rosen; Sundeep Khosla
Journal:  N Engl J Med       Date:  2010-09-30       Impact factor: 91.245

6.  A semimechanistic and mechanistic population PK-PD model for biomarker response to ibandronate, a new bisphosphonate for the treatment of osteoporosis.

Authors:  Goonaseelan Pillai; Ronald Gieschke; Timothy Goggin; Philippe Jacqmin; Ralph C Schimmer; Jean-Louis Steimer
Journal:  Br J Clin Pharmacol       Date:  2004-12       Impact factor: 4.335

7.  Changes in bone density and turnover explain the reductions in incidence of nonvertebral fractures that occur during treatment with antiresorptive agents.

Authors:  Marc C Hochberg; Susan Greenspan; Richard D Wasnich; Paul Miller; Desmond E Thompson; Philip D Ross
Journal:  J Clin Endocrinol Metab       Date:  2002-04       Impact factor: 5.958

8.  Sensitivity analysis of a novel mathematical model identifies factors determining bone resorption rates.

Authors:  M J Martin; J C Buckland-Wright
Journal:  Bone       Date:  2004-10       Impact factor: 4.398

9.  A theoretical analysis of long-term bisphosphonate effects on trabecular bone volume and microdamage.

Authors:  Jeffry S Nyman; Oscar C Yeh; Scott J Hazelwood; R Bruce Martin
Journal:  Bone       Date:  2004-07       Impact factor: 4.398

Review 10.  Bone physiology, disease and treatment: towards disease system analysis in osteoporosis.

Authors:  Teun M Post; Serge C L M Cremers; Thomas Kerbusch; Meindert Danhof
Journal:  Clin Pharmacokinet       Date:  2010       Impact factor: 6.447

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  4 in total

1.  Selection and Qualification of Simplified QSP Models When Using Model Order Reduction Techniques.

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Journal:  AAPS J       Date:  2017-11-27       Impact factor: 4.009

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

Authors:  Jenny Zheng; Erno van Schaick; Liviawati Sutjandra Wu; Philippe Jacqmin; Juan Jose Perez Ruixo
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-06-30       Impact factor: 2.745

3.  Disease Progression Modeling: Key Concepts and Recent Developments.

Authors:  Sarah F Cook; Robert R Bies
Journal:  Curr Pharmacol Rep       Date:  2016-08-15

4.  p53 plays a central role in the development of osteoporosis.

Authors:  Tao Yu; Xiaomeng You; Haichao Zhou; Alex Kang; Wenbao He; Zihua Li; Bing Li; Jiang Xia; Hui Zhu; Youguang Zhao; Guangrong Yu; Yuan Xiong; Yunfeng Yang
Journal:  Aging (Albany NY)       Date:  2020-06-02       Impact factor: 5.682

  4 in total

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