Mechanistic PK-PD model of alendronate treatment of postmenopausal osteoporosis predicts bone site-specific response

Calvo-Gallego, José Luis; Pivonka, Peter; Ruiz-Lozano, Rocío; Martínez-Reina, Javier

doi:10.3389/fbioe.2022.940620

Cited by 8 publications

(11 citation statements)

References 33 publications

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“…Doing so, we highlight the importance of distinguishing both timelines, age and YSM, in the analysis of the treatment efficacy. This distinction contrasts with previous studies which did not take into account age and consequently could only analyse the effect of YSM (Martínez-Reina and Pivonka, 2019;Martínez-Reina et al, 2021a,b;Calvo-Gallego et al, 2022). In Figure 8 left the treatment commences 10 years after menopause, i.e.…”

Section: Resultsmentioning

confidence: 70%

“…Several mathematical models have been proposed in the literature to predict the bone loss due to age (Lemaire et al, 2004) and menopause (Scheiner et al, 2014;Martínez-Reina and Pivonka, 2019;Martínez-Reina et al, 2021a,b;Calvo-Gallego et al, 2022). These are bone cell population models (BCPM) that can account for the effects of age or menopause and the subsequent estrogen deficiency through the biochemical factors that control bone cell differentiation, survival, action and interconnection between different cell types.…”

Section: Introductionmentioning

confidence: 99%

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An in-silico approach to elucidate the pathways leading to primary osteoporosis: age-related vs. postmenopausal

Ruiz-Lozano,

Calvo-Gallego,

Pivonka

et al. 2024

Preprint

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Numerical models of bone remodelling have traditionally been used to simulate bone loss in postmenopausal women and the response to drug treatments. These models simulate the menopausal estrogen decline by altering certain signalling pathways. However, they overlook the simultaneous effect that ageing can have on bone cells function and thus on bone loss. Considering ageing and estrogen decline together is important for designing osteoporosis treatments that can counteract one of the two factors.A previously developed bone remodelling model was adapted to consider the effect of ageing through: (1) decreased TGF-beta content within bone matrix; (2) increased sclerostin production by non-skeletal cells. Estrogen deficiency is simulated in three different ways: (a) increased RANKL expression; (b) decreased OPG production; (c) increased responsiveness of osteoclasts to RANKL. The effect of ageing was validated using clinical BMD data of vertebral trabecular bone from males. The joint effect of ageing and estrogen deficiency was validated using the corresponding clinical data in women.In ageing, the effect of increased sclerostin production is more important than the decrease in TGF-beta. The three mechanisms used to simulate the effect of estrogen deficiency yielded almost identical responses. The results show that early menopause leads to lower density in the fifth decade, but after the sixth decade density is almost independent of the age at menopause on average. Osteoporosis treatment with denosumab was also simulated, concluding that the treatment proves more necessary after 10 years since menopause or after age 60.

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Section: Resultsmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

An in-silico approach to elucidate the pathways leading to primary osteoporosis: age-related vs. postmenopausal

Ruiz-Lozano,

Calvo-Gallego,

Pivonka

et al. 2024

Preprint

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“…However, this study resulted in a distinctive asymmetric peak shape of the BMDDs (Buenzli et al. 2018 ). We note that even though these models are able to investigate the BMDD, no conclusions can be made regarding the bone microstructure.…”

Section: Introductionmentioning

confidence: 89%

“…In subsequent work, Buenzli et al. ( 2018 ) extended this model to limit mineralisation to various maximum calcium capacities of bone. Results of this extended model show that an abrupt stopping of mineralisation near a maximum calcium capacity induces a pile-up of minerals in the BMDD statistics that is not observed experimentally.…”

Section: Introductionmentioning

confidence: 99%

Bone turnover and mineralisation kinetics control trabecular BMDD and apparent bone density: insights from a discrete statistical bone remodelling model

Castoldi,

Pickering,

Sansalone

et al. 2024

Biomech Model Mechanobiol

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The mechanical quality of trabecular bone is influenced by its mineral content and spatial distribution, which is controlled by bone remodelling and mineralisation. Mineralisation kinetics occur in two phases: a fast primary mineralisation and a secondary mineralisation that can last from several months to years. Variations in bone turnover and mineralisation kinetics can be observed in the bone mineral density distribution (BMDD). Here, we propose a statistical spatio-temporal bone remodelling model to study the effects of bone turnover (associated with the activation frequency $$\mathrm {Ac.f}$$ Ac . f ) and mineralisation kinetics (associated with secondary mineralisation $$T_\textrm{sec}$$ T sec ) on BMDD. In this model, individual basic multicellular units (BMUs) are activated discretely on trabecular surfaces that undergo typical bone remodelling periods. Our results highlight that trabecular BMDD is strongly regulated by $$\mathrm {Ac.f}$$ Ac . f and $$T_\textrm{sec}$$ T sec in a coupled way. Ca wt% increases with lower $$\mathrm {Ac.f}$$ Ac . f and short $$T_\textrm{sec}$$ T sec . For example, a $$\mathrm {Ac.f}=$$ Ac . f = 4 BMU/year/mm$$^3$$ 3 and $$T_\textrm{sec}$$ T sec = 8 years result in a mean Ca wt% of 25, which is in accordance with Ca wt% values reported in quantitative backscattered electron imaging (qBEI) experiments. However, for lower $$\mathrm {Ac.f}$$ Ac . f and shorter $$T_\textrm{sec}$$ T sec (from 0.5 to 4 years) one obtains a high Ca wt% and a very narrow skew BMDD to the right. This close link between $$\mathrm {Ac.f}$$ Ac . f and $$T_\textrm{sec}$$ T sec highlights the importance of considering both characteristics to draw meaningful conclusion about bone quality. Overall, this model represents a new approach to modelling healthy and diseased bone and can aid in developing deeper insights into disease states like osteoporosis.

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“…Over the last decade great efforts have been made to develop sophisticated bone cell population models (BCPM) that take into account the aforementioned regulatory factors ( Komarova et al, 2003 ; Lemaire et al, 2004 ; Pivonka et al, 2008 ; Martínez-Reina and Pivonka, 2019 ; Martínez-Reina et al, 2021a ; Martínez-Reina et al, 2021b ; Calvo-Gallego et al, 2022 ). BCPM are able to simulate the process of BR and take into account all the aforementioned effects, by considering the concentration of the cells and of some biochemical factors involved in that process in a representative volume element (RVE).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal simulations of bone remodelling using a bone cell population model based on cell availability

Calvo-Gallego

Manchado-Morales²,

Pivonka³

et al. 2023

Front. Bioeng. Biotechnol.

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Here we developed a spatio-temporal bone remodeling model to simulate the action of Basic Multicelluar Units (BMUs). This model is based on two major extensions of a temporal-only bone cell population model (BCPM). First, the differentiation into mature resorbing osteoclasts and mature forming osteoblasts from their respective precursor cells was modelled as an intermittent process based on precursor cells availability. Second, the interaction between neighbouring BMUs was considered based on a “metabolic cost” argument which warrants that no new BMU will be activated in the neighbourhood of an existing BMU. With the proposed model we have simulated the phases of the remodelling process obtaining average periods similar to those found in the literature: resorption (∼22 days)—reversal (∼8 days)—formation (∼65 days)—quiescence (560–600 days) and an average BMU activation frequency of ∼1.6 BMUs/year/mm3. We further show here that the resorption and formation phases of the BMU become coordinated only by the presence of TGF-β (transforming growth factor β), i.e., a major coupling factor stored in the bone matrix. TGF-β is released through resorption so upregulating osteoclast apoptosis and accumulation of osteoblast precursors, i.e., facilitating the transition from the resorption to the formation phase at a given remodelling site. Finally, we demonstrate that this model can explain targeted bone remodelling as the BMUs are steered towards damaged bone areas in order to commence bone matrix repair.

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Mechanistic PK-PD model of alendronate treatment of postmenopausal osteoporosis predicts bone site-specific response

Cited by 8 publications

References 33 publications

An in-silico approach to elucidate the pathways leading to primary osteoporosis: age-related vs. postmenopausal

An in-silico approach to elucidate the pathways leading to primary osteoporosis: age-related vs. postmenopausal

Bone turnover and mineralisation kinetics control trabecular BMDD and apparent bone density: insights from a discrete statistical bone remodelling model

Spatio-temporal simulations of bone remodelling using a bone cell population model based on cell availability

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