Ten‐Year Simulation of the Effects of Denosumab on Bone Remodeling in Human Biopsies

Abstract

Postmenopausal osteoporosis is a disease manifesting in degradation of bone mass and microarchitecture, leading to weakening and increased risk of fracture. Clinical trials are an essential tool for evaluating new treatments and may provide further mechanistic understanding of their effects in vivo. However, the histomorphometry from clinical trials is limited to 2D images and reflects single time points. Biochemical markers of bone turnover give global insight into a drug's action, but not the local dynamics of the bone remodeling process and the cells involved. Additionally, comparative trials necessitate separate treatment groups, meaning only aggregated measures can be compared. In this study, in silico modeling based on histomorphometry and pharmacokinetic data was used to assess the effects of treatment versus control on μCT scans of the same biopsy samples over time, matching the changes in bone volume fraction observed in biopsies from denosumab and placebo groups through year 10 of the FREEDOM Extension trial. In the simulation, treatment decreased osteoclast number, which led to a modest increase in trabecular thickness and osteocyte stress shielding. Long‐term bone turnover suppression led to increased RANKL production, followed by a small increase in osteoclast number at the end of the 6‐month–dosing interval, especially at the end of the Extension study. Lack of treatment led to a significant loss of bone mass and structure. The study's results show how in silico models can generate predictions of denosumab cellular action over a 10‐year period, matching static and dynamic morphometric measures assessed in clinical biopsies. The use of in silico models with clinical trial data can be a method to gain further insight into fundamental bone biology and how treatments can perturb this. With rigorous validation, such models could be used for informing the design of clinical trials, such that the number of participants could be reduced to a minimum to show efficacy. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Trabecular bone remodeling in the ageing mouse: a micro-multiphysics agent-basedin silicomodel using single-cell mechanomics

“…This problem has been identified for in silico models of bone mechanobiology (Checa and Prendergast 2009), where simple approaches were adopted to overcome this lack of information (Perier-Metz et al 2020, 2022; Borgiani et al 2021). This limitation might be partially overcome by taking advantage of the mechanical environment as it was performed by Tourolle and colleagues (Tourolle et al 2021). A more synergistic study where the cytokines are experimentally obtained from the same site on a regular basis would help in the calibration of this micro-MPA in silico model.…”

Clinical Data for Parametrization of In Silico Bone Models Incorporating Cell-Cytokine Dynamics: A Systematic Review of Literature

“…In silico simulations have been proposed to provide fast, inexpensive and ethical alternatives to years of costly experimentation on animals and humans (Webster, 2020;Nikolova-Simons et al, 2021;Sarrami-Foroushani et al, 2021). Existing in silico models have been developed to study bone remodeling, its deregulation during metabolic bone diseases and the effect of therapeutics and exercise (Lemaire et al, 2004;Pivonka et al, 2008;Lerebours et al, 2015;Pastrama et al, 2018;Boaretti et al, 2019;Tourolle, 2019;Kameo et al, 2020;Martínez-Reina et al, 2021a;Tourolle et al, 2021).…”

“…β (RANK)-its ligand (RANKL) and Osteoprotegerin (OPG) (Boyce and Xing, 2008); mechanosensitivity via the Wingless and int-1 (Wnt) proteins, sclerostin and parathyroid hormone (PTH) (Jilka et al, 1999;Hadjidakis and Androulakis, 2006;Lewiecki, 2014;Kovács et al, 2019); osteoblastogenesis governed by levels of bone morphogenetic protein (BMP) and transforming growth factor-β (TGF-β) (Hanada et al, 1997;Zimmermann et al, 2005;Giannoudis et al, 2008); and interleukin-governed interplay between bone remodeling and immune reactions (Shaarawy et al, 2003;Pino et al, 2010;Sun et al, 2011;Giganti et al, 2012;Marques et al, 2013). Within the varied spectrum of in silico modeling techniques, only bone cell population dynamics models (Lemaire et al, 2004;Pivonka et al, 2008;Lerebours et al, 2015;Pastrama et al, 2018;Martínez-Reina et al, 2021a) and micro-multiphysics agentbased (micro-MPA) models (Boaretti et al, 2019;Tourolle, 2019;Kameo et al, 2020;Tourolle et al, 2021) explicitly incorporate the complex cellular and molecular mechanisms causing metabolic bone diseases and the pathways involved in their treatments.…”

“…To date, only micro-MPA models have the resolution to predict the effect of the complex cell-cytokine pathways in bone on the bone microarchitecture. Tourolle (Tourolle et al, 2021) and Kameo (Kameo et al, 2020) developed micro-MPA models of osteoporosis and its treatments with bisphosphonates and RANK ligand inhibitors, respectively, using microcomputed tomography (microCT) scans of iliac crest biopsies with resolutions on the order of 10 µm as input.…”

“…Despite the promise of such in silico models, extensive verification and validation are still needed, particularly using human data, for micro-MPA models or bone cell population dynamics models to be applied clinically. In these types of models, parameters for cell and cytokine behavior are set based on experimental values found in literature; however, the settings used vary from one research group to the next (Kameo et al, 2020;Tourolle et al, 2021) as well as from one model to another (Martínez-Reina et al, 2021a;Tourolle et al, 2021). The cellcytokine signaling pathways included in micro-MPA models of bone remodeling were based on earlier bone cell population dynamics models.…”