Computational Simulations of Endocrine Bone Diseases Related to Pathological Glandular PTH Secretion Using a Multi-Scale Bone Cell Population Model

Corinna Modiz^1,2*Natalia M Castoldi¹Stefan Scheiner³Javier Martinez-Reina⁴Jose Luis Calvo-Gallego⁴Vittorio Sansalone²Saulo Martelli¹Peter Pivonka¹

• ¹School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, Queensland, Australia
• ²Univ Gustave Eiffel, CNRS, UMR 8208, MSME, Université Paris-Est Créteil Val de Marne, Créteil, Ile-de-France, France
• ³Institute for Mechanics of Materials and Structures, Vienna University of Technology, Vienna, Vienna, Austria
• ⁴Departamento Ingeniería Mecánica y Fabricación, Sevilla University, Seville, Andalucia, Spain

Bone diseases significantly impact global health by compromising skeletal integrity and quality of life. In disease states linked to parathyroid hormone (PTH) glandular secretion, disrupted PTH patterns typically promote osteoclast proliferation, leading to increased bone resorption. While mathematical modeling has proven valuable in analyzing bone remodeling, current bone cell population models oversimplify PTH secretion by assuming constant levels, limiting their ability to represent disorders characterized by variations in PTH pulse characteristics. To address this, we present a novel semi-coupled approach integrating a two-state PTH receptor model with an established bone cell population model. Instead of conventional Hill-type functions, we implement a cellular activity function derived from the receptor model, incorporating pulsatile PTH patterns, cell dynamics, and intracellular communication pathways. Our numerical simulations demonstrate the model's capability to reproduce various catabolic bone diseases, providing realistic changes in bone volume fraction over a one-year period. Notably, while direct implementation of PTH disease progression in the bone cell population model fails to capture diseases only characterized by altered pulse duration and baseline, such as glucocorticoid-induced osteoporosis, our semi-coupled approach successfully models these conditions. This physiologically more realistic approach to endocrine disease modeling offers potential implications for optimizing therapeutic interventions and understanding disease progression mechanisms.