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<p>Bone degeneration diseases, such as osteoporosis, are skeletal disorders characterized by diminished bone mass and increased susceptibility to fractures and represent a growing global health challenge, particularly in aging populations. The development of effective therapeutic strategies necessitates a deep understanding of the complex biological processes underlying bone remodeling, regeneration, and homeostasis. To address these challenges, computational approaches have played a crucial role in advancing our understanding of bone biology and improving therapeutic strategies. This review explores these contributions across three main areas: (1) elucidating the structural organization and interactions within the bone matrix, particularly between collagen and hydroxyapatite; (2) investigating the regulatory roles of non-collagenous proteins, such as bone morphogenetic proteins, osteocalcin, osteopontin, and fibronectin, in bone mineralization; and (3) facilitating drug discovery and development for bone regeneration by targeting key pathways and molecules, including sclerostin, RANKL, and estrogen receptors. Molecular dynamics and docking have helped identify and optimize natural and synthetic therapeutic agents for these critical pathways. Additionally, we apply bioinformatics tools to analyze bone regeneration and degeneration pathways, emphasizing the need for more accurate computational techniques to reconstruct their interactome. As these techniques continue to evolve, integrating advancements in machine learning, molecular dynamics, and multi-scale modeling, their potential to bridge the gap between experimental research and clinical application is becoming increasingly apparent. A multidisciplinary approach that combines computational predictions with experimental validation and clinical data is poised to drive the development of personalized and effective osteoporosis therapies, ultimately reducing the global burden of this debilitating disease.</p>

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Molecular-level understanding of the aging bone and regeneration mechanisms using computational methods

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月10日

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  • Filip Stojceski,
  • Harry Zaverdas,
  • Andrea Danani,
  • Alessia Mengoni,
  • Mario Ledda,
  • Giuseppe Falvo D’Urso Labate,
  • Athanasios Kalogeras,
  • Konstantinos Theofilatos,
  • Seferina Mavroudi,
  • Gianvito Grasso

摘要

Bone degeneration diseases, such as osteoporosis, are skeletal disorders characterized by diminished bone mass and increased susceptibility to fractures and represent a growing global health challenge, particularly in aging populations. The development of effective therapeutic strategies necessitates a deep understanding of the complex biological processes underlying bone remodeling, regeneration, and homeostasis. To address these challenges, computational approaches have played a crucial role in advancing our understanding of bone biology and improving therapeutic strategies. This review explores these contributions across three main areas: (1) elucidating the structural organization and interactions within the bone matrix, particularly between collagen and hydroxyapatite; (2) investigating the regulatory roles of non-collagenous proteins, such as bone morphogenetic proteins, osteocalcin, osteopontin, and fibronectin, in bone mineralization; and (3) facilitating drug discovery and development for bone regeneration by targeting key pathways and molecules, including sclerostin, RANKL, and estrogen receptors. Molecular dynamics and docking have helped identify and optimize natural and synthetic therapeutic agents for these critical pathways. Additionally, we apply bioinformatics tools to analyze bone regeneration and degeneration pathways, emphasizing the need for more accurate computational techniques to reconstruct their interactome. As these techniques continue to evolve, integrating advancements in machine learning, molecular dynamics, and multi-scale modeling, their potential to bridge the gap between experimental research and clinical application is becoming increasingly apparent. A multidisciplinary approach that combines computational predictions with experimental validation and clinical data is poised to drive the development of personalized and effective osteoporosis therapies, ultimately reducing the global burden of this debilitating disease.