Predicting cortical bone adaptation to axial loading in the mouse tibia.

Area of Science:

• Biomechanics
• Computational Biology
• Orthopedics

Background:

• Bone adaptation to mechanical load is crucial for skeletal health.
• Understanding the specific mechanical cues driving bone adaptation is essential.
• Current models often lack spatial accuracy in predicting bone mechanobiology.

Purpose of the Study:

• To develop and validate a computational model for predicting cortical bone adaptation to mechanical load with spatial accuracy.
• To identify key mechanical stimuli, such as interstitial fluid velocity, that drive bone adaptation.
• To enhance the understanding of bone mechanobiology and inform therapeutic strategies.

Main Methods:

• Utilized an axial tibial loading model in C57BL/6 mice.
• Developed a method for mapping cortical thickness in the mouse tibia diaphysis.
• Employed poroelastic finite-element (FE) models coupled with mechanobiological equations to simulate bone response.

Main Results:

• The computational model accurately predicted the spatial distribution of cortical bone adaptation.
• A statistically significant positive correlation was found between model predictions and experimental data (Kendall's τ = 0.51, p < 0.001).
• Interstitial fluid velocity was identified as a key mechanical stimulus driving adaptation.

Conclusions:

• Computational models can accurately predict spatial cortical bone mechanoadaptation to time-varying mechanical stimuli.
• This approach offers a powerful tool for designing optimized loading protocols and targeted drug therapies.
• The findings advance the understanding of bone mechanobiology and its clinical applications.