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A novel algorithm to predict bone changes in the mouse tibia properties under physiological conditions.

Vee San Cheong1,2, Ana Campos Marin3,4, Damien Lacroix3,4

  • 1Department of Mechanical Engineering, University of Sheffield, Sheffield, UK. v.cheong@sheffield.ac.uk.

Biomechanics and Modeling in Mechanobiology
|December 2, 2019
PubMed
Summary

This study developed a novel model to predict bone adaptation in mouse tibias under physiological loading. The model accurately predicted bone apposition but underestimated bone resorption, highlighting areas for future refinement.

Keywords:
Bone adaptationBone remodellingFinite element analysisIn silico simulationIn vivo micro-computed tomographyValidation

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Area of Science:

  • Biomechanical Engineering
  • Musculoskeletal Research
  • Preclinical Modeling

Background:

  • Understanding bone adaptation to mechanical stimuli is crucial for treating musculoskeletal diseases.
  • Quantifying physiological loading's contribution to mouse tibia adaptation remains a challenge.

Purpose of the Study:

  • To develop and validate a mechanistic model predicting bone adaptation in mouse tibias based on physiological loading.
  • To compare model predictions with longitudinal micro-CT scans and assess sensitivity to various parameters.

Main Methods:

  • A novel mechanistic model was created using micro-finite element analysis (micro-FEA) from micro-CT scans of mouse tibias.
  • The model predicted bone changes based on mechanical stimuli like strain energy density (SED) and maximum principal strain (εmaxprinc).
  • Parametric analysis evaluated model sensitivity to subject-specific vs. averaged parameters and different time points.

Main Results:

  • The model showed no significant difference in bone densitometric properties between predicted and experimental images at week 20.
  • 59% of predicted voxels matched experimental sites for bone apposition and 47% for resorption.
  • The model accurately reproduced bone apposition sites but under-predicted resorption sites by over 85%.

Conclusions:

  • A subject-specific mechanoregulation algorithm shows potential for predicting bone changes in mice under physiological loading.
  • The model's under-prediction of resorption indicates a need to incorporate combined or biological stimuli for improved accuracy.