Development of computational models of bone formation and resorption to predict changes in bone in preclinical intervention studies

We will develop new computational models capable of predicting the extent and anatomical location of bone formation and/or resorption following bone anabolic interventions. We will use models of osteoporosis as proof of concept but the models developed will be applicable to any given intervention of genetic, metabolic, surgical, pharmacological, or functional nature. We will use a combination of in vitro data and data collected by the non-destructive in vivo micro computed tomography (microCT). This will allow the accurate three-dimensional measurement of bone tissue morphometry changes over time due to the intervention under investigation in the same region of the skeleton of the same animal.

Once validated, this quantitative information will be fed into a computer model that will determine the relationship between each specific intervention, the time, and the bone remodelling at each point of the bone. These models will then be used to make predictions on how the tissue morphology in a region of the mouse skeleton would change over a given time because of a given intervention. The accuracy of these predictions will be confirmed at the next time points, by simply comparing the computer predictions to the data generated by the in vivo microCT scanning.

The way in which candidate interventions are approved for human trials involves animal use for proof of principle studies in physiological models and uses large numbers of animals to generate statistically robust data at each time point. This technology will replace most of this in vivo testing and will require reduced number of animals to test at the chosen stratification due to the higher accuracy of the serial in vivo imaging technique. Moreover it will generate better quality data and allow a better understanding of the advantages and limitations of mouse models in testing interventions compared to human subjects.

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Project grant

Grant reference number

NC/K000780/1

Award date:

Jul 2013 - Sep 2015

Grant amount

£364,835