A-S.B.07: Combining mechanistic modelling with biobank data to assess the effect of pharmacological osteoporosis therapy in women with or without HRT
Authors
Tom Michoel
University of Bergen
Susanna Röblitz
University of Bergen
Keywords
mathematical modelling, bone remodelling, parameter estimation, hormone replacement therapy
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Bone mineral density (BMD) is a measure of bone strength and a determinant of fracture risk, and measuring BMD is recommended for women approaching menopause due to estradiol deficiency and the higher risk of low bone mass and osteoporosis, which are linked to increased risk for cardiovascular and other age-related diseases. Many mathematical models have been developed in an effort to predict bone behavior and to simulate the effect of drug treatments.
However, these models are provided with only a single set of parameter values, thus neglecting between-patient variability. In this poster, we demonstrate how cross-sectional data on BMD from UK Biobank can be used to re-estimate the distribution of model parameters in a mechanistic model of bone remodelling for two different patient groups, namely post-menopausal women who are on hormone replacement therapy (HRT) and post-menopausal women who are not. Differences in the parameter distributions between the two groups result in differences in treatment outcome when simulating administration of a sclerostin antibody. The results indicate that the treatment is more efficient in women with HRT since virtual patients generated from the parameter distribution based on HRT tend to have a higher delay in returning to the baseline BMD level after the end of drug administration.
This work demonstrates how cross-sectional data, e.g. from biobanks, can be used to enrich existing mechanistic dynamic models to enable group-specific predictions.
Co-authors: Tom Michoel, Susanna Röblitz
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