Modeling and simulation of bone mineral density in Japanese osteoporosis patients treated with zoledronic acid using tartrate-resistant acid phosphatase 5b, a bone resorption marker.

Mori, Y; Kasai, H; Ose, A; Serada, M; Ishiguro, M; Shiraki, M; Tanigawara, Y · Osteoporos Int · 2018

rct · Level II

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Abstract

Annual intravenous administration of zoledronic acid is used in the treatment of osteoporosis. A mathematical model was developed to predict bone mineral density up to 2 years after two annual doses of zoledronic acid from the early values of a bone resorption marker in osteoporosis patients. The measurement of bone mineral density (BMD) has been used as a surrogate marker instead of the observation of incident fractures to detect the efficacy of treatment. However, this method requires a long time to obtain significant changes. On the other hand, bone resorption markers respond to bone resorption inhibitors within a few weeks. Therefore, the aim of this study was to develop a mathematical model predicting long-term BMD after two annual doses of zoledronic acid (ZOL) using the early response of a bone resorption marker in osteoporosis patients. The model was constructed using 3410 tartrate-resistant acid phosphatase 5b (TRACP-5b) serum concentrations and 1146 lumbar spine (L2-L4) BMD values from 306 patients with primary osteoporosis. A mathematical model was developed to describe the time-dependent profiles of TRACP-5b and BMD. The percentage changes from baseline of the BMD (%BMD) at up to 2 years were predicted from patients' baseline BMD and baseline and 12-week TRACP-5b values by the model obtained. The simulated 90% prediction interval almost covered the observed %BMD distribution at each time point, and the predictions were comparable to the observed %BMD. This is the first model to predict BMD for up to 2 years following two annual doses of ZOL using patients' background characteristics and the early response of TRACP-5b. This model allows us to inform patients at the initial stage of ZOL treatment of their predicted response to treatment.

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