A statistical model to allow the phasing out of the animal testing of demineralised bone matrix products

2007 
- Demineralised bone matrix (DBM) products are complex mixtures of proteins known to influence bone growth, turnover, and repair. They are used extensively in orthopaedic surgery, and are bioassayed in vivo prior to being used in clinical applications. Many factors contribute to the osteogenic potency of DBM, but the relative contributions of these factors, as well as the possibility of interactive effects, are not completely defined. The "gold standard" measure of the therapeutic value of DBM, the in vivo assay for ectopic bone formation, is costly, time-consuming, and involves the use of numerous animal subjects. We have measured the levels of five growth factors released by the collagenase digestion of DBM, and statistically related these levels with osteogenic potency as determined by a standard in vivo model, in order to determine which value or combination of values of growth factors best predict osteogenic activity. We conclude that the level of BMP-2 is the best single predictor of osteogenic potency, and that adding the values of other growth factors only minimally increases the predictive power of the BMP-2 measurement. A small, but significant, interactive effect between BMP-2 and BMP-7 was demonstrated. We present a statistical model based on growth factor (e.g. BMP-2) analysis that best predicts the in vivo assay score for DBM. This model allows the investigator to predict which lots of DBM are likely to exhibit in vivo bioactivity and which are not, thus reducing the need to conduct in vivo testing of insufficiently active lots of DBM. This model uses cut-point analysis to allow the user to assign an estimate of acceptable uncertainty with respect to the "gold standard" test. This procedure will significantly reduce the number of animal subjects used to test DBM products.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    7
    References
    7
    Citations
    NaN
    KQI
    []