Purpose: To maximize the yield of extract from Eucommia ulmoides Oliver and its effect on bone quality.
Methods: Different extraction indices were optimized with response surface methodology (RSM) for maximization of extract yield from Eucommia ulmoides Oliver. Box–Behnken design (BBD) was used to identify the effects of temperature, time, and liquid to solid ratio on extract yield from Eucommia ulmoides Oliver. After 4-week acclimatization, thiry-two rats were randomly assigned to 4 groups (n = 8): group 1 (sham) given vehicle only; group 2 (OVX rats given Eucommia ulmoides Oliver extract at a dose of 4 g/kg; group 3 (OVX + vehicle); group 4 (OVX + EUOE), i.e., OVX rats given Eucommia ulmoides Oliver extract (4 g/kg). Sham rats had intact ovaries. After surgery, the rats received gentamicin intramuscularly for 3 successive days. Two months after surgery, blood and trabecular bones was taken for analysis.
Results: Temperature and liquid-to-solid ratio had marked impact on extract yield from Eucommia ulmoides Oliver, with the best conditions being temperature of 88 °C, time of 137 min, and liquid to solid ratio 16:1. Using these optimized conditions, the maximum yield of extract obtained experimentally (2.53%) was very close to the predicted value of 2.49 %. There was a good fit between the mathematical model evolved and the data on extract yield. The extract significantly (p < 0.01) increased the Ca and P and Cr levels in OVX + EUOE group compared to those in OVX control. Moreover, the extract significantly (p < 0.01) increased macro-mechanical indices of trabecular bone in OVX+EUOE group, relative to those in OVX control.
Conclusion: The yield of Eucommia ulmoides Oliver extract has been successfully optimized using RSM. The extract exhibited strong effects on bone quality.
Keywords: Optimization, Eucommia ulmoides, Box–Behnken design, Response surface methodology, Bone loss, Gene
Simultaneous quantification of eleven bioactive components of male flowers of Eucommia ulmoides oliver by HPLC and their quality evaluation by chemical fingerprint analysis with hierarchical clustering analysis
