In the market of botanical dietary supplements, Cimicifuga heracleifolia(CH) has always been considered as an adulterated species of Cimicifuga racemosa(CR), a conventional American herb with promising benefits to counteract troubles arising from the menopause. However, the detailed comparison of their therapeutic effects is lacking. In present study, the pharmacological and metabolomics studies were comparatively conducted between CH and CR in ovariectomized(OVX) female rats. Specifically, estrogen-like, anti-hyperlipidemia and anti-osteoporosis effects were evaluated through measuring serum biochemical parameters, histopathological examination and micro computed tomography(Micro-CT) scanning. At the same time, a gas chromatography-mass spectrometry(GC-MS)-based serum metabolomics method was employed to profile the metabolite compositional changes. As a result, both CR and CH displayed anti-osteoporosis and anti-hyperlipemia on menopause syndrome. Meanwhile, their potentials in improving the OVX-induced metabolic disorders were discovered. In conclusion, these results demonstrated that CH is therapeutically similar to CR in relieving menopausal symptoms and CH could be considered as a promising alternative to CR instead of an adulterant in the market of botanical dietary supplements.
The nonlinear mixed-effects model with stochastic differential equations （SDEs） is used to model the population pharmacokinetic （PPK） data that are extended from ordinary differential equations （ODEs） by adding a stochastic term to the state equation. Compared with the ODEs, the SDEs can model correlated residuals which are ubiquitous in actual pharmacokinetic problems. The Bayesian estimation is provided for nonlinear mixed-effects models based on stochastic differential equations. Combining the Gibbs and the Metropolis-Hastings algorithms, the population and individual parameter values are given through the parameter posterior predictive distributions. The analysis and simulation results show that the performance of the Bayesian estimation for mixed-effects SDEs model and analysis of population pharmacokinetic data is reliable. The results suggest that the proposed method is feasible for population pharmacokinetic data.
