Near Infrared Spectroscopic Combined with Partial Least Squares and Radial Basis Function Neural Network to Analyze Paclitaxel Concentration in Rat Plasma.

Paclitaxel is known as one of the most effective anticancer drugs. Near Infrared Spectroscopy (NIRS), a rapid, precise and non-destructive approach of analysis, has been widely used for qualitative and quantitative detection. The present study aims to analyze the plasma paclitaxel concentration with NIRS. Various batches of plasma samples were prepared and the concentration of paclitaxel was determined via high performance liquid chromatography tandem mass spectrometry (LC-MS/MS). The outliers and the number of calibration set were confirmed by Monte Carlo algorithm combined with partial least squares (MCPLS). Since NIR spectra may be contaminated by signals from background and noise, a series of preprocessing were performed to improve signal resolution. Moving window PLS and radical basis function neural network (RBFNN) methods were applied to establish calibration model. Although both PLS and RBFNN models are well-fitting, RBFNN-established model displayed better qualities on stability and predictive ability. The correlation coefficients of calibration curve and prediction set (Rc(2) and Rp(2)) are 0.9482 and 0.9544, respectively. Moreover, independent verification test with 20 samples confirmed the well predictive ability of RBFNN model.

Study of the analgesic activities, chronic toxicity and addictive potential of Jia-Yuan-Qing pill in rats.

Jia-Yuan-Qing pill (JYQP) composed of Porcellio laevis Latreille, Corydalis Rhizoma and Radix Cynanchi Paniculati at a ratio of 9:7:7 has been found to be an effective analgesic agent. The present study aimed to evaluate the safety, addictive potential and anti-cancer pain activity of JYQP in a rat model. During the 6-month chronic toxicity test, no significant changes in general behavior, defecation, postural abnormalities, dietary or water intake or blood biochemical parameters were observed in male and female rats. Although a high dose of JYQP (5 g/kg) caused swelling of the liver, spleen and kidney in male and female rats, no pathological changes were observed in all organs examined via hematoxylin and eosin staining. The analgesic effect of JYQP on bone cancer pain was successfully confirmed in a rat model of Walker 256 cell-induced bone cancer. In contrast to morphine, in a physical dependence test, JYQP produced no withdrawal symptoms following chronic administration. The data from this study provide experimental evidence supporting the clinical use of JYQP as an effective, safe and non-addictive agent for the treatment of bone cancer pain.

Near infrared spectroscopic (NIRS) analysis of drug-loading rate and particle size of risperidone microspheres by improved chemometric model.

Microspheres have been developed as drug carriers in controlled drug delivery systems for years. In our present study, near infrared spectroscopy (NIRS) is applied to analyze the particle size and drug loading rate in risperidone poly(d,l-lactide-co-glycolide) (PLGA) microspheres. Various batches of risperidone PLGA microspheres were designed and prepared successfully. The particle size and drug-loading rate of all the samples were determined by a laser diffraction particle size analyzer and high performance liquid chromatography (HPLC) system. Monte Carlo algorithm combined with partial least squares (MCPLS) method was applied to identify the outliers and choose the numbers of calibration set. Furthermore, a series of preprocessing methods were performed to remove signal noise in NIR spectra. Moving window PLS and radical basis function neural network (RBFNN) methods were employed to establish calibration model. Our data demonstrated that PLS-developed model was only suitable for drug loading analysis in risperidone PLGA microspheres. Comparatively, RBFNN-based predictive models possess better fitting quality, predictive effect, and stability for both drug loading rate and particle size analysis. The correlation coefficients of calibration set (Rc(2)) were 0.935 and 0.880, respectively. The performance of optimum RBFNN models was confirmed by independent verification test with 15 samples. Collectively, our method is successfully performed to monitor drug-loading rate and particle size during risperidone PLGA microspheres preparation.

Therapeutic effects of Chinese herbal medicine against neuroendocrinological diseases especially related to hypothalamus-pituitary-adrenal and hypothalamus-pituitary-gonadal axis.

This is a systemic review of plants used traditionally for neuroendocrinological diseases related to hypothalamus-pitutary-adrenal (HPA) and hypothalamus-pitutary-gland (HPG) axis. By searching from PubMed literature search system (1950-2013), Medline (1950-2013) and CNKI (China Journals of Full-text database; 1989-2013), 105 papers met the inclusion criteria were displayed in this review. 96 herbal drugs were classified into two parts which include hormones mainly related to HPA and HPG axis. The full scientific name of each herbal medicine, dose ranges and routes, models or diseases, affect on hormones and pertinent references are presented via synoptic tables. Herbal remedies that have demonstrable the activities of hormones have provided a potential to various diseases related to neunoendocrine and deserve increased attention in future studies. This review provides a basis for herbs use in neuroendocrinological diseases. The data collected here will benefit to further research associated to herbal medicines and hormones.

Studies on the analgesic activities of Jia-Yuan-Qing pill and its safety evaluation in mice.

The analgesic activity of Porcellio laevis Latreille, Rhizoma Corydalis, and Radix Cynanchi Paniculati have been reported in recent years. A new formula named Jia-Yuan-Qing pill (JYQP) is therefore created by combining the three herbs at 9:7:7 ratio according to traditional Chinese theories. The present study aims to evaluate the effect of JYQP as a novel painkiller in various models. Acute toxicity test was applied to evaluate the safety of JYQP. Acetic-acid-induced writhing, hot plate test, formalin test, and naloxone-pretreated writhing test were employed to elaborate the analgesic activity of JYQP and its possible mechanism. A bone cancer pain mouse model was performed to further assess the effect of JYQP in relieving cancer pain. Test on naloxone-precipitated withdrawal symptoms was conduct to examine the physical dependence of mice on JYQP. Data revealed that JYQP reduced writhing and stretching induced by acetic acid; however, this effect could not be blocked by naloxone. JYQP specifically suppressed the phase II reaction time in formalin-treated mice; meanwhile, no analgesic effect of JYQP in hot plate test was observed, indicating that JYQP exerts analgesic activity against inflammatory pain rather than neurogenic pain. Furthermore, JYQP could successfully relieve bone cancer pain in mice. No physical dependence could be observed upon long-term administration in mice. Collectively, our present results provide experimental evidence in supporting clinical use of JYQP as an effective and safe agent for pain treatment.

[Study on the detection of active ingredient contents of Paecilomyces hepiali mycelium via near infrared spectroscopy].

Partial least squares (PLS) and radial basis function neural network (RBFNN) combined with near infrared spectros- copy (NIR) were applied to develop models for cordycepic acid, polysaccharide and adenosine analysis in Paecilomyces hepialid fermentation mycelium. The developed models possess well generalization and predictive ability which can be applied for crude drugs and related productions determination. During the experiment, 214 Paecilomyces hepialid mycelium samples were obtained via chemical mutagenesis combined with submerged fermentation. The contents of cordycepic acid, polysaccharide and adenosine were determined via traditional methods and the near infrared spectroscopy data were collected. The outliers were removed and the numbers of calibration set were confirmed via Monte Carlo partial least square (MCPLS) method. Based on the values of degree of approach (Da), both moving window partial least squares (MWPLS) and moving window radial basis function neural network (MWRBFNN) were applied to optimize characteristic wavelength variables, optimum preprocessing methods and other important variables in the models. After comparison, the RBFNN, RBFNN and PLS models were developed successfully for cordycepic acid, polysaccharide and adenosine detection, and the correlation between reference values and predictive values in both calibration set (R2c) and validation set (R2p) of optimum models was 0.9417 and 0.9663, 0.9803 and 0.9850, and 0.9761 and 0.9728, respectively. All the data suggest that these models possess well fitness and predictive ability.

Biodegradable poly(D, L-lactide-co-glycolide) (PLGA) microspheres for sustained release of risperidone: Zero-order release formulation.

The preparation and investigation of sustained-release risperidone-encapsulated microspheres using erodible poly(D, L-lactide-co-glycolide) (PLGA) of lower molecular weight were performed and compared to that of commercial Risperdal Consta™ for the treatment of schizophrenia. The research included screening and optimizing of suitable commercial polymers of lower molecular weight PLGA50/50 or the blends of these PLGA polymers to prepare microspheres with zero-order release kinetics properties. Solvent evaporation method was applied here while studies of the risperidone loaded microsphere were carried out on its drug encapsulation capacity, morphology, particle size, as well as in vitro release profiles. Results showed that microspheres prepared using 50504A PLGA or blends of 5050-type PLGAs exerted spherical and smooth morphology, with a higher encapsulation efficiency and nearly zero-order release kinetics. These optimized microspheres showed great potential for a better depot preparation than the marketed Risperdal Consta™, which could further improve the patient compliance.

[Application of wavelet transform-radial basis function neural network in NIRS for determination of rifampicin and isoniazide tablets].

A calibration model (WT-RBFNN) combination of wavelet transform (WT) and radial basis function neural network (RBFNN) was proposed for synchronous and rapid determination of rifampicin and isoniazide in Rifampicin and Isoniazide tablets by near infrared reflectance spectroscopy (NIRS). The approximation coefficients were used for input data in RBFNN. The network parameters including the number of hidden layer neurons and spread constant (SC) were investigated. WT-RBFNN model which compressed the original spectra data, removed the noise and the interference of background, and reduced the randomness, the capabilities of prediction were well optimized. The root mean square errors of prediction (RMSEP) for the determination of rifampicin and isoniazide obtained from the optimum WT-RBFNN model are 0.00639 and 0.00587, and the root mean square errors of cross-calibration (RMSECV) for them are 0.00604 and 0.00457, respectively which are superior to those obtained by the optimum RBFNN and PLS models. Regression coefficient (R) between NIRS predicted values and RP-HPLC values for rifampicin and isoniazide are 0.99522 and 0.99392, respectively and the relative error is lower than 2.300%. It was verified that WT-RBFNN model is a suitable approach to dealing with NIRS. The proposed WT-RBFNN model is convenient, and rapid and with no pollution for the determination of rifampicin and isoniazide tablets.

[Study on the extraction of polysaccharides from Marasmius androsaceus Mycelium].

OBJECTIVE: To optimize the extraction condition for the polysaccharides from Marasmius androsaceus Mycelium. METHODS: The single factor tests and response surface methodology (RSM) were applied. Overall research was proceeded by rational desingn. Technological condition parameters were determined according to regression analysis by SAS. RESULTS: The optimum condition was as follows: extraction temperature was 78 degrees C , extraction-duration was 1. 5h, water to material ratio was 36: 1. The optimum extracting rate for the polysaccharide was 11. 59%. CONCLUSION: The RSM is for optimum extraction process and it will improve the extraction conditions of polysaccharides.