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Near-infrared spectroscopy (NIR) has become one of the most important methods for counterfeit drugs identification for its low cost, non-destructive, and on-site detection. However, it is often invalid for unknown samples beyond the scope of modeling samples, as well as it is not efficient in conditions of insufficient samples (insufficient number of samples within the class), unbalanced samples (large difference in the number of samples between classes), and sensitivity of identification results (different tolerance for different errors in application scenarios). To solve these problems, this paper proposes a general method for on-site identification of counterfeit drugs based on Siamese-network modeling with near-infrared spectroscopy, which especially constructs the train set and test set, learning the general knowledge of spectral differences to identify the different drugs by a costumed one-dimensional convolution neural network (1D-CNN), and finally answered the question of whether the on-site two spectra are pointed to the same drug. Based on experimental modeling samples of 1314 spectra, which are involved 9 drugs produced by 25 manufacturers, this paper has constructed and fully trained its model. Then, not known at the time of modeling, 4 drugs produced by 9 manufacturers are used for testing in the on-site application, and the accuracy rate amounts to 97.3%. For generalizing consideration, randomly divided into training and testing categories, the 32015 spectra of 135 drugs produced by 391 manufacturers in the spectral library are handled by the same processing. The generalization model is equally applicable, and the accuracy is above 97%. Compared with traditional binary classification identification methods such as SVM, PLS-DA, Auto-encoding, and one class (OC) threshold identification algorithms such as SVM-OC, SIMCA, conformity test (CT), the proposed method has the best identification ability for unknown samples in modeling.

In order to simple the way of appraising and calibrating relay protection test apparatus by using more than one universal instruments which caused the process inefficient and inconvenient to carry, using MCU and IC circuit, the advance technology, a calibration equipment of the apparatus is designed to measure the AC and DC power and the action time of power on (off). The test results prove that the measuring accuracy of AC, DC volume is respectively 0.5% and 0.2%, reaching the requirement of trade standards of the power, the action time is 0.002%, lower than 0.1%. Multi-kinds of relay protection test apparatus are fully able to be calibrated and made an objective assessment in the field by the equipment which is simple to operate with stable performance and antiinterference ability, meeting the standard of measure. [ABSTRACT FROM AUTHOR]

The combination of near infrared spectrum and pattern recognition methods has a wide application prospect in rapid and nondestructive supervision and management of drugs. The traditional identification methods regard the smallest error rate as the goal while the imbalance of classes is ignored. This makes the positive class is overwhelming covered by the negative class and reduces its effect for the classifier, so that the classification results tend to recognize the negative class correctly, which severely affects the identification accuracy. In this paper, we mainly studied the class imbalance problems of true or false drugs via infrared spectral data of its, and then propose a balance cascading and sparse representation based classification method (BC-SRC) by combining the Balance Cascading with SRC. We sampling majority samples from the majority class for several times, which has the same size as minority samples and the majority samples we sampled can contain all the majority class samples entirely (sampling times is ceiling the result of majority samples number divide minority samples number). We can get sets of results, and then obtain the final predict labels form those results. Experiments of three databases achieved on Matlab2012a shows that the method is effective. From the experimental results, it can be seen that the method is superior to the commonly used Partial Least Squares (PLS), Extreme Learning Machine (ELM) and BP. Particularly, for the imbalanced databases, when the imbalance factor is greater than 10, the proposed method has more stable performance with higher classification accuracy than the existing ones mentioned above.

In order to find out the optimum combination of the evaluation parameters for the selection of the best drug near infrared (NIR) universal quantitative model during model optimization, 13 common evaluation parameters of NIR quantitative models were collected and arranged from commercial chemometrics software or References based on the requirements of validation of quantitative analytical procedures of ICH (International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use). Then all these parameters of 92 drug NIR universal quantitative models were calculated and analyzed. By studying the correlation of these parameters, the optimum combination of evaluation parameters for drug NIR universal quantitative models was determined. And the value range of these parameters in the optimum combination was also obtained. Root mean square error of cross-validation(RMSECV)/root mean square error of prediction (RMSEP), average relative deviation (ARD) and ratio of (standard error of) prediction (validation) to (standard) deviation (RPD) were used as the key parameters to evaluate the model accuracy. Most of RMSECV/RMSEP was within 3%, and the value of RMSECV was roughly equivalent to the average absolute deviation of the corresponding model. Most of RPD was more than 2. The value of ARD was related to the type of universal models (such as the drug preparation and packing) and the content range which the test sample belonged to. Determination coefficient (R2) was used as the key parameter to evaluate the model linearity and most of its values were from 80% to 100%. The ratio of RMSEP to RMSECV was selected as the key evaluation parameter of model robustness and its value was usually within 1.5. The standard deviation of repeated measurement data was chosen to evaluate model precision. And it was an important parameter for standardizing operation of NIR instruments and studying the feasibility of model transfer in different instruments. However, the parameter for NIR universal quantitative models received much less attention in previous studies and it was difficult to give a value range for this parameter at present. All the results can not only provide evidence for evaluation of drug NIR universal quantitative models for the model builders or users, but also supply basic data to establish and improve the parameter evaluation system of drug NIR universal quantitative models.

The molecular descriptors of impurities with known structure in cefdinir were calculated, selected and associated with the chromatographic retention behavior to establish a model. This quantitative structure retention relationships (QSRR) model for the related substances of cefdinir was established under specific chromatographic condition and verified by other impurities. 12 molecular descriptors were used to establish the QSRR model, F_AFRBWF, Blbn_J, SsCH3, SssCH2, SsNH2, SssNH, SssS, SHdCH2, EEM_AFc, EEM_AFpl, EEM_XFpl and Pi_MaxQ. The relativity between true values and predictions in QSRR of cefdinir is R2 = 0.9836 (n = 18), ΔRRT is no more than 0.154, as 10.17% in RRT. The results indicate that the QSRR model for the related substances of cefdinir can be used to evaluate the analysis methods for related substances and predict the chromatographic behavior of new impurities, which will provide a new way for the evaluation of the effectiveness for drug quality control.

Unlabelled: To find a more reasonable index to decide whether the universal quantitative NIR model needs to be updated and to develop a general method to update universal quantitative NIR models, the quantitative models for testing ceftazidime, water and arginine contents in ceftazidime for injection were taken as example. The study was performed by analyzing the similarity between new sample spectra and the training set spectra of the original models. At first, new samples of ceftazidime for injection were divided into five groups by cluster analysis. Then representative samples of each group were selected by sample selection strategy. Spectra of those samples were used to update the original quantitative models. The prediction deviation of the new ceftazidime powder injection samples by the models before and after updating was calculated. Decreasing the prediction deviation was regarded as the standard to decide if the updating was effective. At the same time, the correlation coefficient of new sample spectra and reference sample spectra was defined as the index to study the general method for model updating. (Reference sample refers to training set sample) Finally, the proposed method was validated by updating universal models for testing ceftazidime, water and arginine contents in ceftazidime powder injections. Results show that the correlation coefficient of new sample spectra and training set sample spectra of the original model was calculated within modeling wavelength range. It was proved that when correlation coefficient rT < 96.5%, the model needs to be updated. Accordingly, rT = 96.5% was set as the threshold. The quantitative models were updated by the method mentioned above. As a result, when testing ceftazidime for injection containing sodium carbonate using newly updated models, the average predicting deviation of ceftazidime contents decreased from 8.1% to 2.3%. And the average predicting deviation of water contents decreased from 2.2% to 0.3%. Meanwhile, with regard to samples containing arginine using the updated models, the average predicting deviation of ceftazidime contents decreased from 7.0% to 1.9%. The average predicting deviation of water contents decreased from 0.6% to 0.3%. And that of arginine contents de- creased from 2.3% to 0.4%., Conclusion: The newly updated models can be used for testing ceftazidime, water and arginine contens in ceftazidime for injection samples of domestic market. It is reasonable to set rT as the index to decide whether the model needs updating. Moreover, it is necessary to take PCA scores graph of new sample spectra and training set spectra of the original model into account. The proposed method for updating models can be used as a usual approach. And rT = 96.5% can be set as the threshold to determine whether the model needs to be updated.

Drugs are special goods that are directly related to public health, so their quality should be monitored in any link of their whole lifecycle. With nondestructive, rapid and environmentally friendly characteristics, near infrared technique is highly suitable for monitoring drug quality in the open market as well as the distribution channels. The present paper reviewed the current situation (analytical objects, methods and instruments) about the application of near infrared spectroscopy in monitoring the quality of the final drug products, Chinese crude drug or decoction pieces in domestic circulation since 1997, expounded the unsolved problems and future prospects.

Construction of a successful near infrared analysis model is a complex task. It spends a lot of manpower and material resources, and is restricted by sample collection and model optimization. So it is important to study on the extended application of the existing near infrared (NIR) models. In this paper, cephradine capsules universal quantitative model was used as an example to study on the feasibility of its extended application. Slope/bias correction and piecewise direct standardization correction methods were used to make the universal model to fit to predict the intermediates in manufacturing processes of cephradine capsules, such as the content of powder blend or granules. The results showed that the corrected NIR universal quantitative model can be used for process control although the results of the model correction by slope/bias or piecewise direct standardization were not as good as that of model updating. And it also indicated that the model corrected by slope/bias is better than that by piecewise direct standardization. Model correction provided a new application for NIR universal models in process control.

Twenty-nine derivatives of berberine (1) or pseudoberberine (2) were designed, semisynthesized, and evaluated for their up-regulatory activity on the low-density-lipoprotein receptor (LDLR) expression. SAR analysis revealed that (i) the methylenedioxy group at the 2- and 3-position is an essential element to keep the activity, (ii) the 7-position quaternary ammonium and planar structure of the compound are activity-required, and (iii) addition of electron-donating groups at the 7- or 13-position reduced the activity. Of the compound 1 analogues, compound 2 exhibited an increased activity on LDLR expression compared to 1. In the hyperlipidemic rats, compound 2 (100 (mg/kg)/day) reduced blood CHO and LDL-c by 42.6% and 49.4%, respectively, more efficient than 1 did (p < 0.01 for both). The results were confirmed in the hyperlipidemic mice. LD(50) of 2 in mice was over 5000 mg/kg (oral). We consider compound 2 a promising cholesterol-lowering drug candidate.

Universal quantitative models using NIR reflectance spectroscopy in two different kinds of sampling mode were developed for the analysis of cefoperazone sodium for injection from different manufacturers in China. The quantitative models were established using partial least squares(PLS). Nineteen batches of cefoperazone sodium for injection samples from 9 different manufacturers were predicted by the quantitative models. The root mean square errors of cross validation (RMSECV) and the root mean square errors of prediction (RMSEP) of the model in integrating sphere sampling mode were 0. 99 and 0. 98, respectively. The values of RMSECV and RMSEP of the model in fibre sampling mode were 1. 12 and 1. 17, respectively. Based on the ICH guidelines and characteristics of NIR spectra, the quantitative models were then evaluated in terms of specificity, linearity, accuracy, and precision. The authors' study has shown that it is feasible to build a universal quantitative model in fibre sampling mode for quick analysis of pharmaceutical products from different manufacturers. As a result of its good specificity and applicability, the model could be used for quick, non-destructive prescreening of counterfeit and substandard drugs in the mobile vehicle.

Objective: To investigate the inhibiting effects and mechanism of achyranthes bidentata polysaccharide (ABP) and lycium barbarum polysaccharide (LBP) on nonenzyme glycation in D-galactose induced mouse aging model., Methods: Serum AGE levels were determined by AGE-ELISA, MTT method was used to determine lymphocyte proliferation, IL-2 activity was determined by a bioassay method. Spontaneous motor activity was used to detect mouse's neuromuscular movement, latency of step-through method was used to examine learning and memory abilities of mouse, colormetric assay was used to determine hydroxyproline concentration in mouse skin, pyrogallol autoxidation method was used to determine superoxide dismutase (SOD) activity of erythrocytes., Results: Decreased levels of serum AGE, hydroxyproline concentration in mouse skin and spontaneous motor activity in D-galactose mouse aging model were detected after treated with ABP or LBP, while lymphocyte proliferation and IL-2 activity, learning and memory abilities, SOD activity of erythrocytes, were enhanced., Conclusions: ABP and LBP could inhibit nonenzyme glycation in D-galactose induced mouse aging model in vivo and ABP has a better inhibiting effect than LBP.