RBFOX2/GOLIM4 Splicing Axis Activates Vesicular Transport Pathway to Promote Nasopharyngeal Carcinogenesis.

20-30% of patients with nasopharyngeal carcinoma (NPC) develop distant metastasis or recurrence leading to poor survival, of which the underlying key molecular events have yet to be addressed. Here alternative splicing events in 85 NPC samples are profiled using transcriptome analysis and it is revealed that the long isoform of GOLIM4 (-L) with exon-7 is highly expressed in NPC and associated with poor prognosis. Lines of evidence demonstrate the pro-tumorigenic function of GOLIM4-L in NPC cells. It is further revealed that RBFOX2 binds to a GGAA motif in exon-7 and promotes its inclusion forming GOLIM4-L. RBFOX2 knockdown suppresses the tumorigenesis of NPC cells, phenocopying GOLIM4-L knockdown, which is significantly rescued by GOLIM4-L overexpression. High expression of RBFOX2 is correlated with the exon-7 inclusion of GOLIM4 in NPC biopsies and associated with worse prognosis. It is observed that RBFOX2 and GOLIM4 can influence vesicle-mediated transport through maintaining the organization of Golgi apparatus. Finally, it is revealed that RAB26 interacts with GOLIM4 and mediates its tumorigenic potentials in NPC cells. Taken together, the findings provide insights into how alternative splicing contributes to NPC development, by highlighting a functional link between GOLIM4-L and its splicing regulator RBFOX2 activating vesicle-mediated transport involving RAB26.

Germline Polymorphisms and Length of Survival of Nasopharyngeal Carcinoma: An Exome-Wide Association Study in Multiple Cohorts.

Germline polymorphisms are linked with differential survival outcomes in cancers but are not well studied in nasopharyngeal carcinoma (NPC). Here, a two-phase association study is conducted to discover germline polymorphisms that are associated with the prognosis of NPC. The discovery phase includes two consecutive hospital cohorts of patients with NPC from Southern China. Exome-wide genotypes at 246 173 single nucleotide polymorphisms (SNPs) are determined, followed by survival analysis for each SNP under Cox proportional hazard regression model. Candidate SNP is replicated in another two independent cohorts from Southern China and Singapore. Meta-analysis of all samples (n = 5553) confirms that the presence of rs1131636-T, located in the 3'-UTR of RPA1, confers an inferior overall survival (HR = 1.33, 95% CI = 1.20-1.47, P = 6.31 × 10-8). Bioinformatics and biological assays show that rs1131636 has regulatory effects on upstream RPA1. Functional studies further demonstrate that RPA1 promotes the growth, invasion, migration, and radioresistance of NPC cells. Additionally, miR-1253 is identified as a suppressor for RPA1 expression, likely through regulation of its binding affinity to rs1131636 locus. Collectively, these findings provide a promising biomarker aiding in stratifying patients with poor survival, as well as a potential drug target for NPC.

[Drug Discrimination Method Based on Near Infrared Reflectance Spectrum and Balance Cascading Sparse Representation Based Classification].

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.

[Study on the Selection of Parameters for Evaluating Drug NIR Universal Quantitative Models].

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.

Isolation, identification and characterization of related substances in furbenicillin.

Furbenicillin is a broad-spectrum semisynthetic penicillin with strong antibacterial activity against Gram-negative bacteria. In this study, three impurities in furbenicillin, including an unknown epimer, were determined. On the basis of a complete analysis of the spectrum (MS, (1)H,(13)C, 2D NMR and CD) and the results of chemical methods, the unknown epimer impurity was identified as 10-epi-furbenicillin (impurity 1). Isolation and structure elucidation of impurity 1 was also reported here for the first time.

[Construction of the quantitative structure retention relationship of cefdinir related substances].

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.

Identification of a new isomer from a reversible isomerization of ceftriaxone in aqueous solution.

A reversible isomerization of ceftriaxone in aqueous solution was observed, and the structure of the isomer was determined by mass spectrometry and various 1D and 2D NMR techniques. The mechanism of isomerization was also discussed. Finally, molecular docking simulations were performed and the antimicrobial activities of the isomers were measured. This showed that the biological activity of ceftriaxone was stronger than that of its isomer. The results reported in this article may be important to quality control requirements and to the stability of ceftriaxone products.

[Research progress in using near infrared for monitoring drug quality in domestic circulation].

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.

[Update of near-infrared models for testing ceftazidime, water and arginine in ceftazidime for injection].

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.

A training set selection strategy for a universal near-infrared quantitative model.

The purpose of this article is to propose an empirical solution to the problem of how many clusters of complex samples should be selected to construct the training set for a universal near infrared quantitative model based on the Naes method. The sample spectra were hierarchically classified into clusters by Ward's algorithm and Euclidean distance. If the sample spectra were classified into two clusters, the 1/50 of the largest Heterogeneity value in the cluster with larger variation was set as the threshold to determine the total number of clusters. One sample was then randomly selected from each cluster to construct the training set, and the number of samples in training set equaled the number of clusters. In this study, 98 batches of rifampicin capsules with API contents ranging from 50.1% to 99.4% were studied with this strategy. The root mean square errors of cross validation and prediction were 2.54% and 2.31% for the model for rifampicin capsules, respectively. Then, we evaluated this model in terms of outlier diagnostics, accuracy, precision, and robustness. We also used the strategy of training set sample selection to revalidate the models for cefradine capsules, roxithromycin tablets, and erythromycin ethylsuccinate tablets, and the results were satisfactory. In conclusion, all results showed that this training set sample selection strategy assisted in the quick and accurate construction of quantitative models using near-infrared spectroscopy.

[Feasibility of the extended application of near infrared universal quantitative models].

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.

Construction of identification system for non-invasive analysis of macrolides tablets using near infrared diffuse reflectance spectroscopy.

A NIR identification system consisting of two proposed models (Model I and Model II) has been developed for the analysis of 10 different products of macrolides tablets from different manufacturers. A total of 253 batches of the 10 products from 93 manufacturers in China were used for the system construction. First, a universal classification model (termed Model I) was constructed for 10 products using all the samples with the objective to distinguish the macrolides homologues which have the similar molecular structures. Secondly, 10 models (termed Model II) were developed separately for each product by using their samples. For each type of macrolides products, the two qualitative models are used in tandem as an identification system in mobile labs. Only when Model I and Model II both show acceptance results can an unknown sample be identified as "genuine". Internal and external validation showed almost 100% correct identity. Our study has shown that the analytical accuracy can be greatly improved when using this identification system and it will be efficient for quickly prescreening the drug quality in the open market and distribution channels.

Berberine analogues as a novel class of the low-density-lipoprotein receptor up-regulators: synthesis, structure-activity relationships, and cholesterol-lowering efficacy.

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.

Feasibility and extension of universal quantitative models for moisture content determination in beta-lactam powder injections by near-infrared spectroscopy.

In present work, we investigated the feasibility of universal calibration models for moisture content determination of a much complicated products system of powder injections to simulate the process of building universal models for drug preparations with same INN (International Nonproprietary Name) from diverse formulations and sources. We also extended the applicability of universal model by model updating and calibration transfer. Firstly, a moisture content quantitative model for ceftriaxone sodium for injection was developed, the results show that calibration model established for products of some manufacturers is also available for the products of others. Then, we further constructed a multiplex calibration model for seven cephalosporins for injection ranging from 0.40 to 9.90%, yielding RMSECV and RMSEP of 0.283 and 0.261, respectively. However, this multiplex model could not predict samples of another cephalosporin (ceftezole sodium) and one penicillins (penicillin G procaine) for injection accurately. With regard to such limits and the extension of universal models, two solutions are proposed: model updating (MU) and calibration transfer. Overall, model updating is a robust method for the analytical problem under consideration. When timely model updating is impractical, piecewise direct standardization (PDS) algorithm is more desirable and applied to transfer calibration model between different powder injections. Both two solutions have proven to be effective to extend the applicability of original universal models for the new products emerging.

Construction of universal quantitative models for determination of roxithromycin and erythromycin ethylsuccinate in tablets from different manufacturers using near infrared reflectance spectroscopy.

Universal quantitative models using NIR reflectance spectroscopy were developed for the analysis of API contents (active pharmaceutical ingredient) in roxithromycin and erythromycin ethylsuccinate tablets from different manufacturers in China. The two quantitative models were built from 78 batches of roxithromycin samples from 18 different manufacturers with the API content range from 19.5% to 73.9%, and 66 batches erythromycin ethylsuccinate tablets from 36 manufacturers with the API content range from 28.1% to 70.9%. Three different spectrometers were used for model construction in order to have robust and universal models. The root mean square errors of cross validation (RMSECV) and the root mean square errors of prediction (RMSEP) of the model for roxithromycin tablets were 1.84% and 1.45%, respectively. The values of RMSECV and RMSEP of the model for erythromycin ethylsuccinate tablets were 2.31% and 2.16%, respectively. Based on the ICH guidelines and characteristics of NIR spectroscopy, the quantitative models were then evaluated in terms of specificity, linearity, accuracy, precision, robustness and model transferability. Our study has shown that it is feasible to build a universal quantitative model for quick analysis of pharmaceutical products from different manufacturers. Therefore, the NIR method could be used as an effective method for quick, non-destructive inspection of medicines in the distribution channels or open market.

[Construction of universal quantitative models for determination of cefoperazone sodium for injection from different manufacturers using near infrared reflectance spectroscopy].

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.

Inhibiting effects of Achyranthes bidentata polysaccharide and Lycium barbarum polysaccharide on nonenzyme glycation in D-galactose induced mouse aging model.

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.