DESIGN OF MICROPHYSIOMETER BASED ON MULTIPARAMETER CELL-BASED BIOSENSORS FOR QUICK DRUG ANALYSIS

Cellular metabolism arouses the changes of substance in extracellular physiological microenvironment,and the metabolic level reflects the physiological state of cells.This paper developed a novel microphysiometer automatic analysis instrument based on multiparameter cell-based biosensors for quick drug analysis.This study included the multiparameter cell-based biosensors,cell culture chamber,drug auto-injection detection and analysis.The analysis instrument was capable of real-time detection for the acidic product and other chemical parameters generated by the cellular metabolism in the micro-volume.Finally,the paper employs human breast cancer cell line MCF-7 and drug experiments to verify the performance of microphysiometer,and study effects of different drugs on cell metabolism.Further,the research explores drug analysis method of the multiparameter microphysiometer.The results showed that the cell-based microphysiometer system provides a utility platform for rapid,long-term and automatic cell physiological environment detection and drug analysis.

Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

With the increasing usage of drugs to remedy different diseases,drug safety has become crucial over the past few years.Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae.Such diversification is both helpful and danger-ous as such medicine proves to be more effective or shows side effects to different patients.Despite clinical trials,side effects are reported when the medicine is used by the mass public,of which several such experiences are shared on social media platforms.A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed.Sentiment analysis of drug reviews has a large poten-tial for providing valuable insights into these cases.Therefore,this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques.A dataset acquired from the‘Drugs.Com’contain-ing reviews of drug-related side effects and reactions,is used for experiments.A lexicon-based approach,Textblob is used to extract the positive,negative or neu-tral sentiment from the review text.Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory(CNN-LSTM)network.The CNN is used at thefirst level to extract the appropriate features while LSTM is used at the second level.Several well-known machine learning models including logistic regression,random for-est,decision tree,and AdaBoost are evaluated using term frequency-inverse docu-ment frequency(TF-IDF),a bag of words(BoW),feature union of(TF-IDF+BoW),and lexicon-based methods.Performance analysis with machine learning models,long short term memory and convolutional neural network models,and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy.We also performed a statistical sig-nificance T-test to show the significance of the proposed CNN-LSTM model in comparison with other approaches.

Prognostic model and treatment plan analysis of hepatocellular carcinoma based on genes related to glutamine metabolism

Objective:To identify the prognosis of hepatocellular carcinoma(HCC)and the effect of anti-cancer drug therapy by screening glutamine metabolism-related signature genes because glutamine metabolism plays an important role in tumor development.Methods:We obtained gene expression samples of normal liver tissue and hepatocellular carcinoma from the TCGA database and GEO database,screened for differentially expressed glutamine metabolismrelated genes(GMRGs),constructed a prognostic model by lasso regression and step cox analysis,and assessed the differences in drug sensitivity between high-and low-risk groups.Results:We screened 23 differentially expressed GMRGs by differential analysis,and correlation loop plots and PPI protein interaction networks indicated that these differential genes were strongly correlated.The four most characterized genes(CAD,PPAT,PYCR3,and SLC7A11)were obtained by lasso regression and step cox,and a risk model was constructed and confirmed to have reliable predictive power in the TCGA dataset and GEO dataset.Finally,immunotherapy is better in the high-risk group than in the low-risk group,and chemotherapy and targeted drug therapy are better in the low-risk group than in the high-risk group.Conclusion:In conclusion,we have developed a reliable prognostic risk model characterized by glutamine metabolism-related genes,which may provide a viable basis for the prognosis and Treatment options of HCC patients.

Bi_(2)O_(3)/ZnO nanocomposite:Synthesis,characterizations and its application in electrochemical detection of balofloxacin as an antibiotic drug

In the present work,a chemically modified electrode has been fabricated utilizing Bi_(2)O_(3)/ZnO nanocomposite.The nanocomposite was synthesized by simple sonochemical method and characterized for its structural and morphological properties by using XRD,FESEM,EDAX,HRTEM and XPS techniques.The results clearly indicated co-existence of Bi_(2)O_(3) and ZnO in the nanocomposite with chemical interaction between them.Bi_(2)O_(3)/ZnO nanocomposite based glassy carbon electrode(GCE)was utilized for sensitive voltammetric detection of an anti-biotic drug(balofloxacin).The modification amplified the electroactive surface area of the sensor,thus providing more sites for oxidation of analyte.Cyclic and square wave voltammograms revealed that Bi_(2)O_(3)/ZnO modified electrode provides excellent electrocatalytic action towards balofloxacin oxidation.The current exhibited a wide linear response in concentration range of 150e1000 nM and detection limit of 40.5 nM was attained.The modified electrode offered advantages in terms of simplicity of preparation,fair stability(RSD 1.45%),appreciable reproducibility(RSD 2.03%)and selectivity.The proposed sensor was applied for determining balofloxacin in commercial pharmaceutical formulations and blood serum samples with the mean recoveries of 99.09% and 99.5%,respectively.

2009年至2011年我院病区口服降压药物应用分析

目的分析医院病区口服降压药的应用情况,为临床合理用药提供参考。方法对2009年至2011年口服降压药的种类、用量、销售金额、用药频度(DDDs)及限定日费用(DDC)等进行统计、分析。结果医院病区口服降压药用量呈上升趋势,钙通道阻滞剂(CCB)、血管紧张素转换酶抑制剂(ACEI)、血管紧张素Ⅱ拮抗剂(ARB)为临床一线药,ARB用量及比例逐年上升,ACEI用量及比例呈下降趋势。结论医院病区口服降压药临床使用基本合理。

31例盐酸头孢替安药品不良事件分析

目的:分析院内盐酸头孢替安致不良事件的(ADE)发生原因和特点,为临床安全应用提供药师建议。方法:对我院2010~2016年报告的31例盐酸头孢替安ADE报告进行回顾性归纳分析。结果:31例报告中女性患者明显多于男性,发生年龄段51~60岁最多,不良事件损害表现以皮肤黏膜损害、循环系统、消化系统损害为主,严重不良事件为过敏性休克、呼吸困难、多型性红斑。结论:盐酸头孢替安临床应用不良事件的发生并不单纯是由于药物本身特性以及患者个体的差异导致,药物的选择、给药剂量、给药频次的不当等也是导致不良事件发生的因素,提示临床合理选择抗菌药物并严格按照药品说明书的给药剂量和给药频次,加强给药后的观察以及发生不良事件的及时干预,可以减少或避免不良事件的发生,提高临床安全用药水平。

Derivative Spectrophotometric and Isocratic High Performance Liquid Chromatographic Methods for Simultaneous Determination of Repaglinide and Metformin Hydrochloride in Pharmaceutical Preparations

In this study, a derivative spectrophotometric method and one HPLC method were developed and validated for analysis of anti-diabetic drugs, repaglinide (RPG) and metformine hydrochloride (MTF) in tablets. The spectrophotometric methods were based on zero-crossing first-derivative and fourth-derivative spectrophotometric method for simultaneous analysis of RPG (308 nm) and MTF (267 nm), respectively. Linear relationship between the absorbance at λmax and the drug concentration was found to be in the ranges of 5.0 - 50.0 μg·mL-1 for both RPG and MTF. The quantification limits for RPG and MTF were found to be 0.568 and 1.156 μg·mL-1, respectively. The detection limits were 0.170 and 0.347 μg·mL-1 for RPG and MTF, respectively. The second method is a rapid stability-indicating isocratic HPLC method developed for the determination of RPG and MTF. A linear response was observed within the concentration range of 5.0 - 50.0 μg·mL-1 for both RPG and MTF. The quantification limits for RPG and MTF were found to be 1.821 and 1.653 μg·mL-1, respectively. The detection limits were 0.601 and 0.545 μg·mL-1 for RPG and MTF, respectively. The proposed methods were successfully applied to the tablet analysis with good accuracy and precision.

SynergyFinder Plus:Toward Better Interpretation and Annotation of Drug Combination Screening Datasets

Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The Synergy Finder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the Synergy Finder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated Synergy Finder R package includes five main innovations. 1) We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape. 2) We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values. 3)We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy. 4) We evaluate drug combination synergy and sensitivity to provide an unbiased interpretation of the clinical potential. 5) We enable fast annotation of drugs and cell lines, including their chemical and target information. These annotations will improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.s ynergyfinderplus.org as a user-friendly interface to enable a more fexible and versatile analysis of drug combination data.

Metabolomics Combined with Network Pharmacology Uncovers Effective Targets of Tao-Hong-Si-Wu Decoction for Its Protection from Sepsis-Associated Acute Lung Injury

Sepsis-induced acute lung injury(ALI)is a leading cause of death among septic complications.Tao-Hong-Si-Wu decoction(TSD),a classical recipe from traditional Chinese medicine used for treating ischemic stroke,has been recently reported to alleviate inflammation and inflammation-stimulated injuries related to the pathology of ALI.Here,we first observed the therapeutic effect of TSD on sepsis-induced ALI.Based on integrated metabolomics and network pharmacology analysis(NPA)techniques,we aim to understand the mechanism of TSD alleviating ALI.TSD’s effects were observed in rats modeled by cecal ligation and puncture(CLP)and rat macrophages stimulated by lipopolysaccharide(LPS).Metabolomics analyses were applied to determine the ingredients in the medicine and key metabolites correlated to the NPA for the prediction of TSD targets.Gene and protein expressions of the key predicted targets were evaluated in the lung tissue and macrophages of septic model rat by quantitative polymerase chain reaction(PCR)and enzyme-linked immunosorbent assays,respectively.TSD improved survival rate and protected against lung injury in CLP rats.Eleven endogenous metabolites were related to TSD’s actions.TSD significantly suppressed IL-6 and TNF-αsecretions and their gene expressions both in the lung tissue of the model rats and in LPS-stimulated macrophages.TSD also restored decreased lung protein expression of VEGFA in septic model rats.Targeted proteins and their affecting metabolites were finally validated in an external test set of rats.This study shows that metabolomics coupled with NPA is a promising approach to explore potential targets of medicine with complex compositions.

Pharmaceutical applications of framework nucleic acids

DNA is a biological polymer that encodes and stores genetic information in all living organism. Particularly, the precise nucleobase pairing inside DNA is exploited for the self-assembling of nanostructures with defined size, shape and functionality. These DNA nanostructures are known as framework nucleic acids(FNAs) for their skeleton-like features. Recently, FNAs have been explored in various fields ranging from physics, chemistry to biology. In this review, we mainly focus on the recent progress of FNAs in a pharmaceutical perspective. We summarize the advantages and applications of FNAs for drug discovery, drug delivery and drug analysis. We further discuss the drawbacks of FNAs and provide an outlook on the pharmaceutical research direction of FNAs in the future.