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.

Real-World Evidence Supporting New Drug Review and Approval in European Union and Its Enlightenment to China

Objective To analyze the application of real-world evidence(RWE)in the field of medicine in European Union,and provide suggestions for RWE supporting the review and approval of new drugs in China.Methods The European Medicines Agency(EMA)and other databases were used to search relevant documents for analyzing the European Union’s new drug review and approval process with the support of RWE.Results and Conclusion The European Union carrying out new drug review and approval with the support of RWE has just begun.The decision-making process includes three stages such as new drug research and development,review,and approval.However,there are some challenges in data quality,research methods,evidence sufficiency,and research process of RWE supporting the European Union in reviewing and approving new drugs.At present,RWE can accurately grasp the clinical effects of drugs and improve the safety and effectiveness in the process of assisting the review and approval of new drugs.At the same time,RWE also can promote the development and application of Traditional Chinese Medicine(TCM)and help find out the potential value of TCM such as new indications.

Status and Thoughts of Chinese Patent Medicines Seekina Annroval in the US Market

Veregen^TM and Fulyzaq are the first two botanical drug products that were approved by the Food and Drug Administration （FDA） to market in the US in recent years. Additional herbal medicines, including Compound Danshen Dripping Pills （复方丹参滴丸）, Fuzheng Huayu Tablets （扶正化瘀片）, Xuezhikang Capsule （血脂康胶囊）, Guizhi Fuling Capsule （桂枝茯苓胶囊）, Kanglaite Capsule （康莱特胶囊） and Kanglaite Injection （康莱特注射液）, have filed the investigational new drug （IND） application to the FDA and are in phaseⅡ or phase Ⅲ clinical development. In order to gain better understanding of the process of botanical drug approval in the US, this article examines the aforementioned drugs by looking at their composition, indication, prior clinical experience and clinical development process, and summarizes key features that enabled IND filing and marketing approval by the FDA.