SpectraTr:A novel deep learning model for qualitative analysis of drug spectroscopy based on transformer structure

The drug supervision methods based on near-infrared spectroscopy analysis are heavily dependent on the chemometrics model which characterizes the relationship between spectral data and drug categories.The preliminary application of convolution neural network in spectral analysis demonstrates excellent end-to-end prediction ability,but it is sensitive to the hyper-parameters of the network.The transformer is a deep-learning model based on self-attention mechanism that compares convolutional neural networks(CNNs)in predictive performance and has an easy-todesign model structure.Hence,a novel calibration model named SpectraTr,based on the transformer structure,is proposed and used for the qualitative analysis of drug spectrum.The experimental results of seven classes of drug and 18 classes of drug show that the proposed SpectraTr model can automatically extract features from a huge number of spectra,is not dependent on pre-processing algorithms,and is insensitive to model hyperparameters.When the ratio of the training set to test set is 8:2,the prediction accuracy of the SpectraTr model reaches 100%and 99.52%,respectively,which outperforms PLS DA,SVM,SAE,and CNN.The model is also tested on a public drug data set,and achieved classification accuracy of 96.97%without preprocessing algorithm,which is 34.85%,28.28%,5.05%,and 2.73%higher than PLS DA,SVM,SAE,and CNN,respectively.The research shows that the SpectraTr model performs exceptionally well in spectral analysis and is expected to be a novel deep calibration model after Autoencoder networks(AEs)and CNN.

An overview of management of narcotics and psychotropic drugs in China

Narcotics and psychotropic drugs are known as controlled drugs with special management and super vision due to their psychic and physiological dependence. Based on the literature review, experts interview and policy comparative analysis, our study summarized and reviewed the status of related legislation and regulations since the enactment of the Narcotics and Psychotropic Drugs Regulations in 2005. We found the problems of legal loopholes, the complexity of supervision system and the irrational use of narcotics in the treatment of chronic non-cancer. Our analysis suggested that China should reinforce legislation, strengthen the cooperation among departments, establish the information network and improve the guideline of narcotics and psychotropic drugs for clinical treatment as quickly as possible.

Risk management of drug products in FDA: the development and implication

This paper presented detailed information about the timeline of development of drug risk management in FDA. The time process was divided into three stages： the launch of laws and regulations of drug risk management, pre-market approval and post-market safety supervision, adverse drug reaction and risk communication. To address the problems existing in drug risk management in China, suggestions to further and improve the development of drug risk management are proposed.