Research On Quantitative Detection Method Of Anti-tuberculosis Compound Drugs Based On Terahertz Time-domain Spectroscopy

Tuberculosis is one of the top ten infectious diseases with high infection and high fatality rate in the world,and our country is the second high-burden country of tuberculosis in the world after India.In our country,the treatment of tuberculosis has become one of the public health issues that need urgent attention.In the"Thirteenth Five-Year"National Tuberculosis Prevention and Control Plan,it clearly states that the primary task of treating tuberculosis is to ensure the quality of anti-tuberculosis drugs.Currently,the recommended drug for the treatment of tuberculosis is an anti-tuberculosis compound drug.The efficacy of compound drugs is based on the comprehensive action of multiple main ingredients.If the content of active ingredients is insufficient,not only can not cure the patient,but also lead to the growth of drug-resistant tuberculosis strains,which will cause the patient to develop drug resistance and further aggravate the disease.Therefore,the detection of the content of each active ingredient in the anti-tuberculosis compound medicine is an important aspect of its quality control.At present,the commonly used methods for detecting the content of anti-tuberculosis compound drugs mainly have problems such as cumbersome testing steps,long testing time,and environmental pollution by the testing reagents,etc.Therefore,it is urgent to find a simple,fast and green method for detecting the content of anti-tuberculosis compound drugs.Terahertz time-domain spectroscopy(THz-TDS)technology is an emerging spectroscopic detection technology.Because it generally does not cause ionization damage to substances,and is very sensitive to the collective motion mode of drug molecules(lattice vibration,isomer configuration differences,etc.),it is widely used in the field of medical testing.In this paper,the dual anti-tuberculosis compound drug(INZ+RIF)and triple anti-tuberculosis compound drug(INZ+RIF+PNZ)composed of isoniazid(INZ),pyrazinamide(PNZ)and rifampicin(RIF)as the research object were studied,and took the THz-TDS as the main technical line.Firstly,the terahertz spectra of INZ,PNZ and RIF were analyzed,and then the integrated learning algorithm and convolutional neural network combined with THZ-TDS technology were used to carry out the application research on the content detection of each component in the dual and triple compound drugs,respectively.The specific research contents and results are as follows:(1)Researched the terahertz spectra of INZ,RIF and PNZ.The terahertz absorption spectra of INZ,PNZ,and RIF were obtained using THz-TDS technology,and theoretical simulations based on single molecules,dimers and crystal structures were performed using density functional theory(DFT),respectively.The experimental results showed that:INZ had three vibration absorption peaks,located at 1.16THz,1.46THz and 1.56THz respectively,in the band of 0.3-1.8THz.The vibration absorption peak at 1.16THz came from intramolecular interactions,while the vibration absorption peak at 1.46THz and 1.56THz came from intermolecular interactions;PNZ had three vibration absorption peaks,located at 0.50THz?0.71THz and 1.42THz respectively,in the band of 0.3-1.8THz,which all came from intermolecular interactions.RIF had not obtained obvious characteristic absorption peaks through experiments and simulation calculations.The results of this study show that the main components of the three anti-tuberculosis compound drugs have significantly different terahertz absorption characteristics in the terahertz band,which can provide theoretical basis for qualitative identification and quantitative analysis of active components in compound drugs.(2)In order to improve the prediction accuracy and generalization ability of the model,a quantitative modeling method based on integrated algorithm combined with THz-TDS technology was proposed.Three different fusion algorithms,stacking,blending,and averaging,combined with THz-TDS technology,were used to establish a quantitative analysis model of dual and triple anti-tuberculosis compound drugs.In these fusion algorithms,gradient boosting decision tree regression(GBDT),Gaussian kernel support vector machine regression(RBF-SVR)and linear kernel support vector machine regression(Linear-SVR)are used as elementary learner,and the linear-SVR is used as secondary learner.The experimental results show that:Compared with the prediction results of a single model GBDT,RBF-SVR and Linear-SVR,all the three integrated model algorithms improved the prediction performance significantly,among which the stacking integrated model had the best prediction accuracy and model generalization ability.The R~2 predicted by the stacking integrated model for all analytes was greater than 0.99.The root mean square error(RMSEP)of stacking model for the prediction of INZ and RIF in dual anti-tuberculosis compound drug was 0.686%and1.009%,respectively,and the root mean square error(RMSEP)of stacking model for the prediction of INZ?PNZ and RIF in triple anti-tuberculosis compound drug was0.717%?1.021%,0.607%,respectively.(3)The quantitative analysis model of anti-tuberculosis compound drugs was established by CNN combined with THZ-TDS technology.The CNN was an"end-to-end"quantitative modeling method.Due to its unique internal convolutional kernel structure,CNN model had a strong ability to extract spectral features.First of all,the overall architecture of the CNN model,important structural parameters,classifier selection,loss function definition and training methods were optimized and improved.Then,the best CNN architecture was determined,which was used to complete the quantitative analysis of the components in the dual and triple anti-tuberculosis compound drugs.The experimental results showed that:Compared with the stacking model,the CNN model further improved the prediction accuracy.The RMSEP predicted by the CNN model for all analytes was less than 0.8%.Finally,in order to explore the processing mechanism of the convolution check spectrum in the CNN model,the CNN model was analyzed visually.The analysis of the visualization results showed that CNN could complete the conversion of different spectra and extract the characteristics of different levels of spectra,which further proved that CNN had unique advantages in the quantitative analysis of terahertz spectra.