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Analyst ; 146(12): 3888-3898, 2021 Jun 14.
Artigo em Inglês | MEDLINE | ID: mdl-34042921

RESUMO

Chiral drugs are drugs with chiral or asymmetric centres in their molecular structure. Different enantiomers of the same chiral drug have noticeably different pharmacological activities and pharmacokinetic properties. However, its distinction has been perplexing scholars for many years in the qualitative and quantitative detection of antagonistic drugs. Conventional detection methods, such as polarimetry, circular dichroism, and high-performance liquid chromatography, are time consuming, cause sample loss and have cumbersome operations, and they can be applied only to the sampling method. In this paper, we propose a fast, accurate, qualitative and quantitative method for the study of chiral drugs based on linearly polarized terahertz (THz) spectroscopy and imaging technology. Taking ibuprofen as an example, based on the THz absorption spectra of the enantiomers RS-ibuprofen, (R)-(-)-ibuprofen, and (S)-(+)-ibuprofen, their characteristic peak frequencies, peak amplitude differences and peak area differences were extracted to qualitatively and quantitatively distinguish and identify the three substances. THz spectral imaging provides more intuitive results than those obtained from previous methods. In quantitative identification, the stability and detection accuracy of THz spectroscopy are much greater than those of Raman spectroscopy (88.8-99.8% vs. 21.42-94.62%, respectively). The qualitative recognition accuracy was 100%, and the quantitative recognition standard deviation was less than 0.01, and it is also a non-destructive testing method. Furthermore, the above method combined with principal component analysis (PCA) and the support vector machine (SVM) neural network classification algorithm was applied to the analysis of other chiral drugs. These results are significant for the rapid, accurate and non-destructive identification of chiral drugs.


Assuntos
Preparações Farmacêuticas , Espectroscopia Terahertz , Redes Neurais de Computação , Análise de Componente Principal , Máquina de Vetores de Suporte
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