Hyphenated Chromatographic Analysis For Antitussive Herb And Evaluation In Silico For Pressor Toxicology

Currently, the discovery of new synthetic drugs has shown a trend of slowing down, and the new drugs will spend a lot of money and time before the commercialization. Natural medicines and their preparations have been widely used for thousands of years in most countries around the world, which has a significant effect. Therefore, natural medicines have become a hot research topic. However, natural medicine is a "black analysis system", we should solve the separation and identification problem of the active ingredients came from natural medicines, even toxicology screening and evaluation. In this study, a detailed discussion was done based on antitussive natural medicine, which involves the use of chemometric methods used for complex analytical system and toxicological research.1. Chemometric resolution methods were used in the natural medicine data came from high performance liquid chromatography-diode array detector (HPLC-DAD), gas chromatography-mass spectrometry (GC-MS), ultra performance liquid chromatography-mass spectrometry (UPLC-MS), and pure chromatographic curve, pure UV spectra and pure mass spectrometry were obtained. These chemometrics methods include smoothing and filtering, ordinary manual linear deduction, adaptive iteratively reweighted penalized least squares (airPLS), heuristic evolving latent projections (HELP) and alternative moving window factor analysis (AMWFA), selective ion analysis (SIA) and so on. These methods possess practical value in laboratory when facing complicated components analysis. Simultaneously, the common and different features among HELP, SIA and AMWFA were compared by using some experimental data.2. Temperature-programmed retention indices (PTRIs) were applied in the further identification of chemical composition from the essential oils; the equivalent chain length (ECL), fraction chain length (FCL), an established special retention indices library integrated with mass spectrometry were also applied to further identify the composition of fatty acids including total fatty acids, esterified fatty acids, free fatty acids; In addition, a quantitative structure-retention relationship (QSRR) model with good predictive ability was established and the in-silico RI was applied in qualitative identification combined with NIST MS library search results. Candidate compounds were found to have a moderate matching between the predicted RI values against the experimentally determined values, and incorrect formulas were excluded.3. Accurate mass determination was obtained through two methods. The first method is high resolution instruments, such as liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). After accurate mass determination, an established monomer search database was used for qualitative, the main ingredients could be locked, and the results showed that this method is effective and feasible. For some isomer, we could determine the peak elution order using some references as well as the chemical structure analysis; the second method is mathematical process for the data came from low resolution instruments, we used a simple external calibration method to obtain the accurate mass of molecular ion or key fragment, which included overlapped isotopes structures resolution and Gaussian fitting using Origin software. The calibration method was able to distinguish different molecular weights among a large number of known NIST MS library search results.4. A method was applied to evaluate pressor mechanisms through compound-protein interactions. Our method assumed that the compounds with different pressor mechanisms should bind to different target proteins, and thereby these mechanisms could be differentiated using compound-protein interactions. Phytochemical components and tested target proteins related to blood pressure (BP) elevation were collected. Then, in silico compound-protein interactions prediction probabilities were calculated using a random forest model, which have been implemented in a web server, and the credibility was judged using related literature and other methods. Further, a heat map was constructed, it clearly showed different prediction probabilities accompanied with hierarchical clustering analysis results. Followed by a compound-protein interaction network was depicted according to the results, we can see the connectivity layout of phytochemical components with different target proteins within the BP elevation network, which guided the hypothesis generation of poly-pharmacology. Lastly, principal components analysis (PCA) was carried out upon the prediction probabilities, and pressor targets could be divided into three large classes. This work explored the possibility for pharmacological or toxicological mechanism classification using compound-protein interactions. Such approaches could also be used to deduce pharmacological or toxicological mechanisms for uncharacterized compounds.