A strategy to improve the identification reliability of the chemical constituents by high-resolution mass spectrometry-based isomer structure prediction combined with a quantitative structure retention relationship analysis: Phthalide compounds in Chuanxiong as a test case

A comparative study of the predictive performance of different descriptor calculation tools: Molecular-based elution order modeling and interpretation of retention mechanism for isomeric compounds from METLIN database

In the pharmaceutical industry, the need for analytical standards is a bottleneck for comprehensive evaluation and quality control of intermediate and end products. These are complex mixtures containing structurally related molecules. In this regard, chromatographic peak annotation, especially for critical pairs of isomers and closest structural analogs, can be supported by using a Quantitative Structure Retention Relationship (QSRR) approach. In our study, we investigated the fundamental basis of the reversed-phase (RP) retention mechanism for 1141 isomeric compounds from the METLIN SMRT dataset. Nine different descriptor calculation tools combined with different feature selection methods (genetic algorithm (GA), stepwise, Boruta) and machine learning (ML) approaches (support vector machine (SVM), multiple linear regression (MLR), random forest (RF), XGBoost) were applied to provide a reliable molecular structure-based interpretation of RP retention behaviour of the isomeric compounds. Strict internal and external validation metrics were used to select models with the best predictive capabilities (rtest > 0.73, order of elution > 60 %). For the developed models, mean absolute errors were in the range of 60 to 110 s. Stepwise and GA showed the most suitable performance as descriptor selection methods, while SVM and XGBoost modeling gave satisfactory predictive characteristics in most cases. Validation performed on the published experimental data for structurally related pharmaceutical compounds confirmed the best accuracy of MLR modeling in combination with GA feature selection of general physico-chemical properties. The resulting models will be useful for the prediction of separation and identification of structurally related compounds in pharmaceutical analysis, providing a simultaneous understanding of the interaction mechanisms leading to their retention under RP conditions.

Danggui Shaoyao San: Chemical characterization and inhibition of oxidative stress and inflammation to treat CCl4-induced hepatic fibrosis

ETHNOPHARMACOLOGICAL RELEVANCE

Danggui Shaoyao San (DSS) has effective in treating hepatic ascites and liver disease.

AIM OF THE STUDY

To explore the chemical characterization of DSS and protective effect on CCl4-induced hepatic fibrosis and its mechanism, especially its anti-oxidative stress and anti-inflammation.

MATERIALS AND METHODS

The chemical characterization of DSS was determined by HPLC-Q-Exactive Orbitrap MS. And the antioxidant activity of DSS in vitro was determined. The hepatic fibrosis model was established using intragastric administration of 40% CCl4/soybean oil (v/v) twice weekly for 13 weeks. From 6th week, the DSS group and the positive control group were given DSS (2, 4, 8 g/kg/d) and silymarin (50 mg/kg/d), respectively. The livers of rats were examined histologically by H&E. The ALT, AST, ALB and TBIL were determined, and hepatic fibrosis markers (HA, LN, CIV, PIIINP), oxidative stress (SOD, MDA, GST, GSH) and inflammatory factor (IL-6, TNF-α) were tested using ELISA kits. In addition, the levels of TAC, TOS, LOOH and AOPP in the liver were also determined.

RESULTS

The chemical characterization of DSS was determined by HPLC-Q-Exactive Orbitrap MS. The results show that DSS mainly includes triterpenoids, monoterpenes, phenols, sesquiterpenes, butyl phthalide, etc., and DSS has good antioxidant activity in vitro. In addition, the ALT, AST and TBIL of rats were remarkably reduced after treatment with DSS at three doses. Liver histopathological analysis showed that DSS alleviated the inflammatory infiltration, hepatocyte swelling, necrosis and hepatic fibrosis induced by CCl4. DSS significantly decreased HA, IV-C, PIIINP and LN. Further determination showed that DSS significantly increased TAC, OSI and decreased TOC, LOOH and MDA, indicating that DSS could regulate redox balance and reduce lipid peroxidation in vivo. DSS also increased the activity of GST, SOD and GSH concentration. In addition, DSS also reduced IL-6 and TNF-α.

CONCLUSIONS

In this study, we described the chemical characterization of DSS and found that it has good antioxidant activity. We proved that DSS has the functions of reducing oxidative stress, anti-inflammatory, protecting liver cells and reducing hepatic fibrosis.

A practical strategy enabling more reliable identification of ginsenosides from Panax quinquefolius flower by dimension-enhanced liquid chromatography/mass spectrometry and quantitative structure-retention relationship-based retention behavior prediction

To accurately identify the metabolites is crucial in a number of research fields, and discovery of new compounds from the natural products can benefit the development of new drugs. However, the preferable phytochemistry or liquid chromatography/mass spectrometry approach is time-/labor-extensive or receives unconvincing identifications. Herein, we presented a strategy, by integrating offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), exclusion list-containing high-definition data-dependent acquisition (HDDDA-EL), and quantitative structure-retention relationship (QSRR) prediction of the retention time (tR), to facilitate the in-depth and more reliable identification of herbal components and thus to discover new compounds more efficiently. Using the saponins in Panax quinquefolius flower (PQF) as a case, high orthogonality (0.79) in separating ginsenosides was enabled by configuring the XBridge Amide and CSH C18 columns. HDDDA-EL could improve the coverage in MS2 acquisition by 2.26 folds compared with HDDDA (2933 VS 1298). Utilizing 106 reference compounds, an accurate QSRR prediction model (R2 = 0.9985 for the training set and R2 = 0.88 for the validation set) was developed based on Gradient Boosting Machine (GBM), by which the predicted tR matching could significantly reduce the isomeric candidates identification for unknown ginsenosides. Isolation and establishment of the structures of two malonylginsenosides by NMR partially verified the practicability of the integral strategy. By these efforts, 421 ginsenosides were identified or tentatively characterized, and 284 thereof were not ever reported from the Panax species. The current strategy is thus powerful in the comprehensive metabolites characterization and rapid discovery of new compounds from the natural products.

New Evidence for Artemisia absinthium as an Alternative to Classical Antibiotics: Chemical Analysis of Phenolic Compounds, Screening for Antimicrobial Activity

Artemisia absinthium, an important herb of the Artemisia genus, was evaluated in this study for its potential as an alternative to classical antibiotics. The antimicrobial activity of methanol extracts of A. absinthium (MEAA) was evaluated using the broth microdilution method, revealing that A. absinthium exhibited broad-spectrum antibacterial and antifungal activity. Ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF-MS) was used to analyze the chemical profile of the MEAA, with a focus on flavonoids, quinic acids, and glucaric acids. A total of 90 compounds were identified, 69 of which were described for the first time in A. absinthium. Additionally, a new class of caffeoyl methyl glucaric acids was identified. The main active compounds were quantified and screened for antimicrobial activity. A. absinthium was found to be rich in quinic acids and flavonoids. The screening for antimicrobial activity also revealed that salicylic acid, caffeic acid, casticin, and 3,4-dicaffeoylquinic acid had varying degrees of antimicrobial activity. The acute toxicity of MEAA was examined following OECD guidelines. The administration of 5000 mg/kg bw of MEAA did not result in mortality in male and female mice. Furthermore, there were no observed effects on the visceral organs or general behavior of the mice, demonstrating the good safety of MEAA. This study provides new evidence for the use of A. absinthium as an alternative to classical antibiotics in addressing the problem of bacterial resistance.

Identification of endogenous carbonyl steroids in human serum by chemical derivatization, hydrogen/deuterium exchange mass spectrometry and the quantitative structure-retention relationship

Steroids are tetracyclic aliphatic compounds, and most of them contain carbonyl groups. The disordered homeostasis of steroids is closely related to the occurrence and progression of various diseases. Due to high structural similarity, low concentrations in vivo, poor ionization efficiency, and interference from endogenous substances, it is very challenging to comprehensively and unambiguously identify endogenous steroids in biological matrix. Herein, an integrated strategy was developed for the characterization of endogenous steroids in serum based on chemical derivatization, ultra-performance liquid chromatography quadrupole Exactive mass spectrometry (UPLC-Q-Exactive-MS/MS), hydrogen/deuterium (H/D) exchange, and a quantitative structure-retention relationship (QSRR) model. To enhance the mass spectrometry (MS) response of carbonyl steroids, the ketonic carbonyl group was derivatized by Girard T (GT). Firstly, the fragmentation rules of derivatized carbonyl steroid standards by GT were summarized. Then, carbonyl steroids in serum were derivatized by GT and identified based on the fragmentation rules or by comparing retention time and MS/MS spectra with those of standards. H/D exchange MS was utilized to distinguish derivatized steroid isomers for the first time. Finally, a QSRR model was constructed to predict the retention time of the unknown steroid derivatives. With this strategy, 93 carbonyl steroids were identified from human serum, and 30 of them were determined to be dicarbonyl steroids by the charge number of characteristic ions and the number of exchangeable hrdrogen or comparing with standards. The QSRR model built by the machine learning algorithms has an excellent regression correlation, thus the accurate structures of 14 carbonyl steroids were determined, among which three steroids were reported for the first time in human serum. This study provides a new analytical method for the comprehensive and reliable identification of carbonyl steroids in biological matrix.

Extract identification and evaluation of the cytotoxic activity of Polygala fallax Hemsl in Heilongjiang ethnic medicine against tumors

BACKGROUND

Heilongjiang Province is a frontier province with distinctive characteristics, fertile land and rich products.

OBJECTIVE

This study provides a new method for qualitatively studying flavonoids in traditional Chinese medicine and a new auxiliary means for identifying flavonoid isomers.

METHODS

The flavonoids in Polygala fallax Hemsl were identified by ultra-performance liquid chromatography-photo-diode array (PDA)-quadrupole-electro- static field orbitrap mass spectrometry tandem by UV Spectrum, primary and secondary high-resolution mass spectrometry (MS1/MS2) cleavage of fragments combined with databases, mass spectrometry cleavage patterns and literature.

RESULTS

The established QSRR model was used to verify the flavonoids identified from the Polygala fallax Hemsl.

CONCLUSION

The structure of multiple Polygala fallax Hemsl has been identified using various spectral methods. The tumor cytotoxic activity of the isolated compounds was evaluated. This paper is of great significance for further elucidating the pharmacodynamic substance basis and further developing and utilizing Polygala fallax Hemsl.

Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods

Complex systems exist widely, including medicines from natural products, functional foods, and biological samples. The biological activity of complex systems is often the result of the synergistic effect of multiple components. In the quality evaluation of complex samples, multicomponent quantitative analysis (MCQA) is usually needed. To overcome the difficulty in obtaining standard products, scholars have proposed achieving MCQA through the "single standard to determine multiple components (SSDMC)" approach. This method has been used in the determination of multicomponent content in natural source drugs and the analysis of impurities in chemical drugs and has been included in the Chinese Pharmacopoeia. Depending on a convenient (ultra) high-performance liquid chromatography method, how can the repeatability and robustness of the MCQA method be improved? How can the chromatography conditions be optimized to improve the number of quantitative components? How can computer software technology be introduced to improve the efficiency of multicomponent analysis (MCA)? These are the key problems that remain to be solved in practical MCQA. First, this review article summarizes the calculation methods of relative correction factors in the SSDMC approach in the past five years, as well as the method robustness and accuracy evaluation. Second, it also summarizes methods to improve peak capacity and quantitative accuracy in MCA, including column selection and two-dimensional chromatographic analysis technology. Finally, computer software technologies for predicting chromatographic conditions and analytical parameters are introduced, which provides an idea for intelligent method development in MCA. This paper aims to provide methodological ideas for the improvement of complex system analysis, especially MCQA.

Improved structural annotation of triterpene metabolites of traditional Chinese medicine in vivo based on quantitative structure-retention relationships combined with characteristic ions: Alismatis Rhizoma as an example

As a fast, sensitive and selective method, liquid chromatography-tandem high-resolution mass spectrometry (LC-HRMS) has been used for studying the in vivo metabolism of traditional Chinese medicine (TCM). However, the rapid discovery and characterization of metabolites, especially isomers, remain challenging due to their complexity and low concentration in vivo. This study proposed a strategy to improve the structural annotation of prototypes and metabolites through characteristic ions and a quantitative structure-retention relationship (QSRR) model, and Alismatis Rhizoma (AR) triterpenes were used as an example. This strategy consists of four steps. First, based on an in-house database reported previously, prototypes and metabolites in biosamples were preliminarily identified. Second, the candidate structures of prototype compounds and metabolites were determined by characteristic ions, databases or potential metabolic pathways. Then, a QSRR model was established to predict the retention times of the proposed structure. Finally, the structures of unknown prototypes and metabolites were determined by matching experimental retention times with the predicted values. The QSRR model built by the genetic algorithm-multiple linear regression (GA-MLR) has excellent regression correlation (R² = 0.9966). Based on this strategy, a total of 118 compounds were identified, including 47 prototypes and 71 metabolites, among which 61 unknown compounds were reasonably characterized. The typical compound identified by this strategy was successfully validated using a triterpene standard. This strategy can improve the annotation confidence of in vivo metabolites of TCM and facilitate further pharmacological research.

Classification-based strategies to simplify complex traditional Chinese medicine (TCM) researches through liquid chromatography-mass spectrometry in the last decade (2011-2020): Theory, technical route and difficulty

The difficulty of traditional Chinese medicine (TCM) researches lies in the complexity of components, metabolites, and bioactivities. For a long time, there has been a lack of connections among the three parts, which is not conducive to the systematic elucidation of TCM effectiveness. To overcome this problem, a classification-based methodology for simplifying TCM researches was refined from literature in the past 10 years (2011-2020). The theoretical basis of this methodology is set theory, and its core concept is classification. Its starting point is that "although TCM may contain hundreds of compounds, the vast majority of these compounds are structurally similar". The methodology is composed by research strategies for components, metabolites and bioactivities of TCM, which are the three main parts of the review. Technical route, key steps and difficulty are introduced in each part. Two perspectives are highlighted in this review: set theory is a theoretical basis for all strategies from a conceptual perspective, and liquid chromatography-mass spectrometry (LC-MS) is a common tool for all strategies from a technical perspective. The significance of these strategies is to simplify complex TCM researches, integrate isolated TCM researches, and build a bridge between traditional medicines and modern medicines. Potential research hotspots in the future, such as discovery of bioactive ingredients from TCM metabolites, are also discussed. The classification-based methodology is a summary of research experience in the past 10 years. We believe it will definitely provide support and reference for the following TCM researches.

Insight into chemical basis of traditional Chinese medicine based on the state-of-the-art techniques of liquid chromatography-mass spectrometry

Traditional Chinese medicine (TCM) has been an indispensable source of drugs for curing various human diseases. However, the inherent chemical diversity and complexity of TCM restricted the safety and efficacy of its usage. Over the past few decades, the combination of liquid chromatography with mass spectrometry has contributed greatly to the TCM qualitative analysis. And novel approaches have been continuously introduced to improve the analytical performance, including both the data acquisition methods to generate a large and informative dataset, and the data post-processing tools to extract the structure-related MS information. Furthermore, the fast-developing computer techniques and big data analytics have markedly enriched the data processing tools, bringing benefits of high efficiency and accuracy. To provide an up-to-date review of the latest techniques on the TCM qualitative analysis, multiple data-independent acquisition methods and data-dependent acquisition methods (precursor ion list, dynamic exclusion, mass tag, precursor ion scan, neutral loss scan, and multiple reaction monitoring) and post-processing techniques (mass defect filtering, diagnostic ion filtering, neutral loss filtering, mass spectral trees similarity filter, molecular networking, statistical analysis, database matching, etc.) were summarized and categorized. Applications of each technique and integrated analytical strategies were highlighted, discussion and future perspectives were proposed as well.

A strategy for intelligent chemical profiling-guided precise quantitation of multi-components in traditional Chinese medicine formulae-QiangHuoShengShi decoction

Due to the tremendous clinical value, more and more Traditional Chinese Medicines (TCMs) and their formulae are attracted by world's attention. QiangHuoShengShi (QHSS) decoction is one of classic TCM formulae, which is clinically used for treating various rheumatic diseases. However, the phytochemical constituents of QHSS have rarely been reported. A simple, intelligent, and comprehensive strategy was developed to characterize the phytochemical-fingerprint and quantify the chemical-markers for precise quality evaluation of QHSS. Firstly, a new deep-learning assisted mass defect filter (MDF) method was built for rapid and accurate classification of mass spectrum (MS) ions acquired by ultra-high performance liquid chromatography quadrupole time of flight tandem mass spectrometry (UHPLC-Q-TOF/MS). Subsequently, herb species-specific chemical-category and characteristic identification were used for further characterization of multi-components. As the result, seven major types of compounds in QHSS were intelligently differentiated and 183 phytochemical compounds were tentatively identified. Finally, a sensitive scheduled multiple reaction monitoring (sMRM) detection method was applied to precisely quantify 37 target analytes in QHSS decoction. This integrated strategy would provide an alternative method for chemical-material basis study of more herbal medicine or natural products.