Chemometrics applied to quality control and metabolomics for traditional Chinese medicines

Chemical composition combined with network pharmacology and quality markers analysis for the quality evaluation of Qing-fei-da-yuan granules

Qing-fei-da-yuan granules (QFDYGs) had been proved to be an effective TCM prescription for treating coronavirus disease 2019 (COVID-19), which are composed of a variety of TCMs, and characterized by multiple components, multiple targets and overall regulation. It is meaningful to further study the chemical composition and pharmacology of QFDYGs for quality evaluation. However, due to the complexity of the components of QFDYGs, there are no reliable and simple analytical methods for current quality evaluation. In this work, antipyretic activity assessment of QFDYGs in the LPS-induced New Zealand rabbit model was carried out to verify the efficacy firstly. It was proved that QFDYGs can be used to relieve fever to help preventing or controlling the prevalence of influenza and pneumonia. Subsequently, UHPLC-ESI-QTOF-MS/MS combined with network pharmacology, quality markers and fingerprint analysis were used to establish the quality control condition. The chemical compositions were analyzed by UHPLC-ESI-QTOF-MS/MS, and 79 of them were identified, such as arecoline, mangiferin, paeoniflorin, etc. Then, the network pharmacology strategy based on 45 candidate components (CCs) in conjunction with influenza and pneumonia diseases was employed to screen the potential active ingredients. According to the drug-CCs-genes-diseases (D-CCs-G-D) networks, baicalein, honokiol, baicalin, paeoniflorin, saikosaponin A, glycyrrhizic acid and hesperidin were selected as quality markers. And a method for content determination of the 7 quality markers was established by optimizing extraction methods, chromatographic conditions and methodological verification. Finally, the quality of 15 batches of QFDYGs was evaluated by using the 7 quality markers combined with fingerprints and principal component analysis (PCA). The analyzed results showed that baicalin, paeoniflorin, glycyrrhizic acid and hesperidin were the high content and stable quality markers. QFDYGs were characterized by overall consistency and individual ingredient differences among the 15 batches. Our quality evaluation study will provide reference for the further development and research of QFDYGs.

Discovery and determination of misuse and chemotypes of Pogostemon cablin by liquid chromatography-quadrupole time-of-flight mass spectrometry and liquid chromatography with diode-array detector

Traditional Chinese medicine (TCM) has garnered significant scientific interest in healthcare but faces increased regulatory scrutiny due to concerns about uncontrolled usage. This study focuses on characterizing Pogostemon cablin (PC) to mitigate potential misuse and identify chemotype differences. Leveraging untargeted metabolomics, we identified 222 distinctive features effectively differentiating PC from Agastache rugosa (AR), reducing misidentification risks. Pogostone and tilianin emerged as potential markers, leading to a high-performance liquid chromatography-diode array detection (HPLC-DAD) method development for PC and AR discrimination. Evaluation of PC chromatograms revealed notable profile and pogostone level differences among samples, suggesting chemotype associations. Untargeted metabolic profiling identified 78 features with significant differences, highlighting 7,3',4'-tri-O-methyleriodictyol as a potential discriminatory marker between PC chemotypes. The developed HPLC-DAD method quantified pogostone and 7,3',4'-tri-O-methyleriodictyol, effectively discriminating PC chemotypes. This platform differentiates PC and AR and distinguishes chemical types within PC, like pogostone-type and patchoulol-type. Applied to local TCM stores, it ensures PC authenticity. This approach addresses TCM control concerns, enhancing understanding and application of herbal medicine by providing insights into PC chemical composition and discrimination.

Discovery of bioactive-chemical Q-markers of Acanthopanax sessiliflorus leaves: An integrated strategy of plant metabolomics, fingerprint and spectrum-efficacy relationship research

An integrated bioactive-chemical quality markers (Q-markers) discovery strategy, which was based on the LC-MS plant metabolomics, HPLC fingerprint as well as the chemical spectrum-efficacy relationships, was designed to develop a methodology for accurate and comprehensive evaluation of the quality of Acanthopanax sessiliflorus leaves (ASL). Firstly, a high resolution and sensitivity UHPLC-Q-Orbitrap MS method was used for plant metabolomics analysis to obtain component characterization and screen potential chemical markers that differentiate between different harvesting periods. A total of 53 chemical components were identified, and 8 potential chemical markers were discovered, such as sucrose, maltol and phenylalanine. Secondly, a selective HPLC fingerprint analysis of ASL and its pancreatic lipase activity assay method was successfully investigated in vitro. In the study of chemical spectrum-efficacy relationships, neochlorogenic acid, chlorogenic acid, caffeic acid and hyperoside were screened and showed the inhibited pancreatic lipase activity with IC50 values, 0.16 ± 0.01, 0.13 ± 0.01, 0.31 ± 0.01, and 0.44 ± 0.02 mg/mL, respectively, which indicated the above four constituents were selected as the bioactive-chemical Q-markers of ASL. Finally, an accurate and reliable quantitative HPLC assay was developed and validated for simultaneous determination of four bioactive-chemical Q-markers in ASL, and their content levels in ASL varied widely in different harvesting periods. The systematic and efficient screening strategy for bioactive-chemical Q-markers in this study, based on " LC-MS plant metabolomics, HPLC fingerprint, and spectrum-efficacy relationships," could have effectively improved the quality assessment level of ASL.

Application of Data Fusion in Traditional Chinese Medicine: A Review

Traditional Chinese medicine is characterized by numerous chemical constituents, complex components, and unpredictable interactions among constituents. Therefore, a single analytical technique is usually unable to obtain comprehensive chemical information. Data fusion is an information processing technology that can improve the accuracy of test results by fusing data from multiple devices, which has a broad application prospect by utilizing chemometrics methods, adopting low-level, mid-level, and high-level data fusion techniques, and establishing final classification or prediction models. This paper summarizes the current status of the application of data fusion strategies based on spectroscopy, mass spectrometry, chromatography, and sensor technologies in traditional Chinese medicine (TCM) in light of the latest research progress of data fusion technology at home and abroad. It also gives an outlook on the development of data fusion technology in TCM analysis to provide references for the research and development of TCM.

Comprehensive HPLC fingerprint analysis based on a two-step extraction method for quality evaluation of Perilla frutescens (L.) Britt

Abundant chemical components are key to ensure the evaluation accuracy of fingerprint analysis of traditional Chinese medicines (TCMs). A two-step extraction method combining supercritical fluid extraction (SFE) and water ultrasonic extraction was established for the quality evaluation of Perilla frutescens (L.) Britt. Weakly polar components were extracted under optimal SFE conditions (15% co-solvent (EtOH : n-hexane = 1 : 14, (v/v)), 40 °C, 250 bar, and 30 min), and polar components were subsequently extracted by an ultrasonic step (100% water as solvent, 40 °C, and 45 min). Then, HPLC methods were established, which were validated to be accurate, stable, and reliable. In this work, 25 batches of samples were evaluated and the data were analysed by similarity analysis (SA) and hierarchical cluster analysis (HCA). The similarity values of SFE extracts and aqueous extracts were respectively 0.616-0.999, and 0.252-0.997, proving the importance of the extraction method for the accuracy of the subsequent fingerprint analysis results. For the HCA, 25 samples were divided into two categories (leaves and stems), among which four batches of leaves with less similarity were considered as stems, indicating that quality differences of P. frutescens depending on medicinal parts and origin exist. The two-step extraction method developed in this work has been proved to be suitable for the quality evaluation of TCMs with complex compositions.

Quality assessment of traditional Chinese medicine based on data fusion combined with machine learning: A review

The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recently, modern analytical technologies have been extensively investigated and utilized to analyze the chemical composition of TCM. However, a single analytical technique has some limitations, and judging the quality of TCM only from the characteristics of the components is not enough to reflect the overall view of TCM. Thus, the development of multi-source information fusion technology and machine learning (ML) has further improved QATCM. Data information from different analytical instruments can better understand the connection between herbal samples from multiple aspects. This review focuses on the use of data fusion (DF) and ML in QATCM, including chromatography, spectroscopy, and other electronic sensors. The common data structures and DF strategies are introduced, followed by ML methods, including fast-growing deep learning. Finally, DF strategies combined with ML methods are discussed and illustrated for research on applications such as source identification, species identification, and content prediction in TCM. This review demonstrates the validity and accuracy of QATCM-based DF and ML strategies and provides a reference for developing and applying QATCM methods.

Quantitative fingerprint and antioxidative properties of Artemisia argyi leaves combined with chemometrics

Quantitative fingerprint and differences of Artemisia argyi from different varieties, picking time, aging year, and origins were analyzed combing with chemometrics. The antioxidant activity was determined and antioxidant markers of Artemisia argyi were screened. Variety WA3 was significantly different from that of the other varieties. Fingerprint peak response and antioxidant activity of A. argyi picked in December were lower than samples collected in May and August. Fresh A. argyi leaves were significantly superior to withered leaves and stems. Artemisia argyi aging 1-5 years presented a classification trend. Antioxidant activity of A. argyi produced in Nanyang was generally superior to others origins. Peak 9, isochlorogenic acid A, and 6-methoxyluteolin contributed greatly for classification of A. argyi from different variety, picking time, aging year, and origin. Isochlorogenic acid A, isochlorogenic acid C, 6-methoxyluteolin, and chlorogenic acid were selected as antioxidant marker of A. argyi. The method based on quantitative fingerprint, antioxidant activity evaluation, and chemometrics was reliable to analyze the differences of A. argyi samples from different sources.

Coicis Semen for the treatment of malignant tumors of the female reproductive system: A review of traditional Chinese medicinal uses, phytochemistry, pharmacokinetics, and pharmacodynamics

Coicis Semen is an important food product and traditional Chinese medicine (TCM) derived from the dried and mature seeds of Coix lacryma-jobi L.var.ma-yuen (Roman.) Stapf. An increasing number of studies have investigated its use, either alone or in combination with other botanical drugs, to treat female reproductive system malignancies, and its pharmacological effects have been confirmed clinically. This review aims to provide an overview of Coicis Semen's historical role in treating female reproductive system malignancies based on TCM theory, to summarize clinical trials results, and to analyze information pertaining to the main phytochemical components, pharmacokinetics, related anti-cancer pharmacological effects, and toxicology of Coicis Semen. Information on Coicis Semen was collected from internationally accepted scientific databases. Seventy-four clinical trials were identified that used Coicis Semen in combination with other Chinese medicine to treat female reproductive system malignancies, most of which demonstrated good anti-tumor efficacy and few adverse reactions. To date, more than 80 individual compounds have been isolated from this botanical drug. In terms of anti-tumor effects, Coix seed oil has been studied the most. Pharmacokinetic data suggest that the active ingredients in Coicis Semen are widely distributed after administration, and Coicis Semen and its active compounds play a beneficial role in treating female reproductive system malignancies. Mechanistically, the anti-cancer effects may be related to inhibition of tumor cell proliferation and promotion of apoptosis, inhibition of tumor angiogenesis, suppression of the chronic inflammatory microenvironment of tumors, modulation of immune function, and regulation of the female reproductive system. Most acute toxicity and genotoxicity studies have shown that Coicis Semen is non-toxic. However, the existing studies have many limitations, and the future research direction should emphasize 1) the relationship between drug concentration and pharmacological action as well as toxicity; 2) the structural modification or the synthesis of analogues led by the active ingredients of Coicis Semen to enhance pharmacological activities and bioavailability; 3) accurately revealing the anti-cancer pharmacological effects of Coicis Semen and its compounds through multi-omics technology. We hope that this review can determine future directions and inform novel drug development for treating female reproductive malignancies.

A stepwise strategy integrating metabolomics and pseudotargeted spectrum–effect relationship to elucidate the potential hepatotoxic components in Polygonum multiflorum

Polygonummultiflorum (PM) Thunb., a typical Chinese herbal medicine with different therapeutic effect in raw and processed forms, has been used worldwide for thousands of years. However, hepatotoxicity caused by PM has raised considerable concern in recent decades. The exploration of toxic components in PM has been a great challenge for a long time. In this study, we developed a stepwise strategy integrating metabolomics and pseudotargeted spectrum–effect relationship to illuminate the potential hepatotoxic components in PM. First, 112 components were tentatively identified using ultraperformance liquid chromatography-quadrupole-time-of-flight-mass spectrometry (UPLC-Q-TOF-MS). Second, based on the theory of toxicity attenuation after processing, we combined the UPLC-Q-TOF-MS method and plant metabolomics to screen out the reduced differential components in PM between raw and processed PM. Third, the proposed pseudotargeted MS of 16 differential components was established and applied to 50 batches of PM for quantitative analysis. Fourth, the hepatocytotoxicity of 50 batches of PM was investigated on two hepatocytes, LO2 and HepG2. Last, three mathematical models, gray relational analysis, orthogonal partial least squares analysis, and back propagation artificial neural network, were established to further identify the key variables affecting hepatotoxicity in PM by combining quantitative spectral information with toxicity to hepatocytes of 50 batches of PM. The results suggested that 16 components may have different degrees of hepatotoxicity, which may lead to hepatotoxicity through synergistic effects. Three components (emodin dianthrones, emodin-8-O-β-D-glucopyranoside, PM 14-17) were screened to have significant hepatotoxicity and could be used as toxicity markers in PM as well as for further studies on the mechanism of toxicity. Above all, the study established an effective strategy to explore the hepatotoxic material basis in PM but also provides reference information for in-depth investigations on the hepatotoxicity of PM.

Metabolomic analysis of serum from pure coronary artery ectasia patients based on UPLC-QE/MS technique

BACKGROUND

Coronary artery ectasia (CAE) is a cardiovascular disorder characterized by abnormal coronary artery dilation and disturbed coronary flow. The exact pathophysiology of CAE is still unclear. We aimed to investigate differences in metabolomic profiles between CAE patients and healthy controls.

METHODS

Radial artery blood samples were collected from 14 pure CAE patients, 12 mixed CAE patients with atherosclerosis, and 14 controls with normal angiography. Differential serum metabolites were analyzed by untargeted ultra-high performance liquid chromatography-mass spectrometry. Serum ICAM-1, VEGF, ROS, and glutathione levels were also measured.

RESULTS

Ten metabolites distinguished pure CAE patients from controls and mixed CAE, including 1-cyano-2-hydroxy-3-butene, 2,3-dihydro-6-methyl-5-(5-methyl-2-furanyl)-1H-pyrrolizine, 2-propionylpyrrole, 2-pyrrolidinone, 3-(2-furanylmethylene)pyrrolidine, D-alanine, furanofukinin, o-ethyltoluene, rotundine A, and SM(d18:1/18:1(9Z)). Related metabolic pathways include amino acid metabolism, sphingolipid dysfunction, energy metabolism, mitochondrial dysfunction, and oxidative stress. Serum concentrations of ICAM-1, VEGF and ROS were significantly elevated in CAE patients compared to controls, while glutathione decreased significantly in CAE patients. Moreover, ICAM-1 levels were negatively correlated with 2-propionylpyrrole, and VEGF levels were negatively correlated with SM(d18:1/18:1(9Z)), while GSH and ROS levels were correlated with the abundance of SM(d18:1/18:1(9Z)), further confirming systemic inflammation and oxidative stress in CAE.

CONCLUSIONS

This is the first report describing differential serum metabolomic profiles of pure CAE patients compared to mixed CAE and healthy controls, which revealed 10 potential biomarkers that can provide an early diagnosis of pure CAE. These discriminatory metabolites and related metabolic pathways can help to better understand the pathogenesis of pure CAE.

Rapid discrimination and screening of volatile markers for varietal recognition of Curcumae Radix using ATR-FTIR and HS-GC-MS combined with chemometrics

ETHNOPHARMACOLOGICAL RELEVANCE

Curcumae Radix (Yujin) has a long medicinal use history in China, which is used to cure diseases like jaundice, cholelithiasis caused by dampness-heat of gallbladder and liver, and so on. It comes from the dried tuberous roots of C. kwangsiensis (Guiyujin), C. longa (Huangyujin), C. phaeocaulis (Lvyujin) and C. wenyujin (Wenyujin). Though there are differences in chemical compositions and pharmacological activities among the four species of Yujin, they have not been differentiated well in clinical application due to their similar morphological characterizations.

AIM OF THE STUDY

In this study, the four species of Yujin were rapidly and accurately discriminated. The potential volatile markers for varietal recognition were identified.

MATERIALS AND METHODS

Attenuated total reflection fourier transformed infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to rapidly discriminate the four species of Yujin. Headspace-gas chromatography-mass spectrometry (HS-GC-MS) technology coupled with chemometrics was employed to characterize volatile profiling, differentiate species and select potential markers for varietal recognition of Yujin.

RESULTS

By applying PCA (principal components analysis) and HCA (hierarchical cluster analysis), HS-GC-MS realized complete differentiation of the four species of Yujin, while ATR-FTIR only recognized Guiyuijin. Back propagation neural network (BP-NN), KNN (K-nearest neighbor) and LDA (linear discriminant analysis) models based on spectral data achieved 100% discriminant accuracies. Support vector machines (SVM), KNN and PLS-DA (partial least square discriminant analysis) models based on volatile compounds also realized 100% discriminant accuracies. Additionally, the potential volatile markers for varietal recognition of Yujin were screened using PLS-DA, including 2 for Guiyujin, 6 for Lvyujin, 9 for Wenyujin and 13 for Huangyujin.

CONCLUSIONS

The present study developed reliable methods for the varietal discrimination and volatile compounds characterization of Yujin, which will provide references for its quality control and clinical efficacy.

Identification of Novel Biomarkers for Evaluating Disease Severity in House-Dust-Mite-Induced Allergic Rhinitis by Serum Metabolomics

The aim of this study was to identify differences in serum metabolomics profiles of house-dust-mite (HDM)-induced allergic rhinitis (AR) patients compared to controls and to explore novel biomarkers reflecting disease severity. Serum samples were collected from 29 healthy controls and HDM-induced 72 AR patients, including 30 mild patients (MAR) and 42 moderate to severe AR patients (MSAR). Metabolomics detection was performed, and orthogonal partial least square discriminate analysis was applied to assess the differences between AR patients and controls and for subgroups based on disease severity. These analysis results successfully revealed distinct metabolite signatures which distinguished MAR patients and MSAR patients from controls. MSAR patients also could be discriminated from MAR patients based on their metabolic fingerprints. Most observed metabolite changes were related to glycine, serine, and threonine metabolism, pyrimidine metabolism, sphingolipid metabolism, arginine and proline metabolism, and fatty acid metabolism. Levels of sarcosine, sphingosine-1-phosphate, cytidine, and linoleic acid significantly correlated with the total nasal symptom score and visual analogue scale in AR patients. These results suggest that metabolomics profiling may provide novel insights into the pathophysiological mechanisms of HDM-induced AR and contribute to its evaluation of disease severity.

Bioactivity-guided separation of antifungal compounds by preparative high-performance liquid chromatography

Bioactivity-guided chromatographic methods are of great significance for the isolation of the active compounds in complex samples. In this study, four anti-fungal compounds were located by activity screening and successfully isolated from a microbial fermentation sample by preparative high-performance liquid chromatography. Separation performance of columns including C18, positively charged C18, negatively charged C18 and C8 were firstly investigated. And it showed a better capacity of mixed-mode stationary phases for retention and separation. Therefore, the positively charged C18 column was used to separate the sample into several fractions, among which the active one was identified by the antifungal test. And then the active fraction was enriched and separated again by successively using the negatively charged C18 and C8 columns to obtain four compounds, which were identified as polyoxins A, K, F and H. With activity verification, four polyoxins were found to have good inhibitory effects against the three fungal plant diseases including rice sheath blight, tomato grey mould disease, and apple spot leaf disease.

The Role of Serum Metabolomics in Distinguishing Chronic Rhinosinusitis With Nasal Polyp Phenotypes

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous disease characterized by different clinical features and treatment responsiveness. This study aimed to compare the serum metabolomics profiles between eosinophilic CRSwNP (eCRSwNP) and non-eosinophilic CRSwNP (neCRSwNP) and healthy controls (HC) and explore objective biomarkers for distinguishing eCRSwNP before surgery. Methods: Serum samples were collected from 33 neCRSwNP patients, 37 eCRSwNP patients, and 29 HC. Serum metabolomics profiles were investigated by ultra-high-performance liquid chromatography-mass spectrometry. Results: The analysis results revealed that neCRSwNP, eCRSwNP, and HC exhibited distinctive metabolite signatures. In addition, eCRSwNP could be distinguished from neCRSwNP referring to their serum metabolic profiles, and the top ten different metabolites were citrulline, choline, linoleic acid, adenosine, glycocholic acid, L-serine, triethanolamine, 4-guanidinobutyric acid, methylmalonic acid, and L-methionine, which were related to several most important pathways including arginine and proline metabolism; glycine, serine, and threonine metabolism; linoleic acid metabolism; and purine metabolism. Among these distinctive metabolites, citrulline, linoleic acid, adenosine, and 4-guanidinobutyric acid showed good predictabilities, and the serum levels of citrulline, linoleic acid, and adenosine were significantly correlated with tissue eosinophil (T-EOS) percentage and T-EOS count. Conclusion: eCRSwNP patients exhibited discriminative serum metabolic signatures in comparison with neCRSwNP patients and HC. These results suggested that metabolomics profiles contributed to understanding the pathophysiological mechanisms of CRSwNP and distinguishing its phenotypes.

Integrated assessment of medicinal rhubarb by combination of delayed luminescence and HPLC fingerprint with emphasized on bioactivities based quality control

Background

To promote herbal medicine depends largely on its quality. Chromatographic fingerprint is a frequent approach for quality assessment of herbs however with challenges on robust and reproducibility. To develop rapid, cheap and comprehensive measurements as complementary tools for herbal quality control are still urgently needed. Moreover, biological activities are essential for herbal quality, and should be taken into consideration with emphasized in quality control.

Methods

In this research, HPLC fingerprint and delayed luminescence (DL, a rapid and systematic tool) were used to measure the rhubarb samples of multiple species. Statistics were explored to classify these rhubarb samples using data obtained from two analytic methods. In addition, DL properties were linked to specific chemical components which may reflect bioactivities of rhubarb using Spearman's rank correlation. Moreover, mice model was used to evaluate the cathartic effect between rhubarb samples stratifying by two analytic methods.

Results

We found that there was no significant difference of chemical fingerprints and DL signals among the different species of medicinal rhubarb. However, our results show a high similarity between HPLC fingerprint analysis and DL measurements in classification of these rhubarb samples into two sub-groups. In addition, the two sub-groups of rhubarb samples that may have different cathartic activities.

Conclusion

This approach provides new leads for development of herbal quality assessment based on bioactivity. In conclusion, integrated assessment by measuring HPLC fingerprint and DL with emphasized on bioactivity may provide novel strategy for herbal quality control.

Chemometrics Approaches in Forced Degradation Studies of Pharmaceutical Drugs

Chemometrics is the chemistry field responsible for planning and extracting the maximum of information of experiments from chemical data using mathematical tools (linear algebra, statistics, and so on). Active pharmaceutical ingredients (APIs) can form impurities when exposed to excipients or environmental variables such as light, high temperatures, acidic or basic conditions, humidity, and oxidative environment. By considering that these impurities can affect the safety and efficacy of the drug product, it is necessary to know how these impurities are yielded and to establish the pathway of their formation. In this context, forced degradation studies of pharmaceutical drugs have been used for the characterization of physicochemical stability of APIs. These studies are also essential in the validation of analytical methodologies, in order to prove the selectivity of methods for the API and its impurities and to create strategies to avoid the formation of degradation products. This review aims to demonstrate how forced degradation studies have been actually performed and the applications of chemometric tools in related studies. Some papers are going to be discussed to exemplify the chemometric applications in forced degradation studies.

Investigating the Protective Effect of Gross Saponins of Tribulus terrestris Fruit against Ischemic Stroke in Rat Using Metabolomics and Network Pharmacology

Stroke is one of the leading causes of death and long-term disability worldwide. Gross saponins of Tribulus terrestris fruit (GSTTF) has been used for neuroprotective therapy on convalescents of ischemic stroke. But the related therapeutic mechanisms have not yet been well investigated. This study aimed to investigate the protective effects of GSTTF on ischemic stroke using metabolomics coupled with network pharmacology analysis. The rat urine sample was collected and profiled by an LC-MS-based metabolomics approach. The pathway analysis was performed based on the highlighted biomarkers, then the network pharmacology approach was applied to screen the potential therapeutic targets of GSTTF. Metabolomics analysis showed that a series of metabolic perturbations occurred in the middle cerebral artery occlusion (MCAO) group compared with the sham group. Gross saponins of Tribulus terrestris fruit can change the MCAO-induced urine metabolic deviations in a reverse manner via regulating multiple metabolic pathways. Two proteins, inducible nitric oxide synthase (NOS2) and glycogen synthase kinase-3 beta (GSK3B), were highlighted by the network pharmacology analysis, which may be the potential therapeutic targets for the GSTTF against ischemic stroke. This study provides an overview of the mechanism of MCAO-induced ischemic stroke and investigates the efficacy of GSTTF in the treatment of ischemic stroke. Further study is needed to reveal its underlying mechanisms more clearly.

Gas chromatographic fingerprint analysis of secondary metabolites of Stachys lanata (Stachys byzantine C. Koch) combined with antioxidant activity modelling using multivariate chemometric methods

S. lanata has been traditionally used as a medicinal plant due to its various biological activities such as antioxidant activity. Therefore, identification and quality control studies of this plant are of great importance. To this end, gas chromatography (GC) combined with chemometrics was proposed for fingerprint analysis of S. lanata samples. This study sought to classify GC fingerprints of twenty-eight S. lanata samples from eight different regions of Iran and more importantly, to correlate fingerprints to the antioxidant activity to select S. lanata volatile antioxidant markers. S. lanata samples were classified into five and three classes using partial least squares-discriminant analysis (PLS-DA) according to their GC fingerprints and antioxidant peaks, respectively. The results of PLS regression (PLS-R) and variable importance in projection (VIP) showed that phenol, 2,4-bis (1,1-dimethylethyl)-, hexadecanoic acid- ethyl ester, vitamin E, Beta- sitosterol, and 1- monolinoleoylglycerol trimethylsily ether are volatile antioxidant markers of S. lanata samples.

HPLC Coupled with Chemometric Analysis and LC–MS Studies of Three Flavonoids in Tephrosia purpurea (L.) Pers Revealed Impact of Chemodiversity on the Quest for the Chemical Markers

Globally, Tephrosia purpurea (L.) Pers is used as an important component in herbal drug formulations for liver health. The present study is aimed to develop a suitable analytical approach for simultaneous analysis of three flavonoids (rutin, deguelin and rotenone) to establish quality control methods for plant. A novel High-performance liquid chromatography photodiode array detector (HPLC-PDA) method has been developed to quantify these flavonoids in T. purpurea. The method was validated, and data were subjected to chemometric analysis to select most optimal marker compound. The method that was found linear with R2 values ranges from 0.996 to 0.998 with good recoveries. Intra- and inter-day precision values were <2. HPLC analysis revealed high level of chemodiversity. Quantity of all the three chemical markers was found significantly disparate in samples from different locations. Deguelin was detectable only in three out of total eight samples. However, liquid chromatography–mass spectrometry analysis was found sufficiently sensitive to detect all the compounds in all samples. Thus, results suggest to apply combination of approaches to enhance confidence in chromatographic methods for quality control of herbal drugs. Principal component analysis ranked the markers as Rutin>Rotenone>Deguelin. This comprehensive approach employing multichromatography platforms can be successfully utilized in analysis of these bioactive markers and routine standardization of herbal material and formulations containing T. purpurea.

High-Resolution Liquid Chromatography-Mass Spectrometry-Based Metabolomic Discrimination of Citrus-Type Crude Drugs and Comparison with Nuclear Magnetic Resonance Spectroscopy-Based Metabolomics

Five Citrus-type crude drugs (40 samples) were classified using liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. The following six flavonoid derivatives were identified as contributors from the loading plots of multivariate analysis: naringin (1), neohesperidin (2), neoeriocitrin (3), narirutin (9), hesperidin (10), and 3,5,6,7,8,3',4'-heptamethoxyflavone (12). Three coumarin derivatives, namely, meranzin (6), meranzin hydrate (7), and meranzin glucoside (8), were also identified as contributors. Furthermore, compared with our previous studies on proton (¹H) and ¹³C NMR spectroscopy-based metabolomics, the present study revealed that the Citrus-type crude drugs were distinguished with the same pattern; however, the contributors differed between the ¹H and ¹³C NMR spectroscopy-based metabolomics. The high dynamic range of NMR spectroscopy provided broad coverage of the metabolomes including the primary and secondary metabolites. However, LC-MS appeared to be superior in detecting secondary metabolites with high sensitivity, some of which occurred in quantities that were undetectable using NMR spectroscopy.

Novel Surface-Enhanced Raman Spectroscopy Techniques for DNA, Protein and Drug Detection

Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopic technique in which the Raman scattering signal strength of molecules, absorbed by rough metals or the surface of nanoparticles, experiences an exponential growth (10³-10⁶ times and even 10¹⁴-10¹⁵ times) because of electromagnetic or chemical enhancements. Nowadays, SERS has attracted tremendous attention in the field of analytical chemistry due to its specific advantages, including high selectivity, rich informative spectral properties, nondestructive testing, and the prominent multiplexing capabilities of Raman spectroscopy. In this review, we present the applications of state-of-the-art SERS for the detection of DNA, proteins and drugs. Moreover, we focus on highlighting the merits and mechanisms of achieving enhanced SERS signals for food safety and clinical treatment. The machine learning techniques, combined with SERS detection, are also indicated herein. This review concludes with recommendations for future studies on the development of SERS.

Anchor assisted warping of the chromatograms: A novel procedure to correct the drifts in the chromatographic peak positions

The present work introduces anchor assisted warping (AAW) technique to correct the drift in the chromatographic peak positions. The proposed approach finds the suitable anchors in an automatic fashion without asking for any inputs from the user. These anchors are further used as reference peaks to correct the drifts in the peak positions. The AAW technique also reduces the computational burden and data storage issues while carrying out the alignment. The proposed approach is successfully used to align the chromatograms having the complex drifts in the peak positions. Various calculated statistical parameters clearly suggests that AAW algorithm provides a simple and user-friendly technique to correct the drifts in the peak position. The AAW technique can be integrated in the data analysis work-packages to ensure that chromatograms are compared on the correct frame. It will certainly reduce the false outcomes of the data analysis routines. In summary, AAW technique can be useful tool in speeding up the chemical and biological research.

13C-NMR-based metabolic fingerprinting of Citrus-type crude drugs

Five Citrus-type crude drugs (40 samples) were classified using ¹³C-NMR spectra-based metabolomics. The following eight metabolites were identified from the loading plots of multivariate analysis of the ¹³C-NMR spectra; naringin, neohesperidin, narirutin, synephrine, sucrose, α-glucose, β-glucose, and limonene. ¹³C-NMR spectra-based metabolic fingerprinting is a promising strategy for classifying crude drugs.

From quality markers to data mining and intelligence assessment: A smart quality-evaluation strategy for traditional Chinese medicine based on quality markers

BACKGROUND

The quality of traditional Chinese medicine (TCM) forms the foundation of its clinical efficacy. The standardization of TCM is the most important task of TCM modernization. In recent years, there has been great progress in the quality control of TCM. However, there are still many issues related to the current quality standards, and it is difficult to objectively evaluate and effectively control the quality of TCM.

PURPOSE

To face these challenge, we summarized the current quality marker (Q-marker) research based on its characteristics and benefits, and proposed a reasonable and intelligentized quality evaluation strategy for the development and application of Q-markers.

METHODS

Ultra-performance liquid chromatography-quadrupole/time-of-flight with partial least squares-discriminant analysis was suggested to screen the chemical markers from Chinese medicinal materials (CMM), and a bioactive-guided evaluation method was used to select the Q-markers. Near-infrared spectroscopy (NIRS), based on the distinctive wavenumber zones or points from the Q-markers, was developed for its determination. Then, artificial intelligence algorithms were used to clarify the complex relationship between the Q-markers and their integral functions. Internet and mobile communication technology helped us to perform remote analysis and determine the information feedback of test samples.

CHAPTERS

The quality control research, evaluation, standard establishment and quality control of TCM must be based on the systematic analysis of Q-markers to study and describe the material basis of TCM efficacy, define the chemical markers in the plant body, and understand the process of herb drug acquisition, change and transmission laws affecting metabolism and exposure. Based on the advantages of chemometrics, new sensor technologies, including infrared spectroscopy, hyperspectral imaging, chemical imaging, electronic nose and electronic tongue, have become increasingly important in the quality evaluation of CMM. Inspired by the concept of Q-marker, the quantitation can be achieved with the help of artificial intelligence, and these subtle differences can be discovered, allowing the quantitative analysis by NIRS and providing a quick and easy detection method for CMM quality evaluations.

CONCLUSION

The concept of Q-markers focused on unique CMM differences, dynamic changes and their transmission and traceability to establish an overall quality control and traceability system. Based on the basic attributes, an integration model and artificial intelligence research path was proposed, with the hope of providing new ideas and perspectives for the TCM quality management.

Discrimination and identification of Q-markers based on 'Spider-web' mode for quality control of traditional Chinese medicine

BACKGROUND

The safety and effectiveness of traditional Chinese medicine (TCM) in clinical practice is directly related to the quality of TCM. And, the quality control of TCM is a pivotal issue to the quality of TCM, but also an obstacle impeding the modernization of TCM.

PURPOSE

The purpose of this work is to compile and develop a strategy based on discrimination and identification of quality markers (Q-markers) for quality control of TCM.

METHODS

Mainly established by seven variables derived from four dimensions including content, stability, pharmacokinetics and pharmacology, the 'Spider-web' mode was undertaken to assess the Q-marker property of candidate compounds originated from TCM by taking regression area (A) and coefficient variation (CV) of the tested compounds into account. The importance index (ImI), ImI = A × 1/CV, was suggested to focus Q-markers.

RESULTS

The compounds with larger regression area (A) and less coefficient variation (CV) are preferentially adopted as Q-markers, which should possess the satisfactory properties of content, stability, pharmacokinetics and pharmacological activity. To the contrary, the compounds are excluded on the grounds of the unsatisfactory Q-markers' property, less regression area (A) and larger coefficient variation (CV), which cannot represent the quality of TCM.

CONCLUSIONS

The 'Spider-web' mode can filter out the redundant constituents and focus on the key indexes of quality control - Q-markers. The screened Q-markers possess the optimal integrated properties of content, stability, pharmacokinetics and pharmacology among the numerous and complicated ingredients of TCM, which can comprehensively characterize inherent quality of TCM. In summary, the novel strategy established in this work provides a valuable perspective for the quality control of TCM.

Liver metabolomics study reveals protective function of Phyllanthus urinaria against CCl4-induced liver injury

Phyllanthus Urinaria L. (PUL) is a traditional Chinese medicine used to treat hepatic and renal disorders. However, the mechanism of its hepatoprotective action is not fully understood. In the present study, blood biochemical indexes and liver histopathological changes were used to estimate the extent of hepatic injury. GC/MS and LC/MS-based untargeted metabolomics were used in combination to characterize the potential biomarkers associated with the protective activity of PUL against CCl₄-induced liver injury in rats. PUL treatment could reverse the increase in ALT, AST and ALP induced by CCl₄ and attenuate the pathological changes in rat liver. Significant changes in liver metabolic profiling were observed in PUL-treated group compared with liver injury model group. Seventeen biomarkers related to the hepatoprotective effects of PUL against CCl₄-induced liver injury were screened out using nonparametric test and Pearson's correlation analysis (OPLS-DA). The results suggested that the potential hepatoprotective effects of PUL in attenuating CCl₄-induced hepatotoxicity could be partially attributed to regulating L-carnitine, taurocholic acid, and amino acids metabolism, which may become promising targets for treatment of liver toxicity. In conclusion, this study provides new insights into the mechanism of the hepatoprotection of Phyllanthus Urinaria.

Discrimination of Radix Polygoni Multiflori from different geographical areas by UPLC-QTOF/MS combined with chemometrics

Background

Nowadays, Radix Polygoni Multiflori (RPM, Heshouwu in Chinese) from different geographical origins were used in clinic. In order to characterize the chemical profiles of different geographical origins of RPM samples, ultra-high performance liquid chromatography quadrupole time of flight mass spectrometry (UPLC-QTOF/MS) combined with chemometrics (partial least squared discriminant analysis, PLS‑DA) method was applied in the present study.

Methods

The chromatography, chemical composition and MS information of RPM samples from 18 geographical origins were acquired and profiled by UPLC-QTOF/MS. The chemical markers contributing the differentiation of RPM samples were observed and characterized by supervised PLS‑DA method of chemometrics.

Results

The chemical composition differences of RPM samples derived from 18 different geographical origins were observed. Nine chemical markers were tentatively identified which could be used as specific chemical markers for the differentiation of geographical RPM samples.

Conclusions

UPLC-QTOF/MS method coupled with chemometrics analysis has potential to be used for discriminating different geographical TCMs. Results will help to develop strategies for conservation and utilization of RPM samples.

Electronic supplementary material

The online version of this article (10.1186/s13020-017-0155-8) contains supplementary material, which is available to authorized users.

PG-Metrics: A chemometric-based approach for classifying bacterial peptidoglycan data sets and uncovering their subjacent chemical variability

Bacteria cells are protected from osmotic and environmental stresses by an exoskeleton-like polymeric structure called peptidoglycan (PG) or murein sacculus. This structure is fundamental for bacteria’s viability and thus, the mechanisms underlying cell wall assembly and how it is modulated serve as targets for many of our most successful antibiotics. Therefore, it is now more important than ever to understand the genetics and structural chemistry of the bacterial cell walls in order to find new and effective methods of blocking it for the treatment of disease. In the last decades, liquid chromatography and mass spectrometry have been demonstrated to provide the required resolution and sensitivity to characterize the fine chemical structure of PG. However, the large volume of data sets that can be produced by these instruments today are difficult to handle without a proper data analysis workflow. Here, we present PG-metrics, a chemometric based pipeline that allows fast and easy classification of bacteria according to their muropeptide chromatographic profiles and identification of the subjacent PG chemical variability between e.g. bacterial species, growth conditions and, mutant libraries. The pipeline is successfully validated here using PG samples from different bacterial species and mutants in cell wall proteins. The obtained results clearly demonstrated that PG-metrics pipeline is a valuable bioanalytical tool that can lead us to cell wall classification and biomarker discovery.

Comparative Analysis of Compatibility Effects on Invigorating Blood Circulation for Cyperi Rhizoma Series of Herb Pairs Using Untargeted Metabolomics

The mutual-assistance compatibility of Cyperi Rhizoma (Xiangfu, XF) and Angelicae Sinensis Radix (Danggui, DG), Chuanxiong Rhizoma (Chuanxiong, CX), Paeoniae Radix Alba (Baishao, BS), or Corydalis Rhizoma (Yanhusuo, YH), found in a traditional Chinese medicine (TCM) named Xiang-Fu-Si-Wu Decoction (XFSWD), can produce synergistic and promoting blood effects. Nowadays, XFSWD has been proved to be effective in activating blood circulation and dissipating blood stasis. However, the role of the herb pairs synergistic effects in the formula were poorly understood. In order to quantitatively assess the compatibility effects of herb pairs, mass spectrometry-based untargeted metabolomics studies were performed. The plasma and urine metabolic profiles of acute blood stasis rats induced by adrenaline hydrochloride and ice water and administered with Cyperi Rhizoma-Angelicae Sinensis Radix (XD), Cyperi Rhizoma-Chuanxiong Rhizoma (XC), Cyperi Rhizoma-Paeoniae Radix Alba (XB), Cyperi Rhizoma-Corydalis Rhizoma (XY) were compared. Relative peak area of identified metabolites was calculated and principal component analysis (PCA) score plot from the potential markers was used to visualize the overall differences. Then, the metabolites results were used with biochemistry indicators and genes expression values as parameters to quantitatively evaluate the compatibility effects of XF series of herb pairs by PCA and correlation analysis. The collective results indicated that the four XF herb pairs regulated glycerophospholipid metabolism, steroid hormone biosynthesis and arachidonic acid metabolism pathway. XD was more prominent in regulating the blood stasis during the four XF herb pairs. This study demonstrated that metabolomics was a useful tool to efficacy evaluation and compatibility effects of TCM elucidation.