Application of Metabolomics to Quality Control of Natural Product Derived Medicines

Insights into Metabolites Profiling and Pharmacological Investigation of Aconitum heterophyllum wall ex. Royle Stem through Experimental and Bioinformatics Techniques

The Aconitum genus is a leading source of a wide range of structurally diverse metabolites with significant pharmacological implications. The present study investigated metabolite profiling, pharmacological investigation, anticancer potential, and molecular docking analysis of the stem part of Aconitum heterophyllum (AHS). The metabolite profiling of the AHS extract was experimentally examined using LC-MS/MS-orbitrap in both modes (ESI+/ESI-) and GC-MS in EI mode. The in vitro MTT model was used to study the anticancer potential, while the in vivo animal model was used to study the anti-inflammatory and antinociceptive activities. The MOE software was used for the molecular docking study. A total of 118 novel and previously known metabolites, among 44 metabolites (26 in ESI+ positive mode and 18 in ESI- negative mode) in the MeOH extract, while 74 metabolites (46 in ESI+ and 28 in ESI- mode) were identified in the n-hexane extract via LCMS/MS. The identified metabolites include 24 phenolic compounds, 18 alkaloids, 10 flavonoids, 24 terpenoids, 2 coumarins, 2 lignans, and 38 other fatty acids and organic compounds. The major bioactive metabolites identified were hordenine, hernagine, formononetin, chrysin, N-methylhernagine, guineesine, shogaol, kauralexin, colneleate, zerumbone, medicarpin, boldine, miraxinthin-v, and lariciresinol-4-O-glucoside. Furthermore, the GC-MS study helped in the identification of volatile and nonvolatile chemical constituents based on the mass spectrum and retention indices. The methanol extract significantly inhibited tumor progression in H9c2 and MDCK cancer cells with IC50 values of 186.39 and 199.63 μg/mL. In comparison, the positive control aconitine exhibited potent IC50 values (132.32 and 141.58 μg/mL) against H9c2 and MDCK cell lines. The anti-inflammatory (carrageenan-induced hind paw edema) and antinociceptive (acetic acid-induced writhing) effects were significantly dose-dependent, (p < 0.001) and (p < 0.05), respectively. In addition, a molecular docking study was conducted on identified ligands against the anti-inflammatory enzyme (COX-2) (PDB ID: 5JVZ) and the cancer enzyme ADAM10 (PDB ID: 6BDZ) which confirmed the anti-inflammatory and anticancer effects in an in silico model. Among all ligands, L2, L3, and L7 exhibit the most potent potential for inhibiting COX-2 inflammation with binding energies of -7.3424, -7.0427, and -8.3562 kcal/mol. Conversely, against ADAM10 cancer protein, ligands L1, L4, L6, and L7, with binding energies of -8.0650, -7.7276, -7.0454, and -7.2080 kcal/mol, demonstrated notable effectiveness. Overall, the identified metabolites revealed in this AHS research study hold promise for discovering novel possibilities in the disciplines of chemotaxonomy and pharmacology.

Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study

Herbal medicines are popular natural medicines that have been used for decades. The use of alternative medicines continues to expand rapidly across the world. The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications, as their therapeutic potential varies between different geographic origins, plant species, and varieties. Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach. Their quality should be considered based on a complete metabolic profile, as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds. A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization. In this study, a rapid and effective quality assessment system for geographical traceability, species, and variety-specific authenticity of the widely used natural medicines turmeric, Ocimum, and Withania somnifera was investigated using Fourier transform near-infrared (FT-NIR) spectroscopy-based metabolic fingerprinting. Four different geographical origins of turmeric, five different Ocimum species, and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches. Extremely good discrimination (R2 > 0.98, Q2 > 0.97, and accuracy = 1.0) with sensitivity and specificity of 100% was achieved using this metabolic fingerprinting strategy. Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.

Authentication of milk thistle commercial products using UHPLC-QTOF-ESI + MS metabolomics and DNA metabarcoding

BACKGROUND

Milk thistle is one of the most popular hepatoprotectants, and is often sold in combination with other ingredients. Botanical supplements are known to be vulnerable to contamination and adulteration, and emerging technologies show promise to improve their quality control.

METHODS

Untargeted and semi-targeted metabolomics based on UHPLC-QTOF-ESI⁺MS techniques, UV spectrometry, and DNA metabarcoding using Illumina MiSeq were used to authenticate eighteen milk thistle botanical formulations (teas, capsules, tablets, emulsion).

RESULTS

Untargeted metabolomics separated 217 molecules and by multivariate analysis the discrimination between the different preparations was established. The semi-targeted metabolomics focused on 63 phytochemicals, mainly silymarin flavonolignans and flavonoids, that may be considered as putative biomarkers of authenticity. All formulations contained molecules from silymarin complexes at different levels. The quantitative evaluation of silybins was done using in parallel UV spectrometry and UHPLC-QTOF-ESI⁺MS and their correlations were compared. DNA metabarcoding detected milk thistle in eleven out of sixteen retained preparations, whereas two others had incomplete evidence of milk thistle despite metabolomics validating specific metabolites, e.g., silymarin complex, identified and quantified in all samples. Meanwhile, the DNA metabarcoding provided insights into the total species composition allowing the interpretation of the results in a broad context.

CONCLUSION

Our study emphasizes that combining spectroscopic, chromatographic, and genetic techniques bring complementary information to guarantee the quality of the botanical formulations.

Mass spectrometry-based metabolomic analysis as a tool for quality control of natural complex products

Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.

Detecting and Profiling of Milk Thistle Metabolites in Food Supplements: A Safety-Oriented Approach by Advanced Analytics

Milk thistle (Silybum marianum (L.) Gaertn.) is among the top-selling botanicals used as a supportive treatment for liver diseases. Silymarin, a mixture of unique flavonolignan metabolites, is the main bioactive component of milk thistle. The biological activities of silymarin have been well described in the literature, and its use is considered safe and well-tolerated in appropriate doses. However, commercial preparations do not always contain the recommended concentrations of silymarin, failing to provide the expected therapeutic effect. While the poor quality of raw material may explain the low concentrations of silymarin, its deliberate removal is suspected to be an adulteration. Toxic contaminants and foreign matters were also detected in milk thistle preparations, raising serious health concerns. Standard methods for determination of silymarin components include thin-layer chromatography (TLC), high-performance thin-layer chromatography (HPTLC), and high-performance liquid chromatography (HPLC) with various detectors, but nuclear magnetic resonance (NMR) and ultra-high-performance liquid chromatography (UHPLC) have also been applied. This review surveys the extraction techniques of main milk thistle metabolites and the quality, efficacy, and safety of the derived food supplements. Advanced analytical authentication approaches are discussed with a focus on DNA barcoding and metabarcoding to complement orthogonal chemical characterization and fingerprinting of herbal products.

Everyday Evaluation of Herb/Dietary Supplement–Drug Interaction: A Pilot Study

A lack of reliable information hinders the clinician evaluation of suspected herb–drug interactions. This pilot study was a survey-based study conceived as a descriptive analysis of real-life experiences with herb–drug interaction from the perspective of herbalists, licensed health-care providers, and lay persons. Reported dietary supplement–drug interactions were evaluated against the resources most commonly cited for the evaluation of potential supplement–drug interactions. Disproportionality analyses were performed using tools available to most clinicians using data from the U.S. Federal Adverse Event Reporting System (FAERS) and the US Center for Food Safety and Applied Nutrition (CFSAN) Adverse Event Reporting System (CAERS). Secondary aims of the study included exploration of the reasons for respondent use of dietary supplements and qualitative analysis of respondent’s perceptions of dietary supplement–drug interaction. While agreement among reported supplement–drug interactions with commonly cited resources for supplement–drug interaction evaluation and via disproportionality analyses through FAERS was low, agreement using data from CAERS was high.

Metabolomics and Network Pharmacology in the Exploration of the Multi-Targeted Therapeutic Approach of Traditional Medicinal Plants

Metabolomic is generally characterized as a comprehensive and the most copious analytical technique for the identification of targeted and untargeted metabolite diversity in a biological system. Recently, it has exponentially been used for phytochemical analysis and variability among plant metabolites, followed by chemometric analysis. Network pharmacology analysis is a computational technique used for the determination of multi-mechanistic and therapeutic evaluation of chemicals via interaction with the genomes involved in targeted or untargeted diseases. In considering the facts, the present review aims to explore the role of metabolomics and network pharmacology in the scientific validation of therapeutic claims as well as to evaluate the multi-targeted therapeutic approach of traditional Indian medicinal plants. The data was collected from different electronic scientific databases such as Google Scholar, Science Direct, ACS publication, PubMed, Springer, etc., using different keywords such as metabolomics, techniques used in metabolomics, chemometric analysis, a bioinformatic tool for drug discovery and development, network pharmacology, methodology and its role in biological evaluation of chemicals, etc. The screened articles were gathered and evaluated by different experts for their exclusion and inclusion in the final draft of the manuscript. The review findings suggest that metabolomics is one of the recent most precious and effective techniques for metabolite identification in the plant matrix. Various chemometric techniques are copiously used for metabolites discrimination analysis hence validating the unique characteristic of herbal medicines and their derived products concerning their authenticity. Network pharmacology remains the only option for the unique and effective analysis of hundreds of chemicals or metabolites via genomic interaction and thus validating the multi-mechanistic and therapeutic approach to explore the pharmacological aspects of herbal medicines for the management of the disease.

The Anti-Cancer Effects of Mitochondrial-Targeted Triphenylphosphonium-Resveratrol Conjugate on Breast Cancer Cells

Breast cancer is the most commonly diagnosed cancer in women. Resveratrol, a naturally occurring phytochemical, shows great promise in developing novel anti-cancer therapies. This study hypothesized that the mitochondria-targeted delivery of resveratrol would increase its potency and induce mitochondria-mediated apoptosis. The targeted delivery of resveratrol was achieved by conjugating resveratrol to triphenylphosphonium (TPP). The anti-cancer effects of TPP-resveratrol were studied in the murine breast cancer 4T1 and the human breast cancer MDA-MB-231 cell lines. Flow cytometry was used to study apoptosis induction, cell cycle arrest, and mitochondrial membrane potential loss. The morphological changes in the mitochondria in MDA-MB-231 cells after TPP-resveratrol treatments were examined using transmission electron microscopy. Moreover, the changes in MDA-MB-231 cell metabolism after resveratrol and TPP-resveratrol treatments were studied using metabolomic analysis. We demonstrate that TPP-resveratrol significantly improved cytotoxicity in 4T1 cells and MDA-MB-231 cells by inducing apoptosis and mitochondrial membrane potential loss. Swollen and vacuolated mitochondria were observed after the TPP-resveratrol treatment. Meanwhile, TPP-resveratrol treatment down-regulated amino acid and energy metabolism and caused the dysfunction of purine and pyrimidine metabolism. Our results provide evidence supporting the targeted delivery of resveratrol to mitochondria and suggest that TPP-resveratrol may be an effective agent for breast cancer treatment.

Simultaneous UHPLC-MS Quantification of Catechins and Untargeted Metabolomic Profiling for Proof-of-Concept Authenticity Determination of Maytenus ssp. Samples

Due to the widespread use of Maytenus ilicifolia leaves in the treatment of gastric ulcers, herbal medicines derived from such species are distributed by the national health system in Brazil. A related species, Maytenus aquifolium, is also used for the same disorders, and both are popularly known as Espinheira-santa. Due to their popular use, the quality and efficiency of the herbal medicines derived from these species is an important public health issue. The purpose of this study was to develop and test an analytical method that could quantify the content of catechin and epicatechin in dry Maytenus spp. leaves and simultaneously obtain their chemical profile to determine authenticity of the leaf samples. Ultra-high performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) was used to quantify these isomers, in the selected ion monitoring (SIM) mode, while simultaneously analyzing the extract in full-scan mode. This approach was successfully applied to the analysis of commercial and authentic samples of Maytenus spp. Fewer than half the samples presented the minimum epicatechin content of 2.8 mg per g of dry leaf mass, as specified in the 6th Brazilian Pharmacopoeia (2019) for M. ilicifolia. Furthermore, by using untargeted metabolomics, it was observed that the chemical profile of most the samples was not compatible with M. ilicifolia leaves, indicating the need for stricter quality control of this material. The method described herein could be used for this control; moreover, its concept could be adapted and used for an ample variety of medicinal plant products.

Application of chemometrics using direct spectroscopic methods as a QC tool in pharmaceutical industry and their validation

This present review described the application of chemometrics using direct spectroscopic methods at the quality control (QC) laboratory of Pharmaceutical Industries. Using chemometrics methods, all QC assessments during the fabrication processes of the drug preparations can be well performed. Chemometrics methods have some advantages compared to the conventional methods, i.e., non-destructive, can be performed directly to intake samples without any extractions, unnecessary performing stability studies, and cost-effective. To achieve reliable results of analyses, all methods must be validated first prior to routine applications. According to the current Pharmacopeia, the validation parameters are specificity/selectivity, accuracy, repeatability, intermediate precision, range, detection limit, quantification limit and robustness. These validation data must meet the acceptance criteria, that have been described by the analytical target profile (ATP) of the drug preparations.

TLC-Based Metabolite Profiling and Bioactivity-Based Scientific Validation for Use of Water Extracts in AYUSH Formulations

We aimed to develop a chromatographic method for scientific validation of water extract of some important Indian traditional plants used in AYUSH-based formulation as immunomodulator and to evaluate their bioactive potential. Fruits of Phyllanthus emblica L. and Piper nigrum L., stem of Tinospora cordifolia (Willd.) Miers, rhizome of Curcuma longa L., leaves of Ocimum sanctum L. and Achillea millefolium L., roots of Withania somnifera L., and stem bark of Azadirachta indica A. Juss. were coarsely powdered and extracted in three different solvents (water, ethanol, and hydroethanol). The antioxidant potential was determined through 1, 1-diphenyl-2-picrylhydrazyl and ferric reducing capacity methods. Thin-layer chromatography (TLC) was carried out for the comparative metabolite profiling of the extracts using toluene, ethyl acetate, and formic acid (5 : 4 : 1, v/v/v) as a solvent system. In vitro immunomodulatory activity of the extracts has been tested on splenocyte proliferation and pinocytic assay. Hydroethanolic extract (HEE) of most of the plant materials has the highest phenolic and flavonoid contents, followed by water extract (WE) and ethanolic extract (EE), whereas the water extracts of most of the plant material showed better antioxidant activity. Almost all extract exhibited splenocyte proliferation and pinocytic activity in a dose-dependent manner. But water extract showed significantly higher splenocyte proliferation and pinocytic activity as compared to the other two extracts. TLC analysis resulted in detection of totally 63 and 56 metabolites at 254 nm and 366 nm, respectively. Through principal component analysis (PCA), it was observed that metabolite pattern of different extracts from same plant materials may be different or similar. This preliminary result can be used for quality evaluation and to develop a synergy-based polyherbal combination of water extracts of selected plant materials.

Unravelling the Anticancer Mechanisms of Traditional Herbal Medicines with Metabolomics

Metabolite profiling of cancer cells presents many opportunities for anticancer drug discovery. The Chinese, Indian, and African flora, in particular, offers a diverse source of anticancer therapeutics as documented in traditional folklores. In-depth scientific information relating to mechanisms of action, quality control, and safety profile will promote their extensive usage in cancer therapy. Metabolomics may be a more holistic strategy to gain valuable insights into the anticancer mechanisms of action of plants but this has remained largely unexplored. This review, therefore, presents the available metabolomics studies on the anticancer effects of herbal medicines commonly used in Africa and Asia. In addition, we present some scientifically understudied ‘candidate plants’ for cancer metabolomics studies and highlight the relevance of metabolomics in addressing other challenges facing the drug development of anticancer herbs. Finally, we discussed the challenges of using metabolomics to uncover the underlying mechanisms of potential anticancer herbs and the progress made in this regard.

Dissecting coffee seeds metabolome in context of genotype, roasting degree, and blending in the Middle East using NMR and GC/MS techniques

With a favored taste and various bioactivities, coffee has been consumed as a daily beverage worldwide. The current study presented a multi-faceted comparative metabolomics approach dissecting commercially available coffee products in the Middle East region for quality assessment and functional food purposes using NMR and GC/MS platforms. NMR metabolites fingerprinting led to identification of 18 metabolites and quantification (qNMR) of six prominent markers for standardization purposes. An increase of β-ethanolamine (MEA) reported for the first time, 5-(hydroxymethyl) furfural (5-HMF), concurrent with a reduction in chlorogenic acid, kahweol, and sucrose levels post roasting as revealed using multivariate data analyses (MVA). The diterpenes kahweol and cafestol were identified in green and roasted Coffea arabica, while 16-O-methyl cafestol in roasted C. robusta. Moreover, GC/MS identified a total of 143 metabolites belonging to 15 different chemical classes, with fructose found enriched in green C. robusta versus fatty acids abundance, i.e., palmitic and stearic acids in C. arabica confirming NMR results. These potential results aided to identify novel quality control attributes, i.e., ethanolamine, for coffee in the Middle East region and have yet to be confirmed in other coffee specimens.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

Plasma metabolites were associated with spatial working memory in major depressive disorder

Major depressive disorder (MDD) is a common disease with both affective and cognitive disorders. Alterations in metabolic systems of MDD patients have been reported, but the underlying mechanisms still remains unclear. We sought to identify abnormal metabolites in MDD by metabolomics and to explore the association between differential metabolites and neurocognitive dysfunction.Plasma samples from 53 MDD patients and 83 sex-, gender-, BMI-matched healthy controls (HCs) were collected. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) system was then used to detect metabolites in those samples. Two different algorithms were applied to identify differential metabolites in 2 groups. Of the 136 participants, 35 MDD patients and 48 HCs had completed spatial working memory test. Spearman rank correlation coefficient was applied to explore the relationship between differential metabolites and working memory in these 2 groups.The top 5 metabolites which were found in sparse partial least squares-discriminant analysis (sPLS-DA) model and random forest (RF) model were the same, and significant difference was found in 3 metabolites between MDD and HCs, namely, gamma-glutamyl leucine, leucine-enkephalin, and valeric acid. In addition, MDD patients had higher scores in spatial working memory (SWM) between errors and total errors than HCs. Valeric acid was positively correlated with working memory in MDD group.Gamma-glutamyl leucine, leucine-enkephalin, and valeric acid were preliminarily proven to be decreased in MDD patients. In addition, MDD patients performed worse in working memory than HCs. Dysfunction in working memory of MDD individuals was associated with valeric acid.

Chemotypic variation of non-volatile constituents of Artemisia afra (African wormwood) from South Africa

Artemisia afra (African wormwood) is a popular medicinal plant of southern Africa and is an excellent candidate for commercialisation. This current study was aimed at exploring the phytochemistry and chemical variation of non-volatile compounds within wild populations of A. afra, and developing chromatographic quality control protocols for raw materials based on the identification of marker compounds. Chromatographic data, from samples representing 12 distinct populations, were obtained using liquid chromatography-mass spectrometry. An untargeted chemometric approach revealed three clusters. Marker compounds for each cluster, revealed through discriminant analysis, were isolated and identified using NMR spectroscopy, as acacetin (1) (Group 1), chrysoeriol (2) (Group 2), and 3,5-di-O-caffeoylquinic acid (3) and scopoletin (4) (Group 3). In addition, (3) and rutin (5), (both reported for the first time from A. afra), and (1), (2), (4) and 4-caffeoylquinic acid (6) were established as reliable markers for species identification, since they were abundant in most samples. Quantitative analysis using a validated method established (4) as the dominant compound in the samples (1080-19,600 μg/g dry weight (d.w.)), followed by (5) (49.5-2490 μg/g d.w.). A high performance thin layer chromatography (HPTLC) method was developed. The Rf values and colours of the bands corresponding to the marker compounds were recorded so that these compounds could be easily identified for quality control purposes. Multivariate analysis of the data using the rTLC online application confirmed the presence of different chemical groupings within the samples. It was deduced that quantitative, rather than qualitative differences, characterised the samples. Future research should focus on comparing the efficacy of the various chemical clusters in multi-target biological assays aligned to the traditional use of the plant.

UPLC-MS/MS-based molecular networking and NMR structural determination for the untargeted phytochemical characterization of the fruit of Crescentia cujete (Bignoniaceae)

The fruit pulp of Crescentia cujete is traditionally used in folk medicine for the treatment of a variety of respiratory conditions and gastrointestinal disorders. Due to the lack of a comprehensive phytochemical description of the fruit of this plant, its active compounds and rational quality control parameters have not yet been described. An untargeted metabolomics approach combining UPLC-MS/MS-based molecular networking with conventional isolation and NMR methods was carried out for the phytochemical profiling of the fruit pulp of Crescentia cujete. Sixty-six metabolites, including nine n-alkyl glycosides, twenty-three phenolic acid derivatives (such as cinnamoyl and benzoyl derivatives), fifteen flavonoids, four phenylethanoid derivatives and fifteen iridoid glycosides were identified at different levels of confirmation: eighteen confirmed structures (Level 1), six probable structures (Level 2) and forty two tentative candidates (Level 3). Among these, all four phenylethanoid derivatives were described for the first time within this species. In addition, 8-epi-eranthemoside, crescentiol A and crescentiol B were reported as three undescribed iridoid glucosides. The use of molecular networking has resulted in a detailed phytochemical overview of this species. This work provides a useful tool for further development and validation of appropriate analytical methods for routine quality control assessment of commercially available products containing the fruit of this species and further interpretation of their related pharmacological effects.

High-Resolution Magic Angle Spinning (HR-MAS) NMR-Based Fingerprints Determination in the Medicinal Plant Berberis laurina

Berberis laurina (Berberidaceae) is a well-known medicinal plant used in traditional medicine since ancient times; however, it is scarcely studied to a large-scale fingerprint. This work presents a broad-range fingerprints determination through high-resolution magical angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy, a well-established flexible analytical method and one of most powerful "omics" platforms. It had been intended to describe a large range of chemical compositions in all plant parts. Beyond that, HR-MAS NMR allowed the direct investigation of botanical material (leaves, stems, and roots) in their natural, unaltered states, preventing molecular changes. The study revealed 17 metabolites, including caffeic acid, and berberine, a remarkable alkaloid from the genus Berberis L. The metabolic pattern changes of the leaves in the course of time were found to be seasonally dependent, probably due to the variability of seasonal and environmental trends. This metabolites overview is of great importance in understanding plant (bio)chemistry and mediating plant survival and is influenceable by interacting environmental means. Moreover, the study will be helpful in medicinal purposes, health sciences, crop evaluations, and genetic and biotechnological research.

Metabolomic biomarkers of polycystic ovary syndrome related-obesity: a review of the literature

Background and objectives: Polycystic ovary syndrome (PCOS) displays a phenotype-dependent cardio-metabolic risk. By performing a systematic search of the literature, we aimed to summarize metabolomic signatures associated with obesity in PCOS women.

Data sources and study eligibility criteria: We conducted a comprehensive search including: Embase, PubMed, and Web of Science until 31st of May 2019. We used the terms: metabolomics and polycystic ovary syndrome. We excluded the following papers: animal studies, studies that included only lean PCOS women, reviews, meta-analyses, results of interventional studies, those that did not apply metabolomic techniques.

Results: The lipid signature in obese women with PCOS showed increased levels of free fatty acids (carnitine, adipic acid, linoleic acid, oleic acid) and lower levels of lysophosphatidylcholines and glycerolphosphocholine compared with non-obese PCOS women. Regarding carbohydrate metabolism, a decrease in citric and lactic acid levels characterized obese PCOS women. Decreased lactic acid in obese PCOS women suggests augmented insulin stimulated glucose muscle use in lean, but not in obese women. Considering amino acid metabolomic markers, valine, glycine, serine, threonine, isoleucine and lysine were higher in obese PCOS women. Patients with visceral obesity presented a diminished uptake of essential amino acids, BCAA, leucine and serine in the skeletal muscle. α-ketoglutarate was significantly higher in obese women with PCOS in comparison with lean women with PCOS, distinguishing these 2 subgroups of PCOS with high ‘predictive accuracy’.

Limitations: Overall, a small number of studies have focused on the impact of obesity on the metabolic fingerprints of PCOS women. There is need for properly controlled, high-quality studies.

Conclusions: There is compelling evidence of significant alterations in carbohydrate, lipid, and amino acid metabolism in women with PCOS and obesity. Metabolomics may identify new metabolic pathways involved in PCOS and improve our understanding of the complex relation between PCOS and obesity in order to personalize PCOS therapy.

Exploring Metabolic Signature of Protein Energy Wasting in Hemodialysis Patients

End-stage renal disease patients undergoing maintenance hemodialysis (HD) are vulnerable to the protein energy wasting (PEW) syndrome. Identification and diagnosis of PEW relies on clinical processes of judgment dependent on fulfilling multiple criteria drawn from serum biochemistry, weight status, predictive muscle mass, dietary energy and protein intakes. Therefore, we sought to explore the biomarkers’ signature with plasma metabolites of PEW by using ¹H-nuclear magnetic resonance for an untargeted metabolomics approach in the HD population, to understand metabolic alteration of PEW. In this case-controlled study, a total of 53 patients undergoing chronic HD were identified having PEW based on established diagnostic criteria and were age- and sex-matched with non-PEW (n = 53) HD patients. Fasting predialysis plasma samples were analyzed. Partial least square discriminant analysis demonstrated a significant separation between groups for specific metabolic pattern alterations. Further quantitative analysis showed that the level of 3-hydroxybutyrate, acetate, arabinose, maltose, ribose, sucrose and tartrate were significantly increased whilst creatinine was significantly decreased (all p < 0.05) in PEW subjects. Pathway analysis indicated that PEW-related metabolites reflected perturbations in fatty acid mechanism and induction of glyoxylate and dicarboxylate pathway attributed to gluconeogenesis. These results provide preliminary data in understanding metabolic alteration of PEW and corresponding abnormal metabolites that could potentially serve as biomarkers of PEW.

Simultaneous Quantitative Analysis of Q-Marker with One Single Reference in Glycyrrhiza uralensis Fisch

In traditional Chinese medicine (TCM) studies, it is difficult to choose evaluation markers for the strict quality control of herbs. A high performance liquid chromatography coupled with metabolomics for simultaneous quantitative analysis of quality markers (Q-markers) in Glycyrrhiza uralensis Fisch was established, which could not only ensure the quality and batch-to-batch consistency of TCMs, but also achieve a quantitative analysis of multi-components by the single reference standard. Based on the construction of chromatographic profiles by high performance liquid chromatography (HPLC) and HPLC-Q-Exactive/MS methods, different multivariate analyses were employed. Seven quantitative indices were selected as the Q-markers, and a reliable quantification method was established. The quantitative method was acceptable with good linearity with correlation coefficients >0.9993 and satisfactory repeatability (relative standard deviation (RSD) < 0.05%), precision (RSD < 0.24%), reproducibility (RSD < 0.97%), stability (RSD < 2.52%) and recoveries (96.96%-98.52%, RSD < 3.24%), and no significant differences were observed between the external standard method and the new method as determined by calculating standard method difference. Overall, the study suggests that the simultaneous quantitative analysis of main Q-marker in G. uralensis Fisch with one single marker can be considered good quality criteria for performing quality control of G. uralensis Fisch.

Identifying novel treeline biomarkers in lake sediments using an untargeted screening approach

Paleolimnology uses sedimentary biomarkers as proxies to reconstruct long-term changes in environmental conditions from lake sediment cores. This work describes an untargeted metabolomics-based approach and uniquely applies it to the field of paleolimnology to identify novel sediment biomarkers to track long-term patterns in treeline dynamics. We identified new potential biomarkers across the Canadian northern Arctic, non-alpine, treeline using high-resolution accurate mass spectrometry, and pattern recognition analysis. This method was applied to 120 sediment core extracts from 14 boreal, 25 forest-tundra, and 21 tundra lakes to assess long-term fluctuations in treeline position. High resolution accurate mass spectrometry resolved many compounds from complex mixtures with low mass accuracy errors. This generated a large dataset that required metabolomics styled statistical analyses to identify potential biomarkers. In total, 29 potential biomarkers discriminated between boreal and tundra lakes. Tetrapyrrole-type phorbides and squalene derivatives dominated in boreal regions, while biohopane-type lipids were in the tundra regions. Tetrapyrroles were in both surface and subsurface sediments of boreal lakes indicating these compounds can survive long-term burial in sediments. At the ecozone level, tetrapyrroles were more abundant in boreal Taiga Shield, and Taiga Plains. Boreal plant extracts belonging to Pinaceae and Ericaceae also contained tetrapyrroles. Squalene derivatives demonstrated long-term preservation, but wider distribution than tetrapyrroles. Hopanoids were present in tundra and forest-tundra lake regions, specifically the Low Arctic and Taiga Shield, and were absent in all boreal lake sediments. Herein, we describe a method that can systematically identify new paleolimnological biomarkers. Novel biomarkers would facilitate multi-proxy paleolimnological studies and potentially lead to more accurate paleoenvironmental reconstructions.

Integrated analytical assets aid botanical authenticity and adulteration management

This article reviews and develops a perspective for the meaning of authenticity in the context of quality assessment of botanical materials and the challenges associated with discerning adulterations vs. contaminations vs. impurities. Authentic botanicals are by definition non-adulterated, a mutually exclusive relationship that is confirmed through the application of a multilayered set of analytical methods designed to validate the (chemo)taxonomic identity of a botanical and certify that it is devoid of any adulteration. In practice, the ever-increasing sophistication in the process of intentional adulteration, as well as the growing number of botanicals entering the market, altogether necessitate a constant adaptation and reinforcement of authentication methods with new approaches, especially new technologies. This article summarizes the set of analytical methods - classical and contemporary - that can be employed in the authentication of botanicals. Particular emphasis is placed on the application of untargeted metabolomics and chemometrics. An NMR-based untargeted metabolomic model is proposed as a rapid, systematic, and complementary screening for the discrimination of authentic vs. potentially adulterated botanicals. Such analytical model can help advance the evaluation of botanical integrity in natural product research.

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.