Key words
plant metabonomics - large-scale production - discrimination - processing - pharmaceutics
Abbreviations
TCM:
traditional Chinese medicine
CE:
capillary electrophoresis
MVDA:
multivariate data analysis
PCA:
principal component analysis
HCA:
hierarchical cluster analysis
PLS:
partial least squares
OSC:
orthogonal signal correction
OPLS:
orthogonal partial least squares
O2PLS:
bidirectional orthogonal partial least squares
SIMCA:
soft independent modeling of class analogy
PLS-DA:
partial least squares discriminant analysis
kNN:
k nearest neighbors
ANN:
artificial neural networks
MEND:
matched filtration with experimental noise determination
AFLP:
amplified fragment length polymorphism
TOF:
time of flight
UPLC-QTOF-MS:
ultra performance liquid chromatography quadrupole time of flight high definition
mass spectrometry
BP-ANN:
back propagation artificial neural network
ELSD:
evaporative light scattering detector
HPTLC:
high performance thin layer chromatography
DART:
direct analysis in real time
PAD:
photodiode array detector
LS-SVM:
least squares support vector machine
RBF:
radial basis function
NIRS:
near-infrared spectroscopy
WG:
white ginseng
RG:
red ginseng
LTQ:
linear trap quadrupole
LDA:
linear discriminant analysis
SA:
similarity analysis
COW:
correlation optimized warping
UPGMA:
unweighted pair group method with arithmetic mean
FSMWEFA:
fixed size moving window-evolving factor analysis
HELP:
heuristic evolving latent projection
LA:
licochalcone A
RRLC:
rapid resolution liquid chromatography
UFLC:
ultrafast liquid chromatography
MAS-NMR:
magic angle spinning nuclear magnetic resonance
IOP:
iterative optimization procedure
SFA:
subwindow factor analysis
OPR:
orthogonal projection resolution
EWOP:
evolving window orthogonal projection
5-GGMF:
5-(α-D-glucopyranosyl-(1–6)-α-D-glucopyranosyloxymethyl)-2-furancarboxaldehyde
NMR:
nuclear magnetic resonance spectroscopy
MS:
mass spectrometry
TOF:
time of flight
UPLC-QTOFMS:
ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry
UPLC–QTOF–HDMS:
ultra-performance liquid chromatography-quadrupole time-of-flight high-definition
mass spectrometry
UPLC-PAD:
ultra-performance liquid chromatography with photodiode array detector
LC-LTQ-Orbitrap:
LC coupled with ESI hybrid linear trap quadrupole orbitrap
Introduction
Metabonomics is an emerging subject of the post-genome era, which, together with
genomics, transcriptomics, and proteomics, jointly constitutes the “Systems Biology”
[1]. It is the branch of science concerned with the
quantitative understanding of the metabolite components of integrated living systems
and their dynamic responses to changes in both endogenous (such as those associated
with physiology and development) and exogenous factors (such as environmental
factors and xenobiotics) [2]. The success of the
application of metabonomics has been illustrated in the literature from the
perspective of the diagnosis of diseases such as diabetes [3], hypertension [4], and cancers [5], [6]. In recent years, a
wide range of analytical metabonomic techniques have been implemented in research
addressing TCM whose qualitative analysis is difficult because of the complexity and
diversity of its components. In general, one or two biomarkers are used for
identification and authentication of the herbal products. However, this approach
does not provide information on the overall chemical composition of the plant
extract, which is known to vary widely according to geographical origin, source,
cultivar condition, harvesting and processing methods, and storage. Metabonomics,
through achieving complete scans, addresses the shortfalls of single-component
analysis. The rapid development of analytical instruments is accelerating research
on TCM [7]. Additionally, multivariate statistical
methods are increasingly improving, allowing the implementation of robust solutions
[8].
Within the TCM practice, the majority of species used are plants. The multi-varieties
employed in TCM are the main cause of confusion in the herbal medicine market. The
identification of these varieties, as the first step in the production of Chinese
medicinal preparations, is of great significance for ensuring the safety and
effectiveness of clinical treatment. The quality and contents of the active
components of herbs are highly variable depending on the species, parts of the
herbs, cultivated geographic region, and planting period involved. Adulterants
should be distinguishable from plant material and play the role of challenging
substances. The processing of a characteristic portion, as the second step in
production, appears to be of significance in clinical applications and has been
proven to satisfy the requirements of therapeutics. It is essential to unify the
degree of processing. In pharmaceutical production, extracts are commonly used.
Metabonomics can be effectively applied for the quality control of plant
extracts.
Identifying the plant material, processing, and pharmaceutical production is the
sequence of manufacture for Chinese medicinal preparations. Here we demonstrate the
application of metabonomics in the discrimination of TCM species, TCM production
processes, and quality control. To avoid ambiguities, we also illustrated the
factors affecting the identification step, the methods used in processing, and the
forms of the pharmaceuticals.
Analytical Techniques
In recent years, many metabonomic-based methods have been implemented to facilitate
research in the field of TCM. In the pharmacopeia, single-component analyses are
employed in most research addressing TCM. Nevertheless, the lack of
representativeness of single-component analyses seems to account for a deficiency
of
convincing data. Metabonomics, through achieving complete scans, addresses the
shortfalls of single-component analysis.
The rapid development of analytical instruments is accelerating research on TCM [7]. Metabonomics measures the multi-parametric response
of biological systems to a stimulus, typically employing analytical technologies
such as NMR or MS to obtain comprehensive profiling and comparison of metabolic
“fingerprints” [9]. In addition, other chemical
analytical equipments and techniques, such as UV and IR spectroscopy were also
employed. For biomarker identification, it is also possible to separate out
substances of interest on a larger scale from a complex biological system using
techniques such as LC, multidimensional liquid separation systems, GC, and CE.
Especially multidimensional liquid separation systems have the potential to become
a
powerful approach for enriching, separating, and quantifying a large variety of
exogenous and endogenous compounds in complex biological samples and TCM
preparations, with a powerful separation ability, high resolution and sensitivity,
high-peak capacity, and excellent detection in comparison with one-dimensional HPLC.
However, every analytical technique has its advantages and drawbacks, as shown in
[Table 1]. Multi-analysis techniques can partially
overcome the shortcomings of individual analytical techniques. It is believed that
with the further development of metabonomics analysis techniques, especially those
employing multi-analysis, metabonomics will strongly promote TCM research and be
beneficial to its modernization in terms of extending the application of modern
methods in the assessment of TCM safety, assisting in the formulation of TCM safety
norms, and establishing international standards [7].
Table 1 Comparison of analytical techniques.
|
|
Advantages and problems
|
|
LC [7], [96], [97], [98], [99]
|
|
|
NMR [100]
|
-
Noninvasive and nondestructive for samples
-
Quantitative and simultaneous detection unbiased for any
molecules
-
High throughput
-
Produces rich, dynamic molecular information
-
Requires little or no sample preparation
-
Good resolution and reproducibility
|
|
GC [7], [101]
|
-
High sensitive detection for almost both volatile
chemical and nonvolatile compounds
-
Has more peak capacity and can accommodate more complex
mixtures
-
Unsuitable for nonvolatile and thermally unstable
compounds
|
|
CE and HPCE [7], [102]
|
-
High speed and short analysis time
-
Less sample and solvent consumption
-
Appropriate for complex samples
-
Lower operating cost
-
Lower sensitivity than HPLC
|
|
MS [15], [103], [104]
|
-
Realize identification and quantification of volatile and
thermally stable components
-
Used for ionization of polar to nonpolar components
-
Associates with LC overcoming problems
-
Being destructive
-
Requiring preknowledge about samples
-
High recurrent expenditures
|
|
UV [105]
|
|
|
IR [106], [107], [108]
|
|
Data Processing Methods
The progress of metabonomics research will be illustrated. First, the proposed TCM
component should be extracted. Second, analytical tools should be applied. Third,
the chemical profile should be obtained. Combining data under a multivariate data
analysis, validating models, molding, and applying diagnostic tools are the
consecutive steps [10] supported by chemometrics and
mathematical statistics.
Chemometrics are basically classified into two main categories: pattern recognition
methods (unsupervised and supervised), when a qualitative evaluation is involved,
and multivariate calibration for quantitative purposes. Data resulting from
metabonomics-based work are typically high-dimensional data, requiring MVDA methods
for interpretation. Most metabonomics data analysis methods are based on the
classification of samples into different groups (e.g., by treatment or genotype),
both via supervised (e.g., discriminant function analysis or artificial neural
networks) and unsupervised data analysis methods (e.g., PCA or HCA) [11]. It is also possible to use MVDA to conduct
regression modeling between two blocks of data, usually denoted as X and Y. In
metabonomics-based NP studies, X may represent signals from different metabolites
present in plant extracts sampled at regular time intervals, while Y represents
responses (e.g., the quality of product, bioactivity, or yield). The model then can
be used to predict Y from X, which is achieved through new observations. The most
common MVDA method employed for this type of modeling is the PLS method [12]. Recently, OSC, OPLS, and O2PLS were utilized [13], [14]. The specific
methods include SIMCA, PLS-DA, kNN, and ANN. Moreover, PCA and HCA are widely used
in metabonomics research.
Model validation consists of cross validation, permutation testing, and external
validation [10]. There are two significant procedures:
noise filtration and peak matching [15]. Nonlinear
noise filtration is extensively employed, substituting a point with the average of
the surrounding points so as to filter noise successfully [16]. Matched filtration is another method setting up a standard mode of a
peak and comparing its width; a narrower peak is regarded as noise [17]. Andreev et al. [18]
developed MEND, improving the identification function. As for peak matching,
identifying the retention time of the internal standard substance under the same
conditions is the main method employed.
The main diagnostic tools applied include score plots, loading plots, VIP, DModX,
and
regression coefficients [10]. MetExtract, a new
software tool for the automated comprehensive extraction of metabolite-derived LC/MS
signals in metabonomics research, was recently reported in the literature [19].
Bioactivity and Profiles
The main research methods of metabonomics are metabolomic fingerprinting and
metabolomic profiling analysis. Metabolomic profiling can be divided into two parts,
targeted and nontargeted metabolic profiling analysis.
The targeted metabolic profiling aims to search one biomarker. Several components
were usually chosen as marker compounds to assess the quality. These biomarkers were
proved to be constituents that discriminate the different species, different parts,
different cultivated geographic regions, different planting periods, and the
processing products. In the present paper, the bioactivities of the biomarkers were
obtained via PubChem (http://www.ncbi.nlm.nih.gov/pccompound) and related
literatures.
However, these few selected markers sometimes are not unique to a particular herb
since they might be present in many plants belonging to various families. In
addition, the selection of suitable markers is sometimes difficult and subjective.
Furthermore, adulterators are continuously trying to develop ways to make their
productsʼ chemical profile similar to the authentic medicinal herbal product. Under
these specific circumstances, the marker approach, on the one hand, is unable to
confirm the identity of a specific plant. On the other hand, the influences of the
other inner chemicals present may be ignored. Therefore, in some cases, its use may
be inappropriate for quality control purposes [20], [21]. The objective of “nontargeted”
analysis is to describe metabolic events by determining all detectable metabolites
[22]. Of the various profiling techniques,
nontargeted analysis using UPLC–MS is a promising tool for investigating the
diversity of phytochemicals [23]. Thus, it is believed
that nontargeted metabolic profiling analysis will play an important role as an
effective tool in terms of high-throughput elucidation of metabolic phenotypes.
Identification of Traditional Chinese Medicine Components
Identification of Traditional Chinese Medicine Components
The identification of traditional Chinese medicine components plays a key role in
ensuring the safety and effectiveness of clinical treatments. The quality and
contents of the active constituents in herbs are highly variable depending on the
species, parts of the plant, cultivated geographic region, and planting period
involved. Adulterants are assumed to be distinguishable from plant material and play
the role of challenging substances. Therefore, during large-scale production, the
identification of these components is of vital significance. Moreover, many
applications employed in the development of metabolic fingerprinting, which will be
explained below, using appropriate analysis methods coupled with multivariate
analysis, have been investigated and applied to discriminate between closely related
plant species in performing quality control assessments of herbal drugs and to
identify their different geographic origins. In addition, analyzing components is
a
robust way to control plant quality.
Here, the identification of TCM components can be divided into four categories: the
various species and adulterants, the different parts of herbs, the planting period
and the cultivated geographic region.
Identification of species and adulterants
Different species may contain approximately the same components, while the
contents of these components vary, which influence the therapeutic effect. Two
leguminous plants, Astragalus membranaceus (Fisch.) Bge. var.
mongholicus (bge.) Hsiao and Astragalus membranaceus (Fish.)
Bge, are important medical herbs that share great similarities regarding their
morphology, chemical constituents, and genomic DNA sequences. The identification
of different medicinal species directly affects their pharmacological and
clinical effects. Amplified AFLP-based genetic fingerprinting and
GC–TOF/MS-based metabolic fingerprinting were used to successfully discriminate
between the two species. The differences in some soluble sugars, fatty acids,
proline, and polyamine reflected the plantsʼ adaptation to different growth
environments. Using multivariate and univariate statistical analyses, three AFLP
markers and eight metabolites were identified as candidate DNA and metabolic
markers to distinguish between the two herb materials [24]. In another study, metabolite profiling of five medicinal
Panax herbs, which included P. ginseng (Chinese ginseng),
P. notoginseng, P. japonicus, P. quinquefolium L., and P.
ginseng (Korean ginseng), was performed using UPLC-QTOFMS and
multivariate statistical analysis techniques. PCA of the analytical data showed
that the five Panax herbs could be separated into five different groups
of phytochemicals [25]. HPLC fingerprinting was
used for comparison of three closely related species of Pericarpium Citri
(Citrus reticulata ‘Chachi’, Citrus reticulata ‘Dahongpao’, and
Citrus erythrosa Tanaka), and PLS-DA identified hesperidin, tangeretin,
and nobiletin as potential biomarkers for their classification [26]. In a similar case, PCA and HCA as well as SIMCA
and a BP-ANN were applied to identify and distinguish Epimedium
wushanense and Epimedium koreanum based on their secondary
metabolites. The SIMCA method failed to identify one sample, whereas BP-ANN
precisely predicted the whole test set [27]. PCA
was able to discriminate between ten Aristolochia species on the basis of
their essential oil profiles, showing that 2 h of hydrodistillation produce the
best outcome when the oils are used for discriminating between species [28]. Coincidentally, Sun et al. [29] drew on the same plant. In the Chinese
Pharmacopoeia 2010, only two Aconitum species are recorded. One is the
root of Aconitum kusnezoffii Reichb., namely “Caowu” in Chinese. The
other species was Aconitum carmichaelii Debx. Two herbal drugs are
derived from this species. The two species were distinguished successfully using
UPLC–QTOF–HDMS, combining with PCA and S-plot. Moreover, a PCA score plot
clearly demonstrated discrimination between Artemisia annua and
Artemisia afra on the basis of phenylpropanoids (caffeic acid,
chlorogenic acid, dicaffeoyl quinic acid, and ferulic acid) [30]. Spectral fingerprinting via NIR has been
utilized for the rapid identification and counterfeit detection of
Eleutherococcus senticosus, and PCA, DA, SIMCA, and PLS-DA were found
to allow good discrimination between E. senticosus and other herbs both
related to and not related to the Araliaceae family [31]. PCA has been successfully applied for distinguishing Angelica
sinensis from related Apiaceae (syn. Umbelliferae) herbs based on
complete HPLC fingerprints [32]. The same
biomarkers were recognized by PLS-DA for the discrimination of authentic
Pericarpium Citri from commercial samples, mixed peel samples, and other citrus
peels [33]. 1H-NMR spectroscopy and
multivariate data analyses were applied to discriminate two Bupleurum
species (B. chinense and B. scorzonerifolium) and to explore the
influences of habitat and culture methods on the quality of Radix Bupleuri
plants based on their metabonomic profiles [34].
The quality of Radix Bupleuri plants was evaluated via HPLC-ELSD analysis and
HPTLC based on analysis of their principal bioactive components (saikosaponins).
The acquired data were processed using ANNs and kNN to distinguish between
different species of the genus [35]. DART-MS
provides a novel mass spectrometric ion source by producing [M + H]+
molecular ion species. In analyses of Glycyrrhiza inflata Batalin, the
peak at m/z 339 originates mainly from the [M + H]+ of LA, a
species-specific compound. These results indicate that G. inflata can be
differentiated from the other two species based on detection of LA peaks using
DART-MS analysis [36]. In addition, chromatographic
fingerprinting via GC-MS coupled with SA and PCA has been undertaken for
discriminating Scutellaria barbata D. Don from adulterants. The results
showed that the samples could be identified based on differences between the
samples and various adulterants [37]. Similarity
analysis and HCA were applied for the first time to identify and distinguish
genuine Aconitum kusnezoffii from its adulterants, which demonstrated the
feasibility of linking the HCA approach to chemotaxonomic analysis on the basis
of the presence of alkaloids [38]. To discriminate
and assess the quality of Curcuma phaeocaulis, C. kwangsiensis, and C.
wenyujin from different ecotypes, a metabonomics analysis was carried
out via GC‐MS coupled with multivariate statistical analysis. Characterization
of phytochemicals in essential oils was performed by automated matching to the
MS library and comparison of their mass spectra, which discriminated among the
different plant parts [39]. Curcuma plants,
such as Curcuma wenyujin Y. H. Chen et C. Ling and Curcuma longa
L., were also distinguished successfully via HPLC-DAD-MS [40].
Identification of different medicinal parts of herbs
The choice of different parts of herbs determines the resulting curative effect,
which is the purpose of therapy. The contents of the active components of
diverse plant parts were identified. In the long history of the use of medicinal
plant preparations, different plant parts have been regarded as different drugs.
R. Jurišić Grubešić et al. [41] identified the
variation in total polyphenol contents, employing Folin–Ciocalteuʼs reagent,
between different parts of Plantago plants (leaves: up to 10.15 %; stems:
up to 4.34 %; and flowers: up to 5.56 %). The content of tannins in stems ranged
from 0.28 % to 1.00 %, while leaves and flowers contained tannins at
concentrations of 2.26 % and 2.21 % based on UV–Vis spectrophotometry.
Metabolite profiling of different parts of Panax notoginseng was carried
out using UPLC–ESI-MS and multivariate statistical analysis. PCA of the
UPLC–ESI-MS data showed a clear separation of the compositions among the flower
buds, roots, and rhizomes of P. notoginseng. The saponins accounting for
these variations were identified based on corresponding loading weights and were
further verified based on the accurate mass, tandem mass, and retention time of
available standard saponins using UPLC–QTOF-MS [42]. Moreover, each extract from 24 mulberry leaf samples, divided into
six locations from the tip of the stem in each of four strains, was analyzed via
pattern recognition methods, including PCA and SIMCA. The 24 extracts from
mulberry leaves showed independent spectra in 1H-NMR analyses [43]. Aconitum carmichaelii Debx., another
example for the application of plant metabonomics in the discrimination of
different parts of herb plants, was studied by Sun et al. [29]. The mother root is named “Chuanwu”, while the
daughter or lateral root of Aconitum carmichaelii Debx. is known as
“Shengfuzi”. Shengfuzi has been prescribed more frequently than Chuanwu to treat
rheumatic diseases. The analytical techniques, UPLC–Q-TOF–HDMS, as well as the
data processing methods, PCA, and S-plot, were the main measures in this
study.
Differentiation of distinct cultivated geographic regions
The environments of the cultivated geographic regions of medicinal plants,
including their temperature, humidity, soil, and climate, are determinant
factors. Therefore, the cultivated geographic region influences the growth of
herbs. Wei-Jun Kong et al. [44] utilized UPLC-PAD
analysis to examine the five active alkaloids in Rhizoma Coptidis Chinensis,
successfully grouping the plants in accordance with their province of origin.
Moreover, the LS-SVM, RBF-ANN, PLS-DA, and kNN methods were applied for the
classification of Rhizoma Corydalis, and in general, no statistically
significant differences were found between these four methods. NIRS was used to
identify Rhizoma Corydalis plants from two different geographical origins [45]. Another example of the application of the HCA
technique was its use for the classification of Isatis indigotica roots
collected from different regions based on HPLC fingerprinting [46]. Ganoderma lucidum samples from different
cultivated geographic regions were evaluated using HPLC fingerprinting. The HCA,
PCA, PLS-DA, and SIMCA techniques were employed to classify samples in
accordance with their province of origin [47]. In
addition, the essential oils of the Cinnamon Cortex specimens obtained from
different localities have been analyzed via GC-MS [48]. Furthermore, the volatile oils of Artemisia capillaris Herba
from different locations were investigated through GC-MS to develop a
characteristic fingerprint of this raw herb [49].
The discrimination of Schizonepeta tenuifolia Briq. from different
origins has also been achieved via PCA and HCA, which classified the samples
into two main groups on the basis of five marker compounds [50]. It is also worth noting that the combination of
NIR spectroscopy with DA and PLS-DA analysis was applied in geographical origin
discrimination for Radix Scutellaria Baicalensis [51]. A nontargeted procedure was applied for 1H-NMR
spectroscopic fingerprinting of extracts from Rhodiola rosea rhizomes for
pattern recognition analysis and identification of secondary metabolites
responsible for differences in sample composition. For this purpose, plants from
three different geographic areas (the Swiss Alps, Finland, and the Altai region
in Siberia) were investigated [52]. Furthermore,
quantitative estimates of the major isoflavones in Pueraria lobata were
produced, and the studied samples were classified through PCA based on the
amounts of puerarin, daidzin, daidzein, and genistin present [53]. Metabolite fingerprinting was applied in an
attempt to evaluate the quality of dried Angelica acutiloba roots. An
enhanced understanding of the dominance of the relationship of the cultivation
area with the evaluated quality was conceptualized and applied to the
construction of a PLS-DA classification model, which provided the basis for
accurate and reliable predictivity [54].
Additionally, PCA was performed using the data generated through HPLC-DAD-ELSD
analysis for quality control of Polygala japonica from different
localities in China [55]. Recently, Suzuki et al.
[56] classified Sophora flavescens grown
in Japan and China via NMR.
Differentiation of distinct planting periods
The planting period is also a vital factor in the quality of a crude drug due to
the duration over which a plant obtains nutrition from the soil.
An UPLC-Q-TOF-MS-based metabolomic technique was applied for metabolite profiling
in 60 Panax ginseng samples aged from 1 to 6 years [57]. Ginseng is an important herbal resource
worldwide, and adulteration or falsification of the cultivation age has been a
serious problem for ginseng in the commercial market. In this study, ginseng
roots cultivated for 2–6 years under good agricultural practices standard
guidelines were analyzed via NMR-based metabolomics techniques using two
solvents [58]. Moreover, it has been demonstrated
that July might be the best harvest time for Pericarpium Citri Reticulatae
Viride, while November and December are better for Pericarpium Citri
Reticulatae. Furthermore, hesperidin, nobiletin, and tangeretin were
screened as chemical markers based on PCA loadings. The HPLC–HELP–PCA strategy
has shown potential in the optimization of harvest times [26]. Recently, Xue et al. [59] utilized
GC-MS to investigate the flower buds of Tussilago farfara in different
development stages.
Collectively, medicinal herbs from different species and the different parts of
the same plant usually exhibit different efficacy, pharmacological actions, and
clinical indications due to the significant differences in the types and
quantity of the constituents. The species diversity seems to be a significant
factor to influence the quality assessment. In addition, chemical constituents
of the same plant may be various due to different cultivation areas, climatic
conditions, and cultivation ages. For example, ginseng of cultivation ages from
4 to 6 years is the most demanded ginseng in the market. However, age and
cultivation areas can hardly be determined by the herbʼs physical appearance
alone. Accordingly, confused clinical application led to the consumption of
incorrect forms of plant material, improper use, and undesirable effects. Hence,
an effective method applied in quality control is urgently demanded for the
identification step of medicinal herbs. Since some samples share similarities in
morphology but with subtle variations in certain ingredients, metabonomics can
provide a platform to use analytical techniques coupled with multivariate
statistics for the differentiation of these complex samples. Metabonomics
information not only assist in the establishment of a deeper understanding of
the complex interactive nature of plant metabolic networks and their responses
to environmental change but also provide unique insights into the fundamental
nature of plant phenotypes in relation to development, physiology, tissue
identity, resistance, biodiversity, and so on. To make them clear, the plant
material, analysis technique, chemometrics methods, biomarkers, and bioactivity
aspects are summarized in [Table 2].
Table 2 Application of plant metabonomics in
TCM.
|
TCM materials
|
Purpose
|
Analysis techniques
|
Chemometrics methods
|
Biomarkers
|
Bioactivity
|
Ref.
|
|
* Activity obtained from PubChem
(http://www.ncbi.nlm.nih.gov/pccompound). A1: Identification
of species and adulterants; A2: identification of different
medicinal parts of herbs; A3: differentiation of distinct
cultivated geographic regions; A4: differentiation of
distinct planting periods; B: processing
|
|
Two Astragalus plants
|
A1
|
GC-TOF/MS
|
AFLP
|
Three AFLP markers, eight metabolites
|
Antiperspirant and antidiuretic
|
[24]
|
|
Five Panax plants
|
A1
|
UPLC-QTOF-MS
|
PCA
|
Ginsenoside Rf, Rb, Rb2, 20(s)-pseudoginsenosidde, F11
|
Antineoplastic, hypolipidemic agents*
|
[25]
|
|
Panax notoginseng
|
A2
|
UPLC-QTOF-MS
|
PCA
|
Saponins
|
Prevention and treatment of cerebrovascular diseases, immune
regulation, heap to protection, anticarcinogenesis,
neuroprotective effect
|
[42]
|
|
Panax ginseng
|
A4
|
UPLC-QTOF-MS
|
PCAHCA RF PAM PLS-DA
|
\
|
\
|
[57]
|
|
Panax ginseng
|
B
|
UPLC-QTOF-MS
|
PCA
|
\
|
\
|
[62]
|
|
Panax notoginseng
|
B
|
UPLC-QTOF-MS
|
PCA, PLS-DA
|
\
|
\
|
[76]
|
|
Panax ginseng
|
A4
|
NMR
|
PCA, PLS-DA
|
Amino acids, organic acids, sugars
|
Cardiovascular control of blood pressure
|
[58]
|
|
Three tangerine peels
|
A1, 4
|
HPLC-DAD
|
PCA, HELP
|
Hesperidin, tangeretin, nobiletin
|
Protective effect on myocardial ischemia
|
[26]
|
|
Mallotus plants
|
A1, 2, 3
|
LC-MS
|
PLS-DA
|
Senkyunolide A
|
Antioxidants
|
[33], [109]
|
|
Two Epimedium plants
|
A1
|
HPLC
|
PCA,HCA,SIMCA,BP-ANN
|
Flavonoids
|
Influence on sexual function, anti-aging, effect on immune
system, anti-inflammatory, antitussive, expectorant,
antiasthma
|
[27]
|
|
Rhizoma Coptidis
|
A3
|
UPLC
|
SA, HCA, PCA
|
Berberine, coptisine, palmatine, jateorrhizine,
epiberberine
|
Efficacy of suppressing fever, dispelling dampness, removing
toxicosis and anti-microbes*
|
[44]
|
|
Five Bupleurum plants
|
A1
|
1H-NMR
HPLC-ELSD
HPTLC
|
ANNs, kNN
|
Saikosaponins
|
Anti-inflammatory, antineoplastic, immunosuppressive
agents*
|
[34]
[35]
|
|
Ten Aristolochia plants
|
A1
|
GC-MS
|
PCA
|
Essential oils
|
Abortifacients, stomachics, anti-ophidians, antiasthmatics,
expectorants, slimming therapies
|
[28]
|
|
Two Artemisia plants
|
A1
|
NMR
|
PCA
|
Polar components
|
Antiplasmodial
|
[30]
|
|
Artemisia capillaris herba
|
A3
|
GC-MS
|
EWOP, FSMWEFA
|
Essential oils
|
Choleretic, anti-inflammatory and diuretic agent in the
treatment of epidemic hepatitis
|
[49]
|
|
Three Curcuma plants
|
A1
|
GC-MS
|
PCA, PLS-DA
|
Essential oils
|
Against skin diseases, colic inflammatory disorders, insect
repellants, antimicrobial
|
[39]
|
|
Two Curcuma plants
|
A1
|
HPLC-DAD-MS, GC-MS
|
PCA
|
Curcumin, demethoxycurcumin, bisdemethoxycurcumin,
dihydrocurcumin, ar-turmerone, α,β-turmerone,
zingiberene
|
Against skin diseases, colic inflammatory disorders, insect
repellants, antimicrobial, antidiabetic medications
|
[40]
|
|
Rhizoma Corydalis
|
A3
|
NIRS
|
WT, LS-SVM, PLS-DA, KNN
|
\
|
\
|
[45]
|
|
Eleutherococcus senticosus
|
A1
|
NIRS
|
PCA, DA, SIMCA and PLS-DA
|
\
|
\
|
[31]
|
|
Angelica sinensis
|
A1
|
HPLC
|
PCA
|
Senkyunolide A
|
Treatment of gynecological diseases
|
[32]
|
|
Plantago L.
|
A2
|
UV
|
UPGMA, PCA
|
Polyphenols, tannins
|
Diuretic
|
[41]
|
|
Three Glycyrrhiza plants
|
A1
|
DART-MS
|
\
|
Licochalcone A
|
Antimicrobial activity, antiplasmodial activity,
antileishmanial activity*
|
[36]
|
|
Mulberry leaf
|
A3
|
1H-NMR
|
PCA, SIMCA
|
\
|
\
|
[43]
|
|
Isatis indigotica
|
A3
|
RP-HPLC
|
HCA
|
Indirubin, indigotin
|
Anti-inflammatory, inhibition of leucine-rich repeat
kinase-2, proliferative and androgenic effects*
|
[46]
|
|
Cortex Cinnamomi
|
A3
|
GC-MS
|
IOP, HELP, SFA, OPR
|
Essential oils
|
Antimicrobial activities
|
[48], [110]
|
|
Ganoderma lucidum
|
A3
|
NIRS
|
HCA, PCA, PLS-DA, SIMCA
|
Triterpenoidsaponins, polysaccharides
|
Inhibitors of the in vitro human recombinant aldose
reductase*
|
[47]
|
|
Schizonepeta tenuifolia Briq.
|
A3
|
GC-MS
|
PCA,HCA
|
2-Hydroxy-2-isopropenyl-5-methylc, cis-pulegone
oxide,menthone, pulegone, cyclohexanone, schizonal
|
Antifungal properties, decrease in ambulation, and increase
in pentobarbital-induced sleeping time
|
[50], [111]
|
|
Scutellaria barbata D. Don
|
A1
|
GC-MS
|
SA, PCA
|
86 Compounds
|
Antimicrobial, protecting liver and biliary
|
[37]
|
|
Radix Scutellaria Baicalensis
|
A3
|
NIR
|
DA, S-DA
|
\
|
\
|
[51]
|
|
Rhodiola rosea rhizomes
|
A3
|
1H-NMR
|
PCA
|
Salidroside,rosavin
|
Protective effects on LPS-induced acute lung injury*
|
[52]
|
|
Pueraria lobata
|
A3
|
RRLC
|
PCA
|
Isoflavonoids
|
Antioxidant, estrogen-like effect, and treatment of
osteoporosis
|
[53], [112]
[113]
|
|
Angelica acutiloba
|
A3
|
Pyrolyser-coupled (PY-GC-MS)
|
PCA, PLS-DA
|
\
|
\
|
[54]
|
|
Polygala japonica
|
A3
|
HPLC-DAD-ELSD
|
PCA, COW
|
\
|
\
|
[55]
|
|
Sophora flavescens
|
A3
|
NMR
|
PCA
|
Kurarinol
|
Relative inhibition or inhibition of phosphodiesterase
3,4,5*
|
[56]
|
|
Tussilago farfara
|
A4
|
GC-MS
|
PCA
|
Fifty-four metabolites were identified, including 35 polar
metabolites and 19 nonpolar compounds
|
\
|
[59]
|
|
Atractylis chinensis DC
|
B
|
HPLC
|
PCA, kNN, LDA
|
\
|
\
|
[81]
|
|
Aconitum kusnezoffii
|
A1
|
HPLC
|
HCA
|
Mesaconitine, aconitine, hypaconitine
|
Voltage-gated sodium channel agonists, immunologic*
|
[38]
|
|
Fuzi
|
B
|
UPLC-Q-TOF-HDMS
|
PCA, PLS-DA, OPLS-DA
|
Aconitine, mesaconitine, hypaconitine, deoxyaconitine,
10-OH-mesaconitine
|
Voltage-gated sodium channel agonists, immunologic*
|
[61]
|
|
Two Carmichaelii plants
|
A1, B
|
UPLC-Q-TOF-HDMS
|
PCA, S-plot
|
22 Types of alkaloids
|
Voltage-gated sodium channel agonists, immunologic*
|
[29]
|
|
Radix Rehmanniae
|
B
|
UHPLC-TOFMS
|
PCA, OPLS-DA
|
Leonurideor, its isomer, 5-GGMF
|
Antitumor, antidiabetic, neuroprotective*
|
[77]
|
|
Ligustrum lucidum
|
B
|
UPLC-QTOF-MS
|
PCA
|
Ligustaloside B
|
Antidiabetic, antioxidant
|
[78], [114]
|
|
Fructus Xanthii
|
B
|
GC-MS
|
PCA
|
14 Polar compounds
|
Treatment of cramping and numbness of the limbs, ulcer,
sinusitis, catarrhs and pruritus
|
[80]
|
Processing
Processing is the second step in the production of Chinese medicinal preparations.
TCM-specific production steps include storing, washing, rinsing, drying,
remoistening, and cutting, and eventually, unique processing techniques, such as
stir frying, steaming, or calcining are performed to satisfy different clinical
therapeutic requirements. First, the effect of processing is thought to enhance the
therapeutic efficiency in Sophora japonica L. [60]. Drug processing can also weaken the structure of plants so that the
active components can be extracted easily. Additionally, additives react with the
compounds present in plants generating new components dissolved in solvents. For
instance, alkaloids dissolve in acidic solvents, so vinegar is widely applied to
crude drugs to enrich alkaline substances. Second, processing has been reported to
reduce the toxicity of the crude drugs, as described with Fuzi [61]. As evidence has accumulated, it has been shown that
poisonous protein is one of the causes of accidental side effects. As proteins are
thermo-sensitive, crude drugs should be subjected to heat treatment. Third, the
expansion of applications is another important unexpected impact. Finally, after
processing, the generation of several new compounds has been reported [62]. However, the dosage of additives and the time of
heat treatment should be considered during processing, as they are the major factors
that affect quality control.
In the large-scale production, it is difficult to guarantee the purity of all
products. Researchers spend long periods finding solutions to quality control. The
development of metabonomics has provided a necessary way to understand cellular
responses to mutations at all levels of gene products [63]. In recent years, a wide range of metabonomic analytical techniques
have been implemented in research on TCM [64]. Several
cases illustrate the possibilities of the application of metabonomics in quality
control during the processing of TCM materials. Ginseng has been employed in TCM for
over two thousand years and is now widely used around the world as an elixir [63]. In Asia, there are two types of ginseng that are
commonly found in the herbal medicine market: WG and RG. In the practice of
traditional Chinese medicine, WG and RG have been used for different purposes. WG
is
traditionally produced via sun drying of fresh ginseng, and RG is manufactured by
steaming fresh ginseng at 95–100 °C for 2–3 h and then drying it. WG is used to
“supply qi and promote the production of body fluids” as well as enhance physical
fitness and disease resistance, while RG has a “warming effect” and is used for
“boosting yang” and replenishing vital essence [64].
Ginsenosides Rb1, Rb2, Rc, Rd, Rg1, and Re are the major constituents of both WG and
RG, while ginsenosides Rg3, Rg5, Rg6, Rh1, Rh2, Rk1, Rs3, and F4 are known to be
unique constituents of RG [65], [66], [67], [68], [69], [70], [71], [72]. These unique ginsenosides found in RG have been reported to be converted
from the ginsenosides found in fresh ginseng after steaming [70], [73], [74]. In one study, ginseng was processed under temperatures of 100, 140, and
180 °C, with or without vinegar; the duration of exposure to each temperature was
10, 30, and 50 min, respectively, and there was a clear separation in the score
plots obtained for the various treatment conditions. The major compounds
contributing to the separation of 50 % methanol extracts of vinegar-treated ginseng
subjected to various processing conditions were valine, lactate, alanine, arginine,
glucose, fructose, and sucrose. As the temperature increased, the valine, arginine,
glucose, fructose, and sucrose concentrations decreased, whereas lactate, glucose,
and fructose increased in the vinegar-treated samples compared to
non-vinegar-treated samples [62]. Moreover, UPLC/TOFMS
had been demonstrated to be a powerful tool for use in herbal metabonomics to
discriminate differentially processed herbs, such as raw and steamed P.
notoginseng
[75]. An UHPLC-TOF-MS-based metabonomics platform
coupled with PCA and PLS-DA was developed for Panax notoginseng to establish
a correlation between the duration of steaming and the maximum production of
bioactive ginsenosides [76]. A similar study was
performed to determine chemical markers for discriminating between raw and processed
Radix Rehmanniae samples [77]. In addition, the three
types of products obtained from the processing of Ligustrum lucidum fruits
have been distinguished, which correspond to steam treatment processing products,
vinegar treatment processing products, and the fruits processed with wine. There are
differences in metabolite profiles among the crude and different types of processed
fruits of L. lucidum. Ligustaloside B was identified as a chemical marker for
such variations, and its contents in crude L. lucidum specimens were found to
be significantly higher than in processed samples. This study indicated that
UPLC-QTOF-MS coupled with multivariate statistics is able to provide quality control
for the crude and processed fruits of L. lucidum, and these results provide
the basis for determining the appropriate mechanism of processing [78]. The products of the processing of Polygala Radix
were also successfully distinguished [79]. One study
was designed to perform a comprehensive metabonomics analysis of Fuzi and its
processed products, Yanfuzi, Heishunpian, and Baifupian, via UPLC-Q-TOF-HDMS
combined with pattern recognition methods. Differences in the metabolic profiles of
Fuzi and its processed preparations were clearly observed based on PCA of the
obtained MS spectra. Significant changes in 19 metabolite biomarkers were detected
in the Fuzi samples and the three preparations [61].
Similarity analysis and PCA were applied to address the issue of the various quality
changes that occur during the process of toasting Fructus Xanthii supplied by
different producing areas. A high similarity was observed between different samples,
which indicates that the proportion and distribution of the components in most
extracts of F. xanthii show a high level of consistency [80]. HPLC fingerprints together with metal profiles were
employed to assess the quality control procedures applied to Atractylis
chinensis. A separate data matrix and combined data matrices were analyzed
via PCA, kNN, and LDA. The PCA results from the combined data matrices indicated
that the samples were discriminated on the basis of the applied processing methods.
Within each group, the samples were reasonably well grouped according to their
geographical origin and classification using kNN, and LDA results supported the PCA
results [81].
As the raw and processed forms of herbs have different pharmacological actions, it
is
pertinent to administer the correct form of herb to avoid any undesirable
consequences. Even the duration of the processing procedure, the processing
adjuvants, and its dosage arouse the subtle changes in the contents of compounds.
Therefore, it is of paramount importance to characterize the specific forms. The
collection of quoted literature data is shown in [Table
2].
Pharmaceuticals
The last step in the production of Chinese medicinal preparations, obtaining plant
extracts, can also be subjected to quality control using metabonomic profiles. The
extract is a contributing factor to the quality and toxicity of the drug produced.
However, a prerequisite is that the extracts of intermediate products that are to
be
analyzed are well documented with regard to the production steps they have been
subjected to. Bioactive compounds may be identified if it is possible to obtain or
generate extracts of different materials from the same plant species that are highly
variable in bioactivity. PCA may then be used to discriminate the chemical
fingerprints of the extracts in a way that separates them by their activity or by
spatial origin, and relevant chemical compounds can subsequently be deduced from
their contributions to the respective fingerprints [82], [83]. The majority of TCM products for
oral use are applied as water decoctions [84]. Other
oral preparations include macerates in aqueous ethanol and powdered drugs suspended
in water or prepared in pills, with honey, water, or rice gruel as an excipient
[85], [86]. An HPLC
fingerprinting analysis was developed to assess the quality and comparative contents
of cinnamon bark and cinnamon twig components. PCA and PLS-DA allowed good
discrimination of these samples, and cinnamaldehyde was found to be the most
abundant marker component [87]. Several examples are
found in the literature of utilizing HPLC together with different chemometric
methods for the analysis of complex mixtures, including the resolution of HPLC
fingerprints of complex, many-component substances found in Huoxiang Zhengqi
tincture samples from a batch from a given manufacturer, or from different producers
[88]. Another example of investigating complex
mixtures is the analysis of nine bioactive compounds from a Yiqing preparation which
is composed of three TCMs, to assess the consistency of the quality among 12
manufacturers based on SA. The results showed that HPLC fingerprinting could serve
as the first tool for revealing the consistency of the quality of Yiqing via
similarity comparisons [89]. LC-LTQ-Orbitrap MS was
applied for the simultaneous identification and quantification of multi-constituent
Xin-Ke-Shu, a TCM preparation [90].
It is complicated during the pharmaceutical process. On the one hand, the
pharmaceutical excipients hold back the analysis. On the other hand, a Chinese
patent drug usually consists of many sorts of herbs; the constituentsʼ analysis is
a
bottleneck in quality control. Plant metabonomics, a platform aimed at the complex
ingredients, can help to better understand the nature of these problems.
Conclusions and Perspectives
Conclusions and Perspectives
Plant metabonomics can be applied in the discrimination, processing, and
pharmaceutical preparation steps of TCM products, which represent the entire
production process ([Table 2]). As the first step in
the production of Chinese medicinal preparations, the identification of plant
varieties used, involving species, parts of the herbs, cultivated geographic region,
and planting period, is of great significance for ensuring the safety and
effectiveness of clinical treatment since the quality and contents of the active
constituents depend on these factors. Adulterants are assumed to be distinguishable
from plant materials and play the role of challenging substances. The processing,
as
the second step in production, appears to be of significance in clinical
applications and has been proven to satisfy the requirements of therapeutics. As
different forms of TCMs show different pharmacological actions, it is pertinent to
administer the correct form of herbs to avoid any undesirable consequences.
Therefore, it is of paramount importance to characterize the specific form of TCM.
Metabonomics can also be applied to control the content of extracts, which is
crucial to the pharmaceutical production. The complexity and diversity of the
components of TCM preparations make qualitative analyses difficult. In the
pharmacopeia, single-component analysis is used on TCM mostly. Nevertheless, it
lacks representativeness. In contrast to single-component analyses, metabonomics
achieves comprehensive scans, addressing some of the shortfalls of single-component
analysis. As shown by the three points we have just illustrated, plant metabonomics
can play a vital role in quality assessments during the large-scale production of
TCM preparations.
To facilitate the application of plant metabonomics in the quality assessment, on
the
one hand, we are supposed to utilize the new and effective techniques so that they
will support metabonomics studies adequately. On the other hand, exploring potential
research points of metabonomics in TCM seems necessary. With the appearance of RRLC
and UFLC, the methods of analysis come to a new era. Shorter analysis time and the
more efficient separation are the advantages of these methods [91]. MAS-NMR, another sharp technique, enhances the
resolution of solid samples and plays an important role in the overlap of peaks
[92]. Cristina Daolio et al. [93] applied MAS-NMR classifying commercial catuaba
successfully. An LC-MS-NMR platform was demonstrated, which combines two innovations
in microscale analysis, nanoSplitter LC-MS and microdroplet NMR, for the
identification of unknown compounds found at low concentrations in complex sample
matrixes as frequently encountered in metabonomics or natural product discovery
[94]. Therefore, in the future, employing new and
effective chromatographic or spectroscopic techniques for metabonomics studies seems
to be an important tendency. What concerns exploring potential research points, in
recent years, metabonomics was reported to the study of pharmacology in some terms.
Employing a metabonomics platform, Yiqing Lin et al. [94] identified the active cyanobacterial metabolite. Kashif Ali et al.
[95] also managed to screen the anti-TNFα
activity in crude extracts of grapes and other berries by NMR spectroscopy and
chemometric. Using this approach, compounds related to activity can be identified
without extensive and elaborate chromatographic separation, and it thus allows rapid
identification of extracts with biological activity. Moreover, screening the active
compounds and effective parts seems to grow into a vital process in future studies.
As a consequence, the results of quantitative assays will become more instructive
and convincing. In a word, with the development of analysis methods and the
exploration of potential research points, the application of metabonomics in TCM
quality assessment tends to become more prevalent and considered in the future.
Acknowledgements
This study was financially supported by the National Natural Science Foundation of
China (Projects No. 81001623 and No. 30902000).
