Introduction
Introduction
Natural products and particularly plants have long been a productive source of lead
molecules or even drugs themselves. Specifically in the case of anticancer drugs,
the fact that more than 50 % of the modern oncological drugs are derived from natural
products speaks for itself [1].
The traditional way of finding a lead from natural products is based on bioactivity-guided
fractionation. For this approach, it is a prerequisite to have a well-established
bioassay system which has high-throughput capacity and reliability. The procedure
of finding a new active compound is very painstaking since it takes many separation
and isolation steps until an active pure compound is finally obtained from the crude
extracts. The final structure elucidation may even be a more difficult task as often
very complex structures are found. However, the major problem is not only the process,
but rather the fact that the bioassays are usually designed to detect single targets.
When, as is commonly the case, the activity is due to a mixture of compounds and their
synergistic effect, it is not easy to find novel leads [2], [3].
In the last decades, pharmaceutical companies and researchers diverted their attention
from plants and microorganisms, turning instead to combinatorial chemistry. The reason
for this change in attitude towards natural product research was not the conviction
that there was no potential in this field, but rather disenchantment with the ratio
of hits versus effort/time and expense. The problem clearly was the methodology or
approach which was used, that was too expensive or time-consuming or both at the same
time. A new strategy for the detection of the active compounds is necessary to get
natural product research out of its stalemate. The decreasing number of novel drugs
every year makes for a renewed interest in developing novel approaches.
As other reviews have already suggested [2], [3], [4], [5], a new approach to find a lead compound from natural products has to be developed.
One of the promising tools for new strategies is the use of metabolomics. Metabolomics
has been applied in many fields in plant science and natural products chemistry, including
the quality control of medicinal plants [6], [7], [8], the monitoring of the biochemical response of plants to diverse stress conditions
[9], [10], [11], and the search for differences between genetically modified plants and controls
[12], [13], among others. This is possible through the general overview of metabolites provided
by the metabolomic analysis of organisms submitted to different conditions and consequently
allows a better understanding of the biochemical status of biological systems [14].
Metabolomics of human biofluids or tissues, often referred to as “Metabonomics”, has
been used for the analysis of the metabolic or physiological state of biological systems
to provide reliable diagnosis and markers of diseases or toxicity [15], [16].
Metabolites are the end product of gene expression and enzyme activity of organisms.
The metabolome thus reflects the state of an ongoing biological situation, and the
changes in metabolite concentration and/or composition may describe the actual biochemical
status of a system better than genomics and proteomics [14]. Measuring metabolite changes resulting from the response of cells upon treatment
(with natural products or pure compounds) may help us not only to monitor the response
of the cells but also eventually to detect activities of the treated materials themselves.
However, to date, the metabolomic approach is more focused on finding system phenomena
while the detection of lead compounds is still in a very premature state. Our intention
in this review is to explore the feasibility of using metabolomic analysis in the
search for lead compounds from natural sources. Due to the amount of existing literature
and the importance of the issue, we have restricted this to anticancer lead-finding.
A general overview of methods which are currently used for anticancer activity, measurement,
and metabolomic applications in the cancer research field are summarized in the first
two sections.
Among many analytical platforms utilized in metabolomic research, NMR has some important
advantages over other techniques, the most prominent of which are the broad spectrum
of metabolites detected, ease of quantitation, its reproducibility, and straightforward
metabolite identification [17]. Particularly in metabolomic analysis of biofluids, urine, blood, and culture extracts,
NMR has an edge over other methods, since it allows the introduction of samples directly
into the instrument with very simple sample-preparation procedures [18]. Moreover, solid samples from tissues can be directly analyzed using MAS (Magic
angle spinning) NMR [19]. Thus, NMR is considered as a method of choice in metabolomic analysis of biofluids
(metabonomics). Metabolomics in cancer research mostly deals with cell cultures and
animal tissues for which NMR is undoubtedly the most useful and popular method of
analysis, therefore our review will give special attention to the application of NMR-based
metabolomics in cancer research.
Current Methods for Anticancer Activity Measurements
Current Methods for Anticancer Activity Measurements
The initial screening methods used to find anticancer leads in natural products are
mostly either cell-based or mechanism-based in vitro assays. Cell-based assays usually evaluate the cytotoxicity of plant extracts on
cell lines derived from major human neoplasmas. This type of test is routinely conducted
with some of 60 cell lines representing 9 distinct types of tumors, which were established
by NCI in 1990 [20]. Thanks to the amount of work dedicated to the identification of molecular targets
in these cell lines, mechanism-based assays have been established [21]. The targets that have been detected are at different levels: genes (p53, Ras gene) or RNA levels (tyrosine kinase, phosphatases) and enzyme activity (proteasome
inhibition assay, histone deacetylase inhibition assay, DT-diapharose) [22]. Using these in vitro assays, many natural products have been tested and new leads have been identified
in the last decades [21], [23], [24].
These assays are quite simple, relatively fast, robust, and easy to set up but often
too permissive, resulting in false-positive or false-negative results [22]. To overcome this problem, attempts have been made to develop multicellular tumor
spheroid models which have intermediate complexity between in vivo models and in vitro monolayer cultures rather than monolayer culture models. This model has a great potential
as it mimics the structural and functional properties of normal and tumor tissues
much better, bridging the gap between cell-based assays and animal studies [25]. However, the application of this model to high-throughput screening in anticancer
lead-finding has still to be validated. Notwithstanding, this in vitro assay is designed to select compounds for secondary and more comprehensive in vivo testing to ensure that only a small number of compounds with the best chances of
success will make it through to the following, increasingly expensive phases of the
drug development process.
There are plenty of reviews which deal with the state-of-the-art methods for detecting
anticancer activity in natural products, including, among others, an excellent article
by Kinghorn and his team [24], [26], [27], [28], [29], [30]. To date, more than 85 000 compounds have been screened using in vitro assays [22], and judging by scientific journals, these still seem to be the major tools used
for this objective.
Application of Metabolomics in Cancer Research
Application of Metabolomics in Cancer Research
It is quite obvious that monitoring chemical components of tumor cells can provide
a better understanding of the complex biochemical process occurring in a cancer. Malignant
cells undergo significant changes in their metabolism including a redistribution of
metabolic networks, and these changes can result in a metabolic snapshot which is
clearly different from that of normal cells [31]. Metabolomic analysis has been applied in many different fields in cancer research
including the detection of biomarkers for diagnosis [32], the monitoring of drug response [33], and treatments or prediction of their toxicity [34]. It should be also strongly emphasized that one of the major benefits of metabolomic
analysis is that metabolic profiling can usually be achieved using urine or plasma
samples apart from the tumor cell itself, increasing the possibility of carrying out
large-scale research in a noninvasive manner [35]. Some of the applications of metabolomic studies of cancer cells or samples obtained
from cancer patients are described in the following section.
Diagnosis (Biomarkers)
Biomarkers are widely used in clinical medicine for the purpose of diagnosis or prediction
of disease. Finding a new biomarker or, better still, a quantitative biomarker is
of great importance in the development of sensitive and specific tests to detect the
presence of malignant tumors. NMR-based metabolomics particularly is considered to
be a very fast and noninvasive method for the identification of new biomarkers for
clinical diagnosis [36]. Indeed, several metabolites have been detected to be metabolic biomarkers of tumors
thanks to the application of an NMR-based metabolomic approach. In breast cancer cells,
one of the best applications of metabolomics in cancer diagnosis so far, the level
of total choline-containing compounds (tCho) was found to be increased, whilst the
levels of glycerophosphocholine and glucose were decreased as compared to benign tumors
and healthy tissue [37], [38], [39]. This result is well reflected in the study by Yang et al. [40], who showed that the metabolic activity of several pathways was increased in breast
cancer epithelial cell lines, leading to the upregulation of fatty acid synthesis
among other changes. Similarly to breast cancer, prostate cancer cells exhibit a distinct
metabolic profile characterized by high tCho and phosphocholine levels, along with
an increase in the glycolytic products lactate and alanine [39], [41]. Another good example is provided by the metabolomic study of liver cancer by Yang
et al. [42]. They demonstrated that it was possible to distinguish between cancer patients and
healthy volunteers, and even more, they were able to distinguish between patients
with hepatocirrhosis and hepatitis from patients with liver cancer, with an HPLC-based
analysis of urine samples. In this study, several metabolites having cis-diol structures together with typical nucleotide markers (pseudouridin, dihydrouridine)
proved to be useful for the differentiation of patients' status [42].
All studies above have demonstrated that the level of several metabolites appeared
to be important biomarkers (quantitative biomarkers) in the differentiation of diseased
and normal conditions. On the other hand, these biomarkers can also be used to monitor
the efficacy of a treatment, for instance a decrease in the tCho signal in the 1H‐NMR spectra can be associated to the positive response to chemotherapy or radiation
in breast, prostate, and brain tumors. Metabolites which were found to be biomarkers
in different tumors are listed in an excellent review on this topic [43].
For a biomarker to be really useful, it is essential for it to be detectable in the
early stages of the development of a malignant tumor. In cancer, the early detection
is very important since it can greatly increase the chance of total remission. Metabolomic
analysis has also proved to be useful for this purpose. Odunsi et al. [44] reported the adoption of a metabolomic approach to discriminate between women with
epithelial ovarian cancer (EOC) and healthy controls by analyzing their blood serum
samples. 1H‐NMR spectra showed substantial differences between blood samples of the EOC patients
and healthy subjects, and the consequent PCA analysis of NMR data showed a clear separation
of all sera from the patients with malignant tumors, pre-menopausal women, and patients
with benign ovarian disease. This clearly proves that the metabolomic analysis of
blood samples has a great potential as a novel strategy for the early detection of
epithelial ovarian cancer.
Many of these applications have a weakness consisting in the limited number of samples
which were used and there might be sample-to-sample variation. This may prove to be
an obstacle for the definition of biomarkers in different tumor types and pathological
stages of the tumor. Thus, the first requisite for this application to be generalized
is that a sufficient number of samples is analyzed and that the techniques are fully
standardized in order to improve the reproducibility of these technologies. Quantitative
biomarkers or general pattern recognition of tumor cells or samples from cancer patients
would be a good way for diagnosis and prediction of disease.
Toxicity assessment
Metabolomics is a useful tool to monitor early changes in metabolic pathways and therefore
be used to predict an anticancer agent's toxicity at an early stage [34]. There are not that many reports on studies made on this aspect in cancer research,
but the use of metabolomics in toxicity assessment of drug treatment has been explored
opening the possibility for its application to toxicity prediction in cancer therapy.
Robertson et al. [45] evaluated the feasibility of applying this technology to screen the toxicity in
male rats exposed to toxic agents. Spectra changes were observed shortly after exposure
to toxic agents and during the recovery phases. PCA analysis revealed that the metabolites
from the samples of all different toxin-treated rats were well clustered in localized
regions, which also identified the treataa ed groups but not the control groups. The
results demonstrated that this metabolomic approach could readily distinguish the
onset and reversal of toxicity with good agreement with clinical biochemistry.
The early diagnosis of toxicity is very necessary in cancer therapaay. This is the
case of anthracyclines, which are known to have a potential cardiotoxic effect [35]. The toxicity is cumulative, dose-related, and irreversible, thus, to monitor the
early signs of cardiotoxicity will give an opportunity of early intervention, allowing
attenuation or abrogation of the cardiac dysfunction.
Response to treatment
Perhaps the most important contribution which metabolomics can make to the search
for an anticancer lead from natural products is providing the means to monitor the
effects of anticancer drugs. Cancer is a highly complex disease and abnormalities
appear in several genes, proteins, and biochemical pathways. Thus, monitoring all
interconnected cellular networks will provide much more information than the use of
single molecular markers. This technology can provide a view of all metabolic changes
occurring after exposure to a certain anticancer agent. The resulting data provide
a view of perturbations which might occur in a large number of tissues. These results
can then be used to decide whether to continue the treatment based on the observation
of the effectiveness of the drug and eventually to shed more light on its mechanism
of action [46].
The application of 1H‐NMR to the prediction of resistance or response to chemotherapy by a neuroblastoma
was reported by Lindskog et al. [33]. Several cytotoxic drugs such as cisplatin, etoposide, and irinotecan were used
to treat cell lines with sensitivity to these, and the cells were examined by 1H‐NMR ([Fig. 1]). Cytotoxic drug treatment was found to produce an increase in methylene and polyunsaturated
fatty acid levels and a decrease in choline levels in drug-sensitive neuroblastoma
cells. Methylene/choline ratio correlated with cell death and increased in cisplatin-treated
drug sensitive cells but not in drug-resistant cell lines, indicating that this ratio
can be used as an indicator of cytotoxic responses. A similar study by Morse et al.
[47] was performed on breast tumor models. The treatment with docetaxel, an antimicrotubule
agent, decreased phosphocholine (PC) levels significantly and increased glycerophosphocholine
(GPC) levels in the cell extracts.
Fig. 1 1H‐NMR spectra of cytotoxic drug-sensitive and ‐resistant neuroblastoma cell lines.
Four neuroblastoma cell lines [drug-sensitive SH-SY5Y and IMR-32 cell lines, drug-resistant
phenotypes SK‐N‐FI, SKN‐BE (2)] incubated without (NT) or with cisplatin (cis, 5 µM)
for 48 hours. Adapted from [33].
Another interesting application, monitoring P-containing metabolites by 31P NMR, allowed the detection of phospholipid metabolism changes since it allows the
quantitative analysis of phosphate metabolites in cancer cells [48]. This provides information concerning key components of energy and phospholipids
metabolism. Human colon cancer cells were treated with a novel anticancer drug: 17-allylamino,17-demethoxygeldanamycin,
and 31P‐NMR spectroscopy allowed the screening of surrogate markers of drug activity. The
increased phosphomonoesters/phosphodiesters ratio was found to correlate with a favorable
tumor response, indicating that this ratio can be used as a surrogate marker of response.
A similar study was conducted on breast cancer cells by Sterin et al. [49]. Several anticancer agents were tested on the breast cancer cell lines showing different
hormonal response and metastatic potential. The 31P NMR spectra of perfused cells revealed a correlation between the mode of action
of anticancer drugs and the observed changes in cell metabolic profiles. Briefly,
cells treated with antimicrotubule drugs (paclitaxel, vincristine, colchichine) showed
a notorious increase in intracellular glycerophosphorylcholine (GPC) while other drugs
(methotrexate and doxorubicin) did not affect their levels whatsoever. This study
showed that 31P -NMR can be used as a diagnostic tool for decisions about treatment of cancers and
a new approach to understand the mechanism of action of anticancer drugs. These studies
suggest that tumor metabolic profile changes could be helpful in predicting the response
to anticancer treatments faster than current methods (e.g., imaging).
Recently, Chung and Griffiths [50] published a review on the characteristic metabolic profiles of cancer cells by NMR
after responding to different anticancer treatments with all kinds of drugs from conventional
cytotoxic drugs to novel anticancer drugs. This may be very useful to obtain an overview
of the different effects of drugs on a cancer cell metabolome.
Metabolomics: Tool for Finding Anticancer Activities
Metabolomics: Tool for Finding Anticancer Activities
Metabolomics has been applied in many fields in natural products including the study
of the metabolism and biosynthetic pathways under different conditions [14], [51]. However, so far, its potential in the detection of active compounds (new leads)
still remains untapped. Recently several attempts have been made [6], [52], [53], [54], and these have shown the feasibility of using metabolomics for this purpose.
Detecting activities in combination with metabolomics can be performed using two different
approaches, in silico and in situ. Using the in silico approach a correlation between two different data sets – e.g., metabolomics and bioactivity
results – is sought with the help of bioinformatics. On the other hand, the in situ approach consists in the monitoring of the metabolic changes of systems, e.g., tumor
cells, microorganism cultures, upon the treatment with tested materials (plant extracts
or single pure compound).
Even though most applications reported so far [6], [52], [53] do not refer to anticancer drug research, there is no reason not to adapt this method
to this purpose. Therefore, the techniques/approaches which have been applied in other
fields of natural products lead-finding will be reviewed.
In silico approach – correlation by chemometric methods
A good example of the successful application of this approach can be found in a recently
published paper on the metabolomic study of Galphimia glauca [54]. Galphimia glauca is a Mexican plant which has been used in traditional medicine for the treatment
of central nervous disorders. Six different collections of this plant from the whole
Mexican area showed different degrees of sedative and anxiolytic activities when tested
on animal models. Only two collections were highly active and the others did not show
significant activity. Metabolic profiling of six collections was performed using 1H‐NMR and analyzed further by partial least square-discriminant Analysis (PLS‐DA)
using previous information on their activities ([Fig. 2]). Interpretation of the PLS‐DA loading plot clearly demonstrated that the signal
strongly correlated with the above-mentioned activities, and this signal was identified
as corresponding to galphimine ([Fig. 3]). When targeted HPLC analysis was performed, it was proven that the two collections
possessing strong activities contained a high amount of galphimine while both other
(less active) samples did not.
Fig. 2 Score (A) and loading (B) plots of PLS‐DA models (PLS component 1 vs. PLS component 2) based on 1H‐NMR resonances and sedative activity of G. glauca crude extracts based on two classes, active and inactive plants. Active plant materials
include GM, MT, and QJ. Inactive plant materials include MS, MC, and JG. Adapted from
[54].
Fig. 3 Structure of galphimine series and 1H‐NMR spectra of: A resonances for vinylic protons H-1 and H-2 of an HPLC peak containing the diastereomeric
pair of galphimines B and F, B an MeOH crude extract from GM sample (active) showing the resonances for H-1 (δ = 6.55–6.63) and H-2 (δ = 6.00–6.05) for the galphimine series and C a MeOH crude extract from one individual of JG sample (inactive) without galphimines.
Adapted from [54].
Before this application, Roos et al. [6] had attempted to correlate metabolomes of St. John's wort extract with opioid receptor
binding activity. In this study, they showed that NMR combined with multivariate data
analysis could effectively predict the activity of preparations, based on a database
they constructed. This was suitable for the quality control of preparations made of
this plant since it provided a correlation with its efficacy, but they did not attempt
to identify the corresponding active compound/s.
In these applications, the use of an appropriate chemometric tool is very critical
since it determines the main output from the huge data sets. One of the useful methods
to correlate two different data sets (even two different types of “omics” data) is
the PLS/OPLS regression method. PLS is a method for relating two data matrices, X
and Y, by a linear multivariate model, but goes beyond traditional regression in that
it also models the structure of two data sets. It provides an approach to the quantitative
modeling of the often complicated relationships between predictors, X (metabolome),
and responses, Y (bioactivity) [55], [56].
On the other hand, the OPLS method is designed to separate the variance of the data
matrix X according to the variance of the data matrix Y, into three parts: the first
part represents the variance that is related to Y, the second part, the interfering
systematic variation not related (orthogonal) to Y, and the last part contains some
residual variance not interfering with the prediction of Y. OPLS is an extension of
PLS and has similar objectives to orthogonal signal correction but is integrated directly
in the modeling, which allows an easier validation of orthogonal components [57].
The direct correlation and PLS-based approaches were also adopted by Rantalainen et
al. [58] in an integrated analysis of NMR metabolic profiles and 2-D differential in gel
electrophoresis (DIGE) proteomic data from a murine cancer xenograft model. Metabolites
strongly correlating positively or negatively to proteins can be determined in this
way. Other useful methods that can be used for this purpose are well described in
the review [57], [59].
It has to be noted that in such correlations between activity in in vivo assays and the metabolome of extracts, the compounds found to correlate to activity
not necessarily are active in itself, they could also be pro-drugs or take part in
synergistic activity. This is in fact a major advantage of this approach if compared
with the classical bioassay-guided isolation of active compounds in which such compounds
will not be found.
In situ approach – metabolic footprinting/fingerprinting
Numerous different terms have been used for metabolomic analysis, e.g., metabonomic
analysis, metabolic profiling, metabolic fingerprinting, and metabolic footprinting
[60]. Among these, metabolic footprinting (exometabolomic) is often applied to the measurement
of all extracellular metabolites present, for example, in a culture media. The identified
compounds are either metabolites which were secreted by the cells into the medium
or medium components biochemically transformed by the organism. On the other hand,
metabolic fingerprinting measures a large number of intracellular metabolites detected
by a selected analytical technique in defined conditions [60]. Due to the fact that the separation of extracellular media is much easier than
any other culture media, metabolic footprinting of microorganisms has been amongst
the frontier research fields [61]. Even though metabolic footprinting represents only a fraction of the extracellular
contents, it has a tight relationship with the intracellular metabolism, thus it can
provide important information and a picture of the intracellular metabolic status.
If this technique is to be used to measure the anticancer activity of natural products,
the main strategies to study this should be based on the comparison of the metabolic
footprint/fingerprint of cancer cells before/after treatment. We have already seen
that metabolomic analysis can be used as a tool to monitor the response of anticancer
drugs in tumor cells or the patients' response to anticancer treatment. A similar
method could be used to monitor activities as their metabolic profiles can be directly
compared to those of positive controls. In this way, biomarkers of activity or the
pattern of activity could be detected and subsequently could be identified.
This approach has appeared in the work of Zhi Biao-Yi et al. [52], in which the effect of different antibiotics with different modes of action on
the microbial metabolomics was studied. Their results allowed them to conclude that
dihydrocucurbitacin F-25-O-acetate, a major constituent of the Chinese plant Hemsleya pengxianensis, showed antimicrobial activity. The metabolome of a Staphylococcus aureus culture treated with a plant extract, dihydrocucurbitacin F-25-O-acetate, and several
known antibiotics (streptomycin, vancomycin, erythromycin, rifampicin, etc.) were
compared. PCA analysis revealed that dihydrocucurbitacin F-25-O-acetate was the component
responsible for the main antimicrobial activity on Staphylococcus aureus in H. pengxianensis through its ability to inhibit cell wall synthesis, as in the case of vancomycin
([Fig. 4]).
Fig. 4 PCA scatter plot of HPLC profile of controls and cultures treated with different
drugs and extract of Hemsleya pengxianensis W. J. Chang [controls (+); norfloxacin (×); acheomycin (◊); lincolmensin (▴); cefataxime
(*); vancomycin (○); rifampicin (□); erythromycin (▵); chloromycetin (▿); streptomycin
(▾); dihydrocucurbitacin F-25-O-acetate (★); extract of Hemsleya pengxianensis (☆)]. Adapted from [52].
Similar studies aimed at seeking to discover the mode of action behind antibacterial
[53], antifungal [62], and anti-inflammatory activities have been published [63].
One of the advantages of this approach is that it can also be used to predict the
mode of actions and not only to select active samples. In order to find an anticancer
agent, it is essential that the metabolic footprint/fingerprint of the target cancer
cells is analyzed first so that the metabolic footprint/fingerprint of the same cancer
cells after treatment with plant extracts can be compared to detect changes. In NMR-based
metabolomic analysis, metadata of two different measurements can be easily compared
to find changes if existent. One of the possible ways to achieve this is to subtract
the obtained spectra, thus revealing the signals that have changed. For this purpose,
HSQC (heteronuclear single quantum coherence) spectroscopy is very useful. In this
case, as the 13C chemical shift is relatively insensitive to the change of pH and concentration of
samples, it is much more reliable and reproducible to use this rather than 1H‐NMR [64]. Recently this technique was used for the comparison of two different species of
Ilex and revealed that the signals of saponins constituted the main difference between
the species ([Fig. 5]) [65].
Fig. 5 HSQC spectra of I. brevicuspis (black) and I. dumosa var. guaranina (gray) in the range of δ 4.0 – δ 5.5 of 1H and δ 90 – δ 115 of 13C for HSQC. 1: HSQC correlation between anomeric protons and carbons of sugars in
saponins, 2: HSQC correlation between glucosyl H-1 and C-1 rutin, 3: HSQC correlation
between H-1 and C-1 of arbutin, 4: HSQC correlation between rhamnosyl H-1 and C-1
of rutin. Adapted from [65].
In addition, HSQC can be used for a quick screen of the presence of certain metabolites
within an extract (a mixture) by comparison of the HSQC spectra of mixtures of known
reference compounds [66].
To increase the efficiency of activity screening, the material to be tested from the
plant also has to be defined. Usually, the screening material – plant extracts or
fractions of extracts – were used for initial testing. In conventional extraction
methods, plants or parts of plants are extracted with organic solvent/s, and dried
extracts are subjected to screening. Depending on the properties of extraction solvent,
the metabolome of the extract will vary, and consequently its activity will surely
also differ. It is even possible that some methods may not extract the active metabolites
efficiently resulting in low concentrations which may not be sufficient to produce
any desirable cellular effect. To improve the potential of finding active compounds
and at the same time to reduce elaborated fractionation steps, alternative fast extraction
methods should also be considered. This can be accomplished by combining different
solvents in one system, applying gradients of polarity and extracting them consecutively
in an on-line system. This approach has been developed in our lab and has proved to
be very successful allowing the extraction of metabolites ranging from very nonpolar
to polar compounds from one single plant sample (unpublished results).
Perspectives
Perspectives
Metabolomics is a very promising tool to use for the detection of activities from
natural products as it has been used in many similar fields, allowing the accumulation
of vast experience. What should not be underestimated from the start, is the importance
of the interpretation of the biological information revealed by this powerful technique.
Therefore, the first requirement is that the obtained data are reliable and reproducible.
As many researchers have mentioned, drawing conclusions on the basis of small experiments
is very risky [46]. To obtain reliable information from an experiment, a sufficient number of samples
have to be analyzed. Furthermore, the communication with metadata obtained from other
experiments or groups is only possible if the firm standardization of all processes,
from the sample preparation to the measurement and data analysis, needs to be done
beforehand. At the same time, if all data have been measured under the same conditions,
particularly NMR data can be stored forever and whenever new data is generated, this
can be included for use in a new data analysis.
Another important aspect to consider is the assay system required for this approach
which has to be fast and simple and suitable to be applied to high-throughput screening.
An NMR-based technique – but also other techniques which are used in the metabolomic
field – has a great potential for a high-throughput application. Some obstacles are
still present, namely the need for fast metabolite identification. The improvements
made to NMR sensitivity tend to make fast identification feasible, particularly in
mixtures. On the other hand, the existence of public databases of metabolites will
again contribute to their identification.
Lastly, whichever compound determined in principle to be active will still have to
be submitted to testing on a more solid model system in order to confirm this activity.
However, this feature can improve the efficiency and provide shortcuts in the bioassay
guided lead-finding research from natural products, thus recovering this prolific
source of potential anticancer drugs. Moreover, it can offer a chance to reveal the
complex mechanism and synergism of metabolites.
