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.


Introduction
Medicinal plants have been considered an essential source for drug discovery and development as they are enriched in verities of phytochemicals of different categories [1]. Due to the multiplicity of the phytochemicals, they have been associated with the multitargeted therapeutic response and thus dealing with the alleviation of several acute and chronic ailments. However, over the past decades, the use of medicinal plants and their derivative products has seen rapid growth due to their minimal side effects, easy availability, and economically thus maintaining sustainable development in the health care system and economy of the country [2,3]. Furthermore, medicinal plants are accepted as the main source for new drug discovery and development, as more than approx. 50% of existing drugs or pharmaceuticals are derived from natural resources. A major contribution to their regulatory aspects includes taking a look at their uncountable use in the health care system,

Need for Metabolomic Study for Medicinal Plants
We are aware of the multi-mechanistic and therapeutic action of medicinal plants and their derived products exhibited by the varieties of the metabolites present in the plant matrix, thus, alleviating several acute and chronic ailments. During this period, omics technology advancements were leveraged to create strategies for different aspects of drug research [22,23]. In order to explore the diversity of the metabolites in a plant matrix, there are various chromatographic and spectrometric techniques such as GC-MS, LC-MS, NMR, FT-IR, etc., which deal with the qualitative and quantitative exploration of the phytochemicals and each technique works based on their designed principles, hence exploring the complete phytochemistry phenomenon. The study of the medicinal effect of plants exponentially put many research orientations to the plant metabolites to obviate the insufficiency for medicinal plants or their derived products that evades them from meeting their worldwide requirements [24,25]. Plant secondary metabolites are one of the main principles for their quality, safety, and efficacy-based scientific validation; hence, they are exponentially used as the first choice of parameter for their regulatory aspects or authentication, although it has been reported that only safety and efficacy data is not sufficiently meeting the optimum criteria for their scientific evaluation and health prospects [26]. Throughout history, traditional medicines have been used as folk regimens, and in recent past decades, these are providing a key role in the production of many natural products to treat a variety of ailments. It has been reported that secondary plant metabolites play an essential role as exogenous and endogenous agents for treating varieties of pathogens and thus producing a large-scale antibiotic only by the plant sources, which significantly act against the different microorganisms which cause several pathophysiological changes or health issues [27,28]. Based on the multi-mechanistic and therapeutic effect of the plant secondary metabolites, these have been termed as the economical as well as the potential therapeutic weapons for treating several ailments induced by the exogenous and endogenous PAMPs or DAMPs onsets.

Challenges in Metabolomics Analysis
It is known that medicinal plants deal with the diversity of metabolites even with different chemical or structural behavior. Furthermore, to reach perfection in exploring the phytochemistry of medicinal plants, a single analytical method does not put into their high throughput phytochemical screening [29]. As each technique works based on its designed principle, multiple methods or techniques are required to explore qualitative and quantitative phytochemical profiling in a medicinal plant [30]. Some approaches need to be thought of during metabolomics studies using modern analytical techniques, such as the extraction of phytochemical constituents from a plant matrix. It is processed based on their ethnopharmacological prospective or targeted bioassay, so it is necessary to extract phytochemicals using traditional or modern reference, understand the phytochemical behavior of the targeted or untargeted metabolites such as probable solubility/polarity, chemical nature of molecule (acidic or basic) and complexity of the molecule/molecular size. Furthermore, in metabolomic analysis, the preparation of the sample is one of the essential steps which support the whole outcome of the study. The targeted solvent and its chemical behavior, solvent ionization followed by its dielectric behavior, and sample amount need to be appropriate for accurate and precise analysis. Chromatographic conditions, mobile and stationary phase (nature of stationary phase) used for separation of the targeted or untargeted metabolites should be followed based on the chemical nature of the metabolites. Last but not least, the ionization process, source of ionization, and flighting distance and time need to be adjusted and favored also based on the chemical nature and complexity of the metabolites. Moreover, the repeatability of the optimized or non-optimized chromatographic and spectroscopic methods needs to be validated to reproduce robustness [31].
Based on the above facts and assessment for metabolomic analysis helps us to unravel and even reach perfection in qualitative and quantitative phytochemical analysis of medicinal plants. Furthermore, in a single study, quantitative-based standardization of the medicinal plant needs at least four marker constituents to validate the authenticity aspects for their regulatory purpose [32]. A systematic method for metabolomic approach and statistical analysis has been depicted in Figure 1 and previously reported methods for quality and quantitative metabolomic analysis of medicinal plants using the modern analytical technique are summarized in Table 1. of the metabolites. Last but not least, the ionization process, source of ionization, and flighting distance and time need to be adjusted and favored also based on the chemical nature and complexity of the metabolites. Moreover, the repeatability of the optimized or non-optimized chromatographic and spectroscopic methods needs to be validated to reproduce robustness [31].
Based on the above facts and assessment for metabolomic analysis helps us to unravel and even reach perfection in qualitative and quantitative phytochemical analysis of medicinal plants. Furthermore, in a single study, quantitative-based standardization of the medicinal plant needs at least four marker constituents to validate the authenticity aspects for their regulatory purpose [32]. A systematic method for metabolomic approach and statistical analysis has been depicted in Figure 1 and previously reported methods for quality and quantitative metabolomic analysis of medicinal plants using the modern analytical technique are summarized in Table 1.

Needs of Network Pharmacology for Therapeutic Exploration of Medicinal Plants
Computational techniques have been growing and exotically contributing to drug discovery and development via assessing wide varieties of genes and their expression behavior to the chemical or bioactive principles which are involved in the pathophysiology of any ailments. Compounds and protein interaction and biomolecular accession provide a high throughput orientation to explore better pharmacological validation concerning the targeted or untargeted metabolites. Besides this approach, the one-drug/one-target/one-disease strategy for drug development is currently confronted with several difficulties in terms of sustainability, efficacy, and safety. Recently, strategies for network biology and pharmacology, respectively, have acquired recognition [33,34].
Network biology and polypharmacology are one of the most conventional approaches to exploring multi-mechanistic and therapeutic targets for integrating omics data and developing multitarget drugs. Hence, both approaches are combined and acknowledged as network pharmacology. This approach does not look only at the consequence of drugs in terms of the effect on both the interactome as well as the diseasome level and covers multiple interactions with multiple proteins/genes in a single time [18]. By enabling an objective examination of prospective target areas, it also seeks to identify

Needs of Network Pharmacology for Therapeutic Exploration of Medicinal Plants
Computational techniques have been growing and exotically contributing to drug discovery and development via assessing wide varieties of genes and their expression behavior to the chemical or bioactive principles which are involved in the pathophysiology of any ailments. Compounds and protein interaction and biomolecular accession provide a high throughput orientation to explore better pharmacological validation concerning the targeted or untargeted metabolites. Besides this approach, the one-drug/one-target/onedisease strategy for drug development is currently confronted with several difficulties in terms of sustainability, efficacy, and safety. Recently, strategies for network biology and pharmacology, respectively, have acquired recognition [33,34].
Network biology and polypharmacology are one of the most conventional approaches to exploring multi-mechanistic and therapeutic targets for integrating omics data and developing multitarget drugs. Hence, both approaches are combined and acknowledged as network pharmacology. This approach does not look only at the consequence of drugs in terms of the effect on both the interactome as well as the diseasome level and covers multiple interactions with multiple proteins/genes in a single time [18]. By enabling an objective examination of prospective target areas, it also seeks to identify novel drug leads and targets and to repurpose current drug molecules for various therapeutic diseases [35]. Traditional knowledge about the plant and its omics plays a vital role in the further discovery, development as well as repurposing of existing drugs. However, this approach requires the right protocol and guidance for screening the right targets and new scaffolds which are being used for the evaluation of drug molecules. During the analysis of the network biology or pharmacology, the rationale design and therapeutic targets should be known (Cho et al., 2012, Hopkins, 2008, Ellingson et al., 2014 [33,34]. Such open new therapeutic options have also aimed to improve the safety and efficacy of existing medications. Network pharmacology analysis has been generally classified into two different approaches such as untargeted network pharmacology analysis and target-based network pharmacology analysis. A systematic approach for network biology as well as polypharmacology has been depicted in Figure 2. novel drug leads and targets and to repurpose current drug molecules for various therapeutic diseases [35]. Traditional knowledge about the plant and its omics plays a vital role in the further discovery, development as well as repurposing of existing drugs. However, this approach requires the right protocol and guidance for screening the right targets and new scaffolds which are being used for the evaluation of drug molecules. During the analysis of the network biology or pharmacology, the rationale design and therapeutic targets should be known (Cho et al., 2012, Hopkins, 2008, Ellingson et al., 2014 [33,34]. Such open new therapeutic options have also aimed to improve the safety and efficacy of existing medications. Network pharmacology analysis has been generally classified into two different approaches such as untargeted network pharmacology analysis and target-based network pharmacology analysis. A systematic approach for network biology as well as polypharmacology has been depicted in Figure 2.

Untargeted Network Pharmacology Analysis
In this analysis, the active molecule can be fixed or can not be fixed, which needs to be explored biologically based on the interaction of targets/genes targets that would be interacted with the molecule are completely unfixed. A random analysis is done using the software commands (Cytoscape) of the STRING app to expand the network. During this process, the number of interactors to expand the network depends on the information retrieval that exists in the database (https://jensenlab.org/, 19 october 2022), while the type of interactors to the expanded network should remain to the Homo sapiens species only. The process is repeated till no interactors are further connected via expanding the network. No further expansion of the network represents no additional genomic information exists in the reported database. Thereafter, the statistical information of the generated network about the number of nods, edges, network density, heterogeneity, etc., is imported through a network analyzer. In one example, the constructed network with statistical summary has been depicted in Figure 3.

Untargeted Network Pharmacology Analysis
In this analysis, the active molecule can be fixed or can not be fixed, which needs to be explored biologically based on the interaction of targets/genes targets that would be interacted with the molecule are completely unfixed. A random analysis is done using the software commands (Cytoscape) of the STRING app to expand the network. During this process, the number of interactors to expand the network depends on the information retrieval that exists in the database (https://jensenlab.org/, 19 October 2022), while the type of interactors to the expanded network should remain to the Homo sapiens species only. The process is repeated till no interactors are further connected via expanding the network. No further expansion of the network represents no additional genomic information exists in the reported database. Thereafter, the statistical information of the generated network about the number of nods, edges, network density, heterogeneity, etc., is imported through a network analyzer. In one example, the constructed network with statistical summary has been depicted in Figure 3. represents the untargeted genes PPI network with partial interaction (small circle) and significant interaction (big circle), (B) represents selective PPI network with the specific protein (CAT) while (C) represents the common PPI network with partial and significant interaction of proteins/genes. (D) represents the histogram of the analyzed proteins based on their enrichment concerning cellular compartments (mitochondria), while (E) represents the regression analysis of the analyzed genes between clustering coefficient and closeness centrality. (F) represents the CPI interaction network Figure 3. A systematic approach for network biology and pharmacology of phytochemicals, (A) represents the untargeted genes PPI network with partial interaction (small circle) and significant interaction (big circle), (B) represents selective PPI network with the specific protein (CAT) while (C) represents the common PPI network with partial and significant interaction of proteins/genes. (D) represents the histogram of the analyzed proteins based on their enrichment concerning cellular compartments (mitochondria), while (E) represents the regression analysis of the analyzed genes between clustering coefficient and closeness centrality. (F) represents the CPI interaction network which represents the interaction of two phytoconstituents with specific genes in the form of different colored edges. Each color of the edges represents a different database source. Each figure was generated as an example through the Cytoscape tool.

Target Based Network Pharmacology Analysis
In this analysis, the active components and each target are fixed already concerning the disease. During this analysis, a set of active components and genes involved in the pathophysiology of the disease are simultaneously analyzed for their interaction in the form of nods and edges. The genes are generally screened for the Genecard database (genecards.org), which is a free online database that involves an n-number of genes involved in diseases. STITCH is an open-source platform for rapid analysis of the active components and the genes which are needed to be analyzed. Each component and selected gene are inserted into the STITCH platform of the Cytoscape software. It gives multiple possible matches at the fixed or optimum confidence score of 0.04 and maximum additional interactor [34,36]. During network analysis, several additional interactors were found to be connected with the genes, which are generally removed through network interpretation analysis. Statistical summary, betweenness among the network, and strength of the network binging between the nodes as gene and the active component are evaluated as network data acquisition or summary of the network. Furthermore, there are various new approaches, such as a network histogram analysis which gives information about the enrichment of the genes biologically active or based on cellular organelles or tissue. Multi-statistical components analysis, principal components analysis, principal coordinates analysis, t-distribution analysis, etc., is performed to retrieve information about the biological enrichment of the gene or based on their pathophysiological actions. An example of the analysis has been subjected in Figure 3.
Previously, several studies have been reported based on the targeted and untargeted network pharmacology analysis, which revealed the multi-mechanistic and therapeutic role of various biologically active metabolites in the alleviation and treatment of diseases. In a study reported by Gautam evaluated multi-mechanistic and therapeutic potential of Momordica charantia in the alleviation of diabetic nephropathy. In this study, the author explored the therapeutic potential of several metabolites of Momordica charantia and reported that quercetin, ferulic acid, caffeic acid, and catechin are the potential metabolites that exhibit a significant interaction with the genes such as G6PD, MAPK1, MAPK3, NOS, PTGS2, PON1, ILs, etc. and alleviating diabetic nephropathy and its associated complications [34]. Gaurav et al. reported the multi-mechanistic and therapeutic potential of polyphenols present in Boerhaavia diffusa and Tinospora cordifolia against glomerulopathy, inflammation, oxidative stress, etc. The outcome of the study showed that Boerhaavia diffusa and Tinospora cordifolia contains several metabolites, such as polyphenols which poly a significant role in the alleviation of kidney disease and its associated complication via the regulation of several genomes such as CASP3, CASP7, CASP8, CASP9, MAPK1, MAPK3, TNF and TP53 involved in the several pathways AGE/RAGE pathway, signaling to interleukins, response to the inorganic substances, TNF signaling pathway, lipid localization, regulation of defense response, RAS and bradykinin pathway in COVID-19, circulatory system process, cellular response to nitrogen compounds, etc. [33].

Gene Ontology or Gene-Disease Association Network Analysis
Gene ontology analysis showed the multi-targeted and mechanistic approach of the targeted genes that are screened through the protein-protein network (PPI) and compoundprotein interaction (CPI) network analysis. it gives information about that how genes play multi-targeted effect in pathophysiology involved in the disease via regulation of several positive and negative biological pathways. There are several platforms such as Cytoscape, network analyst (https://www.networkanalyst.ca/, 19 October 2022), Metascape (https://metascape.org, 19 October 2022), STRING (https://string-db.org/, 19 October 2022), DisGeNET (https://www.disgenet.org/, 19 October 2022), etc. outlined in Table 2. These are the freely available gene analysis tools for gene ontology analysis and give enriched information about gene-gene interaction and their involvement in diseases. The genes which are involved significantly in the alleviation of the disease through any specific pathway are also revealed through gene ontology analysis which is expressed in form of the p-value. Furthermore, the gene enrichment analysis also reproduces a robust approach for biological or physiological regulation of the plant growth or production of secondary metabolites [33,34]. A systematic approach for the evaluation of gene and disease association has been depicted in Figure 4. Exploration of multi-mechanistic and therapeutic action of traditional medicines through network biology, polypharmacology and gene ontology analysis has been increased exponentially. Vadivu et al. reported that mapping genes involved in the diseases with standardized ontology analysis gives more improvement in analyzing the biological performance of the medicinal plants and their therapeutic options [38]. Several studies have been conducted to investigate multi-targeted effects or molecular mechanisms of some herbal medicines or their derived products using Network pharmacology approaches (Table 3)  Gupta et al. evaluated functional annotation and comparative analysis of Withania somnifera Leaf and root transcriptomes to identify putative genes involved in the withanolides biosynthesis through various approaches of gene enrichment analysis. The outcome of the study showed that, to assign functional GO annotation in terms of biological process, molecular function, and cellular component groups, the TAIR10 database's best hit for each unigene. The wide range of GO annotations for unigenes emphasizes the variety of genes that are probably represented in the transcriptome data for the Withania leaf and root. A significant number of unigenes are involved in the production of several secondary metabolites by mapping these unigenes onto KEGG. It was also found that the genes for all of the enzymes involved in the biosynthesis of the triterpenoid backbone (including the MVA and MEP routes) up to 24-methylene cholesterol, which is thought to be a precursor for the biosynthesis of withanolide, based on the annotation. Additionally, certain contigs associated with the enzymes used in these processes displayed differential expression [37].
Exploration of multi-mechanistic and therapeutic action of traditional medicines through network biology, polypharmacology and gene ontology analysis has been increased exponentially. Vadivu et al. reported that mapping genes involved in the diseases with standardized ontology analysis gives more improvement in analyzing the biological performance of the medicinal plants and their therapeutic options [38]. Several studies have been conducted to investigate multi-targeted effects or molecular mechanisms of some herbal medicines or their derived products using Network pharmacology approaches (Table 3). Gaurav et al. reported the multi-therapeutic use of two Indian traditional medicinal plants such as Boerhaavia diffusa and Tinospora cordifolia, in the regulation of several pathways such as signaling to interleukins, response to the inorganic substances, TNF signaling pathway, lipid localization, regulation of defense response, RAS and bradykinin pathway in COVID-19, circulatory system process, cellular response to nitrogen compounds, etc. [33]. Gautam et al. validated the traditional pharmacological claim of Momordica charantia in alleviating diabetic nephropathy and its associated complications via regulation of several pathophysiological pathways such as metabolic regulation, increased uptake of glucose to the muscle, obviating insulin resistance, biochemical markers regulation, oxidative stress, and inflammation, etc. [34].
Physostigma venenosum potential targets were identified using a combination of network pharmacology and virtual reverse pharmacology techniques for the treatment of vascular dementia (VaD). This work used PASS prediction to find known VaD-related targets interacting with phytomolecules. More than 200 proteins connected to VaD were studied since its expression has increased. In order to broaden the scope of the search, an extra 34 genes were chosen. It was done because, from a pharmacological perspective, it is typically simpler to inhibit the function of proteins than to activate them. Thirty-four mechanisms of action (MOAs) predicted by the PASS program were found to exist. It was found that eseridine, physostigmine, physovenine, and eseramine can be predicted with a probability Pa of more than 0.5 for their dual MOAs (inhibition of acetylcholinesterase and butyrylcholinesterase) and pharmacological effects related to the treatment of VaD (cognition disorder treatment and nootropic). Acetylcholinesterase and butyrylcholinesterase are both inhibited by eseridine42. Physostigmine holds patents for treating dementia and Alzheimer's disease as well as inhibiting butyrylcholinesterase, acetylcholinesterase inhibition, and memory enhancement. While eseramine has a less inhibitory effect against acetylcholinesterase than physostigmine, physovenine was patented as an inhibitor of acetylcholinesterase and butyrylcholinesterase as well as for the treatment of AD and dementia. The anti-acetylcholinesterase and anti-butyrylcholinesterase activity of several phytomolecules may have additive or synergistic effects on the therapeutic benefits of Physostigma venenosum in the treatment of VaD. It is now understood that this plant is utilized to cure atherosclerosis [39].
Khanal et al. performed gene ontology enrichment analysis of α-amylase inhibitors from Duranta repens (D. repens) in diabetes mellitus. Through network pharmacology analysis between -amylase inhibitors, regulated proteins, and expression pathways, it was possible to determine the -amylase inhibitory action of substances from Duranta repens. In the compound-protein-pathway network, it was discovered that the PI3K-Akt signaling route and the p53 signaling pathway were anticipated to be substantially regulated. Scutellarein was also projected as the main hit based on its ability to inhibit -amylase, its affinity for binding, and its involvement in regulated pathways. Additionally, AGE-RAGE and the FoxO signaling pathways, which are implicated in diabetic complications, were projected to be modulated by -amylase inhibitors. It was shown that D. repens may not only block -amylase in the GI tract, but may also be absorbed into the systemic circulation and influence several pathways implicated in the etiology of diabetes mellitus to generate a synergistic or cumulative impact [40].

Future Perspectives
Quality, efficacy, and safety issues about herbal medicines often concern researchers or the healthcare system due to their complex phytochemical matrix responsible for exhibiting the pharmacological effect. In a plant matrix, it is challenging to justify which type of phytochemicals is responsible for exhibiting the pharmacological effect and to what extent. Besides that, herbal medicines and their derived products still represent the need for their comprehensive safety concern. Studying the phytochemicals of medicinal plants using modern analytical techniques such as LC-MS, GC-MS, and NMR, etc., are the most precise, accurate, and robust techniques for the qualitative and quantitative-based standardization of medicinal plants. These techniques have been exponentially used to explore the metabolomics pattern of medicinal plants. Furthermore, bioassay-based exploration of metabolomics patterns of medicinal plants brings to reach several facts concerning efficacy and safety. Qualitative and quantitative phytochemicals-based exploration of medicinal plants leads to unfolding their authentication concerns and provides accuracy in the regulatory-based exploration of medicinal plants. In recent times, several types of research have been conducted to explore the metabolomic pattern of medicinal plants using single technology such as LC-MS, GC-MS, NMR, etc. As we are aware of the complexity of the phytochemicals and their different chemical nature, single analytical methods cannot be accurate and precise enough to explore the complete phytochemical profile of the compounds. Plant metabolomics is well explored using different analytical methods based on the targeted phytochemical's nature.
With the expansion of research and development in medical technologies, more animals are being employed in the study. All throughout the world, millions of experimental animals are utilized each year. Animals' suffering, mortality, and misery during scientific studies have long been a topic of controversy. Animal research has several critical and concerning drawbacks outside the main ethical issue, such as the need for trained labor, lengthy protocols, and high expense. In order to address the problems with animal testing and prevent unethical practices, several alternatives have been presented. For the use of animals in laboratories, the three Rs (3Rs: reduction means minimum use of animals, refinement means minimum pain or distress to the animals, and replacement means employing the use of non-sentient material) are being used as a strategy [112].
The different fundamental principles of biology can be better understood by computational methods. In order to create novel drugs, specialized computer models and software are used. Without dissecting animals, computer-generated simulations are used to estimate the numerous potential biological and toxic consequences of a chemical or prospective medication candidate. For in vivo testing, only the most promising compounds discovered via initial screening are employed. For instance, in vivo testing is required to determine a drug's receptor binding location. The receptor binding site for a prospective therapeutic molecule is predicted using computer software called Computer Aided Drug Design (CADD) [112]. By identifying potential binding sites, CADD prevents the testing of undesired compounds with little biological activity. Also, with the help of such software programs, we can tailor-make a new drug for the specific binding site, and then in the final stage, animal testing is done to obtain confirmatory results [113][114][115][116].
In considering the 3Rs strategy, computational or in-silico approaches remain an exponential contribution to drug discovery and development, as well as drug repurposing via approaching biomolecular targets for potential therapeutic effects. Network pharmacology is one of the most efficient and conventional approaches to exploring the multi-targeted and therapeutic effects of chemicals. It is well-defined in the exploration of polypharmacology of medicinal plants or their derived products, even exponentially used to validate their traditional therapeutic claim via approaching different biological targets. However, the traditional drug research approaches are not suitable enough to study the biomolecular therapeutic application of complex components with many targets. Network biology or network polypharmacology provides a novel research technique for studying several active ingredients with their and predicting effector targets and effector mechanisms involved in the treatment of certain diseases.
Different methods and alternatives to organisms provide a contributing hand concerning the computational approach, especially network biology, and polypharmacology is well enough for drug discovery and development via approaching their biomolecular and multi-targeted effects. Moreover, in the establishment or validation of the therapeutic effect of traditional medicinal plants or any herbal formulae, metabolomics and network pharmacology provides a contribution to validate them based on multi-targeted and therapeutic effect. The findings provided by the network pharmacology for predicting effector targets and effector mechanisms are based on reported databases. It would be an effective and alternative tool for the biological or therapeutic exploration of active constituents, thus contributing to scientific validation of medicinal plants concerning their safety and efficacy.

Conclusions
This review enlightens the systematic approach of metabolomics and network pharmacology for multi-mechanistic and therapeutic exploration of traditional Indian medicinal plants or active constituents, as well as to identify biomolecular targets concerning pathophysiological alterations. In Network biology, gene enrichment analysis is generally used to predict the pathways involved in diseases. Through this study, the synergism between numerous components, targets, and pathways may also be predicted, validating the mechanistic evidence to support clinical practice and pharmaceutical impact. It also provides ideas for the establishment of further scientific evidence. Although this approach provides a theoretical basis for better clinical development of drugs for the treatment of any acute and chronic ailments, the research conducted on network pharmacology is based on database mining research, and the mechanism of drug action is only predicted. Considering the facts of metabolomics and network pharmacology, it can be one of the most promising computational techniques for the exploration of multi-therapeutic effects in both the assessment of