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ORIGINAL ARTICLE
Year : 2022  |  Volume : 12  |  Issue : 3  |  Page : 142-152

Screening and Selection of Hispaglabridin B as a Lead Compound in Colon Cancer Treatment: In Silico Approach


Department of Biotechnology, Mepco Schlenk Engineering College (Autonomous), Sivakasi, India

Date of Submission29-Mar-2022
Date of Decision03-May-2022
Date of Web Publication3-Oct-2022

Correspondence Address:
Sankar Malayandi
Department of Biotechnology, Mepco Schlenk Engineering College (Autonomous), Sivakasi -626005
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijnpnd.ijnpnd_11_22

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   Abstract 


Introduction: Colon cancer is the third largest cause of cancer-related death according to a survey report by GLOBOCAN 2020. Though several common conditions, including family history and personal health care, are reported as the causes of colon cancer, the real cause of colorectal cancer is unrevealed. Treatment with chemical drugs like 5-fluorouracil in combination with radiation therapy can help to shrink tumor size. Surgical procedures can also be performed at the early stage along with the administration of drugs. However, the available treatment strategies are equally toxic to healthy cells and, in general, are nonspecific. The side effects of chemical drug administration are very devastating. Hence, natural phytochemicals can be a better choice for treating cancer. Materials and methods: In this article, in silico screening of plenty of phytochemicals from 200 different plants was performed. Databases such as PubChem, Drugbank, ChemSpider, eMolecules, and Chembank were used for extracting structures of phytochemicals including flavonoids, alkaloids, peptides, steroids, or any other organic compounds, and used as ligands. Vital proteins involved in colon cancer pathways are extracted from the protein data bank based on the output from the KEGG pathway database and Cytoscape network analysis. AutoDockPyRx Python prescription-0.8 was used to predict the possible ligands and their targets using a structure-based drug discovery approach. Results and Discussion: Hispaglabridin B showed interaction with a maximum number of target proteins at low binding energies. Swiss target prediction was used to find other potent targets for the selected ligand. The binding pocket analysis showed that hispaglabridin B binds to the same position as the known inhibitor of the target protein. The amino acids involved in the protein and hispaglabridin B interaction were also studied. Polar, hydrophobic, hydrogen bond, and charge-based interactions were dominant between hispaglabridin B and its targets. PASS online was used to check the biological potential of hispaglabridin B. The drug likeliness properties and ADME characteristics of hispaglabridin B were checked using DruLiTo and Swiss ADME, respectively. The toxicity of hispaglabridin B was analyzed using preADMET and was found safe. Hispaglabridin B was not available in the drug bank, and its structure was predicted to be an isoflavonoid. Isoflavanols are polycyclic compounds containing a hydroxylated isoflavone skeleton and an aromatic hetero-polycyclic molecular framework. From the literature, the most abundant source of hispaglabridin B was found to be Glycyrrhiza glabra.Conclusion: Hence, it is concluded that hispaglabridin B could be a potential lead for developing an effective colon cancer drug.

Keywords: AutoDock, colon cancer Glycyrrhiza glabera, hispaglabridin B, in silico


How to cite this article:
Malayandi S, Marimuthu S, Jayanthi Antonisamy A. Screening and Selection of Hispaglabridin B as a Lead Compound in Colon Cancer Treatment: In Silico Approach. Int J Nutr Pharmacol Neurol Dis 2022;12:142-52

How to cite this URL:
Malayandi S, Marimuthu S, Jayanthi Antonisamy A. Screening and Selection of Hispaglabridin B as a Lead Compound in Colon Cancer Treatment: In Silico Approach. Int J Nutr Pharmacol Neurol Dis [serial online] 2022 [cited 2022 Dec 8];12:142-52. Available from: https://www.ijnpnd.com/text.asp?2022/12/3/142/357210




   Introduction Top


The colon, which is the large intestine, forms the last part of the digestive system and plays an essential role in the removal of water, salt, and nutrients from the stool. The colon is divided into four parts: the ascending colon, the transverse colon, the descending colon, and the sigmoid colon. The ascending colon is of diameter 6 to 7 cm. The cecum is the part of the large intestine and is about 8 to 10.5 cm. It joins the large intestine and the small intestine. The unwanted materials travel upward by action of peristalsis. The ascending colon is otherwise called a spiral colon. The transverse colon is the mobile part of the colon where the watershed area is present. The descending colon, also known as the distal gut, stores feces, which are then emptied into the rectum. The sigmoid colon is a curved part that connects the descending colon with the rectum. According to the survey report by GLOBOCAN 2020, an international Agency for Research on Cancer, 10% of new cases of colorectal cancer may go more than 10 lakhs in India.[1],[2] It also remained the second leading cause of cancer-related death (9.4%) next to lung cancer.[3],[4],[5] Signs and symptoms of colon cancer are highly varying majorly showing changes in bowel habits. The real cause of colorectal cancer is unknown. Factors such as age (90% of colon cancer patients are aged above 50), family history of colon cancer, presence of colorectal polyps, and personal history of uterine, breast, or ovarian cancer can increase the chances of colon cancer.[6] A physical examination is done based on family history and polyps are often detected and removed by colonoscopy.[7] FDA approved drugs including capecitabine, 5-fluorouracil, irinotecan, oxaliplatin, and trifluridine/tipiracil are commonly used for chemotherapy.[8],[9] Neoadjuvant therapy involving radiation before surgery is also in practice to reduce the size of colon cancer.[10],[11] Chemotherapy treatments often damage healthy cells and tissues and are nonspecific. Nausea, vomiting, diarrhea, fatigue, peripheral neuropathy-inability to tolerate cold, and tingling/pain are the commonly reported side effects. Prolonged or enhanced exposure to drugs such as 5-fluorouracil and capecitabine affects healthy metabolism resulting in severe toxicity or death.[12],[13],[14] Phytochemicals from many traditional plants have often proved a success for cancer treatment with less or no side effects.[15],[16],[17],[18],[19]

The development of omics tools has minimized the problem of side effects by their ability to link diseases to proteins.[20] Structure-based drug designing (SBDD) is an in silico approach that helps to predict suitable ligands from phytochemical databases against specific target proteins involved in disease pathways.[21] It is a cyclic process that involves the following steps: (i) identification of target protein structure available or designed using homology modeling, (ii) in silico studies to identify potential ligands, (iii) determining the potency, efficacy, and affinity of the ligand with the receptor, (iv) biological activity of the ligand-receptor complex obtained and correlation to the structure, and (v) the cycle again repeats for finding a ligand that can bind with more affinity by incorporating some molecular modifications.[22] The enhanced feature of the structure-activity relationship also provides the actual visualization of the small molecule that is docked with the protein.

Molecular docking is a powerful tool in SBDD that provides visualization of drug compounds into the active site of protein and helps in the identification of bioactive HIT compound.[23] Advances in system biology have revealed the fact that a single drug may target multiple proteins; these types of drugs show higher efficacy and less toxicity. In cases of complex diseases like cancer, the number of a target protein is huge; also, there exists a high degree of interaction between these proteins. One protein may regulate several other proteins and hence the proteins that have higher interaction with other proteins involved in cancer proliferation may be considered while selecting lead molecules.[24] As the traditional concept of directing a single target of a disease with a single-drug approach to drug discovery is currently facing numerous challenges of safety, efficacy, and sustainability, a new discipline network pharmacology, based on omics data integration and multi-target drug development, is recently gaining importance in novel drug discovery.[25]

In this study, network construction is used to shortlist the most interacting key target proteins involved in pathways related to the emergence and progression of colon cancer and molecular docking is used to find possible lead compounds from phytochemicals of medicinal plants to treat colon cancer.


   Materials and methods Top


Target protein identification

Candidate protein targets associated with colon cancer were identified from the KEGG pathway database and literature survey. Pathway identifiers identify the pathway maps in the database. The code prefix was used to search the pathways in Homo sapiens and the keyword colon cancer was used. The candidate targets were given as input to STRING (Search Tool for Retrieval of Interacting Genes/Proteins) database 11.5 (https://string-db.org/) with a search limited to the key word “Homo sapiens” was used to generate protein-protein interactions (PPI). The TSV (tab-separated values) format of the output from the STRING database was given as input to Cytoscape3.9.0. Cytoscape is an open-source platform that aids to visualize the complicated network of interactions among proteins.[26]

Ligand preparation

The traditional medicinal plants were chosen based on the literature survey and data retrieved from Indian Medicinal Plants, Phytochemistry and Therapeutics. For every phytochemical, the 3D conformation was downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/) in SDF format.[27] PubChem is a database maintained by NCBI. It contains information about the molecules and their bioassays. The SDF file of the ligand molecule was converted to the PBD format using open babel in the Auto dock.[28]

Target preparation

The key protein targets were downloaded from the Protein Data Bank (PDB) database in the PDB format (http://www.rcsb.org). Each protein has a unique PDB identifier, which is a four-character alphanumeric identifier. The protein data bank database is a repertoire of information on protein structure identified by X-ray diffraction, electron microscopy, and nuclear magnetic resonance. The PDB structures of selected target proteins with the ligand bound to them are downloaded and saved in PDB format. The downloaded protein file was prepared for docking using molecular visualization software PyMOL after removing water molecules and adding hydrogen bonds, which are essential to provide interaction with the ligands. In the preparation of proteins for docking, the residues within 6 A° of ligand bound to the target were selected as the active site and saved in a separate PDB file to be used for molecular docking purposes.[29]

Molecular docking

Docking was carried out using AutoDockPyRx Python prescription-0.8, which is a practical screening tool used to screen libraries of compounds against potential targets. The AutoDock wizard in the PyRx was selected followed by the addition of desired protein downloaded in the PDB format.[30] The missing atoms in the PDB files were corrected using PyMOL. All the water molecules obstructing the docking procedure were removed. The small molecules, phytochemicals of selected plants, were loaded using the open babel option, which can load files in SDF format into PyRx. Finally, it was converted to a file supporting docking format (pdbqt). The auto grid box was set with dimensions X: 25.000, Y: 25.000, Z: 25.000, and the run AutoDock vina was selected.[31] The docking results were displayed as a table, which showed the binding energy for the protein and the small molecule.

Post-docking analysis

Amino acids are polar, nonpolar, positively charged, and negatively charged based on their side chains. Charge-based interactions, electrostatic, van der Waals, and hydrophobic interactions of amino acids at the binding site with functional groups of ligand and inhibitors confer stability to the protein-ligand complex. The amino acids interacting with the known inhibitor for each of the screened targets and selected ligand were visualized using Maestro Version 11.9.011, MMshare Version 4.5.011, Release 2019-1.2D. Interaction diagram between the proteins and the phytochemicals to be used as a ligand was obtained as an image. The docked results from PyRx were opened in PyMOL as a pdbqt format in the order of protein molecule followed by the inhibitor. The protein object was selected as the surface, and the color was set to gray. The inhibitor was selected to show as sticks, and a different color was used to differentiate it from the protein. The image was saved in png format.

Ligand property analysis

The ligand properties were analyzed on DruLiTo, PASS online (http://way2drug.com/passonline/), and Swiss Absorption Distribution Metabolism Excretion (ADME) (http://www.swissadme.ch/). DruLiTo is a visualization tool to find the likeliness of the ligand molecule as a drug. It works on Lipinski’s rule, Ghose filter, BBB rule, Veber rule, and MDDR-like rule to check for the drug likeliness. The PASS online was used to find the characteristics of the drug molecule. The toxicity and ADME (Absorption Distribution Metabolism Excretion) of the drug were tested using preADMET. The presence of the commercial drug similar to the lead molecule based on the ligand properties was analyzed on DruLiTo, PASS online, and Swiss ADME. The other possible targets for the ligand molecule were predicted using Swiss target prediction (http://www.swisstargetprediction.ch/). The presence of a drug similar to a lead molecule in the drug bank database (https://go.drugbank.com/) was also checked.


   Results and Discussion Top


Target protein selection

Wnt/β-catenin signaling pathway, PI3K signaling pathway, mitogen-activated protein kinase (MAPK) pathway, ErbB pathway, mTOR pathway, p53 pathway, and apoptotic pathway were identified as significant pathways in colon cancer using the KEGG pathway database. It is reported that activation of Wnt or genetic mutation of Wnt components results in the accumulation of β-catenin in the cytoplasm and further translocation into the nucleus. Nuclear accumulation of β-catenin is observed in 80% of colorectal carcinoma. The downstream binding of β-catenin promotes the transcription of target genes such as Jun, c-Myc, and CyclinD-1 in a tissue-specific manner that encodes for oncoproteins mostly.[32] Hence, targeting β-catenin pathway is a promising approach as it is involved in cell survival, migration, and angiogenesis.[33] The tumor-promoting role of the PI3-K/Akt/PTEN signal transduction pathway and its association with the antiapoptotic family of proteins is well established. Downregulation of (Phosphatidylinositol 3-kinase) PI3-K/Akt/PTEN pathway resulted in induction of apoptosis, increasing ROS generation, and decrease in mitochondrial membrane potential upon treatment of colon cancer with curcumin and diclofenac. The above results augmented the selection of this pathway in the present study.[34] MAPK signaling pathways follow a cascade of phosphorylation events upon response to extracellular signals. Mutations in KRAS and BRAF lead to constitutive activation of the (Extracellular signal-regulated protein kinase) ERK1/2 signaling cascade of the MAPK pathway. Also, ERK1/2 which shares 84 % homology, is reported to role play in cell proliferation, metastasis like epithelial to mesenchymal transition (EMT), migration, or invasion. The specific role of ERK2 as a mediator of EMT in both breast and colon tumors had been reported. This necessities choosing the MAPK pathway for analyzing the interaction of proteins.[35],[36] The membrane proteins upstream to the PI3K pathway, proteins in PI3K signaling cascade, or the downstream proteins could be targeted. Inhibiting the constitutive expression of STAT in cancer could prevent the tumor cell survival and migration and induce the cells to undergo apoptosis. The Ras/extracellular signal-regulated kinase (ERK) inhibitor is a novel finding for chemotherapy since it regulates cell proliferation, survival, growth and motility, and tumorigenesis.[37] Activating tumor suppressor genes like p53is a direct route to kill the cancer cells which have survival advantage over other cells because of escaping apoptosis.[38] Also, membrane proteins like VEGFR2, EGFR, PDGFR, and insulin-like growth factors can be the ideal targets to inhibit the angiogenesis process.[39],[40],[41] The expression of these proteins is upregulated in tumor cells, and a variety of inhibitors are available on the market. The malfunctioning of proapoptotic, antiapoptotic, and tumor suppressor proteins is the primary reason for the spread of tumors. Proteins such as β-catenin, PI3K, ProCaspase 3, Bcl-2, p53, and pRb are also selected as targets for inhibition to prevent cancer growth.[42],[43],[44],[45]

Protein-protein interaction network

The PPI was analyzed using STRING database 11.5 (https://string-db.org/). With Homo sapiens as the organism selected and by using the option of multiple proteins, candidate targets are given as input to the STRING database. Network construction is carried out with a full STRING network focusing on both physical and functional protein associations, with high confidence of 0.7. The output of STRING is representing the PPI with circles denoting the proteins referred to as nodes. The number of nodes is 57 and the edges representing the interactions among proteins are about 291. In this network interaction, the average node degree and average local clustering coefficient are about 10.2 and 0.659, respectively. Also, the expected number of edges is 75 with PPI enrichment P < 1.0e-16. The average node degree indicates the number of targets that are connected to the key target protein. The greater the average node degree, the stronger the role of the key target protein corresponding to this node in the network.[46] When the TSV format of output from STRING is given as input to Cytoscape, the network analysis for the output of the STRING database was obtained as a degree layout circle as shown in [Figure 1] proteins such as VEGFR (KDR), HIF, PI3K, ß-catenin (CCND1), Caspase 3 (CASP3), IAP, Bcl2, p53, DCC, RB1, PDGFR, FGFR were chosen as protein targets for colon cancer.
Figure 1 STRING database output on protein-protein interaction network related to colon cancer

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Molecular docking

Docking using AutoDockPyRx was performed for 180 phytochemicals from 120 plants and 13 selected protein targets. PyRx was loaded with macromolecule and ligand as protein targets and phytochemicals, respectively. They were converted to pdbqt files similar to PDB with inclusion of partial atomic charges (Q) and atom types (T) for each ligand. The lower the binding energy score, the greater the binding affinity between small molecules and selected protein targets. During docking, exhaustiveness for all selected targets was set as 8. Nine poses were predicted for each ligand-protein interaction. The binding energies of nine docked conformations of each ligand against the protein were documented.[47] The docking results of the small molecules with 13 selected target proteins based on binding energy (kcal/mol)) for interaction between ligands and target proteins are mentioned in supplementary details. For each protein top, 10 interacting ligands were selected based on the maximum binding affinity. Among the selected compounds, interestingly the compound hispaglabridin B PubChem id: 5318057 had interaction with 9 out of 13 target proteins with greater affinity.

Lead compound selection

A Cytoscape network was constructed with the top 10 ligands for every selected protein as listed in [Table 1]. On doing the network analysis, the degree layout was selected to be circular. The ligands that interact with more than three proteins are selected separately and shown in [Figure 2]. Dark green-colored ligand interacts with nine proteins, light green colored ligand interacts with six proteins, red colored ligand interacts with five proteins, yellow colored ligand interacts with four proteins, and blue colored ligand interacts with three proteins. The ligand that interacts with a maximum number of target proteins (9 out of 13) was found to be hispaglabridin B. In the selection of a drug lead molecule, balance between affinity and selectivity is needed.[48] In order to ensure the selectivity of binding of hispaglabridin B with the nine protein targets, docking analysis with known inhibitors is performed and binding energies are compared as shown in [Table 2]. The binding energy of hispaglabridin B with target proteins such as HIF, Bcl-2, procaspase 3, and Rb-1 is almost equal to or lesser than that of commercial inhibitors of those proteins. The higher negative energy indicates the stronger binding between the protein and the hispaglabridin B. This shows that the interaction between protein and hispaglabridin B is stable.
Figure 2 Selection of proteins and ligands with maximum interaction

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Table 1 Top 10 ligands interacting with target proteins

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Table 2 Comparative binding energy of hispaglabridin B with known inhibitor

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Post-docking analysis

The output pdbqt file of docking corresponding to nine poses is analyzed and the best model with the lowest binding energy is selected for further analysis. Analysis of position and binding pocket interactions was performed. Hispaglabridin B binds in the same pocket as that of the known inhibitor of the target proteins. The protein was viewed as a surface represented by gray color, red color indicates the hispaglabridin B binding, and green color indicates the inhibitor binding site ([Figure 3]a–i).. In the case of HIF, hispaglabridin B is binding to the interior of the protein than to the surface and also has lower binding energy (–6.3 kcal/mol) than the inhibitor as shown in [Table 2]. The amino acids at the binding site were also visualized as shown in [Figure 4]. Hydrogen bonding, charge based, and polar interactions were dominating in the binding pockets leading to very stable interactions. The selected molecule hispaglabridinB was analyzed for other possible targets binding using Swiss target prediction. The majority of the protein targets bound by hispaglabridin B were found to be the receptors responsible for causing cancer as shown in [Figure 5]. Hence, hispaglabridin B could act as an effective inhibitor for these multiple proteins.[49],[50]
Figure 3 Image of binding pocket a) Interaction of VEGFR2 with inhibitor and Hispaglabridin B. b) Interaction of IFG with inhibitor and Hispaglabridin B. c) Interaction of HIF with inhibitor and Hispaglabridin B d) Interaction of IAP with inhibitor and Hispaglabridin B e) Interaction of procaspase-3 with inhibitor and Hispaglabridin B f) Interaction of Bc1-2 with inhibitor g) Interaction of RB 1 with inhibitor and Hispaglabridin B h) Interaction of p-53 with inhibitor and Hispaglabridin B i) Interaction of DCC with inhibitor and Hispaglabridin B. Red color indicates the Hispaglabridin B binding, green color indicates the inhibitor binding site

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Figure 4 Amino acid interaction of HIF with known inhibitor and Hispaglabridin B: a) The critical amino acid residues of HIF involved in interaction with the known inhibitor is shown in the figure. b) The hydroxyl group in Hispaglabridin B is involved in hydrogen bonding with Ser 276 residue of HIF. Charge based interaction is observed between Lys 253 and Hispaglabridin B. Hydrophobic interaction is involved between Phe 254, Phe 280 residues and Hispaglabridin B

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Figure 5 Possible targets for Hispaglabridin B

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Drug likeliness of the selected lead compound

The activity of the selected molecule was analyzed on PASS online website. When the value of probability to be active (Pa) is greater than 0.7 for a ligand, it is most likely to express biological activity. The Pa and Pi scores vary in the range of 0.00 to 1.00 and their sum is not equal to 1 as their potentialities are predicted independently. [Table 3] shows that hispaglabridin B has potent biological activity. The lead molecule hispaglabridin B has a molecular weight of about 390.18; log P was 3.431; the number of hydrogen bond acceptors was four; and the number of hydrogen bond donors was one. Hence, the likeliness of the lead molecule was found to be reasonable based on Lipinski’s rule of five. The prediction of absorption, distribution, metabolism, and excretion for the lead compound was done using the Swiss ADME website[51] and the result is shown in [Figure 6]. Accordingly, it showed higher gastrointestinal absorption and moderate solubility in water with a bioavailability score of 0.55. This score is indicating that the lead compound has better pharmacokinetic properties.[52] The toxicity analysis was done preADMET. This in silico test was done to check the impact of the drug on the environment. The result obtained (0.006) suggests that the toxicity of algae was less. So, the drug is environment-friendly. The Ames test result tells us about the carcinogenicity of the molecule. Hispaglabridin B was found to be a non-mutagen. The test Medak and minnow tells us that the compound is friendly to fish [Table 4]. When the drug bank database was browsed for the details of hispaglabridin B, no available drug was found. Also, the possible predictions regarding its taxonomy and its structure were obtained from the drug bank. This compound belongs to a group of organic compounds known as isoflavonoids. These are polycyclic compounds containing a hydroxylated isoflavone skeleton and aromatic hetero-polycyclic molecular framework. A literature survey indicated the natural availability of hispaglabridin B from the roots of Glycyrrhiza glabra.[53]
Table 3 Activity analysis for hispaglabridin B

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Table 4 Toxicity analysis for hispaglabridin B

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Figure 6 ADME report predicting the possible use of Hispaglabridin B as a potential drug

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   Conclusion Top


Colon cancer was a significant cause of death in cancer-related diseases. So, we set out to find a natural drug molecule that is less harmful than the drugs available on the market. From the KEGG pathway analysis, the possible protein targets involved in causing colon cancer were selected, the protein targets with maximum interaction were chosen based on network construction, and the phytochemicals from the traditionally used plants were chosen as ligand molecules. The docking studies for the chosen protein targets and the ligand molecules were done and a ligand molecule hispaglabridin B was found to interact with nine protein targets that were chosen. Comparatively, with the approved inhibitor, the molecule hispaglabridin B was found to have stronger binding. The binding pocket and amino acid interactions analyzed using Maestro were similar for hispaglabridin B and the approved inhibitor indicating overlapping of binding sites of the selected compound and inhibitors used as drugs for targeting the proteins. On further analysis, the selected molecule was also found to exhibit drug likeliness with excellent ADME properties and lowered level of toxicity. Thus, hispaglabridin B was chosen as the lead molecule. The other possible targets for the lead molecule were analyzed to prevent the side effects of the lead molecule. Finally, the taxonomy of the lead molecule was found to be polycyclic compounds containing a hydroxylated isoflavone skeleton with an aromatic hetero-polycyclic molecular framework. As a future perspective, optimization of extraction and purification of hispaglabridin B from roots of Glycyrrhiza glabra and assessing their in vitro and in vivo roles in decreasing the progression of colon cancer can be done.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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