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Journal of Drug Delivery and Therapeutics

Open Access to Pharmaceutical and Medical Research

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Open Access   Full Text Article                                                                                                                       Research Article 

In-silico ADMET profile of Ellagic Acid from Syzygium cumini: A Natural Biaryl Polyphenol with Therapeutic Potential to Overcome Diabetic Associated Vascular Complications

S. Ramya¹, M. Murugan², K. Krishnaveni³, M. Sabitha², C. Kandeepan², R. Jayakumararaj⁴*

¹ The Thavaram Trust, Thiruppalai - 625014, Madurai, TamilNadu, India

² PG Department of Zoology, Arulmigu Palaniandavar College of Arts & Culture, Palani-624601, India

³ Department of Zoology, GTN College, Dindigul, TamilNadu, India

⁴ Department of Botany, Government Arts College, Melur – 625106, Madurai, TamilNadu, India

 

INTRODUCTION 

Naturally, EA is present in fruits (pomegranates, persimmons, raspberries, black raspberries, wild strawberries, peaches, plums - Indian black plum), seeds (walnuts, almonds), and vegetables^1,2. Pharmacological attributes of EA includes antioxidant^3,4,5, anti-proliferative⁵, apoptotic^5,6, antimalarial⁷, anti-inflammatory⁸, anti-hepatotoxic^9,10, antitumor¹¹, anti-cholestatic, anti-fibrogenic and antiviral^12,13. EA has been reported for its potential neuroprotective, cytotoxic, anticancer, cardio-protective, anti-tubercular activity and is used in the management of metabolic syndrome. EA has been claimed to improve hepatic functions against toxic and pathological conditions. Derivatives of EA-urolithins and 4,4’-Di -O-methyl EA have been reported to inhibit colon cancer cell proliferation. EA has been reported to block activated pancreatic stellate cells, and exhibit apoptosis inducing activities¹.

EA belongs to the class of hydrolysable tannins (MW 302.194 g/mol; MF C₁₄H₆O₈) with melting point 350°C and is highly thermo-stable due to the presence of four lipophilic rings, four phenolic groups and two lactones (Fig. 1). EA is organic hetero-tetracyclic compound resulting from dimerization of GA by oxidative aromatic coupling with intramolecular lactonisation of carboxylic acid groups¹. EA is a cyclic ketone, (lactone) and a member of catechols - polyphenol, appears as cream-colored needles or yellow powder, odourless, sparingly soluble in water, soluble in alkalies, pyridine, insoluble in ether, when heated emits acrid smoke and irritating vapour^1,14.

Nevertheless a few reports on improving water-solubility and bioavailability of EA are available, a wide gap still exists from practical utility point of view. Currently, synthetic antioxidants are replaced by natural antioxidants as the former are reported to have carcinogenic properties. Plants serve as primary source of natural antioxidant molecules capable of eliminating or neutralizing the harmful reactive oxygen species (ROS). Natural antioxidants (NAs) are free-radical scavengers, reduction agents, pro-oxidant metal complexes, singlet oxygen quenchers. NAs safeguard human body from free radicals and delay progression of chronic illnesses (Cancer, Heart Disease, Stroke) prevent lipid oxidative rancidity, boost plasma’s antioxidant ability and natural immunity of the system^1,14. 

Maintenance of good health is attributed to consumption of fruits, vegetables, herbs, seeds that contain a group of natural polyphenols classified as hydrolysable tannins (HT) viz., ellagitannins (ETs). ETs represent a diverse class of polyphenolic natural products with remarkable structural complexity. Members of ETs include glycosyl esters of EA and/or GA motifs. ETs that contain flavone motifs as part of the structure, referred to as flavano-ellagitannins¹⁵. EA, a component of ellagitannins, is a biaryl polyphenol where two GA motifs are oxidatively coupled via a carbon-carbon bond to join two aryl rings. Further, one or more hexahydroxydiphenoic acid (HHDP) units are ester-linked with a sugar molecule. ETs are rather not/ less absorbed in the gastrointestinal tract (GIT), when hydrolysed yields EA¹⁶. Due to water soluble nature, its metabolism in GIT is further complicated by the irreversible binding to cellular DNA and proteins. Hence, EA has a very low or sometime very poor bioavailability in the system. Additionally, gut bacteria are known to metabolize EA into urolithins that has a better bioavailability compared to EA. Rate of metabolism of EA by gut bacteria and levels of urolithins is attributed to discrepancies in outcomes observed from in vitro vs in vivo studies¹. 

Metabolic inflammation plays a key role in the pathogenesis of diabetes associated complications (diabetic nephropathy, retinopathy, and neuropathy). T2DM is characterized by systemic inflammation, sustained elevation of circulating levels of pro-inflammatory cytokines (IL-6, IL-1β, TNF-α), chemokine-evoked enhanced recruitment of inflammatory cells, activation of inflammatory response to NF-κB and signaling of AMPK and PPAR-γ. On the other hand, treatment, cancer exhibits resistance to drugs, phenomenon referred to as multidrug resistance. MDR is attributed to (a) heightened DNA repair, (b) reduced drug uptake, (c) enhanced drug efflux, (d) mutation of drug targets, (e) altered inherent apoptotic process, and (f) increased drug metabolism¹⁶. Drug resistance has been attributed to increased levels of proteins such as mitogen-activated protein kinases (MAPKs), protein kinase B (PKB), and nuclear factor-κB (NF-κB), and overexpression of a type of ATP-binding cassette (ABC) transporter, referred to as P-glycoprotein (P-gp)¹⁷. Recently, EA has attracted researchers to develop drug leads to overcome MI and MDR¹⁸.

At the biochemical level, several mechanisms have been proposed to be associated with therapeutic action, including efficacy in normalizing lipid metabolism and lipidemic profile, regulating pro-inflammatory mediators, such as IL-6, IL-1β, and TNF-α, upregulating nuclear factor erythroid 2-related factor 2 and inhibiting NF-κB action¹⁹. EA exerts appreciable neuroprotective activity by its free radical-scavenging action, iron chelation, initiation of several cell signalling pathways, and alleviation of mitochondrial dysfunction¹⁹. Presence of poly-oxygenated aryl rings allows EA to quench free radicals, making it highly effective bioactive molecule with significant antioxidant and cytoprotective potential¹⁸. Physico-chemical and biological properties of EA have been extensively worked out and reviewed in literature^1,14,20, however, major pharmacological limitation of EA is accredited to its low solubility that significantly reduces its bioavailability therefore in-depth study on absorption, distribution, metabolism, excretion, and toxicity (ADMET) is warranted.  

MATERIALS AND METHODS

Syzygium cumini - botanical description

Trees, 6-20 m tall; branchlets grayish white when dry, terete; petiole 1-2 cm; leaf blade broad to narrowly elliptic, 6-12×3.5-7 cm, leathery, abaxially slightly pale when dry, adaxially brownish green to blackish brown and slightly glossy when dry, both surfaces with small glands, secondary veins numerous, 1-2 mm apart, and gradually extending into margin, intra-marginal veins ca. 1 mm from margin; base broadly cuneate to rarely rounded, apex rounded to obtuse and with a short cusp; inflorescences axillary on flowering branches or occasionally terminal, paniculate cymes, to 11 cm; hypanthium obconic or long pyriform, ca. 4 mm or 7-8 mm; calyx lobes inconspicuous, 0.3-0.7 mm; petals 4, white or light purple, coherent, ovate and slightly rounded, ca. 2.5 mm; stamens 3-4 mm; style as long as stamens; fruit red to black, ellipsoid to pot-shaped, 1-2 cm, 1-seeded; persistent calyx tube 1-1.5 mm; fl. Feb-Mar or Apr-May; fr. Jun-Sep^21-25,.

GC-MS Analysis 

Leaf samples of S. cumini, were collected from Alagarkovil Reserve Forest (longitude/ latitude geographical coordinates 10.0748° N, 78.2131° E, Eastern Ghats) Dindigul District, Tamil Nadu, India. Phyto-components were identified using GC–MS detection system as described previously²⁶, however with modification, whereby portion of the extract was analysed directly by headspace sampling. GC–MS analysis was accomplished using an Agilent 7890A GC system set up with 5975C VL MSD (Agilent Technologies, CA, and USA). Capillary column used was DB-5MS (30 m × 0.25 mm, film thickness of 0.25 μm; J&W Scientific, CA, USA). Temperature program was set as: initial temperature 50°C held for 1 min, 5°C per min to 100°C, 9°C per min to 200°C held for 7.89 min, and the total run time was 30 min. The flow rate of helium as a carrier gas was 0.811851 mL/ min. MS system was performed in electron ionization (EI) mode with Selected Ion Monitoring (SIM). The ion source temperature and quadruple temperature were set at 230°C and 150°C, respectively. Identification of phyto-components was performed by comparison of their retention times and mass with those of authentic standards spectra using computer searches in NIST 08.L and Wiley 7n.l libraries²⁷. 

ADMET Predictions 

Physicochemical properties were computed using FAF-Drugs4 (28961788)/ RDKit - open-source CIP. Selected phytocompounds were subjected to ADMET prediction using QikProp (version 4.3, Suite 2015-1; Schrödinger, LLC: New York, NY) and toxicity prediction using TOPKAT (Accelrys, Inc., USA). Qik-Prop develops and employs QSAR/QSPR models using partial least squares, principal component analysis and multiple linear regression to predict physico-chemically significant descriptors^28-31. Druggabiity scores were computed using FAF-Drugs4 (28961788)/ FAF-QED (28961788) - open-source CIP. The vNN-ADMET webserver was used to predict absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and predict properties, such as cytotoxicity, mutagenicity, cardiotoxicity, drug-drug interactions, microsomal stability, and drug-induced liver injury^32-35.

 

 

RESULTS AND DISCUSSION 

ChemicaL kingdom	:	Organic compounds
Super class	:	Phenylpropanoids and polyketides
Class	:	Tannins
Subclass	:	Hydrolyzable tannins
PubChem Identifier	:	5281855
ChEBI Identifier	:	4775
CAS Identifier	:	476-66-4
Synonyms	:	Ellagic Acid; Methyl Jasmonate
Canonical SMILES	:	OC1CC2C(=O)OC3C4C2C(C1O)OC(=O)C4CC(C3O)O
InChI Key	:	AFSDNFLWKVMVRB-UHFFFAOYSA-N

Physicochemical Properties 

Despite the efficient pharmacodynamics and safety profile of EA, it suffers from the low bioavailability³⁶. Molecular weight of EA was calculated as 302.19 g/mol; LogP value was predicted as 1.31; LogD value was predicted as 0.53; LogSw value was predicted as -2.83. Number of stereo-centers was predicted as 0; Stereo-chemical complexity was predicted as 0.000; Fsp3 was predicted as 0.000; Topological polar surface area was calculated as 141.34 Ų; Number of hydrogen bond donors was calculated as 4; Number of hydrogen bond acceptors was calculated as 8; Number of smallest set of smallest rings (SSSR) was calculated as 1; Size of the biggest system ring was calculated as 16; Number of rotatable bond was calculated as 0; Number of rigid bond was calculated as 21; Number of charged group was calculated as 0; Total charge of the compound was calculated as 0; Number of carbon atoms was ascertained as 14; Number of heteroatoms was ascertained as 8; Number of heavy atoms was ascertained as 22; Ratio between the number of non-carbon atoms and the number of carbon atoms was ascertained³⁷ as 0.57 (Table 1). 

Druggability Properties 

In silico studies are expected to reduce the risk of late-stage attrition of drug development and to optimize screening and testing by looking at promising Druggability Properties^37, Lipinski's rule of 5 violations was predicted as 0; Veber rule was predicted as Good; Egan rule was predicted as Good; Oral PhysChem score (Traffic Lights) was predicted as 2; GSK's 4/400 score was predicted as Good; Pfizer's 3/75 score was predicted as Good; Weighted quantitative estimate of drug-likeness (QEDw) score was predicted as 0.245; Solubility of EA was predicted as 17843.03; Solubility Forecast Index of EA was predicted as Good (Table 2). 

ADMET Properties  

Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) prediction models, including performance measures were performed online³⁸ (Table 3). In the resent study 15 models covered a diverse set of ADMET endpoints including Maximum Recommended Therapeutic Dose (MRTD),³⁹ chemical mutagenicity,³² human liver microsomal (HLM),³⁴ Pgp inhibitor/substrates.⁵ vNN model for cross validation of ADMET data for EA is provided in Table 4 a,b.

 

Liver Toxicity DILI

Drug-induced liver injury (DILI) is the most commonly cited reason for drug withdrawals from the market. This model predicts whether a compound could cause DILI. The dataset of 1427 compounds were obtained and used.⁴⁰ Dataset contained both pharmaceuticals and non-pharmaceuticals assuming a compound as causing DILI if it was associated with a high risk of DILI and not if there was no such risk.

Cytotoxicity (HepG2)

Cytotoxicity - the degree to which a chemical causes damage to cells. Cytotoxicity prediction model was developed using in vitro data on toxicity against HepG2 cells for 6,097 structurally diverse compounds, collected from ChEMBL considering compounds with an IC₅₀ ≤ 10 μM in the in vitro assay as cytotoxic.

Metabolism HLM

Human liver microsomal (HLM) stability assay is used to identify and exclude compounds that are too rapidly metabolized. For a drug to achieve effective therapeutic concentrations in the body, it cannot be metabolized too rapidly by the liver. Compounds with a half-life of 30 min or longer in an HLM assay is considered as stable; otherwise considered unstable. HLM data was retrieved from the ChEMBL database, manually curated, and classified as stable or unstable compounds based on the reported half-life (T_1/2 > 30 min was considered stable, and T_1/2 < 30 min as unstable. Analysis indicated that in the final dataset of 3219 compounds 2,313 were stable and 1,341 were unstable.³⁴

Cytochrome P450 enzyme (CYP) inhibition

CYPs constitute a superfamily of proteins that play an important role in the metabolism and detoxification of xenobiotics. In vitro data derived from five main drug-metabolizing CYPs viz., 1A2, 3A4, 2D6, 2C9 and 2C19 were used to develop CYP inhibition models⁴¹. CYP inhibitors were retrieved from PubChem and classified a compound with an IC₅₀ ≤ 10 μM for an enzyme as an inhibitor [CYP1A2 (7558), CYP3A4 (8072), CYP2D6 (8155), CYP2C9 (7805), and CYP2C19 (10373)].

Membrane Transporters – BBB

Blood-brain barrier (BBB) is a highly selective barrier that separates the circulating blood from the central nervous system. A vNN-based BBB model was developed, using 353 compounds whose BBB permeability values (log BB) were obtained.⁴² Compounds were classified with log BB values of less than –0.3 and greater than +0.3 as BBB non-permeable and permeable^,43  Fig. 2.

Pgp Substrates and Inhibitors

P-glycoprotein (Pgp) is cell membrane protein that extracts many foreign substances from the cell. Cancer cells often overexpress Pgp, which increases efflux of chemotherapeutic agents from cell and prevents treatment by reducing the effective intracellular concentrations of such agents. Hence, identifying compounds that can either be transported out of cell by Pgp (substrates) or impair Pgp function (inhibitors) is considered as significant aspect of drug lead. Models to predict both Pgp substrates and Pgp inhibitors were developed^44,45 with a dataset consisting of 422 substrates and 400 non-substrates in combination^46,47. Analysis of the final dataset indicates that among the selected substrates 2304 were inhibitors and 822 were non-inhibitors.

hERG (Cardiotoxicity)

Human ether-à-go-go-related gene (hERG) codes for a potassium ion channel involved in the normal cardiac repolarization activity of the heart. Drug-induced blockade of hERG function causes long QT syndrome, which may result in arrhythmia and death. 282 known hERG blockers were retrieved and classified with an IC₅₀ cutoff value of 10 μM or less as blockers.⁴⁰ Analysis with set of 404 compounds with IC₅₀ values greater than 10 μM from ChEMBL classified them as non-blockers.

MMP (Mitochondrial Toxicity)

Given the fundamental role of mitochondria in cellular energetics and oxidative stress, mitochondrial dysfunction has been implicated in cancer, diabetes, neurodegenerative disorders, and cardiovascular diseases. Large dataset of chemical-induced changes in mitochondrial membrane potential (MMP) was used based on the assumption that a compound that causes mitochondrial dysfunction is likely to reduce MMP. vNN-based MMP prediction model was developed, using 6,261 compounds from a library of 10,000 compounds (~8,300 unique chemicals) at 15 conc., each in triplicate, to measure changes in the MMP in HepG2 cells.⁴⁸ Data depicted that 913 compounds decreased MMP, whereas 5,395 compounds had no effect.

Mutagenicity (AMES Test)

Mutagens cause abnormal genetic mutations leading to cancer. A common way to assess a chemical’s mutagenicity is Ames test³⁵. A prediction model was developed using a dataset of 6,512 compounds, of which 3,503 were Ames-positive.

Maximum Recommended Therapeutic Dose - MRTD

Maximum Recommended Therapeutic Dose (MRTD) is an estimated upper daily dose that is safe. A prediction model was developed based on a dataset of MRTD values publically disclosed by FDA of GRAS standard, mostly of single-day oral doses for an average adult with a body weight of 60 kg, for 1,220 compounds (most of which are small organic drugs), excluding organometallics, high-molecular weight polymers (>5,000 Da), nonorganic chemicals, mixtures of chemicals, and very small molecules (<100 Da) using external test set of 160 compounds collected by FDA for validation⁴⁹. The total dataset for model contained 1,184 compounds.³⁵ The predicted MRTD value is reported in mg/day unit based upon an average adult weighing 60 kg. 

Further, Predicted Human Target Proteins (STITCH database) towards Ellagic Acid is provided in Table 5 and Cytoscape network Fig. 3. The major challenging issue in diabetes management is the prevention of diabetes associated complications that remain the leading cause of diabetes-related mortality. Inflammatory response is one of key driving forces to promote the pathogenesis of diabetes and its complications. Bioactive phytochemicals such as EA with significant anti-inflammatory benefits hold great promise for the treatment of diabetes^50,51,52.

CONCLUSION 

Polyphenolic natural products represent a chemically unique class of molecules as potential antioxidants and anticancer agents. EA and its derivatives are plant based natural products with significant health benefits and holds potential for advanced biomedical applications. However, its production on industrial scale for biomedical applications from commercial point of view has been hampered by the lack of in-depth information on EA and its derivatives. In-silico-ADMET prospecting and pharmacoinformatics is expected fulfil the lack of high-grade products with high bioavailability. Accordingly, QSAR knowledge base on structural and functional informatics of ETs and EA derivatives would be the first step towards a wider exploration for biomedical applications.

REFERENCES

1. Agrawal OD, Kulkarni YA. Mini-Review of Analytical Methods used in Quantification of Ellagic Acid. Reviews in Analytical Chemistry. 2020; 39(1):31-44. https://doi.org/10.1515/revac-2020-0113

2. Gajera HP, Gevariya SN, Hirpara DG, Patel SV, Golakiya BA. Antidiabetic and antioxidant functionality associated with phenolic constituents from fruit parts of indigenous black jamun (Syzygium cumini L.) landraces. J Food Sci Technol 2017; 54:3180-3191 https://doi.org/10.1007/s13197-017-2756-8

3. Meyer AS, Heinonen M, Frankel EN. Antioxidant Interactions of Catechin, Cyanidin, Caffeic acid, Quercetin, and Ellagic acid on Human LDL Oxidation. Food Chem. 1998; 61(1-2):71-5. https://doi.org/10.1016/S0308-8146(97)00100-3

4. Priyadarsini KI, Khopde SM, Kumar SS, Mohan H. Free Radical Studies of Ellagic acid, a Natural Phenolic Antioxidant. J Agric Food Chem. 2002; 50(7):2200-6. https://doi.org/10.1021/jf011275g

5. Seeram NP, Adams LS, Henning SM, Niu Y, Zhang Y, Nair MG, et al. In Vitro Antiproliferative, Apoptotic and Antioxidant Activities of Punicalagin, Ellagic acid and a Total Pomegranate Tannin Extract Are Enhanced in Combination with Other Polyphenols as Found in Pomegranate Juice. J Nutr Biochem. 2005; 16(6):360-7. https://doi.org/10.1016/j.jnutbio.2005.01.006

6. Hussein MZ, Al Ali SH, Zainal Z, Hakim MN. Development of Antiproliferative Nanohybrid Compound with Controlled Release Property Using Ellagic acid as the Active Agent. Int J Nanomedicine. 2011; 6(1):1373-83. https://doi.org/10.2147/IJN.S21567 

7. Soh PN, Witkowski B, Olagnier D, Nicolau ML, Garcia-Alvarez MC, Berry A, et al. In Vitro and In Vivo Properties of Ellagic acid in Malaria Treatment. Antimicrob Agents Chemother. 2009; 53(3):1100-6. https://doi.org/10.1128/AAC.01175-08

8. Corbett S, Daniel J, Drayton R, Field M, Steinhardt R, Garrett N. Evaluation of the Anti-Inflammatory Effects of Ellagic acid. J Perianesth Nurs. 2010; 25(4):214-20. https://doi.org/10.1016/j.jopan.2010.05.011

9. García-Niño WR, Zazueta C. Ellagic acid: Pharmacological Activities and Molecular Mechanisms Involved in Liver Protection. Pharmacol Res. 2015; 97:84-103. https://doi.org/10.1016/j.phrs.2015.04.008

10. Ahn D, Putt D, Kresty L, Stoner GD, Fromm D, Hollenberg PF. The Effects of Dietary Ellagic acid on Rat Hepatic and Esophageal Mucosal Cytochromes P450 and Phase II Enzymes. Carcinogenesis. 1996; 17(4):821-8. https://doi.org/10.1093/carcin/17.4.821

11. Umesalma S, Sudhandiran G. Differential Inhibitory Effects of the Polyphenol Ellagic acid on Inflammatory Mediators NF-KB, INOS, COX-2, TNF-α, and IL-6 in 1,2-Dimethylhydrazine-Induced Rat Colon Carcinogenesis. Basic Clin Pharmacol Toxicol. 2010; 107(2):650-5. https://doi.org/10.1111/j.1742-7843.2010.00565.x

12. Chen GH, Lin YL, Hsu WL, Hsieh SK, Tzen JT. Significant Elevation of Antiviral Activity of Strictinin from Pu'er Tea after Thermal Degradation to Ellagic acid and Gallic acid. Yao Wu Shi Pin Fen Xi. 2015; 23(1):116-23. https://doi.org/10.1016/j.jfda.2014.07.007

13. Park SW, Kwon MJ, Yoo JY, Choi HJ, Ahn YJ. Antiviral activity and possible mode of action of ellagic acid identified in Lagerstroemia speciosa Leaves toward Human Rhinoviruses. BMC Complement Altern Med. 2014; 14(1):171. https://doi.org/10.1186/1472-6882-14-171

14. Evtyugin DD, Magina S, Evtuguin DV. Recent advances in the production and applications of ellagic acid and its derivatives. A review. Molecules. 2020 Jan; 25(12):2745. https://doi.org/10.3390/molecules25122745

15. Alfei S, Turrini F, Catena S, Zunin P, Grilli M, Pittaluga AM, Boggia R. Ellagic acid a multi-target bioactive compound for drug discovery in CNS? A narrative review. European journal of medicinal chemistry. 2019 Dec 1; 183:111724. https://doi.org/10.1016/j.ejmech.2019.111724

16. Kumar Singh A, Cabral C, Kumar R, Ganguly R, Kumar Rana H, Gupta A, Rosaria Lauro M, Carbone C, Reis F, Pandey AK. Beneficial effects of dietary polyphenols on gut microbiota and strategies to improve delivery efficiency. Nutrients. 2019 Sep; 11(9):2216. https://doi.org/10.3390/nu11092216

17. Zhang, H.; Xu, H.; Ashby, C.R., Jr.; Assaraf, Y.G.; Chen, Z.S.; Liu, H.M. Chemical molecular-based approach to overcome multidrug resistance in cancer by targeting P-glycoprotein (P-gp). Med. Res. Rev. 2021, 41, 525-555. https://doi.org/10.1002/med.21739

18. Yoganathan S, Alagaratnam A, Acharekar N, Kong J. Ellagic Acid and Schisandrins: Natural Biaryl Polyphenols with Therapeutic Potential to Overcome Multidrug Resistance in Cancer. Cells. 2021 Feb; 10(2):458. https://doi.org/10.3390/cells10020458

19. Gupta A, Singh AK, Kumar R, Jamieson S, Pandey AK, Bishayee A. Neuroprotective Potential of Ellagic Acid: A Critical Review. Advances in Nutrition. 2021:1-28; doi: https://doi.org/10.1093/advances/nmab007

20. Ríos JL, Giner RM, Marín M, Recio MC. A pharmacological update of ellagic acid. Planta medica. 2018 Oct; 84(15):1068-93. https://doi.org/10.1055/a-0633-9492

21. Ruan ZP, Zhang LL, Lin YM. Evaluation of the antioxidant activity of Syzygium cumini leaves. Molecules. 2008; 13(10):2545-56 https://doi.org/10.3390/molecules13102545

22. Ahmed R, Tariq M, Hussain M, Andleeb A, Masoud MS, Ali I, Mraiche F, Hasan A. Phenolic contents-based assessment of therapeutic potential of Syzygium cumini leaves extract. PloS one 2019; 14(8):e0221318 https://doi.org/10.1371/journal.pone.0221318

23. Aqil F, Gupta A, Munagala R, Jeyabalan J, Kausar H, Sharma RJ Antioxidant and antiproliferative activities of anthocyanin/ ellagitannin-enriched extracts from Syzygium cumini L.(Jamun, the Indian Blackberry). Nutrition and Cancer 2012; 64(3):428-38  https://doi.org/10.1080/01635581.2012.657766

24. Ayyappan P, Ganesan K, Jayakumararaj R Ethnobotanic information on uncommon anti-diabetic medicinal plants from Alagarkoil Forest Reserve: Evidence based strategic rationale in management of diabetics. Int J Pharm Res 2019; 16:515-26

25. Ramya S, Neethirajan K, Jayakumararaj R. Profile of bioactive compounds in Syzygium cumini-a review. J. Pharm. Res 2012; 5(8):4548-4553

26. Loganathan T, Barathinivas A, Soorya C, Balamurugan S, Nagajothi TG, Jayakumararaj R. GCMS Profile of Bioactive Secondary Metabolites with Therapeutic Potential in the Ethanolic Leaf Extracts of Azadirachta indica¬: A Sacred Traditional Medicinal Plant of INDIA. Journal of Drug Delivery and Therapeutics. 2021; 11(4-S):119-26. https://doi.org/10.22270/jddt.v11i4-S.4967

27. Soorya C, Balamurugan S, Basha AN, Kandeepan C, Ramya S, Jayakumararaj R. Profile of Bioactive Phyto-compounds in Essential Oil of Cymbopogon martinii from Palani Hills, Western Ghats, INDIA. Journal of Drug Delivery and Therapeutics. 2021; 11(4):60-5. https://doi.org/10.22270/jddt.v11i4.4887

28. Soorya C, Balamurugan S, Ramya S, Neethirajan K, Kandeepan C, Jayakumararaj R. Physicochemical, ADMET and Druggable properties of Myricetin: A Key Flavonoid in Syzygium cumini that regulates metabolic inflammations. Journal of Drug Delivery and Therapeutics. 2021 Jul 15; 11(4):66-73 https://doi.org/10.22270/jddt.v11i4.4890

29. Sabitha, M., Krishnaveni, K., Murugan, M., Basha, A.N., Pallan, G.A., Kandeepan, C., Ramya, S. and Jayakumararaj, R., In-silico ADMET predicated Pharmacoinformatics of Quercetin-3-Galactoside, polyphenolic compound from Azadirachta indica, a sacred tree from Hill Temple in Alagarkovil Reserve Forest, Eastern Ghats, INDIA. Journal of Drug Delivery and Therapeutics, 2021; 11(5-S):77-84 https://doi.org/10.22270/jddt.v11i5-S.5026

30. Loganathan T, Barathinivas A, Soorya C, Balamurugan S, Nagajothi TG, Ramya S, Jayakumararaj R. Physicochemical, Druggable, ADMET Pharmacoinformatics and Therapeutic Potentials of Azadirachtin-a Prenol Lipid (Triterpenoid) from Seed Oil Extracts of Azadirachta indica A. Juss. Journal of Drug Delivery and Therapeutics. 2021; 11(5):33-46. https://doi.org/10.22270/jddt.v11i5.4981

31. Kandeepan C, Kalaimathi RV, Jeevalatha A, Basha AN, Ramya S, Jayakumararaj R. In-silico ADMET Pharmacoinformatics of Geraniol (3, 7-dimethylocta-trans-2, 6-dien-1-ol)-acyclic monoterpene alcohol drug from Leaf Essential Oil of Cymbopogon martinii from Sirumalai Hills (Eastern Ghats), INDIA. Journal of Drug Delivery and Therapeutics. 2021; 11(4-S):109-18. https://doi.org/10.22270/jddt.v11i4-S.4965

32. Liu, R., and A. Wallqvist. Merging applicability domains for in silico assessment of chemical mutagenicity. Journal of Chemical Information and Modeling. 2014; 54(3):793-800 https://doi.org/10.1021/ci500016v

33. Liu, R., G. Tawa, and A. Wallqvist. Locally weighted learning methods for predicting dose-dependent toxicity with application to the human maximum recommended daily dose. Chemical Research in Toxicology. 2012; 25(10):2216-2226 https://doi.org/10.1021/tx300279f

34. Liu, R., P. Schyman, and A. Wallqvist. Critically assessing the predictive power of QSAR models for human liver microsomal stability. Journal of Chemical Information and Modeling. 2015; 55(8):1566-1575 https://doi.org/10.1021/acs.jcim.5b00255

35. Schyman P., R. Liu, V. Desai, and A. Wallqvist. vNN web server for ADMET predictions. Frontiers in Pharmacology. 2017 December 4; 8:889 https://doi.org/10.3389/fphar.2017.00889

36. Seeram NP, Lee R, Heber D. Bioavailability of ellagic acid in human plasma after consumption of ellagitannins from pomegranate (Punica granatum L.) juice. Clinica Chimica Acta. 2004 Oct 1; 348(1-2):63-8. https://doi.org/10.1016/j.cccn.2004.04.029

37. Dharani J, Ravi S. in silico ADMET Screening of Compounds Present in Cyanthillium cinereum (L.) H. Rob. Asian Journal of Chemistry. 2020; 32(6):1421-6. https://doi.org/10.14233/ajchem.2020.22569

38. Mohanraj K, Karthikeyan BS, Vivek-Ananth RP, Chand RB, Aparna SR, Mangalapandi P, Samal A. IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry and Therapeutics. Scientific Reports. 2018; 8(1):1-7. https://doi.org/10.1038/s41598-018-22631-z

39. Liu T, Oprea T, Ursu O, Hasselgren C, Altman RB. Estimation of maximum recommended therapeutic dose using predicted promiscuity and potency. Clinical and translational science. 2016 Dec; 9(6):311-20. https://doi.org/10.1111/cts.12422

40. Schyman, P., R. Liu, and A. Wallqvist. General purpose 2D and 3D similarity approach to identify hERG blockers. Journal of Chemical Information and Modelling. 2016; 56(1):213-222 https://doi.org/10.1021/acs.jcim.5b00616

41. Plonka W, Stork C, Šícho M, Kirchmair J. CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes. Bioorganic & Medicinal Chemistry. 2021; 46:116388. https://doi.org/10.1016/j.bmc.2021.116388

42. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific reports. 2017 Mar 3; 7(1):1-3. https://doi.org/10.1038/srep42717

43. Daina A, Zoete V. A boiled‐egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem. 2016 Jun 6; 11(11):1117. https://doi.org/10.1002/cmdc.201600182

44. Li, D., L. Chen, Y. Li, S. Tian, H. Sun, and T. Hou. ADMET evaluation in drug discovery. 13. Development of in Silico prediction models for P-Glycoprotein substrates. 2014; 11(3):716-726 https://doi.org/10.1021/mp400450m

45. Broccatelli, F., E. Carosati, A. Neri, M. Frosini, L. Goracci, T. Oprea, and G. Cruciani. A novel approach for predicting P-Glycoprotein (ABCB1) inhibition using molecular interaction fields. 2011; 54(6):1740-1751. https://doi.org/10.1021/jm101421d

46. Chen, L., Y. Li, Q. Zhao, H. Peng, and T. Hou. ADME evaluation in drug discovery. 10. Predictions of P-Glycoprotein inhibitors using recursive partitioning and naive bayesian classification techniques. 2011; 8(3):889-900 https://doi.org/10.1021/mp100465q

47. Schyman, P., R. Liu, and A. Wallqvist. Using the variable-nearest neighbour method to identify P-glycoprotein substrates and inhibitors. ACS Omega. 2016; 1(5):923-929 https://doi.org/10.1021/acsomega.6b00247

48. Attene-Ramos, M., R. Huang, S. Michael, K. Witt, A. Richard, R. Tice, A. Simeonov, C. Austin, M. Xia. Profiling of the Tox21 chemical collection for mitochondrial function to identify compounds that acutely decrease mitochondrial membrane potential. 2015; 123(1):49. https://doi.org/10.1289/ehp.1408642

49. Naef R. A generally applicable computer algorithm based on the group additivity method for the calculation of seven molecular descriptors: Heat of combustion, LogPO/W, LogS, refractivity, polarizability, toxicity and LogBB of organic compounds; scope and limits of applicability. Molecules 2015; 20(10):18279-351 https://doi.org/10.3390/molecules201018279

50. Zhou Y, Wu F, Li L, Shen X, Chen G, Wang X, Liang X, Tan M, Huang Z. Computational approaches in preclinical studies on drug discovery and development. Frontiers in Chemistry. 2020; 8:726-31. https://doi.org/10.3389/fchem.2020.00726

51. Kong M, Xie K, Lv M, Li J, Yao J, Yan K, Wu X, Xu Y, Ye D. Anti-inflammatory phytochemicals for the treatment of diabetes and its complications: Lessons learned and future promise. Biomedicine & Pharmacotherapy. 2021; 133:110975. https://doi.org/10.1016/j.biopha.2020.110975

52. Alfei S, Marengo B, Zuccari G. Oxidative stress, antioxidant capabilities, and bioavailability: Ellagic acid or urolithins?. Antioxidants. 2020 Aug; 9(8):707. https://doi.org/10.3390/antiox9080707