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Year : 2018  |  Volume : 50  |  Issue : 5  |  Page : 242--250

In silico approach to study the metabolism and biological activities of oligomeric proanthocyanidin complexes

Sankar Jamuna, Ashokkumar Rathinavel, Sakeena Sadullah Mohammed Sadullah, Sivasitambaram Niranjali Devaraj 
 Department of Biochemistry, University of Madras, Chennai, Tamil Nadu, India

Correspondence Address:
Dr. Sivasitambaram Niranjali Devaraj
Department of Biochemistry, University of Madras, Guindy Campus, Chennai - 600 025, Tamil Nadu


OBJECTIVES: Over the past three decades, numerous studies have focused on the biological activities of oligomeric proanthocyanidins (OPCs) in the prevention of many diseases such as neurodegeneration, atherosclerosis, tumorigenesis, and microbial infections. OPC has redox-active metabolites which could modulate the intracellular redox equilibrium to maintain the antioxidant homeostasis. This redox-modulating efficiency of OPC could provide new insights into therapeutic approaches that could reduce the burden of cardiovascular diseases. The main objective of this study was to explore the biological and metabolic activities of OPC using in silico approaches. METHODS: To validate the above objective, chemoinformatic tools were used to predict the metabolism of OPC after ingestion, based on both the ligand and structure of the constituent compounds. RESULTS: OPC showed possible sites for Phase I metabolism by cytochrome P450, and the metabolites obtained thereafter may be responsible for its biological activities. Absorption, distribution, metabolism, elimination, and toxicity properties showed efficient absorption, distribution, and metabolism of OPC, without toxicity. CONCLUSION: Thus, from the results obtained, OPC could be strongly recommended as a cardioprotective drug.

How to cite this article:
Jamuna S, Rathinavel A, Mohammed Sadullah SS, Devaraj SN. In silico approach to study the metabolism and biological activities of oligomeric proanthocyanidin complexes.Indian J Pharmacol 2018;50:242-250

How to cite this URL:
Jamuna S, Rathinavel A, Mohammed Sadullah SS, Devaraj SN. In silico approach to study the metabolism and biological activities of oligomeric proanthocyanidin complexes. Indian J Pharmacol [serial online] 2018 [cited 2020 Aug 14 ];50:242-250
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Recent scientific reports emphasize numerous biological activities such as antioxidant, anti-inflammatory, anticarcinogenic, antimicrobial, vasodilatory, and cardioprotective effects of flavonoids and polyphenols.[1] Oligomeric proanthocyanidins (OPCs) or condensed tannins, consisting of oligomers or polymeric forms of flavan-3-ols, are among the most abundant polyphenols present in all edible fruits and berries. The normal daily consumption of B-type proanthocyanidins varies from 10 mg to 500 mg/day.[2]

Proanthocyanidins are known to possess antioxidant and radical scavenging activities. OPC has been shown to inhibit low-density lipoprotein oxidation,[3] induce platelet activation and function,[4] and possess anticarcinogenic activity,[5] mainly due to their complex structure and degree of polymerization.[6],[7] Clinical and experimental studies have shown that consumption of proanthocyanidins improves the antioxidant status and atherosclerotic plaque stability, prevents DNA damage, and possesses anticancer activity.[8]

To validate the use of proanthocyanidins as a potential drug, its bioavailability and its efficiency in reaching the target tissue should be taken into consideration. Hence, it is important to gain an understanding about the absorption, distribution, metabolism, and excretion (ADME) of the OPCs. A recent study on proanthocyanidins showed that its degradation takes place in the stomach. It was reported that the strong acid in the stomach (pH 2–4) can degrade the oligomers into monomers and dimers. Few studies have emphasized that proanthocyanidins can be degraded by the colonic microflora which resulted in the production of aromatic acids.[9] [Figure 1] illustrates the possible bioavailability of OPCs during oral administration.{Figure 1}

Various approaches have been used to validate a potential candidate drug. Determination of ADME-toxicity (ADME-Tox) properties is an essential sequel of drug discovery.[10] The identification of metabolites and the modification of the compounds are essential to improve its pharmacokinetic properties. This has been achieved by predicting the possible site of metabolism (SOM) of compounds. In order to address this hypothesis, recent studies have used in silico methods to predict the SOM.[11],[12]

These in silico approaches are broadly classified into two types namely, ligand- and structure-based approaches. Ligand-based models provide indirect information about the active site of a protein based on the shape, electronic properties, conformational changes of substrates, inhibitors, and metabolic products.[13],[14] The ligand-based model also predicts the metabolic fate of a compound depending on its chemical and structural characteristics. A structure-based method provides the substrate's properties, the specificity of interaction between the substrate and the enzyme, rationalization of metabolic reactions, structure elucidation, and computation of dynamics and reactivity of compounds. The combination of both ligand- and structure-based approaches becomes a more promising approach by its synergistic effects between different metabolic reactions. The objectives of this present study are to evaluate the metabolism, bioavailability, toxicity, and biological activity of OPC using different in silico approaches.


Ligand-based metabolism prediction


SmartCYP is a freeware to predict the sites of metabolism of a given compound, most commonly based on the cytochrome P450 (CYP) enzymes. In this study, we have predicted the CYP450-associated sites of epigallocatechin gallate, gallocatechin, gallocatechingallate, procyanidins, and proanthocyanidin molecules. SmartCYP tool accepts SDF format of the molecules and predicts the SOM by recognizing two-dimensional (2D) formats of the molecules and predicts the CYP450-mediated metabolic sites. This tool calculates the oxidation states of aliphatic carbons and aromatic sites by applying density functional theory.[15],[16] SmartCYP is freely available from the Internet at


MetaPrint2D is a new tool for predicting the sites of Phase I metabolism based on data-mining approaches of xenobiotic metabolism. This algorithm is based on the analysis of the prediction of atom center's circular fingerprints of both substrates and metabolites.[17] Metaprint2D can be freely accessed from the web platform ( uk/metaprint2d/).

Chemical structures of potential metabolites


MetaPrint2D-React is an extension of MetaPrint2D extending it from SOM prediction – prediction of the metabolic transformations and metabolites formed. MetaPrint2D-React is available from the Internet at[18] This tool predicts different transformations (addition of oxygen, hydroxylation, oxidation, epoxidation, and elimination reaction) of molecules that can take place at the SOM where the likely metabolite is to be formed.

Toxicity prediction of potential metabolites


This online tool helps the end user to analyze the molecular description and druglikeliness properties of compounds ( Molinspiration server works based on the Lipinski Rules of Five.[19] The most “drug-like” compound must possess the following properties: LogP ≤5, molecular weight ≤500Da, number of hydrogen bond acceptors ≤10, and number of hydrogen bond donors ≤5. Compounds that failed to show these characters are least considered as a drug. Molinspiration server calculates the important molecular properties of compounds based on partition coefficient (LogP), polar surface area, number of hydrogen bond donors and acceptors, and also prediction of bioactivity score for the drug targets such as G-Protein Coupled Receptor (GPCR) ligands, kinase and protease inhibitors (KIs and PIs), ion channel modulators (ICMs), and nuclear receptor ligand (NRL). Topographical polar surface area (TPSA) was used to calculate the percentage of absorption using the following equation: Percentage of absorbance = 109 − 0.345 × TPSA.[20]

admetSAR predictions

The pharmacokinetic properties such as ADMET-Tox of the compounds can be predicted using admetSAR ( database.[21] In admetSAR, web-based query tools incorporating a molecular build-in interface enable the database to be queried by SMILES and structural similarity search. It provides the latest and most comprehensive manually curated data for diverse chemicals associated with known ADMET profiles.

Biological activity spectrum (BAS)

Biologically activity spectrum (BAS) of a compound represents an intrinsic property of the pharmacological effects, physiological and biochemical mechanisms of action, and specific toxicity (mutagenicity, carcinogenicity, teratogenicity, and embryotoxicity), which is largely dependent on the interaction with the biological system.[22] The values vary from 0.000 to 1.000. Only those activity types for which Pa (probability to be active) > Pi (probability to be inactive) were considered possible (

 Results and Discussion

[Figure 2] shows the possible interaction of OPC with CYP450. From the results, the SOM at C1, C2, and C3 sites was predicted, and the ability of the OPC to activate/inhibit the cytochrome system was determined.{Figure 2}

[Figure 3] and [Figure 4] illustrate the SOM predicted by very accurate and efficient tools – Metaprint2D and Metaprint2D react, respectively. Metaprint 2D analysis showed the SOM as follows: the atoms are colored according to the likelihood of a metabolic site. Red circle shows the sites with the highest oxidation reaction, yellow indicates medium, green represents low, and no color represents no likelihood of metabolism occurring at that particular site. The high normalized ratio denotes a more frequent SOM.{Figure 3}{Figure 4}

The Lipinski “rule of five” is commonly used as an index during drug design and development to predict the oral bioavailability of the lead drug molecules. Based on Lipinski's “rule of five,” drug compound should satisfy the following criteria: (1) The molecular weight of the candidate drug should be >500, (2) LogP <5, and (3) More than 5 hydrogen bond donors (OH and NH groups), where hydrogen donor should be <10 (N and O). The predicted molecular properties of OPC using Molinspiration tool are represented in [Table 1]. From the results obtained, epigallocatechin gallate, gallocatechin, and gallocatechin gallate parameters were within the Lipinski Rule of Five, whereas, procyanidins and proanthocyanidin showed increased molecular weight (<500). Moreover, other properties such as LogP, TPSA, and hydrogen donor and acceptor are within the range. Hence, all the compounds of OPC do not violate the Lipinski rule and could be expected to be orally active as they followed Lipinski Rule of Five.{Table 1}

The bioactivity scores of OPC compounds were also predicted using Molinspiration tool and represented in [Figure 5]. If a molecule is predicted to have a bioactivity score of >0.00, it is likely to demonstrate considerable biological activities, whereas the values −0.50–0.00 indicate moderately active molecules, and less that −0.50 is presumed to be inactive. From the results, it is obvious that the physiological actions of OPC could be due to the strong interactions with GPCR ligands, NRLs, and inhibition of proteases and other enzymes. These bioactivity scores obtained suggested that OPC compounds could interact with all the drug targets. All the compounds showed good bioactivity based on the scores obtained for all drug targets.{Figure 5}

ADMET properties of compounds were calculated using admetSAR cheminformatics tool to predict the pharmacokinetic properties for the effectiveness and bioavailability of compounds. From [Table 2], it can be observed that ADMET properties of compounds specify threshold ADMET characteristics for the chemical structure of the molecules based on the available drug databases. The scores of human intestinal absorption (HIA) predict the intestinal absorption after oral administration. Then, the AlogP value and PSA scores of compounds show that they are well absorbed during absorption. The aqueous solubility (LogS) predicts the solubility properties of each compound in water at 25°C. From the results, it has been shown that all the compounds used in this study showed maximum solubility. An important ADMET parameter is blood–brain barrier (BBB), which predicts the bioactivity of drug to reduce neurological burden after oral administration. Among all compounds, procyanidin alone is positive for BBB, i.e., this compound is known to enter the CNS after oral administration. Whether a compound is likely to interact with the carrier proteins, which are available in the blood, is predicted from the plasma protein-binding model. CYP2D6 ADMET properties showed that constituents of OPC inhibit the enzyme CYP450. Finally, from the ADMET results, it was shown that the compounds did not exhibit toxicity.{Table 2}

[Table 3] represents the BAS of OPC compounds. These results reflect the pharamacotherapeutic applications of the compound. All the compounds were analyzed for antioxidant and cardioprotective effects. The obtained results also exhibit higher Pa values for all the compounds which include antioxidant (Pa = 0.827) and cardioprotective (Pa = 0.822) effects. Hence, BAS tools predict the biological activities of all compounds with significant cardioprotective activity.{Table 3}


To understand the biological activity of OPC, this study highlighted the bioavailability and pharmacokinetic properties using in silico approach to predict the possible SOM; biotransformation; Phase I metabolism; ADMET; and biological activities of the constituent compounds, such as epigallocatechin gallate, gallocatechin, gallocatechin gallate, procyanidins, and proanthocyanidins.

CYP450 plays a vital role in the metabolism of xenobiotics. During drug development, it is mandatory to study the metabolic reactions and toxicities. Hence, in this study, we used both structure-based and ligand-based tools to predict the SOM. The scores obtained from SMARTCyp tool for OPC compounds showed that compounds were properly undergoing metabolism in the liver.[23],[24]

Results of Metaprint2D showed high metabolism rate for all the ligands and showed numerous biotransformation reactions such as alkylation, acylation, glucuronidation, sulfation, and methylation. Kim et al.[25] have reported an important secondary metabolite 5-(3',5'-dihydroxyphenyl)-γ-valerolactone (EGC-M5) of green tea catechins, which exerts important immunoregulatory effects. These kinds of biotransformed metabolites of compounds are responsible to elicit biological activities such as antioxidant and anti-inflammatory activities.

Secondary metabolic products such as 4-hydroxybenzoic acid, 3,4-dihydroxybenzoic acid, 3-methoxy-4-hydroxy-hippuric acid, and 3-methoxy-4-hydroxybenzoic acid (vanillic acid) of proanthocyanidins were excreted after 6–48 h.[26] Thus, OPC produces secondary metabolites by conjugating with glucuronide and sulfate to form biologically active metabolites. Serra et al.[27] have also reported that metabolism of procyanidins produces methyl-catechin-sulfate and phenolic acids. Studies reported that secondary metabolites such as phenolic acids and lactones from oligomers form through the fission of heterocyclic and A-rings.[28] The released metabolites are responsible to maintain plasma cholesterol homeostasis and inhibition of monocyte adhesion to the endothelial cells.[29]

Molinspiration tool is very useful and accurate in predicting certain properties of the ligand to assess its efficacy and biological activity based on calculating, cLogp, number of hydrogen donors and acceptors, TPSA, and number of rotatable bonds present in the target ligand. From the results obtained, gallocatechin (molecular weight <500 Da) could easily be transported, diffused, and absorbed, whereas other ligands with high molecular weight (>500 Da) cannot. However, other ligands might be degraded into their respective monomers and dimers by the action of gut and colon microbiota which may increase their absorption rate.

Lipophilicity (LogP) and TPSA values determine the oral bioavailability of a drug. LogP values of all five ligands were within the range, showing that the rate of permeability into a cell for these ligands is considerable. Thereby, OPC could easily pass the phospholipid bilayer because of its lipophilic nature. TPSA is an important parameter which determines drug absorption, bioavailability, permeability, and penetration. This was calculated from the surface area of hydrogen bond between the O and N atoms. Results showed that all ligands exhibit TPSA and number of rotatable bonds with more than the predicted range. Hence, we emphasize that our ligand of interest showed increase in TPSA and rotatable bonds because it has more aromatic hydroxyl groups in its structure. The number of hydrogen donor and acceptor of a ligand determines the flexibility and adaptability of the ligand to bind with the target enzyme or protein. Specific biological activities of a ligand determine the drug likeliness property of ligands. As shown in [Figure 4], all ligands showed >0.000 values for GPCR ligands, ICM, KI, PI, and NRL. All ligands showed the suggested range of biological activities.

Whenever a xenobiotic is introduced into the system, CYP450 and its isoforms, which belong to a family of hemoproteins, play a vital role in drug metabolism and clearance. CYP450 catalyzes the Phase I metabolism which oxygenates lipophilic compounds into water-soluble forms. When a compound fails to activate the CYP450 system, drug molecules accumulate in the tissue, resulting in toxicity. All the ligands of this study showed positive results for HIA but negative results for BBB and Caco-2 cell permeability. They reported the antidiabetic effects of grape seed proanthocyanidins which cross the BBB.[30] All ligands showed no inhibitory side effects in terms of renal cation transport. This tool also analyzes the ability of the ligands to serve as a P-glycoprotein substrate. The results demonstrate that all the ligands showed positive results and were identified as noninhibitors of P-glycoproteins.

This study showed the prediction of toxicity profiles of all the ligands for mutagenicity, tumorigenicity, reproductive effectiveness, irritancy, human ether-a-go-go-related gene inhibition, Ames toxicity, carcinogenicity, fish toxicity, Tetrahymena pyriformis toxicity, honey bee toxicity, biodegradation, acute oral toxicity category, and acute rat toxicity. [Table 2] shows the toxicity profiles for all the ligands. The toxicity profiles of the selected compounds revealed that most of the compounds were not mutagenic, carcinogenic, or tumorigenic. Similarly, the selected compounds were negative for Ames toxicity, were weak inhibitors of human ether-a-go-go-related genes, and exhibit no properties that exert significant toxicity in humans. On the contrary, all the selected compounds were found to present high toxicity for fish, T. pyriformis, and honey bees.

Administration of OPC to animals showed beneficial effects by preventing hepatic and renal toxicities.[31] Stefanovic et al.[32] showed reduced levels of blood urea nitrogen and serum creatinine in experimentally induced myoglobinuric rats. OPC did not possess any known side effects, toxicity, or drug interactions.

The possible biological activities of all the ligands were obtained by using PASS server. The set of pharmacological effects, mechanisms of action, and specific toxicities, that might be exhibited by a compound in its interaction with biological entities, and which is predicted by PASS, is termed the BAS of this compound. The Pa (probability to be active) and Pi (probability to be inactive) values vary from 0 to 1, and Pa + Pi <1, since these probabilities are calculated independently. Pa and Pi are measures of the compound under study belonging to the classes of active and inactive compounds, respectively. From our results, all the ligands showed potent antioxidant and cardioprotective activities within the prescribed range. From the previous reports on OPC, promising cardioprotective and antioxidant activities in experimental animals were seen. Hence, these results using in silico tools strongly recommend that OPC could be a potent cardioprotective agent.


Taken together, all the compounds of OPC showed sites for CYP450 metabolism and Phase I metabolism and possible secondary metabolites of OPC. Thus, OPC could be a potent drug candidate by displaying its prescribed range of clogp value, number of rotatable bonds, number of hydrogen donor and acceptor, solubility, flexibility and adaptability. From the results, all ligands possess, G-Protein Coupled Receptor, ion channel modulator, kinase and protease inhibitor activity and nuclear receptor activity. OPC could be efficiently absorbed, distributed and metabolized. Toxicity studies on OPC revealed that none of the ligands used in this study showed any toxicity. OPC has potent cardioprotective and antioxidant properties as predicted. Hence, OPC may be postulated as a candidate drug for cardiovascular diseases.


All authors would like to thank the University Grants Commission (UGC), New Delhi, India, for providing financial support under UGC-BSR meritorious and UGC-UPE II fellowship.

Financial support and sponsorship

This study was financially supported by the UGC, New Delhi, India, under UGC-BSR meritorious and UGC-UPE II fellowship.

Conflicts of interest

There are no conflicts of interest.


1Williamson G, Manach C. Bioavailability and bioefficacy of polyphenols in humans. II. Review of 93 intervention studies. Am J Clin Nutr 2005;81:243S-55S.
2Gu L, Kelm MA, Hammerstone JF, Beecher G, Holden J, Haytowitz D, et al. Concentrations of proanthocyanidins in common foods and estimations of normal consumption. J Nutr 2004;134:613-7.
3Quettier-Deleu C, Voiselle G, Fruchart JC, Duriez P, Teissier E, Bailleul F, Vasseur J and Trotin. Hawthorn extracts inhibit LDL oxidation. Pharmazie 2014;58:577-581.
4Murphy KJ, Chronopoulos AK, Singh I, Francis MA, Moriarty H, Pike MJ, et al. Dietary flavanols and procyanidin oligomers from cocoa (Theobroma cacao) inhibit platelet function. Am J Clin Nutr 2003;77:1466-73.
5Makabe H. Synthesis and anti-tumor activity of proanthocyanidins. Org Chem Curr Res 2014;3:e133.
6Gresele P, Cerletti C, Guglielmini G, Pignatelli P, de Gaetano G, Violi F, et al. Effects of resveratrol and other wine polyphenols on vascular function: An update. J Nutr Biochem 2011;22:201-11.
7Salvamani S, Gunasekaran B, Shaharuddin NA, Ahmad SA, Shukor MY. Antiartherosclerotic effects of plant flavonoids. BioMed Res Inter 2014;480258.
8Li Q, Wang X, Chen J, Liu C, Li T, McClements DJ, et al. Antioxidant activity of proanthocyanidins-rich fractions from Choerospondias axillaris peels using a combination of chemical-based methods and cellular-based assay. Food Chem 2016;208:309-17.
9Duda-Chodak A, Tarko T, Satora P, Sroka P. Interaction of dietary compounds, especially polyphenols, with the intestinal microbiota: A review. Eur J Nutr 2015;54:325-41.
10Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol 2011;162:1239-49.
11Andrade EL, Bento AF, Cavalli J, Oliveira SK, Freitas CS, Marcon R, et al. Non-clinical studies required for new drug development – Part I: Early in silico and in vitro studies, new target discovery and validation, proof of principles and robustness of animal studies. Braz J Med Biol Res 2016;49:e5644.
12Honório KM, Moda TL, Andricopulo AD. Pharmacokinetic properties and in silico ADME modeling in drug discovery. Med Chem 2013;9:163-76.
13Pelkonen O, Turpeinen M, Raunio H. In vivo in vitroin silico pharmacokinetic modelling in drug development: Current status and future directions. Clin Pharmacokinet 2011;50:483-91.
14Andrade CH, Silva DC, Braga RC. In silico prediction of drug metabolism by P450. Curr Drug Metab 2014;15:514-25.
15Kirchmair J, Williamson MJ, Tyzack JD, Tan L, Bond PJ, Bender A, et al. Computational prediction of metabolism: Sites, products, SAR, P450 enzyme dynamics, and mechanisms. J Chem Inf Model 2012;52:617-48.
16Melo-Filho CC, Braga RC, Andrade CH. Advances in methods for predicting phase I metabolism of polyphenols. Curr Drug Metab 2014;15:120-6.
17Rydberg P, Gloriam DE, Olsen L. The SMARTCyp cytochrome P450 metabolism prediction server. Bioinformatics 2010;26:2988-9.
18Miners JO, Birkett DJ. Cytochrome P4502C9: An enzyme of major importance in human drug metabolism. Br J Clin Pharmacol 1998;45:525-38.
19Carlsson L, Spjuth O, Adams S, Glen RC, Boyer S. Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using metaPrint2D and bioclipse. BMC Bioinformatics 2010;11:362.
20Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 2001;46:3-26.
21Ahsan MJ, Samy JG, Khalilullah H, Nomani MS, Saraswat P, Gaur R, et al. Molecular properties prediction and synthesis of novel 1,3,4-oxadiazole analogues as potent antimicrobial and antitubercular agents. Bioorg Med Chem Lett 2011;21:7246-50.
22Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, et al. AdmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model 2012;52:3099-105.
23Corominas-Faja B, Santangelo E, Cuyàs E, Micol V, Joven J, Ariza X, et al. Computer-aided discovery of biological activity spectra for anti-aging and anti-cancer olive oil oleuropeins. Biomed Res Int 2014;6:731-41.
24Goey AK, Meijerman I, Beijnen JH, Schellens JH. The effect of grape seed extract on the pharmacokinetics of dextromethorphan in healthy volunteers. Eur J Clin Pharmacol 2013;69:1883-90.
25Wanwimolruk S, Prachayasittikul V. Cytochrome P450 enzyme mediated herbal drug interactions (Part 1). EXCLI J 2014;13:347-91.
26Kim YH, Won YS, Yang X, Kumazoe M, Yamashita S, Hara A, et al. Green tea catechin metabolites exert immunoregulatory effects on CD4(+) T cell and natural killer cell activities. J Agric Food Chem 2016;64:3591-7.
27Vidak M, Rozman D, Komel R. Effects of flavonoids from food and dietary supplements on glial and glioblastoma multiforme cells. Molecules 2015;20:19406-32.
28Serra A, Macià A, Rubió L, Anglès N, Ortega N, Morelló JR, et al. Distribution of procyanidins and their metabolites in rat plasma and tissues in relation to ingestion of procyanidin-enriched or procyanidin-rich cocoa creams. Eur J Nutr 2013;52:1029-38.
29Manach C, Williamson G, Morand C, Scalbert A, Rémésy C. Bioavailability and bioefficacy of polyphenols in humans. I. Review of 97 bioavailability studies. Am J Clin Nutr 2005;81:230S-42S.
30Spencer JP, Schroeter H, Kuhnle G, Srai SK, Tyrrell RM, Hahn U, et al. Epicatechin and its in vivo metabolite, 3'-O-methyl epicatechin, protect human fibroblasts from oxidative-stress-induced cell death involving caspase-3 activation. Biochem J 2001;354:493-500.
31Ray SD, Patel D, Wong V, Bagchi D.In vivo protection of DNA damage associated apoptotic and necrotic cell deaths during acetaminop hen-induced nephrotoxicity, amiodarone-induced lung toxicity and doxorubicin-induced cardiotoxicity by a novel IH636 grape seed proanthocyanidin extract. Res Commun Mol Pathol Pharmacol 2000;107:137-66.
32Stefanovic V, Savic V, Vlahovic P, Cvetkovic T, Najman S, Mitic-Zlatkovic M, et al. Reversal of experimental myoglobinuric acute renal failure with bioflavonoids from seeds of grape. Ren Fail 2000;22:255-66.