Home  |  About JAPTR |  Editorial board  |  Search |  Ahead of print  |  Current issue  |  Archives |  Submit article  |  Instructions  |  Subscribe  |  Advertise  |  Contacts  |Login 
Users Online: 2417   Home Print this page Email this page Small font sizeDefault font sizeIncrease font size
     

 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 12  |  Issue : 2  |  Page : 132-139  

Molecular docking studies and ADME-Tox prediction of phytocompounds from Merremia peltata as a potential anti-alopecia treatment


1 Department of Pharmaceutical Analysis and Medicinal Chemistry, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang; Department of Medical Laboratory Technology, Mandala Waluya Kendari High School of Health Sciences, Kendari, Indonesia
2 Department of Medicinal Chemistry, Faculty of Pharmacy, Halu Oleo University, Kendari, Indonesia
3 Department of Pharmaceutical Analysis and Medicinal Chemistry, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia

Date of Submission24-Oct-2020
Date of Decision20-Nov-2020
Date of Acceptance03-Feb-2021
Date of Web Publication27-Apr-2021

Correspondence Address:
Dr. Aliya Nur Hasanah
Jl. Raya Bandung Sumedang KM. 21, Hegarmanah, Kec., Jatinangor, Kabupaten Sumedang, Jawa Barat 45363
Indonesia
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/japtr.JAPTR_222_20

Rights and Permissions
  Abstract 


Alopecia is a condition in which some or all of the hair from the scalp is lost. One recent preventative measure is the inhibition of the enzyme 5-α-reductase. Inhibition of the enzyme 5-α-reductase converts circulating testosterone to its more potent metabolite, dihydrotestosterone. Ethnobotically, Merremia peltata is used as a baldness medicine by utilising compounds contained within the leaves. This research aimed to test activity of 18 known compounds contained within M. peltata) as anti-alopecia. Activity was based on their interaction with the androgen receptor (PDB code 4K7a) using molecular docking and ADME-Tox prediction. The stages of research performed were: preparation of androgen protein structure databases; preparation and optimization of three-dimensional structures of compounds using ChemDraw 8.0; molecular docking to the androgen receptor protein using Autodock 1.5.6.; and ADME-Tox prediction using the pkCSM tool. The following test compounds had strong bond energies (ΔG): compound 16 (olean-12-en-3beta-ol, cinnamate)-7.71 kcal/mol, compound 17 (alpha-amyrine)-6.34 kcal/mol, and Finasteride-6.03 kcal/mol. Interestingly, the ΔG of compound 16 (olean-12-en-3beta-ol, cinnamate) is better than of minoxidil (-4.8 kcal/mol) and also to gold-standard treatment compound, finasteride. ADME-Tox prediction for compound 16 showed favorable results in several metrics such as skin permeability, absorption, and distribution. Compound 16 (olean-12-en-3beta-ol, cinnamate) is therefore a potential androgen receptor antagonist and may be beneficial in the treatment of alopecia.

Keywords: ADME-Tox, alopecia, enzyme 5-α-reductase, Merremia peltata, molecular docking


How to cite this article:
Abdurrahman S, Ruslin R, Hasanah AN, Mustarichie R. Molecular docking studies and ADME-Tox prediction of phytocompounds from Merremia peltata as a potential anti-alopecia treatment. J Adv Pharm Technol Res 2021;12:132-9

How to cite this URL:
Abdurrahman S, Ruslin R, Hasanah AN, Mustarichie R. Molecular docking studies and ADME-Tox prediction of phytocompounds from Merremia peltata as a potential anti-alopecia treatment. J Adv Pharm Technol Res [serial online] 2021 [cited 2021 Jun 16];12:132-9. Available from: https://www.japtr.org/text.asp?2021/12/2/132/314671




  Introduction Top


Alopecia is a medical condition that results in hair loss. In patients, who suffer from alopecia, the underlying abnormalities that cause hair loss are found predominately on the scalp but can extend to all areas of the body.[1],[2] In 2014, alopecia was endured by 35 million men and 21 million women worldwide. Alopecia is caused by various factors, including genetics, environmental causes, and nutritional factors.[3] One effort to prevent baldness is via inhibition of the enzyme 5-α-reductase using the synthetic drug finasteride; however, continuous finasteride treatment can cause serious side effects such as a reduction in libido.[4] In addition to using synthetic drugs, various other methods can be used to treat alopecia, one of which is the utilization of the compounds contained within the Merremia peltata plant, which originates from Indonesia. In ethnobotany, this plant has been used by the people of Konawe, Southeast Sulawesi to treat dandruff and hair growth.[5]

According to Mustarichie et al.,[6] an Erythrina variegate ethanol extract that contains polyphenol compounds, terpenoids, tannins, saponins, and steroids increases hair growth in male rabbits. Furthermore, an in silico study used chemical modeling to identify compounds extracted from E. variegate; they bound Janus kinase 2 (JAK2) and therefore might be effective therapeutic treatments of alopecia.[7] A study that fractionated the extracts of Katuk (Sauropus androgynus (L.) Merr.) leaves showed that the ethanol, n-hexane, ethyl acetate, and water fractions stimulated hair growth.[8] In addition, research conducted by[9] showed that ethanol and n-hexane extracts of cocoa peel, also stimulated hair growth in rabbits. The non-polar n-hexane fraction in this study confirmed that the terpenoid and steroid compounds in the waste cocoa peel (Theobroma cacao L.). According to Perez et al., Honesty et al., Kondengis et al.,[10],[11],[12] the contents of secondary metabolites found in M. peltata leaves are terpenoids, steroids, alkaloids and flavonoids with 18 terpenoid derivatives already identified. Based on research of[6] compounds that have activity as antialopecia were compounds of the terpenoids, flavonoids, and alkaloids. Hence, we hypothesized 18 compounds of terpenoid derivatives identified in M. peltata will have an antialopecia activity too. Identification by using GC-MS on research of Kondengis et al.[12] for M. peltata revealed 18 compounds that we used on this docking study.

Until now, based on literature search, no research already conducted on anti-alopecia and ADMET of M. peltata. Hence, we investigate the anti-alopecia activity of M. peltata by studying molecular interactions between both terpenoid compounds and steroids isolated from this plant and their target proteins 4K7A. The 4K7A PDB receptor is an androgen receptor that acts as a transcription factor in the regulation of gene expression, especially the development of male sexual phenotypes with natural ligands of dihydrotestosterone (DHT) and minoxidil. Androgens have a profound effect on the growth of human scalp and body hair, such as promoting beard growth but leading to hair loss in androgenetic alopecia (AGA) in males.[13],[14] Steroid hormone or DHT cause the baldness process by which has an affinity for the receptor androgens.[15],[16]


  Subjects and Methods Top


Hardware and software

The hardware included a PC running Windows 7 Home 64-bit operating system, Intel® Core (TM) i5-3337U CPU @ 1.80GHz, NVIDIA Ge Force GTS 710M Graphic Card and 4 GB CPU memory (RAM). Analysis was performed with the following software: Discovery Studio Visualizer, AutoDock Tools 1.5.6 and ADME-Tox pkCSM tools.

Materials

The androgen receptor crystal structure (PDB code 4K7A), obtained from http://www. rcsb. org/pdb, is shown in [Figure 1]. Data and structures of minoxidil and finasteride were obtained from https://www.pubchem.org. A total of 18 test ligands were obtained from research journals.[12] The structures are shown in [Table 1].
Figure 1: Androgen receptor (46Ka)

Click here to view
Table 1: Two-dimensional structures of minoxidil, finasteride and test ligands derived from the leaves of Merremia peltata

Click here to view


Preparation of ligand structure

Test ligand structures of the 18 compounds derived from M. peltata leaves,[17] the minoxidil and reference ligand finasteride are shown in [Table 1].

Preparation of protein receptor

The crystal structure of the androgen receptor (PDB code 4K7A) was obtained from http/www/pdbbeta. rscb. org/pdb with a resolution of 2.44 Ǻ. Furthermore, the AutoDock Tools 1.5.6 program were used to use to provide a grid box to determine spatial shape and spatial coordinates as docking materials.[18] Androgen receptor crystal structure used were exist with DHT and minoxidil. As DHT is natural ligand that caused baldness process, and minoxidil is a drug that already known protect against baldness, we used minoxidil position in the crystal structure of androgen receptor as position for all docking analysis.

Validation of the molecular docking method

Validation of the molecular docking method is performed by redocking a minoxidil to a target protein that has been removed from androgen receptor using the AutoDock Tools 1.5.6. Docking validation was carried out by redocking the natural minoxidil ligand at the 4K7A androgen receptor by removing the natural DHT and minoxidil ligands contained in the protein receptor which was then adjusted to the grid box position on the natural minoxidil ligand. The redocking process was then carried out to determine the root-mean-square deviation (RMSD) value by overlaying the natural ligand which was separated before docking and the minoxidil natural ligand that had been redocked. The method is deemed successful if the RMSD value returned is ≤3Å.[19]

Docking simulation of the minoxidil, finasteride, and test ligands (phytocompounds extracted from Merremia peltata leaves)

The three-dimensional (3D) structure of ligands was created and optimized using Chem 3D Ultra 8.0 with MM2 semi-empirical method.[17] The structure of the ligands in the pdb format was converted into. pdbqt format using the AutoDock Tools 1.5.6. The docking method was performed by tethering each ligand to androgen receptors using the tether coordinates (Grid Center) x = 40, y = 40, z = 40 Å and the Grid Box size coordinates x = −2.592 y = 0.864 z = −6.729Å. Docking results were assessed for binding energy and chemical interactions.

Discovery studio visualizer

Discovery Studio is a comprehensive software includes functionality for viewing and editing data along with tools for performing basic data analysis suite for analyzing and modeling molecular structures, and sequences. Visualization of docking result was done using Discovery Studio to determine hydrogen bond distance (Ǻ) and nearest amino acid residue.[20]

Prediction of ADME-Tox

The ADME-Tox SAR program is accessed at http://biosig.unimelb.edu.au/pkcsm/prediction.[21] The structure of the generated compound was changed to a smile format using the PubChem program.


  Results Top


Preparation of protein receptor

The androgen receptor binds to natural ligands with chemical bonds. The structure of the androgen receptor and minoxidil is depicted in [Figure 2] and [Figure 3].
Figure 2: Structure of (a) the androgen receptor (4K7a) and (b) minoxidil which has been separated from its receptor

Click here to view
Figure 3: Overlay of docked pose of minoxidil with that of the co-crystallized ligand of 4K7A

Click here to view


Validation of molecular docking method

The analysis results of the bonds formed are shown in [Table 2].
Table 2: Validation results for the molecular docking method

Click here to view


A hydrogen bond between minoxidil and the androgen receptor formed with amino acid SER865 and GLU793 of the androgen receptor [Figure 4] and [Figure 5].
Figure 4: Visualisation of interactions between minoxidil and androgen receptors (4K7A). Hydrogen bonds are represented by green bonds, minoxidil is represented by grey structures

Click here to view
Figure 5: Overlay of the docked poses of the test compounds on that of minoxidil

Click here to view


Docking simulation of minoxidil, finasteride, and test ligands (phytocompounds extracted from Merremia peltata leaves)

The analysis of docking simulation was performed for binding energy and hydrogen bond. Docking simulation results are shown in [Table 3].
Table 3: Docking simulation results

Click here to view


Visualization of the docking interactions that occur between finasteride and compound 16 to the androgen receptor (4K7A) is shown in [Figure 6].
Figure 6: Visualisation of molecular docking between the androgen receptor and (a) finasteride and (b) compound 16 (olean-12-en-3beta-ol, cinnamate)

Click here to view


ADME-Tox prediction

The pharmacokinetic parameters of absorption and distribution were investigated to select compounds for drug candidates. ADME-Tox prediction values of reference ligand and test ligands (compound 16) are shown in [Table 4] meanwhile data from other ligands are not shown.
Table 4: Absorption and distribution prediction values

Click here to view


The ADME-Tox analysis that predicts metabolism, secretion, and toxicity is shown in [Table 5].
Table 5: Metabolism, excretion and toxicity prediction results

Click here to view



  Discussion Top


Preparation of the androgen receptor protein

The androgen receptor is a nuclear hormone receptor whose activity can be stimulated through the formation of bonding interactions with androgen hormones.[22] The androgen receptor is a transcription factor that regulates gene expression in developing males.[23] Besides the androgen receptor, exploration of the docking process requires a ligand. Ligand selection used in the process of tethering the target protein is based on initial screening results according to Lipinski's Rule of Five.[24] The androgen receptor (4K7A) forms a hydrogen bond of 2.28 and 2.90 Ǻ with minoxidil. The-NH2 and-NO group in minoxidil forms a hydrogen bond with SER865 and GLU793 of the androgen receptor with a binding energy of-4.8 kcal/mol. A smaller ΔG value indicates that the bonds are more balanced. Based on Lipinski's criteria, compounds from M. peltata are predicted to have good bioavailability in the body.[25]

Validation of the molecular docking method

The molecular docking method is validated by redocking minoxidil to the target protein. The redocking results had an RMSD value of 2.31 Å and a bond energy of −4.8 kcal/mol. According to,[19] an RMSD ≤3Å and a bond energy similar to what we obtained with the redocking results indicates that the interaction between the ligand and the receptor is at a low energy condition; thus, the molecule will be more stable. Visualisation of interactions between minoxidil and androgen receptors (4K7A), also its overlay can be seen in [Figure 4] and [Figure 5].

Docking simulation of minoxidil, reference ligand, and test ligands (phytocompounds identified from Merremia peltata leaves)

Docking is a process of tethering interactions between ligands and proteins; it will produces ΔG, which is the stability parameter of the conformation between the ligand and the androgen receptor.[26] Based on the androgen receptor docking results, the ΔG values for the compound 16 (olean-12-en-3beta-ol, cinnamate)-7.71 kcal/mol is close to the value for minoxidil [−4.8 kcal/mol; [Table 2]]. Therefore, compound 16 is potential as an inhibitors of androgen receptor and potential for antialopecia treatments.

Hydrogen bonding between minoxidil and the androgen receptor occurs at SER865 and GLU793, with the other closest amino acid residues being LEU862, LYS,861 and TYR857. Compound 16 has lower binding energy than minoxidil. This can be caused by the presence of proximal amino acids of compound 16 that also found in the finasteride. These include: LYS861 and LUE797 [Figure 3]. Factors that cause the binding energy for finasteride and test compounds to be higher than minoxidil are different amino acid residues forming a hydrogen bond with the androgen receptor. Minoxidil form hydrogen bonds with SER865 and GLU793 of the androgen receptor, finasteride forms hydrogen bonds with ARG854, GLU793 and SER865, whereas compound 16 forms hydrogen bonds with SER865. Additional hydrophobic interactions play a role in determining ligand stability with the androgen receptor. Hydrophobic interactions, which repel liquid, are more likely to group together in the globular structure of proteins.[27] Based on the simulation results of natural ligands, serine is predicted to play an important role in the androgen receptor ligand binding domain.[28]

ADME-Tox prediction

The level of binding of the plasma protein (% PB) to the drug candidate influences the action of the drug, its properties and its efficacy. Therefore, % PB is an important pharmacokinetic factor that determines the dose regimen (frequency) but not the daily dose.[26] Minoxidil has 99% PB value while compound 16 has a PB of 93%. Based on these results, minoxidil and compound 16 have good plasma protein bonding attributes.[29]

Distribution prediction using the pkCSM tool predicts Vdss, BBB permeability and CNS permeability. The higher the Vdss value, the more drug reserves are distributed to the tissue from the plasma.[22] agreed to accept a low distribution volume if the log Vdss value <-0.15 and >0.45. Analysis indicates that the log Vdss value of minoxidil is 0.142, whereas compound 16 has a log Vdss value of 0.09. Based on the definition of an acceptable Vdss value as defined by[30],[31],[32], compound 16 is less favourable than minoxidil.

Caco-2 single cell monolayer permeability is an in vitro model of the intestinal mucosa that is used to predict the absorption of drugs given orally. According to Lee and Chang,[28] a compound is considered to have high Caco-2 permeability if the Papp >8 × 106 cm/s. However, in this study, permeability predictions were made using the pkCSM permeability tool. pkCSM values >0.90 are deemed as high log PapP values and indicate that a compound is permeable.[29],[30],[31]. Minoxidil and compound 16 have PapP values of 0.653 and 1.475, respectively. This suggests that compound 16 has greater Caco-2 permeability than minoxidil.


  Conclusions Top


Based on in silico analysis using the androgen receptor (4K7A), we found that compound 16 (olean-12-en-3beta-ol, cinnamate) had the best binding energy value; indeed, it is close to the value for minoxidil, a natural androgen receptor ligand. ADME-Tox analysis on minoxidil and compound 16 (olean-12-en-3beta-ol, cinnamate) showed a good profile. Hence, compound 16 (olean-12-en-3beta-ol, cinnamate) potentially as successful anti-alopecia drug. Further research is needed to isolate the biological compounds contained within M. peltata leaves and perform in vitro and in vivo tests.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Obasi CJ, Obasi IS, Okafor UC, Uzoka IS. Comparison of anti-dandruff activity of synthetic shampoos and crude plant extracts on dandruff causing isolates. J Biotechnol Biochem 2018;4:42-6.  Back to cited text no. 1
    
2.
Semwal D, Kotiyal R, Chauhan A, Mishari A, Adhikari L, Semalty A, et al. Alopecia and the herbal drugs: An overview of the current status. Adv Biomed Pharm 2015;2:246-54.  Back to cited text no. 2
    
3.
Guo EL, Katta R. Diet and hair loss: Effects of nutrient deficiency and supplement use. Dermatol Pract Concept 2017;7:1-10.  Back to cited text no. 3
    
4.
Kaur H, Babu BR, Maiti S. Perspectives on chemistry and therapeutic applications of Locked Nucleic Acid (LNA). Chem Rev 2007;107:4672-97.  Back to cited text no. 4
    
5.
Ruslin R, Sahidin I. Identification and determination of traditional medicinal plants in southeast Sulawesi community at Prof. Arboretum. Mahmud Hamundu, Haluoleo University. Majalah Farmasi Indones 2008;19:101-7.  Back to cited text no. 5
    
6.
Mustarichie R, Wicaksono IA, Gozali D. Anti-alopecia activity of DADAP (Erythrina variegata L.) leaves ethanol extract. J Pharm Sci 2017;9:6.  Back to cited text no. 6
    
7.
Mustarichie R, Megantara S, Saptarini N. In-silico study of bioactive compounds of natural materials as a JAK-signal transducer and activator of transcription inhibitor for anti-alopecia. Asian J Pharm Clin Res 2017;10:331-6.  Back to cited text no. 7
    
8.
Mustarichie R, Hendriani R, Triarini D. Anti-alopecia characteristic of Sauropus androgynus (L) Merr. ethanol extract and its fractions. Drug Invent Today 2018;10:1302-9.  Back to cited text no. 8
    
9.
Mustarichie R, Hasanah AN. Anti-alopecia activity of waste cacao (Theobroma cacao L.) peels. Drug Invent Today 2019;11:6.  Back to cited text no. 9
    
10.
Perez KJ, Jose MA, Aranico E, Madamba MR. Phytochemical and antibacterial properties of the ethanolic leaf extract of Merremia peltata (L.) Merr. and Rubus spp. Adv Environ Biol 2015;9:50-7.  Back to cited text no. 10
    
11.
Honesty R. Antibacterial Activity Test of Lambuang Aka Leaf Fraction (Merremia peltata (L.) Merr, Thesis. Andalas University. Indonesia: Padang; 2012.  Back to cited text no. 11
    
12.
Kondengis S, Ismail I, Petrus HC. Identification of the composed components extract methanol leaf Kugete (Merremia peltata) scientific papers in the Tunuo village of North Kao North Halmahera. Int J Health Med Curr Res 2017;2:464-8.  Back to cited text no. 12
    
13.
Randall VA, Hibberts NA, Thornton MJ, Hamada K, Merrick AE, Kato S, et al. The hair follicle: A paradoxical androgen target organ. Horm Res 2000;54:243-50.  Back to cited text no. 13
    
14.
Randall VA. Hormonal regulation of hair follicles exhibits a biological paradox. Semin Cell Dev Biol 2007;18:274-85.  Back to cited text no. 14
    
15.
MacLean HE, Chu S, Warne GL, Zajac JD. Related individuals with different androgen receptor gene deletions. J Clin Invest 1993;91:1123-8.  Back to cited text no. 15
    
16.
Chang C, Saltzman A, Yeh S, Young W, Keller E, Lee HJ, et al. Androgen receptor: An overview. Crit Rev Eukaryot Gene Expr 1995;5:97-125.  Back to cited text no. 16
    
17.
Liu JS, Hsu CL, Wu WG. 4K7A: Crystal Structure of the Androgen Receptor Ligand Binding Domain in Complex with Minoxidil. Available from: https://www.rcsb.org/structure/4K7A. [Last accessed on 2020 May 04 ].  Back to cited text no. 17
    
18.
Susanti MP, Saputra PD, Hendrayati PL, Parahyangan DN, Swandari DG. Molecular docking Cyanide and Peionidin As Anti-Inflammatory Atheroslerosis in Silico. Journal of Pharmacy Udayana 2018;7:28-33.  Back to cited text no. 18
    
19.
Jain AN, Nicholls A. Recommendations for evaluation of computational methods. J Comput Aided Mol Des 2008;22:133-9.  Back to cited text no. 19
    
20.
Adeniji SH, Uba S, Uzairu A. In silico study for evaluating the binding mode and interaction of 1, 2, 4-triazole and its derivatives as potent inhibitors against lipoate protein B (LipB). J King Saud Univ Sci 2018;32:1-11.  Back to cited text no. 20
    
21.
Mustarichie R, Warya S, Moektiwardoyo M, Megantara S, Saputri FA. Docking, absorption, distribution, metabolism and toxicity prediction of anticancer compounds found in plants. World J Pharm Pharm Sci 2014;10:72-90.  Back to cited text no. 21
    
22.
Guay AT. Advances in the management of androgen deficiency in women. Med Aspects Hum Sex 2001;1:32-8.  Back to cited text no. 22
    
23.
Culig Z, Klocker H, Bartsch G, Hobisch A. Androgen receptors in prostate cancer. Endocr Relat Cancer 2002;9:155-70.  Back to cited text no. 23
    
24.
Lipinski 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.  Back to cited text no. 24
    
25.
Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 2002;45:2615-23.  Back to cited text no. 25
    
26.
Girija CR, Karunakar P, Poojari CS, Begum NS, Syed AA. Molecular docking studies of curcumin derivatives with multiple protein targets for procarcinogen activating enzyme inhibition. J Proteomics Bioinform 2010;3:200-3.  Back to cited text no. 26
    
27.
Lins L, Brasseur R. The hydrophobic effect in protein folding. FASEB J 1995;9:535-40.  Back to cited text no. 27
    
28.
Lee DK, Chang C. Endocrine mechanisms of disease: Expression and degradation of androgen receptor: Mechanism and clinical implication. J Clin Endocrinol Metab 2003;88:4043-54.  Back to cited text no. 28
    
29.
Ghafourian T, Barzegar-Jalali M, Dastmalchi S, Khavari-Khorasani T, Hakimiha N, Nokhodchi A. QSPR models for the prediction of apparent volume of distribution. Int J Pharm 2006;319:82-97.  Back to cited text no. 29
    
30.
Mannhold R, editor. Molecular Drug Properties. Measurement and Prediction. Weinheim: Wiley-VHC Verlag; 2008.  Back to cited text no. 30
    
31.
Pires DE, Blundell TL, Ascher DB. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 2015;58:4066-72.  Back to cited text no. 31
    
32.
Hou TJ, Zhang W, Xia K, Qiao XB, Xu XJ. ADME evaluation in drug discovery. 5. Correlation of Caco-2 permeation with simple molecular properties. J Chem Inf Comput Sci 2004;44:1585-600.  Back to cited text no. 32
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

Top
 
 
  Search
 
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

 
  In this article
   Abstract
  Introduction
  Subjects and Methods
  Results
  Discussion
  Conclusions
   References
   Article Figures
   Article Tables

 Article Access Statistics
    Viewed380    
    Printed0    
    Emailed0    
    PDF Downloaded60    
    Comments [Add]    

Recommend this journal