worldwide, reaching epidemic proportions in many developed countries including India. Type

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1 6. Studies on mechanism of anti-diabetic action: In-silico and in vitro approaches 6.1 Introduction In the past few decades diabetes mellitus has become a major health problem worldwide, reaching epidemic proportions in many developed countries including India. Type 2 diabetes leads to insulin resistance in which liver and muscle cells do not respond adequately to insulin (Laakso and Lehto, 1997). As a result, blood glucose does not get into these cells, leading to increased blood glucose concentrations resulting in hyperglycemia. NIDDM usually occurs slowly over time. Family history, genetic makeup, low muscle or body activity, junk food or unhealthy diet, and excess body weight usually increase the risk of a person getting affected. Some of the long-term symptoms include deterioration of normal health, atherosclerosis, myocardial infarction and hyperosmolar non ketotic diabetic coma (Olokaba et al. 212). Since synthetic drugs currently available are costly and are also prone to cause side affects. Herbs prescribed in traditional systems of India such as Siddha system could be used for managing diabetes. In the present work one such Siddha anti-diabetic plant drug Ponkorandi is selected and evaluated scientifically for its anti diabetic-efficacy so as to contribute significantly for the suffering diabetic population. It is observed that there are three different sources available for Ponkorandi. Attempts were made in the present dissertation besides standardization to understand the mechanism of anti diabetic action of selected Siddha plant sources. 6.2 Material and Methods Preparation of aqueous extracts Plant materials were cleaned thoroughly using water and cut into small pieces and shade dried and extracted with water. 2 g of plant material was soaked in 2 litres of water and heated in a water bath at 8 C for 1 hour. The crude extracts were filtered through Whatman paper and evaporated in vacuo at 4 C using a rotary evaporator. The yield of the extract was 18% (w/w). The extracts were subjected to in vitro studies. Molecules identified in the bio active extract using LC-MSMS technique were used as ligands against GSK3, a protein which play a vital role in NIDDM patients. 13

2 6.2.2 Amylase inhibition assay 5 μl of sample (Extract) and 5 μl of α-amylase (2 unit/ml, procured from Sigma- Aldrich, USA) were mixed and pre-incubated in 2 mm sodium phosphate buffer (ph6.9) for 1 min at room temperature. Then, 1ml of 1% (w/v) starch dissolved in the buffer was added to the reaction mixture (Amylase and test extract) to make a total volume of 2 ml, and the whole mixture was incubated for 1 min at room temperature. After the incubation, 1ml of dinitrosalicylic acid (DNS) color reagent was added and placed in a boiling water bath for exactly 15min. This mixture was cooled on ice to room temperature and 6 ml of deionized water was added. The absorbance of resulting solution was measured at 54 nm. Acarbose was used as a standard (Bhagyajyothi et al. 212). 6.3 LC-MS/MS Analysis Chemical composition of each fraction was determined by Ultra high performance liquid chromatography (UHPLC + Focused) with mass selective detection, using Ultimate 3 series liquid chromatography (Dionex, USA) coupled with ESI tandem mass spectrometer (microtof-q II) (Bruker, Germany). Components were separated using reverse-phase Acclaim12, RP-C18 12 Å, mm, 3. μm column (Dionex, USA), held at 5 C. Mobile phase consisting of acetonitrile (A) and 1% aqueous formic acid v/v (B), was used with a discontinuous gradient; 99% (B). to.2 min, 25% B at 16 th min, next 3 min to reach % B, 2. min to reach 95% B and was maintained at same condition until the run ends, with a flow rate of.2 ml/min. Chromatographic profiles were acquired in the wavelength at 36 nm. Injection volume was 5 μl. Eluted components were ionized by electrospray ion source (ESI), using N 2 for nebulization (pressure of 34.8 psi) and drying (flow of 7 L/min, temperature of 3 C). Set capillary voltage was 45 V, end plate offset -5 V, collision cell RF 2. Vpp, energy transfer time of 8. µs, pre pulse storage of 3. µs. Data were acquired in MS/MS (Auto) scanning mode. To increase the sensitivity, to lower the noise, and simplify the spectra, negative ionization was used. Generated [M-H] - ions were analyzed using AutoMSn Scan mode, in m/z range 5-1 m/z. 14

3 6.4 In- silico docking studies In- silico docking studies against Diabetes target GSK-3 using the ligands identified in the LC-MSMS analysis of three Salacia species such as Vitexin, Salasones A, Salacinol, Mangiferin and Isorhamnetin were carried out. Glycogen synthase kinase (GSK-3) is one of the common proteins which play a vital role in NIDDM. In mammals, GSK-3 is encoded by two known genes: GSK-3α and GSK-3β. Increased expression of GSK-3 is harmful since it causes the inactivation of multiple blood-glucose-reducing enzymes and disrupts insulin signaling. Studies have shown that introducing competitive inhibitors of GSK-3 leads to increased activity of insulin (Desbois-Mouthon et al., 21). Considering the earlier studies, data strongly recommend that suppressing levels of GSK-3β will be an effective approach for inhibiting diabetes mellitus such kind of computational biology studies helps to facilitate and reduce the time and cost of drug discovery process, which involves diverse methods to discover novel compounds (Usha et al., 213). One such method utilized in the present study is active site and molecular docking analysis of GSK-3β and GK, which enabled us to identify natural inhibitors from the selected plant sources Prediction for protein interaction AutoDock is a suite of automated docking tool. It is designed to predict how small molecules, such as substrates or drug candidates, bind to a receptor of known 3D structure (Usha et al. 213). To the modeled protein Kollmann charges were assigned, through which hydrogens were added, side chains were optimized for hydrogen bonding. The energy minimized protein was then saved in PDB format. Using MGLTools nonpolar hydrogens were merged, AutoDock atom type AD4 and Gasteiger charges were assigned and finally saved in protein.pdbqt format (Scott et al., 1999). 15

4 6.4.2 Prediction for ligand interaction Structure of ligands were drawn using ChemSketch (Morris et al., 1998) optimized with 3D-geometry and the two-dimensional structure of Isorhamnetin, Mangiferin, Salacinol, Salasones A, Vitexin compounds were converted into 3-D structure using the open Babel format molecule converter and saved in PDB format for AutoDock compatibility. MGLTools (The Sripps Research Institute) was used to convert ligand.pdb files to ligand.pdbqt files Active site prediction The active site of the protein is the binding site or usually a pocket at the surface of the protein that contains residues responsible for substrate specificity which often act as proton donors or acceptors. Identification and characterisation of binding site is the key step in structure based drug design. The binding site has been identified by computational and literature reports. The active site region of the protein is identified by Qsite (Guha et al., 26). These servers analytically furnish the area and the volume at the probable active site of each pocket to envisage the binding site Docking protocol Grid parameter files (protein.gpf) and docking parameter files (ligand.dpf) were written using MGLTools Receptor grids were generated using 9x6x6 grid points in xyz with grid spacing of.375 Å. Grid box was centered co crystallized ligand map types were generated using autogrid4. Docking of macromolecule was performed using an empirical free energy function and Lamarckian Genetic Algorithm, with an initial population of 25 randomly placed individuals, a maximum number of 16 energy evaluations, a mutation rate of.2, and a crossover rate of.8. One hundred independent docking runs were performed for each ligand. Results differing by 2. Å in positional root-mean square deviation (RMSD) were clustered together and represented by the result with the most favorable free energy binding. 16

5 Percentage inhibition 6.5 Results and Discussion In vitro studies Figure 13. In vitro alpha amylase inhibitory assay SD mean SR SO SC acarbose SR Salacia reticulata SO Salacia oblonga SC Salacia chinensis The alpha amylase inhibitory assay was performed employing the extracts of selected Salacia species which revealed 6.1% inhibition in Salacia oblonga, 55.5% inhibition in Salacia chinensis and 45.6% inhibition in Salacia reticulata respectively LC-MS/MS Analyis of the aqueous extracts of selected Salacia species The polar constituents are of interest, since most of the traditional preparations are only aqueous extractions. RP-UHPLC-/ESI/MSMS works well for polar molecules and also provides the rapid assessment of plant extracts for the presence of biologically active compounds with a minimum pre-purification. In LC/ESI/MSMS the effluent from an UHPLC+ is introduced into an Electro Spray Ionization source coupled with Dual quadrapole and TOF giving typical and well-understood ESI Spectra. The advantage to acquire ESI fragmentation data from a LC separation lies within the subsequent ability to use existing deconvolution and search programs to match results with wellestablished and commercially available ESI mass spectral databases. Extensive research is needed to develop or upgrade the available MS MS libraries to prevent the difference in 17

6 fragmentation patterns among ESI instruments; this would make the identification of the components in a complex mixture very easy. Figure 14. LC-MS/MS Chromatogram of aqueous extract of Salacia oblonga Intens. x Time [min] S oblonga rpt 1Feb14_GA5_1_59.d: TIC -All MS S oblonga rpt 1Feb14_GA5_1_59.d: TIC -All MSn Figure 15. LC-MS/MS Chromatogram of aqueous extract of Salacia reticulata Figure 16. LC-MS/MS Chromatogram of aqueous extract of Salacia chinensis Intens. x Time [min] S chinensis 1Feb14_GA4_1_587.d: TIC -All MS S chinensis 1Feb14_GA4_1_587.d: TIC -All MSn 18

7 LC-MS/MS spectrum of identified molecules in Salacia oblonga Figure 17: LC-MS/MS spectrum of Friedelane-1,3-dione Intens MS2(439.3), min #1484-#1492 x Friedelane-1,3-dione m/z Figure 18: LC-MS/MS spectrum of Mangiferin Intens MS2(421.1), 9.6min #573 2 Mangiferin m/z 12 19

8 Figure 19: LC-MS/MS spectrum of Kotalanol Intens MS2(423.1), 8.4min #51 Kotalanol m/z Figure 11: LC MS/MS spectrum of Salasones A Intens MS2(455.6), 19.9min # Salasones A m/z 11

9 LC-MS/MS spectrum of identified molecules in Salacia reticulata Figure 111: LC MS/MS spectrum of Mangiferin Intens MS2(421.1), min #717-#725 x Mangiferin m/z Figure 112: LC MS/MS spectrum of Vitexin Intens. 8. -MS2(431.1), 13.3min #794 x1 4 Vitexin m/z 111

10 Figure 113: LC-MS/MS spectrum of Kotalanol Intens. 4. -MS2(423.1), min #158-# Kotalanol m/z Figure 114: LC-MS/MS spectrum of Lambertic Acid Intens. 2. -MS2(315.3), 22.6min # Lambertic acid m/z 112

11 Figure 115: LC-MS/MS spectrum of Salacinol Intens MS2(355.4), min #153-#1511 Salacinol (M+Na) m/z LC-MS/MS spectrum of identified molecules in Salacia chinensis Figure 116: LC MS/MS spectrum of Fisetin Intens. Fisetin MS2(285.5), min #1153-# m/z 113

12 Figure 117: LC MS/MS spectrum of Isorhamnetin Intens MS2(314.5), min #1155-#1169 x Isorhamnetin m/z Figure 118: LC MS/MS spectrum of Salasones A Intens MS2(455.6), 19.9min # Salasones A m/z 114

13 Figure 119: LC MS/MS spectrum of Foliasalaciosides E1 Intens MS2(53.7), min #117-#1127 Foliasalaciosides E m/z By UHPLC, a chromatographic method was successfully developed using Acclaim 12, C18 reverse-phase column from Dionex, coupled with the ESI/MSMS mass spectrometry (Bruker, Germany). The collected data for the LC/ESI/MSMS was analyzed by averaging followed by extracted ion analysis using the Hystar DataAnalysis program. Averaging and AutoMSn options revealed approx 2 ion signatures in the TIC. Present evaluation of the ESI data yielded about ~5 completely unique ion signatures in the analysis of the Salacia plants species studied. microtof-q II was able to identify ~2 constituents by mass spectral matching and several of these assignments were further supported by comparing these data with previously published data of the constituents as well as from the in-built library. Major identified constituents are flavonoids like vitexin, isorhamnetin, fisetin and polyphenols like salacinol, kotalanol and mangiferin (a xanthone phenolic compound). Terpenoids such as salasones and friedelane-1,3-dione were identified. 115

14 Table 22: Major constituents identified in the bioactive extracts AESO AESR AESC Friedelane-1,3-dione Mangiferin Fisetin Mangiferin Vitexin Isorhamnetin Kotalanol Kotalanol Salasones A Salasones A Lambertic Acid Foliasalaciosides E1 - Salacinol (M +Na) - Table 23 Mass data of identified fragments of compound in Salacia oblonga Compound Retention time (min) [M-H] - m/z (M+Na) - Fragment ion Friedelane- 1,3-dione , 351.3, 363.3, , 421.3, Mangiferin , 31.1, 314.1, Kotalanol , 169., 183.1, , 213., 225., , 272.1, 285.1, , 331.1, 387.1, Table 24 Mass data of identified fragments of compound in Salacia reticulata Compound Retention time (min) [M-H] - m/z (M+Na) - Fragment ion Mangiferin , 31.1, 32, 313.1, Vitexin , 311.1, 431 Kotalanol , 181.1, 191.1, , 261.1, Lambertic , 76.9, 16.8, 132.8, , 215.2, 237.2, , 312.2, 468.8, , Salacinol , 183., 195.1, , 239.1, 253.1, 265.1, 269.1, 281.1, 39.3,

15 Table 25 Mass data of identified fragments of compound in Salacia chinensis Compound Retention time (min) [M-H] - m/z (M+Na) - Fragment ion Fisetin , 185.3, 229.3, Isorhamnetin , 199.3, 215.3, , 269.4, 285.5, Salasones A , 171.3, 187.3, , 237.4, 253.4, 273.4, 281.4, 295.5, 315.5, , 367.6, 379.6, , 422.6, 437.6, Folia salaciosides E , 259.3, 31.3, , 365.6, 379.6, , 427.6, 441.7,

16 6.9 In silico studies Cline et al. (22), Henriksen et al. (23) and Ring et al. (23) have used GSK3 as targets in their in silico experiments. The components present in Salacia species like Isorhamnetin, Mangiferin, Salacinol, Salasones A, Vitexin, have been shown to interact with GSK-3 B. As presented in Table 21, the docking scores of the active compounds present in Salacia species, along with their H- bonding interactions suggest that these compounds could inhibit the GSK-3 action. This indicates the potential of the plant extract containing them towards reducing hyperglycemia and necessitates the measurement of their efficacy in diabetes treatment through in-vitro and in-vivo studies. Figure 12. Glycogen synthase kinase (GSK-3) interaction with Isorhamnetin 118

17 Figure 121.Glycogen synthase kinase (GSK-3) interaction with Mangiferin Fig.19. Glycogen synthase kinase (GSK-3) interaction with Salacinol Figure 122. Glycogen synthase kinase (GSK-3) interaction with salacinol 119

18 Figure 123.Glycogen synthase kinase (GSK-3) interaction with Salasones A Figure 124. Glycogen synthase kinase (GSK-3) interaction with Vitexin In this dissertation also GSK-3 is used as a potential target to suggest possible mechanism of anti diabetic action of selected Salacia species in controlling Diabetes.Glycogen synthase kinase (GSK-3) is one of the common proteins which play a vital role in NIDDM. In mammals, GSK-3 is encoded by two known genes: GSK-3α and GSK-3β. Increased expression of GSK-3 is harmful since it causes the inactivation of multiple blood-glucosereducing enzymes and disrupts insulin signaling. Studies have shown that introducing 12

19 competitive inhibitors of GSK-3 leads to increased activity of insulin. Inhibition of GSK-3 may be a therapeutic strategy for increasing insulin sensitivity and improving Glucose clearance in type 2 diabetes. Most recently, small molecule inhibitors of GSK-3 have been developed and assessed for therapeutic potential in several of models of pathophysiology. As proven in practice, it is far easier to develop a small molecule that inhibits an enzyme rather than one that activates it. If the inability of insulin to inhibit GSK-3 is an important element of type 2 diabetes, then a synthetic GSK-3 inhibitor should hold promise by restoring one of the major effects of insulin action. The first promising data that supported this idea came from experiments with lithium. The recent availability of small molecule inhibitors to GSK-3 has resulted in several studies that showed promising results with respect to activation of glycogen synthesis in models of diabetes (Cline et al., 22, Henricksin et al., 23). These experiments have clearly shown that inhibiting GSK-3 is sufficient to promote glycogen deposition and glucose clearance. Hence in the present study also GSK-3, the selected target interacted well with the ligands in LC-MSMS and results are presented in Table-22 & Fig Table 26: Data on molecular docking studies Compound Name Docking score IC 5 value H-Bond interaction Distance Vitexin LYS 65 N-H...O 2.24 ASN64 O-H...O ASN64 N-H...O VAL135 O-H...O 2.29 ARG141 N-H...O 2.13 Salasones A -8 2 LYS163 N-H...O 1.88 LYS85 N-H...O Salacinol -7 3 ASP133 N-H...O 1.91 VAL135 N-H...O 2.53 VAL135 O-H...O ARG141 N-H...O ARG141 N-H...O Mangiferin -7 9 ASN186 O-H...O ASN64 N-H...O VAL135 N-H...O 2.24 ASP133 O-H...O ASP133 O-H...O

20 Isorhamnetin TYR134 N-H...O ASP133 N-H...O ASN64 O-H...O These experiments have clearly shown that inhibiting GSK-3 is sufficient to promote glycogen deposition and glucose clearance. Docking studies, revealed that compounds identified in the bio active extracts of selected salacia, species such as Isorhamnetin, Mangiferin, Salacinol, Salasones A, Vitexin, interacted with GSK-3, as presented in Table 22 & Fig The docking scores of the active compounds present in the selected Salacia species, along with their H-bonding interactions suggest that these compounds could inhibit the GSK-3 action. These data depicted the anti hyperglycemic potential of the selected Salacia extracts containing these compounds. 6.7 Conclusion LC-MS/MS analysis revealed the presence of poly-phenols such as Kotalanol which is known for their alpha amylase inhibitory action. So in vitro alpha amylase inhibitory assay was performed for the selected Salacia extract.the data of the results obtained in the alpha amylase inhibitory assay performed using the selected extracts of species containing polyphenols were encouraging and provided supporting chemical evidences for the anti-diabetic action of the test drugs. In the docking studies Inhibition of GSK3 by the molecules identified in the selected extract suggested that the test drug controls glucose level by glucose clearance in Type 2 diabetes and is a good strategy for increasing insulin sensitivity. This is in agreement with the earlier findings by Bhagyajothi et al. (212) that S. oblonga stimulate secretion of insulin and regeneration of β-cells. Though many reports are available on the anti-diabetic action and mechanism of Kotalanol, Salacinol identified from Salacia species, this is the first report on the binding affinity of Vitexin, Salasones A, Salacinol, Mangiferin and Isorhamnetin against GSK3. 122

21 To sum up the anti-diabetic action of the Siddha drug Ponkorandi might probably be due to the presence of poly-phenols which are known for their alpha amylase inhibitory action and through the inhibition of GSK3which might have helped in the increased insulin sensitivity. After understanding the probable mechanism of anti-diabetic action of extracts of selected Salacia species through in silico and in vitro approaches, the exctracts were subjected to preclinical studies so as to assess the anti-diabetic as well as the efficacy of the extracts against diabetic neuropathy employing in vivo models. 123