INTRODUCTION:

Discovering effective therapeutic drugs for global diseases such as Diabetes Mellitus is a difficult task in modern science. It is a metabolic disorder mainly characterized by hyperglycemia¹. The major cause for the disorder is defects in insulin synthesis², or decrease of cellular responses toward insulin, responsible for Diabetes mellitus³. Abnormality of insulin receptors or effecter enzymes leads to decreased responses towards insulin at cellular level⁴. According to Egyptian manuscript diabetes was three thousand years old, however it is yet a pressing issue for the whole world⁵. Presently rate of this event is expanding around the world⁶. Till date there is no permanent cure of the disorder from traditional as well as modern medicine⁷.Till date large number of plants has been reported as antidiabetic⁸.

Various kinds of phytoconstituents present in plants are responsible for treatment of different disorders and diseases⁹. Therefore, documentation and validation of medicinal plants is enhancing every day in regard to the characterization of phytoconstituents¹⁰. In silico ADME study along with docking of active principles of plants is an immense strategy¹¹. Classification of drug targets achieved by bioinformatics techniques which are further used for In silico approaches¹². It also provides the structural and functional relationship of ligands¹³. Joining in silico strategies of the active constituents from plant with different receptors provides new drug discovery methods¹⁴ . Since older times Rhus Parviflora was used for curing many types diseases and disorders. It was used as sedative, antibacterial , antifungal^15,16, Anti HIV¹⁷, neurotoxicity¹⁸, Cytotoxic and Neuroprotective¹⁹. Plant was already reported to have diverse phytoconstituents^15,19,20,21. Especially Biflavonoids, that should have antidiabetic potential according to their nature^22,23. Taking a gander at the profitability of in silico procedure and conventional utilization of plant, the present research is intended to find out the antidiabetic potential of Rhus parviflora phytoconstituents with the help of in silico and in vitro study.

MATERIAL AND METHOD:

Database and software’s used:

In this research, in silico methods were incorporated: Molegro Virtual Docker software for docking study in addition Openbabel was used for the conversion of formats. IIT Delhi supercomputer facility was used online for Lipinski rule of 5. For tracing the ADME profile of phytoconstituents Swiss ADME was incorporated. Whole in silico study was carried using computer with Window 10, i5 processor with 4GB DDR3 RAM.

Ligand selection and preparation:

All structure of different phytoconstituents of Rhus parviflora was retrieved by using pubchem database²⁴. Any error of these selected compounds was detected and rectified using chemdraw ultra software²⁵. Conversion Format of structure was done by Openbabel software²⁶. Then Marvin sketch software was used for conversion of structure from 2D to 3D along with explicit hydrogen bond²⁷. To make ligand active, energy minimized for flexible docking Chem 3D pro was used. MVD was used for correction of any error in the hybridization or architecture of the ligand and PDB²⁷.

Selection and preparation of PDB:

Protein Data Bank (PDB) (http: //www.rcsb.org/) was used for retrieval of all necessary 3D structure of PDB used in study. Selection of all PDB for different receptor was done on the basis of literature and analysis of other properties like resolution. Total six receptors namely Aldose Reductase, Insulin receptor, Protein Tyrosine Phosphatase1, Human Dipeptidyl Peptidase IV, α- glucosidase α-amylase and were used in study. For every receptor different PDB was selected including1US0, 1IR3, 2F6V, 2OQV, 5NN6 and 2QV4 respectively²³.

ADME studies:

Selected ligand’s first obtained in ‘smiles’ format using Open Babel software. Then Swiss institute of bioinformatics used to conduct ADME studies.³³ for that files (in smile format) were uploaded in the tool and processed. The results provide basic pharmacokinetics parameter of ligand’s like drug likeness, BBB permeation, GI absorption and P-gp inhibitor.

Molegro Virtual Docker:

Molegro virtual docker is fantastic software to find out relationship between different receptors and ligands. In comparison to the other available software, it provides 87% results for predictions of binding tendencies of the ligands. Other softwares provides lesser results predictions as Gold (78.2%), Glide (81.8%), Surflex(75.3%), FlexX2 (57.9%) provides the results as stated.After importing the PDB , water molecules and cofactors were removed from the Selected PDB. Then trace the warning (if any) in protein preparation were repaired afterward. Cavities are then detected where the ligand get fits. Ligand was imported and docking starts after reset the view. Then total no of interactions between PDB and ligands were analyzed and marked. In silico activity of ligands can be assessed by analyzing bond length, moldock score , number of interactions and H bonding.

Table 1: Lipinski’s Rule of five data and ADME Profile of Chemical Constituents

Sr. no.	Ligand	M.W	HBA	HBD	Log P	MR	Lipinski violations	Solubility	GI absorption	BBB permeant	P-gp substrate
1	4’-O-beta-D-Glucosyl-cis-p-coumaric acid	326.3	8	5	1.64	77.26	0	Very soluble	Low	No	No
2	Quercetin-3-O- α-L-rhamnopyranoside	448.38	11	7	1.6	109	2	Soluble	Low	No	No
3	Trans p -coumaric acid-4-O-b-D-glucopyranoside	326.3	8	5	1.64	77.26	0	Very soluble	Low	No	No
4	Trimethyl citrate	234.2	7	1	1.92	50.43	0	Very soluble	High	No	No
5	Kaempferol	286.24	6	4	1.7	76.01	0	Soluble	High	No	No
6	Myrecetin	302.24	7	5	1.63	78.04	0	Soluble	High	No	No
7	Quercetin	302.23	7	5	2.01	74.05	0	Very soluble	High	No	No
8	Fustin	288.25	6	4	1.24	72.73	0	Soluble	High	No	No
9	Taxifolin	304.25	7	5	0.71	74.76	0	Soluble	High	No	No
10	Isorhamentin	316.26	7	4	2.35	82.5	0	Soluble	High	No	No
11	Sitosterol	414.71	1	1	5.07	133.23	1	Poorly soluble	Low	No	No
12	Aureusidin	286.24	6	4	1.73	73.91	0	Soluble	High	No	No
13	Rhamanoside	164.16	5	4	0.24	34.57	0	Highly soluble	High	No	Yes
14	Naringenin	272.25	5	3	1.75	71.57	0	Soluble	High	No	Yes
15	Shikimic acid	174.15	5	4	0.62	38.43	0	Highly soluble	High	No	No

Docking Results:

Molecular docking of all 15 selected constituents with six selected PDB was done by Molegro Virtual Docker. Prediction of binding affinity was best assessed not only on the basis of mol dock score but also bond length, no of hydrogen bond, interactions also play important role. The values for interactions found between PDB and ligands range from 3 to 14. Multiple interactions showed by almost every ligand.

Insulin Receptor (IR):

Moldock score for internal ligand ANP found to be -139.34.In addition, Glibenclamide have mol dock score -125.221 with 3 hydrogen bonds. However, Trans p coumaric acid 4-o-b-D-glucopyranoside, quercetin-3-O-α-L-rhamnopyranoside, Sitosterol have moldock score -100.794, -101.35, -121.28 respectively. Minimum bond length was found for Isorhamnetin is 1.50Å. Quercetin-3-O-α-L-rhamnopyranoside reflected 8 Hydrogen bonds (Figure 1).

FIG. 1: PDB-1IR3, Ligand Quercetin-3-O- α-L-rhamnopyranoside

Aldose Reductase (AR)

Internal ligand LDT_320 shows moldock score with PDB 1US0 is - 147.81. Glibenclamide have mol dock score -204.55. In contrast moldock score of quercetin and aureusidin was found to be -143.961, -148.011, along with hydrogen bonds 7 each. Minimum bond length was found for Rhamanoside is 1.65Å (Figure2).

FIG. 2: PDB-1US0, Ligand Rhamanoside

Protein tyrosine phosphatase I:

Mol dock score for 2F6V and internal ligand SK2-608 found to be -91.25.Glibenclamide has mol dock score -150.417 with 7 hydrogen bonds. In contrast, myrecetin, quercetin, aureusidin and fustin have moldock score -107.688, -107.622, -114.902 and -104.206 respectively. Maximum number of hydrogen bond found 14 with trimethyl citrate (Fig 3).

Fig 3. 2F6V, Ligand Trimethyl Citrate

Human Dipeptidyl Peptidase IV (DPP4)

Mol dock score of internal ligand Ma9_901 with 2OQV was found to be -115.19. Glibenclamide have moldock score -133.91 with 4 hydrogen bonds. On the other hand Aureusidin and Taxifolin have mol dock score -123.282, and 115.959 with number of hydrogen bonds 7, 8 respectively. Trans -p-coumaric acid-4-O-b-D-glucopyranoside forms shortest bond 1.45Å (Fig 4).

Fig 4 2OQV, ligand Trans -p-Coumaric Acid-4-O-b-D-Glucopyranoside

Alpha amylase:

Docking of PDB 2QV4 with internal ligand NAG_497 internal ligand provided Moldock score -64.23. Glibenclamide have moldock score -135.219 With 2 hydrogen bonds. Whereas fustin show 9 interactions.Taxifolin, auresidin, and trans p coumaric acid 4-o-b-D-glucopyranoside show 8 interactions each. Kampferol, myrecetin, quercetin and naringenin shows 7 interactions. Shikimic acid formed shortest bond of 1.67 Å (Fig. 5).

Alpha glucosidase:

Mol dock score of NAG NAG BMA (internal ligand) PDB 5NN6 was -118.19. Glibenclamide have mol dock score -28.0824 with 4 hydrogen bonds. Trans p coumaric acid 4-o-b-D-glucopyranoside, kaempferol, myrecetin and quercetin shows 6 interactions with PDB. Moldock score for 4’-O-beta-D-Glucosyl-cis-p-coumaric acid was found to be -89.945. Shortest bond length of 1.63 Å was found for naringenin. Maximum interactions were found for Trans p coumaric acid 4-o-b-D-glucopyranoside was 6(Fig 6)

FIG. 6: PDB-5NN6, Ligand Trans p Coumaric Acid 4-o-b-D-Glucopyranoside

FIG. 5: PDB-2QV4, Ligand Shikimic Acid

In vitro Alpha amylase Assay:

α amylase inhibition assay was performed with various concentration 8, 15, 30, 60, 125µg/ml of Rhus parviflora petroleum ether, chloroform and methanol extract. Methanol extract of Rhus parviflora (RPME) exhibited 54.64±2.46 percent inhibition in comparison to 67.76±1.97 of standard Acarbose at 125µg/ml, shown in table 4. Inhibition shown by chloroform and petroleum ether extract of Rhus parviflora was found to be insignificant in contrast to that of standard.

Alpha glucosidase Inhibition Assay:

Rhus parviflora extracts were tested at concentrations of 8, 15, 30, 60, and 125g/ml, and RPME showed substantial inhibition compared to normal Acarbose, but RPCE and RPPE showed minimal inhibition (Table 5). The percentage inhibition was observed to rise when the concentration was raised. At 125g/ml, the highest enzyme inhibition with the standard acarbose was 71.351.84.

Table 4. α-Amylase inhibition activity of Rhus Parviflora

Sample

8 µg/ml

15 µg/ml

30 µg/ml

60 µg/ml

125 µg/ml

RPPE

0.74±

0.35

2.16±

1.97

2.87±

2.77

-0.33±

1.97

0.38±

1.84

RPCE

15.28±

1.23

20.24±

2.33

18.47±

3.69

22.01±

0.71

23.43±

0.71

RPME

14.57±

2.9

23.08±

3.09

28.08±

0.62

42.58±

0.71

54.64±

2.46

Acarbose

21.66±

1.06

36.55±

1.63

43.65±

0.94

58.54±

1.28

67.76±

1.97

RPPE: Rhus parviflora petroleum extract, RPCE: Rhus parviflora chloroform extract, RPME: Rhus parviflora methanol extract

Table 5. α- glucosidase inhibition activity of Rhus parviflora

Sample

8 µg/ml

15 µg/ml

30 µg/ml

60 µg/ml

125 µg/ml

RPPE

1.56±

0.35

1.9±

1.25

1.9±

1.39

2.25±

1.59

3.99±

0.35

RPCE

5.38±

2.62

13.36±

1.84

19.61±

2.28

23.78±

0.92

25.51±

1.25

RPME

12.32±

1.25

18.92±

1.59

32.81±

1.51

49.47±

0.35

60.58±

0.6

Acarbose

28.64±

1.51

38.71±

2.17

46.35±

1.93

61.28±

1.51

71.35±

1.84

RPPE: Rhus parviflora petroleum extract, RPCE: Rhus parviflora chloroform extract, RPME: Rhus parviflora methanol extract

CONCLUSION:

The entire study was directed to follow out the antihyperglycemic potential of chemical constituents of Rhus parviflora. Before docking investigation, ADME profile of every constituent was analyzed by in silico procedures. This bioinformatics examination gave the data in regards to various basic parameters needed to check drug likeliness of the chosen constituents. Total 18 constituents from plant were selected by applying Lipinski rule of five. Then MVD was employed for molecular docking studies. A comparative study was then done on internal of each PDB with ligands. Form the study it was concluded that most of the selected ligands show comparable results with Glibenclamide. In addition ligands have more number of bonds with shorter bond length. In addition, results of in vitro antidiabetic study also support the therapeutic potentials of Rhus parviflora. Among all three extracts of plant, methanol extract exhibited highest enzyme inhibition. Looking into the results of both in silico and in vitro study, it tends to be concluded that most of the ligands have extraordinary binding affinity with different receptors, assuming basic part being played in Diabetes mellitus. Thus selected constituent’s presumably play important role in controlling diabetes mellitus. Nonetheless, much more exploration is needed to break down the restorative viability of Rhus parviflora in decrease and control of diabetes.

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Received on 16.05.2021 Modified on 22.08.2021

Research J. Pharm. and Tech 2022; 15(9):3919-3923.

DOI: 10.52711/0974-360X.2022.00656