Docking Analysis of Novel Arylidinemalononitrile derivatives as PPAR-γ Modulators in the Management of type II Diabetes Mellitus

 

Ramsaneh Raghuwanshi*, Hemendra P. Singh

Research Scholar, Faculty of Pharmacy, Bhupal Nobles University, Udaipur, Rajasthan.

 

ABSTRACT:

Prolonged hyperglycemia often associated with the number of complications such as diabetic neuropathy, retinopathy, nephropathy, cardiomyopathy etc. Diabetic neuropathy is damage to nerves in the body that occurs due to high blood glucose level. In the central nervous system, diabetes exacerbates depression, phobias, anorexia, hypolocomotion, anxiety, cognitive dysfunction etc. The design, docking and analysis of novel arylidine malononitrile-based molecules as derivatives as peroxisome proliferator-activated receptor-γ (PPAR-γ) modulators for antidiabetic activity are reported. Docking studies of designed compounds were carried out using GLIDE (Grid-based ligand docking with emergetics) module version 4.5, Schrodinger, LLC, Newyork, NY,2007. The software package running on multi-processor Linux PC. GLIDE has previously been validated& applied successfully to predict the binding orientation of many ligands. Docking studies of compounds were performed using human peroxisomes proliferators activated receptor gamma (PDB ID 2PRG) obtained from the RCSB Protein Data Bank. Different arylidine malononitrile derivatives were docked into the ligand binding domain of PPAR-γ by the Glide XP module of Schrodinger, out of those Eight derivatives (AM01, AM02, AM03, AM04, AM05, AM06, AM07, AM08) having Glide XP scores > -9 as compared to the standard drug, rosiglitazone (Glide XP score = -8.34), showed almost similar interaction with the amino acids such as SER-289, GLN-286, and HIE-323 in the molecular docking studies.

 

 

 

INTRODUCTION:

Diabetes, correctly termed as diabetes mellitus, is a major epidemic of this century and has increased in incidence by 50% over the past 10 years [1,2]. Diabetes Mellitus is a clinical syndrome, characterized by hyperglycemia caused by a relative or absolute deficiency of insulin at the cellular level. It is the most common endocrine disorder, affecting mankind all over the world, prevalence of which is increasing, daily [3].

 

Diabetes mellitus is an endocrinological metabolic disorder characterized by hyperglycemia, glycosuria, hyperlipemia, negative nitrogen balance and sometimes ketonemia [4]. Diabetes Mellitus is an endocrine disorder characterized by altered glucose homeostasis leading to derangements in the carbohydrate, protein and lipid metabolism, resulting from partial or complete deficiency in insulin synthesis or due to peripheral resistance to insulin action [5].

 

DM is commonly associated with low HDL cholesterol and high triglyceride levels. There are a number of agents that both raise HDL-C and lower triglycrides. These include the peroxisome proliferator-activated receptor (PPAR) agonists (α and γ), statin, fibrate and nicotinic acid [6]. Diabetes is becoming the third killer of the health of mankind along with cancer, cardiovascular and cerebrovascular diseases. According to World Health Organization (W.H.O) report, number of diabetic patients is expected to increase from 171 million in year 2000 to 366 million or more by the year 2030 [7]. Chronic hyperglycemia in diabetes is associated with long-term damage, dysfunction, and eventually the failure of organs, especially eyes, kidneys, nerves, and the cardiovascular system [8].

 

Defective insulin secretion, resistance to insulin action and reduction in the bio-antioxidant potential leads to diabetes mellitus. The imbalance between the pro-oxidant and antioxidant homeostasis results in oxidative stress (OS). The role of OS in the pathogenesis of diabetes and its associated diseases (retinopathy, nephropathy, atherosclerosis and coronary artery disease) is well characterized [9]. Non-insulin dependent diabetes mellitus (NIDDM) Type –II occurs at any age, but is more common between 40–80 years of age and also has a strong genetic component. The majority of diabetes (~90%) is Type –II diabetes (T2D) caused by a combination of impaired insulin secretion from pancreatic beta cells and insulin resistance of the peripheral target tissues, especially muscle and liver [10].

 

Prolonged hyperglycemia often associated with the number of complications such as diabetic neuropathy, retinopathy, nephropathy, cardiomyopathy etc. Diabetic neuropathy is damage to nerves in the body that occurs due to high blood glucose level [2]. In the central nervous system, diabetes exacerbates depression, phobias, anorexia, hypolocomotion, anxiety [11-13], cognitive dysfunction etc [14]. Clinically, patients with diabetes are at increased risk of developing depression and cognitive impairment as compared to the general population [14,15]. Dysregulation of insulin signaling pathway has been involved in the pathogenesis of diabetic complications. Rosiglitazone (ROSI), a PPARγ agonist is known to ameliorate neuronal insulin receptor expression during insulin resistance [16,17]. Report suggests that, dysfunctioning of PPARγ receptor is associated with neuronal dysfunction, nephropathy, cardiomyopathy etc [18]. Very few reports are available to understand the role of arylidene malononitrile in diabetes and its associated neuronal dysfunction. Effect of arylidene malononitrile derivatives on diabetes mediated behaviour deficit is not investigated. There is no evidence regarding the use of arylidene malononitrile on comorbidity of depression and diabetes. Investigation of the role of arylidene malononitrile against diabetes mediated neuronal dysfunctions might solve the problems associated with current therapy which is known to induce cardiac dysfunction. Clinical study was carried out in healthy volunteers as well as in diseased patients by considering two parameters namely behavioural and oxidative stress parameters. Behavioural parameters included anxiety, depression, acidity; constipation etc. and these parameters were further marked as mild, moderate and severe according to the intensity [19]

Docking is a method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex. It is frequently used to predict the binding orientation of small molecule drug candidates to their protein targets in order to in turn predict the affinity and activity of the small molecule. Hence docking plays an important role in the rational design of drugs [20]. The process of “DOCKING” a ligand to binding sites tries to mimic the natural course of interaction of the ligand and its receptor via a lowest energy pathway. Usually the receptor is kept rigid while the conformation of the drug molecule is allowed to change. The molecules are physically moved closer to none another and the preferred docked conformation is minimized.

 

METERIALS AND METHODS:

Docking Procedure:

Docking studies of compounds listed in table No.1 were performed using human peroxisomes proliferators activated receptor gamma (PDB ID 2PRG) obtained from the RCSB Protein Data Bank, http://www.rcsb.org/pdb, where the residues were bonded more closely to Rosiglitazone agonist, co-crystallized with PPARγ. In this crystal structure, the LBD forms a homodimer in which both monomers have nearly identical Cα conformations. The structure of chain "A" (monomer of the LBD homodimer) was chosen as the target for docking studies.

 

Experiments were performed using the program GLIDE (Grid-based Ligand Docking with Emergetics) module version 4.5, Schrödinger, LLC, New York, NY, 2007 (Schrödinger Inc.). Coordinates of the full- length substrate-complexed dimmer were prepared for glide 4.0 calculations by running the protein preparation wizard. The p-prescript produces a new receptor file in which all residues are neutralized except those that bridges. The impref script run a series of restrained impact energy minimizations using the impact utility. Minimizations were run until the average root mean square deviation (rmsd) of the non-hydrogen atoms reached 0.3A°.

 

Glide uses two boxes that share a common centre to organize its calculations: a larger enclosing box and a smaller binding box. The grids themselves are calculated within the space defined by enclosing box. The binding box defines the space through which the centre of the defined ligand will be allowed to move during docking calculations. It provides a measure of the effective size of the search space. The only requirement on the enclosing box is that it be large enough to contain all ligand atoms, even when the ligand centre is placed at an edge or vertex of the binding boxes. Grid files were generated using the cocrystallized ligand at the centre of the two boxes. The size of the binding was set at 20A° in order to explore a large region of the protein. The three-dimensional structures of the compounds were constructed using the Maestro interface. The initial geometry of the structures was optimized using the OPLS-2s005 force field performing 1000 steps of conjugate gradient minimization. The compounds were subjected to flexible docking using the pre-computed grid files. For each compound the 100 top scored poses were saved and analyzed. Give the structure of designed compound in figure 1 and table2.

 

 

Table 1: Glide Score of Designed Compounds

S. No.	R	Glide Score
1	2-C l	9.82
2	4-CH3	9.55
3	3-O CH3	9.42
4	2- O CH3	9.23
5	4-F	9.20
6	H	9.08
7	3-NO2	9.01
8	4-Cl	9.00
9	4-OC2H5	8.96
10	2,4Di –C l	8.94
11	Isoniazid	8.82
12	3- CH3	8.73
13	4-Br	8.58
14	4-NO2	8.50
15	4- O CH3	8.46
16	2,3- Di –C l	8.39
17	3,4,5 Tri- O CH3	7.47

 

Figure 1 Arylidene Malononitrile

 

Table 2: Detail of Compounds with Glide Score 9 Were Selected

Compound code	Structure	IUPAC Name
AM-01		2-(4-(2-(5-phenyl-1,3,4-oxadiazol-2-ylthio)ethoxy)benzylidene)malononitrile
AM -02		2-(4-(2-(5-(2-methoxyphenyl)-1,3,4-oxadiazol-2-ylthio)ethoxy)bezylidene)malononitrile
AM -03		2-(4-(2-(5-(3-methoxyphenyl)-1,3,4-oxadiazol-2-ylthio)ethoxy)bezylidene)malononitrile
AM -04		2-(4-(2-(5-(2-chlorophenyl)-1,3,4-oxadiazol-2-ylthio)ethoxy)bezylidene)malononitrile
AM -05		2-(4-(2-(5-(4-chlorophenyl)-1,3,4-oxadiazol-2-ylthio)ethoxy)bezylidene)malononitrile
AM -06		2-(4-(2-(5-(4-fluorophenyl)-1,3,4-oxadiazol-2-ylthio)ethoxy)bezylidene)malononitrile
AM -07		2-(4-(2-(5-p-tolyl-1,3,4-oxadiazol-2-ylthio)ethoxy)bezylidene)malononitrile
AM -08		2-(4-(2-(5-(3-nitrophenyl)-1,3,4-oxadiazol-2-ylthio)ethoxy)bezylidene)malononitrile

 

Table 3: Glide/ Docking (XP Mode) Score of Selected Compound

COMP. CODE	STRUCTURE	HYDROGEN BOND INTERACTION	Gvdw†	GLIDE SCORE
BRL⃰ (Reference ligand)		SER-289	336	8.34
AM-01		SER-289 GLN-286 HIE-323	325	9.08
AM -02		SER-289 GLN-286 HIE-323	351	9.23
AM -03		SER-289 GLN-286  HIE-323	350	9.43
AM -04		SER-289 GLN-286 HIE-323	319	9.83
AM -05		SER-289 GLN-286  HIE-323	311	9.00
AM -06		SER-289 GLN-286  HIE-323	314	9.20
AM -07		SER-289 GLN-286  HIE-323	352	9.55
AM -08		SER-289 GLN-286  HIE-323	323	9.01

 

RESULTS AND DISCUSSION:

Docking studies of designed compounds were carried out by GLIDE (Grid-based Ligand Docking with Energetics, version 4.5, Schrödinger, LLC, New York, NY, 2007) software package running on multiprocessor-Linux PC. GLIDE has previously been reported for the docking of PPARγ and was found successfully to predict the binding orientation of many ligands. On the basis of Glide/docking Score total eight compounds were selected shown in table table No. 3. The detail of their binding pattern at the active site of receptor (PDB ID- 2PRG) was successfully visualized with the help of software shown in fig.2 to 5.

 

Glide Score is based on Chem Score, but includes a steric-clash term and adds buried polar terms devised by Schrodinger to penalize electrostatic mismatches:

 

Glide Score=0.065*vdW + 0.130*Coul + Lipo + Hbond + Metal + BuryP + RotB + Site

 

 

Fig. 2: Active Site of PPARγ with Docked Ligand (AM-02)

 

 

Fig. 3: Hydrogenbond interaction between Malononitrile head group and Amino Acids of Receptor

 

 

FIG.4: Hydrogen bond interaction of AM-04 with receptor

 

Fig.5: Good vander waal's interaction of AM-04 with receptor

 

CONCLUSION:

Designing novel series of PPARγ ligands this was to explore newer acidic head groups for designing novel series of PPARγ ligands this was aimed to improve the potency of compound. It was realized that docking, an imoptant computational technique could be of help in discovering newer scaffolds with desired features and thus employed for this purpose. The PPARγ receptor has both hydrophilic domain, so for proper interaction with receptor the compounds should be ampiphilic in nature. The malononitrile group is successfully completed the requirement of acidic head group and shows the hydrogen bond interaction with the SER-289, GLN-286 and HIE-323 amino acid of the active site of the receptor.

 

REFERENCES:

1.      Shaw, J.E., R.A. Sicree, and P.Z. Zimmet: Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes research and clinical practice 2010; 87(1): p. 4-14.

2.      Danaei, G., et al.: National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2· 7 million participants. The Lancet, 2011; 378(9785): p. 31-40.

3.      Nwauche, Kelechi Thank God, Monago, C.C., Anacletus, F.C. Management of Diabetic Induced Hyperlipidemia with Combined Therapy of Reducdyn and Metformin in Streptozotocin Induced Diabetic Male Rats. Research J. Pharm. and Tech. 7(8): August 2014 Page 910-91.

4.      Surendra Nath Pandeya, Rajeev Kumar, Arun Kumar, Ashish Kumar Pathak. Antidiabetics Review on Natural Products. Research J. Pharm. and Tech. 3(2): April- June 2010; Page300-318.

5.      S. Subramanian, S. Rajeswari, G. Sriram Prasath. Antidiabetic, Antilipidemic and Antioxidant Nature of Tridax procumbens Studied in Alloxan-Induced Experimental Diabetes in Rats: a Biochemical Approach. Asian J. Research Chem. 4(11): Nov., 2011; Page 1732-1738.

6.      Monago C.C., Amachree I., Joshua P.E. Diabinese and Nicotinic acid Combination Reduced Cardiovascular Indices in Experimental DiabetesAsian J. Research Chem. 3(3): July- Sept. 2010; Page 785-790.

7.      Samidha Kamtekar, Vrushali Keer. Management of Diabetes: A Review. Research J. Pharm. and Tech. 7(9): Sept. 2014 Page 1065-1072.

8.      K. Radhika, B. Kumaravel, V. Thamizhiniyan, S. Subramanian. Biochemical evaluation of antidiabetic activity of Piper betel leaves extract in alloxan-induced diabetic rats. Asian J. Research Chem. 6(1): January 2013; Page 76-82.

9.      Swathi N, Subrahmanyam CVS, Satyanarayana K. Synthesis and Quantitative Structure-Antioxidant Activity Relationship Analysis of Thiazolidine-2,4-dione Analogues. Asian J. Research Chem 8(1): January 2015; Page 21-26

10.   Suresh Kumar Sutrakar, Drutpal Singh Baghel. Biochemical Parameters Variations in Type–II Diabetes Mellitus: Special Reference in Rewa Region. Asian J. Research Chem. 7(10): October- 2014; Page 877-881.

11.   Lustman, P.J., L.S. Griffith, and R.E. Clouse: Depression in adults with diabetes: results of 5-yr follow-up study. Diabetes care 1988; 11(8): p. 605-612.

12.   Nouwen, A., et al.: Prevalence of depression in individuals with impaired glucose metabolism or undiagnosed diabetes a systematic review and meta-analysis of the European Depression in Diabetes (EDID) research consortium. Diabetes care 2011; 34(3): p. 752-762.

13.   Patel, S.S., A. Parashar, and M. Udayabanu: Urtica dioica leaves modulates muscarinic cholinergic system in the hippocampus of streptozotocin-induced diabetic mice. Metabolic brain disease 2015; 30(3): p. 803-811.

14.   Lupien, S.B., E.J. Bluhm, and D.N. Ishii: Systemic insulin‐like growth factor‐I administration prevents cognitive impairment in diabetic rats, and brain IGF regulates learning/memory in normal adult rats. Journal of neuroscience research 2003; 74(4): p. 512-523.

15.   Egede, L.E. and C. Ellis: Diabetes and depression: global perspectives. Diabetes research and clinical practice 2010; 87(3): p. 302-312.

16.   Pipatpiboon, N., et al.: PPARγ agonist improves neuronal insulin receptor function in hippocampus and brain mitochondria function in rats with insulin resistance induced by long term high-fat diets. Endocrinology 2011; 153(1): p. 329-338.

17.   Patel, S.S., S. Gupta, and M. Udayabanu: Urtica dioica modulates hippocampal insulin signaling and recognition memory deficit in streptozotocin induced diabetic mice. Metabolic brain disease 2016; 31(3): p. 601-611.

18.   Greene-Schloesser, D., et al.: The peroxisomal proliferator-activated receptor (PPAR) α agonist, fenofibrate, prevents fractionated whole-brain irradiation-induced cognitive impairment. Radiation research 2014; 181(1): p. 33-44.

19.   PB Suruse, A Roy, KR Kakade, VB Mathur, MK Kale. Clinical Study of Marketed Ayurvedic Preparation in Diabetic Induced Stress. Research J. Pharm. and Tech. 1(4): Oct.-Dec. 2008; Page 414-417.

20.   Sandip N. Badeliya, Dhrubo Jyoti Sen. Bioreceptor Platform: A Macromolecular Bed for Drug Design. Asian J. Research Chem. 3(4): Oct. - Dec. 2010; Page 812-820.

 

 

 

 

Received on 19.02.2020           Modified on 29.03.2020

Accepted on 21.05.2020         © RJPT All right reserved