2D-QSAR ANALYSIS OF PHTHALIMIDE DERIVATIVES AS POTENT HYPOGLYCEMIC AGENTSHTML Full Text
2D-QSAR ANALYSIS OF PHTHALIMIDE DERIVATIVES AS POTENT HYPOGLYCEMIC AGENTS
Sumitra Nain*, Garima Mathur and Anu Sharma
Department of Pharmacy, Banasthali University, Banasthali, Rajasthan, India
ABSTRACT: A quantitative structure activity relationship study on series of total thirty three pthalimide analogues reported compounds was taken. Pthalimide analogues have several advantages over present ant diabetic drugs. The present drugs target insulin resistance and insulin insufficiency. So, it is believed that agents will be available alternative to other second line treatment options including sulfonylurea TZDs, DPP-4 inhibitors. Several statistical regression expressions were obtained using stepwise multiple linear regression analysis (MLR) and partial least square analysis (PLS). Pthalimide analogues activity is described by models that are built on simple 2D molecular descriptors and nevertheless are of good quality and predictive power. The results obtained after performing QSAR were; r2 = 0.8986, (MLR method) (equation-1), (equation-2) r2 = 0.6898 (PLS method) The parameters that are found to have significant correlation with hypoglycemic activity are Hosoya Index which is signifies the topological index or Z index, negatively contributing in the biological activity (~40%). The next descriptor is T_N_N_4 i.e. number of Nitrogen atoms (single double or triple bonded) separated from any other Nitrogen atom (single double or triple bonded) by 4 bonds in a molecule. is inversely proportional to the activity (~30%) which mainly indicates the relationship with reference to distance between two nitrogen atoms. The descriptor XK Average Hydrophilicity i.e. Average hydrophilic value also negatively. The r2 and r (CV) 2 values of PCR and PLS models clearly indicate the predictive ability of these models.
Qsar, phthalimide Derivative, Hypoglycemic Activity
INTRODUCTION:A QSAR is a mathematical model or a statistical correlation that relates one or more quantitative parameters derived from chemical structure to a quantitative measure of a property or activity. QSARs are quantitative models yielding a continuous or categorical result
The term quantitative in QSAR refers to the nature of the parameter(s) used to make the prediction. The presence of a quantitative parameter enables the development of a quantitative model. Such a model can be used to predict a qualitative or quantitative endpoint. Diabetes, accounts for over 90% of the reported diabetic cases. Most of the current therapies for type 2 diabetes were developed in the absence of defined molecular targets.
Oral hypoglcemics are anti-diabetic drugs designed to help people with type 2 diabetes.
There are seven distinct classes of hypoglycemic agents (Table 1): biguanides, sulfonylureas, meglitinides, thiazolidinediones, α-glucosidase inhibitors, incretin mimetics and DPP-4 inhibitors.1
TABLE 1: ORAL HYPOGLYCEMIC DRUGS AND THEIR CLASS
Acetohexamide Chlorpropamide Tolazamide Tolbutamide
Glibenclamide/Gliburide Glipizide Glimepiride Gliclazide
||Sitagliptin Vildagliptin Saxagliptin|
- Biguanides: Biguanides are old agents that work by reducing hepatic glucose output and, to a lesser extent, enhancing insulin sensitivity in hepatic and peripheral tissues. In contrast to sulfonylureas, metformin does not directly stimulate insulin secretion; its major effects are to increase insulin action and insulin-mediated glucose utilization in peripheral tissues (such as muscle and liver), particularly after meals, and to decrease hepatic glucose output. Moreover, it has an antilipolytic effect that lowers serum free fatty acid concentrations, thereby reducing substrate availability for gluconeogenesis 2.
- Sulfonylureas: Sulfonylureas (tolbutamide) leads to increased mortality due to cardiovascular events, the use of the first generation sulfonylureas (acetohexamide, chlorpropamide, tolbutamide and tolazamide) quickly fell out of favour. In contrast, the second- generation sulfonylureas (glipizide, gliclazide, glibenclamide, called also glyburide) and glimepiride are widely employed worldwide. They work by stimulating insulin release from the insulin secreting ß- cells located in the pancreas 3 and may slightly improve insulin resistance in peripheral target tissues (muscle, fat) 4. Their receptor is a component of the ATP-dependent potassium channel in the pancreatic β-cells; the binding leads to inhibition of these channels, which alters the resting potential of the cell, leading to calcium influx and stimulation of insulin secretion.
- Meglitinides: The meglitinides, repaglinide and nateglinide, are short-acting glucose-lowering drugs for therapy of patients with type 2 diabetes alone or in combination with metformin. They were designed to achieve more physiologic insulin release and less risk for hypoglycemia. They are structurally different than sulfonylureas but their mechanism of action closely resembles that of sulfonylureas (they act by regulating ATP-dependent potassium channels in pancreatic beta cells), because they stimulate the release of insulin from the pancreatic beta cells through a different binding site on the “sulfonylurea receptor” 5.
- Thiazolidinediones: During the last decade a new class of drugs has been available for treatment of type 2 diabetes: the thiazolidinediones (troglitazone, rosiglitazone and pioglitazone). Actually only two thiazolidinediones (rosiglitazone and pioglitazone) are currently marketed. The majority of data reporting the efficacy of this class comes from studies with troglitazone, results from more recent studies with the newer agents (rosiglitazone and pioglitazone) demonstrating similar properties, although their mechanism of action is not fully understood 6.
Thiazolinediones improve glycemia reducing insulin resistance and preserving pancreatic beta-cell function with different mechanism of action; as example, the predominant effect of metformin is to inhibit hepatic glucose production, whereas thiazolidinediones act mainly by improving peripheral uptake and utilization of glucose in muscle and fat, finally decreasing liver glucose production 7.
- α-Glucosidase Inhibitors: α-Glucosidase inhibitors include acarbose and miglitol. They act on α-glucosidase, an enzyme found in brush border cells of small intestine, cleaving more complex carbohydrates into sugars.
α- Glucosidase inhibits the breakdown and absorption of carbohydrates (dextrins, maltose, sucrose and starch; no effect on glucose); their largest impact is on postprandial hyperglycemia and their effect on FPG levels is modest. They have been associated with a reduction in HbA1c by 0.7 to 1.0 percent and FPG levels by 35 to 40 mg per dL (1.9 to 2.2 mmol per L) 8 .
- Incretin agonist: Incretins (Glucagon Like Peptide-1 and Glucose Dependent Insulinotropic Polypeptide) are enteroendocrine hormones released into bloodstream from L and K cells dispersed throughout the gastrointestinal tract 9.
GLP-1 is secreted in response to nutrients and its levels are decreased in type 2 diabetes; it acts stimulating glucose-dependent insulin release from the pancreatic islets and this is the major advantage over sulfonylureas, as it prevents hypoglycaemia 10.
- DPP-4 inhibitors: DPP-4 rapidly degrades and inactivates GLP-1, GIP, and other peptides in vivo via cleavage of N-terminal two amino acids. Inhibition of this enzyme leads to an increase in circulating endogenous GLP-1 and GIP levels; so that DPP-4 inhibitors are not incretin mimetics, but incretin enhancers. Unlike other GLP-1 based therapies, can be administered orally.
Sitagliptin, vildagliptin and saxagliptin are DPP-IV inhibitors that are approved as initial pharmacologic therapy for the treatment of type 2 diabetes; as a second agent in those who do not respond to a single agent, such as a sulfonylurea, metformin or a thiazolidinedione; and as a third agent when dual therapy with metformin and a sulfonylurea does not provide adequate glycemic control.
The usual dose of sitagliptin is 100 mg once daily, with reduction to 50 mg for moderate to severe renal insufficiency (GFR <30 to 50 mL/min) and 25 mg for severe renal insufficiency (<30 mL/min) 11.
OBJECTIVE: The objective of this study is to develop a best suitable model with predictive power. This study helps to determine the hypoglycemic activity of a drug i.e. its biological activity and potency of drug.
MATERIALS & METHODS:
Data Set: The dataset consist of structurally diverse compounds reported for hypoglycemic activity. The selected series comprises of thirty three phthalimide analogues (Table 1, 2). The hypoglycemic activity of compounds in the series is reported asLog IC50 values refers to experimentally determined concentration required to inhibit 50% of hypoglycemic activity.
The compounds in the selected series were randomly divided into two sets with 25 compounds used as a training set in developing regression models and the remaining 8 as validation set (Test set) in the prediction of biological activity.
2D-QSAR Analysis: The molecular structures of the compounds in selected series were sketched and optimized using V life sciences and were then loaded into a data table within the graphic based QSAR program V life sciences molecular modeling software. The sketched structures were then transferred to three dimensional structures (3D).
The negative logarithm of the original biological activity data were entered into the data table and the entire ranges of molecular properties were calculated using V life sciences technology.
TABLE 1: TRAINING SET WITH BIOLOGICAL ACTIVITY
|Compound||R1||R2||R3||Log IC50 value|
Selection of a series was done of biologically active analogues (training set) with their biological activity. In the table 1, different 25 derivatives with their log IC50 are given. Second step was selection of Test set. Table 2 showing the test set (derivative to be synthesized) from 26 to 33, for which the activity will be predicted. Next step was Calculation of various physicochemical parameters. Figure 1 showing the computer screen for of software for the calculation of physic chemical parameters.
TABLE 2: TEST SET COMPOUNDS
FIGURE 1: WORKSHEET OF TRAINING SET COMPOUNDS WITH THEIR BIOLOGICAL ACTIVITY
TABLE 3: ACTUAL ACTIVITY AND PREDICTED ACTIVITY (A) TRAINING SET (B) TEST SET
After that the QSAR equation was generated and the activities of test compounds are predicted.
(a) Model generated by using principal component regression:
log IC50 = - 0.0001 HosoyaIndex - 1.3219 T_N_N_4 - 79.7487 XK Average Hydrophilicity + 0.3588 T_N_N_5 + 2.0090
Optimum Components = 3, n = 25, Degree of freedom = 21, r2 = 0.8588, q2 = 0.5669
This is statistically significant model because of r2 =0.85 (coefficient of determination >0.7) and q2 =0.56(cross- validation > 0.5). Figure 2 showing the contribution chart of different descriptors used in equitation (a) and table 3 showing the actual and predicted activity of 33 derivatives by using equitation (a).
(b) Model generated by using Multiple regression:
logIC50 = - 0.0000 (±0.0000) Hosoya Index - 1.9775(± 0.0209) T_N_N_4 + 0.7922(± 0.0296) XK Hydrophilic Area + 14.4188(± 4.5621) Most -ve Potential + 3.2401
n = 25, Degree of freedom = 20, r2 = 0.8986, q2 = 0.4301
This is statistically not significant model because of r2 =0.89 (coefficient of determination >0.7) but q2 =0.43(cross- validation > 0.5).
FIGURE 3: CONTRIBUTION CHART FOR MODEL GENERATED BY PCR
(c) Model generated by using Partial Least Squares regression:
logIC50 = - 0.0001 HosoyaIndex - 0.4654 T_O_O_7 - 0.0092 XZ Polarizability+ 2.0719
Optimum Components = 1, n = 25, Degree of freedom = 23, r2 = 0.6898, q2 = 0.5117
This is statistically not significant model because of r2 =0.68 (coefficient of determination >0.7) but q2 =0.51(cross- validation > 0.5).
RESULT & DISCUSSION: The regression coefficient r2 for the models derived using PCR analysis & PLS analysis were compared together with their corresponding r(CV)2 value.
Result of Principal component regression:
|No. of Compounds
CONCLUSION: The regression equation generated in the present study suggests that the chosen properties are equally relevant.
In conclusion the parameters that are found to have significant correlation with hypoglycemic activity are Hosoya Index which is signifies the topological index or Z index, negatively contributing in the biological activity (~40%).
The next descriptor is T_N_N_4 i.e. number of Nitrogen atoms (single double or triple bonded) separated from any other Nitrogen atom (single double or triple bonded) by 4 bonds in a molecule. is inversely proportional to the activity (~30%) which mainly indicates the relationship with reference to distance between two nitrogen atoms. The descriptor XK Average Hydrophilicity i.e. Average hydrophilic value also negatively.
The r2 and r (CV)2 values of PCR and PLS models clearly indicate the predictive ability of these models. The QSAR studies reveals that the derivative no. 28 would be most active for hypoglycemic activity with predicted logIC50 value =-33.074. The derivative no. 27 is next active compound with predicted logIC50 value =-2.194 and the third active compound in derivative no. 29 with predicted logIC50 value =0.2384.
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How to cite this article:
Nain S, Mathur G and Sharma A: 2D-QSAR analysis of Pthalimide derivatives as potent hypoglycemic agents. Int J Pharm Sci Res 2013; 4(11): 4470-76. doi: 10.13040/IJPSR. 0975-8232.4(11).4470-76
All © 2013 are reserved by International Journal of Pharmaceutical Sciences and Research. This Journal licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
Sumitra Nain*, Garima Mathur and Anu Sharma
Department of Pharmacy, Banasthali University, Banasthali, Rajasthan, India
24 June, 2013
12 August, 2013
17 October, 2013
01 November, 2013