3D-QSAR FOR α-GLUCOSIDASE INHIBITORY … FOR α-GLUCOSIDASE INHIBITORY ACTIVITY OF N-(PHENOXYALKYL)...

IJRRAS 11 (3) ● June 2012 www.arpapress.com/Volumes/Vol11Issue3/IJRRAS_11_3_06.pdf

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3D-QSAR FOR α-GLUCOSIDASE INHIBITORY ACTIVITY OF

N-(PHENOXYALKYL) PHTHALIMIDE DERIVATIVES

Samira Mbarki*, Menana El Hallaoui & Khalid Dguigui

LCPPEM, Département de Chimie, Faculté des Sciences Dhar El Mahraz,

Université Sidi Mohammed Ben Abdallah, B.P.1796 ATLAS. Fès. Maroc.

E-mail : [email protected]; [email protected]

ABSTRACT

Three-dimensional quantitative structure–activity relationship (3D-QSAR) models were developed for 37 N-

(phenoxyalkyl)phthalimide derivatives, inhibiting alpha-glucosidase. 3D-QSAR investigations, applied to find a

correlation between the different physicochemical parameters of the compounds studied and their biological activity,

were carried out using multiple linear regression (MLR) and neural network (NN). The results of MLR and NN

showed good correlations (r values of 0.943 and 0.979, respectively) between the descriptors and alpha-Glucosidase

inhibitory activity. To test the performance of this model we have used the cross validation method ( r= 0.866).

Keywords: Alpha-Glucosidase inhibitory activity, 3D-QSAR model, MLR, NN, Cross validation (CV).

1. INTRODUCTION

α-Glucosidase [EC 3.2.1.20; α -d-glucoside glucohydrolase] catalyzes the liberation of α -d-glucose from the non-

reducing end of a substrate, including maltooligosaccharides, aryl- and alkyl- α -d-glucopyranosides. Besides this

hydrolase activity, the enzyme also displays a transferase activity (transglycosylation reaction), which results in the

formation of various α -glucosylated compounds [1-3]. These enzymes have drawn a special interest of the

pharmaceutical research community because it was revealed that the inhibition of its catalytic activity led to the

retardation of glucose absorption and the decrease in postprandial blood glucose level. This indicates that the

effective α-glucosidase inhibitors may serve as chemotherapeutic agents for clinic use in the treatment of diabetes,

hypertension, dyslipidemia, obesity and cardiovascular diseases in patients with metabolic syndrome [4–6].

Consequently, a-glucosidase inhibitors such as acarbose [7] and miglitol[8] (fig. 1) have been approved for clinical

use in the management of type 2 diabetes, as well as the treatment of obesity. Glucosidase inhibition may also retard

cancer growth since the spread of cancer as well as the structural changes of cell surface glycoconjugates within

neoplasmic cells is proliferated by glycosidases. [9] α -Glucosidase inhibitors have also been observed to block viral

infections [10] and proliferation in HIV-infections.[11] In fact, the well established α -glucosidase inhibitor 1-

deoxynojirimycin (dNM, Fig. 1) exhibits potential anti-human HIV activity.[12] As a consequence, many efforts

have been made to develop new a-glucosidase inhibitors, as recently reviewed in an extensive fashion.[13]

Figure 1. Chemical structure of α -Glucosidase inhibitors

In the present work, we focused on new famille of N-(phenoxyalkyl)phthalimide derivatives (fig. 2) of non-sugar-

type sugar mimic a-glucosidase inhibitors.

Figure 2. Chemical structure of N-(phenoxyalkyl)phthalimide derivatives

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IJRRAS 11 (3) ● June 2012 Mbarki & al. ● 3D-QSAR for α -Glucosidase Inhibitory Activity

396

The lack of structural information about the nature of the interactions between α-glucosidases and the inhibitors has

thus made it a difficult task to discover good lead compounds. Quantitative structure-activity relationship (QSAR)

studies are a powerful method for the design of bioactive compounds and the prediction of activity according to the

physical and chemical properties [14–17].

The basis for various quantitative structure–activity relationship (QSAR) methods is the ’description’ of the

molecular structures by means of numbers. At present, there are a large number of molecular descriptors that can be

used in QSAR studies.[18–22]

In this study, Multiple Linear Regression (MLR) analysis and Neural Network (NN) calculations are applied to a

series of 37 N-(phenoxyalkyl)phthalimide derivatives, in order to set up a 3D-QSAR model able to predict alpha-

Glucosidase inhibitory activity.

2. MATERIALS

2-1 Experimental data

The experimental IC50(µM) alpha-Glucosidase inhibitory activity of N-(phenoxyalkyl) phthalimide derivatives are

collected from recent publications (Rossana Pascale et al. 2010) [23]. The observations are converted into minus

logarithm scale logIC50 and are included in Table 2.

2-2 Calculation of molecular descriptors

The initial conformation of the compounds was drawn in ChemDraw Ultra ver. 8.0 and copied to Chem3D Ultra ver.

8.0 in order to create its 3D model. Each model was cleaned up and energy monimization was performed using

allinger’s MM2 force field by fixing root Mean Square Gradient(RMS) to 0.1 Kcal mol-1

A0. Further geometry

optimization was done using semiemperical AM1 (Austin Model 1) Hamiltonian method; closed shell restricted

wave function available in the MOPAC module until the RMS value becomes smaller than 0.001Kcal mol-1

A0.

Energy minimized geometry was used for calculation of various thermodynamic; steric and electronic descriptors

which are mentioned in Table 1.

Table 1: Descriptors chosen for the QSAR model, and used in this study.

Category of descriptors Name of the descriptors

Elrctronic

Steric

Thermodynamic

Electronic energy (ElcE),

Highest occupied molecular orbital energy (HOMO),

Lowest unoccupied molecular orbital energy (LUMO),

Repulsion energy (NRE),

Total energy (E)

Dipole length (μ)

Connolly accessible area (CAA),

Connolly molecular area (CMA)

Connolly solvent excluded volume (CSEV)

Exact mass (EM)

Cluster Count (ClsC S)

Wiener Index (Windx)

Henry’s law constant (H),

Bend energy (Eb),

Heat of formation (Hf),

Partition coefficient (Octanol/Water)(clogP).

Standard Gibbs Free Energy(G)

cMR

logP

3. METHODS

3-1 Multiple linear regressions (MLR)

The statistic technique multiple linear regression is used to study the relation between one dependent variable and

several independent variables. It is a mathematic technique that minimizes differences between actual and predicted

values. The multiple linear regression model (MLR) was generated using the software SYSTAT, version 12, to


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predict α -Glucosidase inhibitory activity. It has served also to select the descriptors used as the input parameters for

a back propagation network (NN).

3-2 Neural network

All the feed-forward NN used in this paper are three-layer networks, the first (input) layer contains eight neurones,

representing the relevant descriptors obtained in MLR technique. Although there are neither theoretical nor

empirical rules to determinate the number of hidden layers or the number of neurone layers, one hidden layer seems

to be sufficient in the most chemical application of ANN. Some authors [24-25] have proposed a parameter ρ,

leading to determine the number of hidden neurons, which plays a major role in determining the best ANN

architecture defined as follows:

ρ = (Number of data points in the training set / Sum of the number of connections in the NN).

The output layer represents the calculated activity values log (IC50 ). The architecture of the ANN used in this work

(5-3-1), is depicted in figure 1.

All calculations of NN are done on Matlab 7 using our program written in C language.

Figure 3 Typical architecture of a three layer artificial neural network

Cross-validation technique

Cross-validation is a popular technique used to explore the reliability of statistical models. Based on this technique,

a number of modified data sets are created by deleting in each case one or a small group these proceedings are

named respectively “leave-one-out” and “leave-some-out” [26-28]. For each data set, an input-output model is

developed. The model is evaluated by measuring its accuracy in predicting the responses of the remaining data (the

ones that have not been used in the development of the model).In this study we used, the leave-one-out (LOO)

procedure.


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Table 2

Compounds studied and their α -Glucosidase inhibitory activity of N (phenoxyalkyl)phthalimide derivatives; values

of log IC50(µM) observed and values calculated log IC50(µM) by MLR and NN

Compd n R1 R2 R3 R4 logIC50 (Obs) logIC50(MLR) logIC50(NN) logIC50(CV

1 2 H H H H 2.471 2.114 2.478 3.291

2 3 H H H H 2.830 1.655 2.834 2.138

3 4 H H H H 1.519 1.476 1.511 2.066

4 5 H H H H 1.305 1.362 1.286 1.194

5 6 H H H H 1.013 1.782 1.03 1.009

6 8 H H H H 0.813 1.403 0.786 0.74

7 9 H H H H 0.483 1.452 0.580 0.562

8 10 H H H H 0.398 1.095 0.414 0.408

9 2 H H Cl H 1.973 1.506 1.822 1.635

10 2 CH3 H Cl H 1.170 0.972 1.400 1.488

11 2 CH3 H Cl CH3 1.114 1.514 0.980 0.921

12 3 H H Cl H 1.771 1.296 1.783 1.082

13 3 Cl Cl H H 1.176 1.145 1.106 1.084

14 3 H Cl Cl H 0.878 0.787 0.880 1.054

15 3 CH3 H Cl H 0.949 0.577 1.073 1.074

16 3 CH3 H Cl CH3 0.978 0.572 0.979 0.912

17 10 H H Cl H 0.079 0.156 0.165 0.159

18 10 H Cl Cl H 0.000 -0.148 0.063 0.078

19 10 CH3 H Cl CH3 0.127 -0.072 0.057 0.357

20 10 Cl H Cl H 0.076 -0.150 0.072 0.071

21 10 H CF3 NO2 H -0.081 -0.026 -0.013 -0.252

22 10 H H CH3 H 0.322 0.350 0.149 0.138

23 10 H H NO2 H -0.066 0.201 -0.055 -0.018

24 10 NO2 H H H 0.250 0.442 0.202 -0.014

25 10 H NO2 H H 0.031 -0.092 -0.007 0.044

26 10 CH3 H H CH3 0.512 0.244 0.205 0.202

27 10 H Cl H H 0.037 0.214 0.148 0.154

28 10 H Cl H CH3 -0.041 0.182 0.082 0.117

29 10 H H CF3 H -0.081 -0.052 -0.086 1.481

30 10 NO2 H Cl H -0.323 -0.037 -0.011 0.029

31 10 H Cl Cl NO2 -0.284 -0.401 0.023 0.180

32 10 Cl H Cl NO2 -0.125 -0.263 -0.011 -0.006

33 10 NO2 H NO2 H -0.013 0.189 -0.012 -0.254

34 10 NO2 H CF3 H 0.117 -0.008 -0.012 -0.203

35 10 CH3 NO2 Cl CH3 0.556 0.024 -0.012 -0.074

36 10 CH3 H NO2 CH3 0.121 0.024 -0.011 -0.041

37 10 H Cl NO2 CH3 -0.187 -0.064 -0.011 0.007


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4. RESULTAS AND DISCUSSION

In this study, Multiple Linear Regression (MLR) analysis and Neural Network (NN) calculations are applied to a

series of 37 N-(phenoxyalkyl)phthalimide derivatives, in order to set up a 3D-QSAR model able to predict α-

Glucosidase inhibitory activity.

4-1 Multiple linear regressions (MLR)

The QSAR model built using multiple linear regression (MLR) method is represented by the following equation:

LogIC50 = 23.063 - 3.937(CMR) – 1.077(H) – 0.057(CAA) + 0.194(CMA)

As indicated in equation above, the most significant descriptors that affect the inhibitory activity are: Connolly

accessible area (CAA), Connolly molecular area (CMA) , Henry’s law constant (H) and cMR (Steric and

Thermodynamic descriptors).

N= 37 r =0.943 SD= 0.288 F-ratio = 23.94

Where n is the number of compounds, r is the correlation coefficient, SD is the standard deviation, F is the Fisher F-

statistic.

Fig-2 displays a plot between observed activity ( logIC50 (Obs)) and predicted activity ( logIC50(MLR))

Figure. 2. A plot between observed activity and MLR predicted one

4-2 Neural networks

Neural networks (NN) can be used to generate predictive models of quantitative structure–activity relationships

(QSAR) between a set of molecular descriptors obtained from the MLR and Observed activity.

N = 37 r = 0.979 SD = 0.155

Fig-3 displays a plot between observed activity ( logIC50 (Obs)) and predicted activity (logIC50(NN))

Figure.3. A plot between observed activity and NN predicted one


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4-3 Validation

Before using a QSAR model to predict the activity of new compounds, we should validate it using a validation

method. In this paper we validated our model with cross validation using LOO procedure.

r = 0.866 SD = 0.404 N = 37 P< 0.0001

Fig-4 displays a plot between observed activity ( logIC50 (Obs)) and predicted activity ( logIC50(CV))

Figure. 4. A plot between observed activity and CV predicted one

QSAR between molecular structure and α -Glucosidase inhibitory activity of N (phenoxyalkyl)phthalimide

derivatives were discovered by two chemometrics methods: MLR and NN.

The correlation coefficient of the Multiple Linear Regression (r = 0.943) , which is comparable to the Neural

Network (r = 0.979 ), indicating the stability of the QSAR model in this study

5. CONCLUSION

The QSAR analysis of N(phenoxyalkyl) phthalimide was successfully carried out. So, QSAR model shows good

correlation between observed activities and predicted ones, as well as a high predictive power of activities

The QSAR model was validated by standard statistical means and through observation on how it reproduces and

explains the quantitative differences seen in the experimentally known activity data.

The detailed structural investigation revealed that the α -Glucosidase inhibitory activity is predominantly

explained by the Connolly accessible area (CAA), Connolly molecular area (CMA) , Henry's law constant (H)

and cMR (Steric and Thermodynamic descriptors).

Finally QSAR is a broadly used tool for developing relationships between the effects (α.g.activities and

properties of interest) of a series of molecules with their structural properties. It is used in many areas of science.

It is a dynamic area that integrates new technologies at a staggering rate.

6. REFERENCES

[1]. Soro RY, Diopoh JK, Willemot RM, Combes D. Enzymatic synthesis of polyglucosyl-fructosides from

sucrose alone by a novel �-glucosidase isolated from the digestive juice of Archachatina ventricosa

(Achatinideae). Enzyme Microb Technol 2007;42:44–51.

[2]. Zdzieblo A, Synowiecki J. New source of the thermostable �-glucosidase suitable for single step starch

processing. Food Chem 2002;79:485–91.

[3]. Yamamoto T, Unno T, Watanabe Y, Yamamoto M, Okuyama M, Mori H, et al. Purification and

characterization of Acremonium implicatum �-glucosidase having regioselectivity for �-1,3-glucosidic

linkage. Biochim Biophys Acta 2004;1700:189–98.

[4]. B.D.Walker, M.Kowalski, W.C.Goh, K.Kozarsky, M.Krieger, C.Rosen, L.Rohrschneider, W.A. Haseltine, J.

Sodroski, Proc. Natl. Acad. Sci. 84 (1987) 8120–8124.

[5]. P.B. Fischer, G.B. Karlsson, T.D. Butters, R.A. Dwek, F.M. Platt, J. Virol. 70 (1996) 7143–7152.

[6]. P.B. Fischer, G.B. Karlsson, R.A. Dwek, F.M. Platt, J. Virol. 70 (1996) 7153–7160.

[7]. Balfour, J. A.; McTavish, D. Drugs 1993, 46, 1025.

[8]. Pogano, G.; Marena, S.; Corgiat-Mansin, L.; Cravero, F.; Giorda, C.; Bozza, M. Diabetes Metab. 1995, 21,

162.


401

[9]. Bernaeki, R. J.; Niedbala, M. J.; Korytryk, W. Cancer Metastasis Rev. 1985, 4, 81.

[10]. Metha, A.; Zitzmann, N.; Rudd, P. M.; Block, T. M.; Dwek, R. A. FEBS Lett. 1998, 430, 17.

[11]. Gruters, R. A.; Neefjes, J. J.; Tersmetti, M.; De Goede, R. E.W.; Tulp, A.; Huisman, H. G.; Miedeme, F.;

Ploegh, H. C. Nature 1987, 330, 74.

[12]. Ratner, L.; Wander Heyden, N. AIDS Res. Hum. Retroviruses 1993, 9, 291.

[13]. De Melo, E. B.; Gomes, A. S.; Carvalho, I. Tetrahedron 2006, 62, 10277.

[14]. M. Karelson (Ed.), Molecular Descriptors in QSAR/QSPR,Wiley-Interscience, 2000.

[15]. M. Fernandez, J. Caballero, A.M. Helguera, E.A. Castroe,M.P. Gonzalez, Bioorg.Med. Chem. 13 (2005)

3269–3277.

[16]. S. Liane, M.P. Gonzalez, Y. Fall, G. Gomez, Eur. J. Med. Chem. 42 (2007) 64–70.

[17]. A. Tropsha, W. Zheng, Curr. Pharm. Des. 7 (2001) 125–133.

[18]. Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors; Wiley-VCH: Germany, 2000.

[19]. Karelson, M. Molecular Descriptors in QSAR/QSPR; John Wiley & Sons: New York, 2000.

[20]. QSPR/QSAR Studies by Molecular Descriptors; Diudea, M. V., Ed.; Nova Science: Huntington, NY, 2001.

[21]. From Chemical Graphs to Three-Dimensional Geometry; Balaban, A. T., Ed.; Plenum: New York, 1997.

[22]. Balaban, A. SAR QSAR Environ. Res. 1998, 8,1.

[23]. Rossana Pascale , Alessia Carocci, Alessia Catalano , Giovanni Lentini, Anna Spagnoletta, Maria Maddalena

Cavalluzzi, Francesco De Santis , Annalisa De Palma , Vito Scalera , Carlo Franchini ’New N-

(phenoxydecyl)phthalimide derivatives displaying potent inhibition activity towards a-glucosidase’ Italy 2010

[24]. So, S.; Richards, G. J. Med. Chem. 1992, 35, 3207.

[25]. Andrea, T. A.; Kalayeh, H. J. Med. Chem. 1991, 34, 2824.

[26]. Efron,B. J.Am.Stat.Assoc. 1983, 78,316.

[27]. Efroymson, M. A. Multiple regression analysis. In Math ematical Methods for Digital Computers

Ralston,A.;Wilf, H.S.,Eds.;Wiley:NewYork,1960.

[28]. Osten,D.W. J.Chemom. 1998, 2,39.

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