Physiochemical Investigation and Role of Indicator Parameter in the Modeling of Tetrahydroimidazole Benzodiazepine -1- one (TIBO): A QSAR Study

 

Lokendra Kumar Ojha*, Ajay M Chaturvedi*, Arpan Bhardwaj*, Abhilash Thakur¹,Mamta Thakur²

*Department of Chemistry, Govt. Madhav Science PG College, Ujjain (MP), INDIA

Department of Applied Science, NITTTR, Bhopal (MP), INDIA

Department of Pharma. Chemistry, Softvision College, Indore (MP), INDIA

*Corresponding Author E-mail: ojha_lokendra@rediffmail.com

 

The aim of the author in this paper is to emphasize the role of indicator parameter for TIBO 89 derivatives with the help of computational drug design. A QSAR study has been carried out to know the drug receptor binding affinity of the TIBO derivatives. In this regard multiple linear regression analysis (MLR) method has been used and different indicator parameter is used to obtain the better mathematical model in the particular set. The substitution at the –Z position on five member ring, –R position on seven member ring and –X position on benzene ring plays vital role in this concern. The statistical analysis multiple R= .9170, standard error of estimation (Se= .5572) and Fisher Ratio (F= 122.383) values gives the satisfactory explanation of the data set to know the effect of indicator parameter on the biological activity.

 

KEYWORDS: Drug design, Multiple Linear Regression (MLR), Biological activity, Physiochemical parameter,   Molecular Modeling

 

INTRODUCTION:

Non-nucleoside reverse transcriptase inhibitors (NNRTIs) are antiretroviral drugs used in the treatment of human immunodeficiency virus (HIV). NNRTIs inhibit reverse transcriptase (RT), an enzyme that controls the replication of the genetic material of HIV Human immunodeficiency virus type-1 (HIV-1) is the causative agent for the transmission and development of the acquired immunodeficiency syndrome (AIDS). AIDS remains one of the most urgent world health problems, being the leading cause of death in Africa and the fourth worldwide.¹   Even if there is no definitive cure for HIV infection, a number of drugs slow or halt disease progression. However, HIV can rapidly become resistant to any single antiretroviral drug, therefore a combination of three or more drugs are usually required to effectively suppress the virus. The highly active antiretroviral therapy (HAART)² consists of the combination of nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs/NtRTIs) with non-nucleoside reverse transcriptase inhibitors (NNRTIs) or protease inhibitors (PIs).²

 

Acquired immunodeficiency syndrome (AIDS) is one of the leading cause of death in the world.  It was identified as a disease in 1981. Two years later the etiology agent for AIDS, the HIV was described.  HIV is a retrovirus and has two major serotypes, HIV-1 and HIV-2.

 

Drug discovery and development are expensive undertakings. The application of computational technology during drug discovery and development offers considerable potential for reducing the number of experimental studies required for compound selection and development and for improving the success rate. The quantitative structure-activity relationships (QSAR) are certainly a major factor in contemporary drug design. Thus, it is quite clear why a large number of users of QSAR^3-4 are located in industrial research units. So, Classical QSAR and 3D-QSAR are highly active areas of research in drug design ^5-6. In structure based drug design, one of the central strategies is to modify lead molecules slightly to obtain or improve certain therapeutic properties^7-8. The rationale behind this approach is that similar molecules bind in a similar fashion to a target receptor, thus possibly inducing the same effect. Nevertheless, the new compound may adopt a different binding mode, due to the presence of internal water molecules.

Table 1 Substituent of TIBO derivatives used in present study

Comp. No.	X	Z	R	X'		Comp. No	X	Z	R	X'
1	H	S	DMAa	5-Me(S)		46	H	O	DMA	5-Me(S)
2	9-Cl	S	DMA	5-Me(S)		47	H	O	DMA	5-Me(S)
3	8-Cl	S	DMA	5-Me(S)		48	H	O	DMA	5-Me(S)
4	8-F	S	DMA	5-Me(S)		49	H	O	DMA	5-Me(S)
5	8-SMe	S	DMA	5-Me(S)		50	H	O	DMA	5-Me(S)
6	8-OMe	S	DMA	5-Me(S)		51	H	O	DMA	5-Me(S)
7	8-OC2H5	S	DMA	5-Me(S)		52	H	O	DMA	5-Me(S)
8	8-CN	O	DMA	5-Me(S)		53	H	O	DMA	5-Me(S)
9	8-CN	S	DMA	5-Me(S)		54	H	O	DMA	5-Me(S)
10	8-CHO	S	DMA	5-Me(S)		55	H	O	DMA	5-Me(S)
11	8-CONH2	O	DMA	5-Me(S)		56	8-Cl	S	DMA	H
12	8-Br	O	DMA	5-Me(S)		57	9-Cl	S	DMA	H
13	8-Br	S	DMA	5-Me(S)		58	H	O	2-MA	5,5-di-Me
14	8-I	O	DMA	5-Me(S)		59	H	O	2-MA	4-Me
15	8-I	S	DMA	5-Me(S)		60	9-Cl	S	2-MA	4-Me(S)
16	8-C=-CH	O	DMA	5-Me(S)		61	9-Cl	S	CPM	4-Me(R)
17	8-C=-CH	S	DMA	5-Me(S)		62	H	O	DMA	4-CHMe2
18	8-Me	O	DMA	5-Me(S)		63	H	O	2-MA	4-CHMe3
19	8-Me	S	DMA	5-Me(S)		64	H	O	2-MA	4-C3H7
20	9-NO2	O	CPMb	5-Me(S)		65	H	O	DMA	7-Me(S)
21	8-NH2	O	CPM	5-Me(S)		66	8-Cl	O	DMA	7-Me(S)
22	8-NMe2	O	CPM	5-Me(S)		67	9-Cl	O	DMA	7-Me(S)
23	9-NH2	O	CPM	5-Me(S)		68	H	S	C3H7	7-Me(S)
24	9-NMe2	O	CPM	5-Me(S)		69	H	S	DMA	7-Me(S)
25	9-NHCOMe	O	CPM	5-Me(S)		70	8-Cl	S	DMA	7-Me(S)
26	9-NO2	S	CPM	5-Me(S)		71	9-Cl	S	DMA	7-Me(S)
27	9-F	S	DMA	5-Me(S)		72	H	O	DMA	4,5-di-Me(cis)
28	9-CF3	O	DMA	5-Me(S)		73	H	S	DMA	4,5-di-Me(cis)
29	9-CF3	S	DMA	5-Me(S)		74	H	S	CPM	4,5-di-Me(trans)
30	9-Me	O	DEAc	5-Me(S)		75	H	S	DMA	4,5-di-Me(trans)
31	10-OMe	O	DMA	5-Me(S)		76	H	S	DMA	5,7-di-Me(trans)
32	10-OMe	S	DMA	5-Me(S)		77	H	S	DMA	5,7-di-Me(cis)
33	9,10-di-Cl	S	DMA	5-Me(S)		78	9-Cl	O	DMA	5,7-di-Me(R,R-trans)
34	10-Br	S	DMA	5-Me(S)		79	9-Cl	S	DMA	5,7-di-Me(R,R-trans)
35	H	O	CH2CH=CH2	5-Me(S)		80	H	S	DMA	4,7-di-Me(trans)
36	H	O	2-MAd	5-Me(S)		81	9-Cl	O	DMA	5-Me(S)
37	H	O	CH2CO2Me	5-Me(S)		82	9-Cl	S	CPM	5-Me(S)
38	H	O	CH2C=-CH	5-Me(S)		83	H	S	CPM	5-Me(S)
39	H	O	CH2-2-furanyl	5-Me(S)		84	H	O	C3H7	5-Me
40	H	O	DMA	5-Me(S)		85	H	S	C3H7	5-Me
41	H	O	DMA	5-Me(S)		86	H	O	2-MA	5-Me
42	H	O	DMA	5-Me(S)		87	H	S	DMA	5-Me
43	H	O	DMA	5-Me(S)		88	H	O	DMA	5-Me(S)
44	H	O	DMA	5-Me(S)		89	H	S	2-MA	5-Me(S)
45	H	O	CPM	5-Me(S)

^a 3,3-Dimethylallyl. ^bCyclopropylmethyl. ^c3,3-Diethylallyl.

^d2-Methylallyl

 

Quantitative structure activity relationship (QSAR) studies are useful tools in the rational search for bioactive molecules. The main success of the QSAR method is the possibility to estimate the characteristics of new chemical

 

 

compounds without the need to synthesize and test them. This analysis represents an attempt to relate structural descriptors of compounds with their physicochemical properties and biological activities. This is widely used for the prediction of physicochemical properties in the chemical, pharmaceutical, and environmental spheres.

Table 2 Indicator parameter used in the study

Comp No	I_Cl	I_S	I_DMA		Comp No	I_Cl	I_S	I_DMA
1	0	1	1		45	0	0	0
2	1	1	1		46	0	0	0
3	1	1	1		47	0	0	0
4	0	1	1		48	0	0	1
5	0	1	1		49	0	0	0
6	0	1	1		50	0	0	0
7	0	1	1		51	0	0	1
8	0	0	1		52	0	0	1
9	0	1	1		53	0	0	0
10	0	1	1		54	0	0	0
11	0	0	1		55	0	0	0
12	0	0	1		56	1	1	1
13	0	1	1		57	1	1	1
14	0	0	1		58	0	0	0
15	0	1	1		59	0	0	0
16	0	0	1		60	1	1	0
17	0	1	1		61	1	1	0
18	0	0	1		62	0	0	0
19	0	1	1		63	0	0	0
20	0	0	0		64	0	0	0
21	0	0	0		65	0	0	1
22	0	0	0		66	1	0	1
23	0	0	0		67	1	0	1
24	0	0	0		68	0	1	0
25	0	0	0		69	0	1	1
26	0	1	0		70	1	1	1
27	0	1	1		71	1	1	1
28	0	0	1		72	0	0	1
29	0	1	1		73	0	1	1
30	0	0	0		74	0	1	0
31	0	0	1		75	0	1	1
32	0	1	1		76	0	1	1
33	0	1	1		77	0	1	1
34	0	1	1		78	1	0	1
35	0	0	0		79	1	1	1
36	0	0	0		80	0	1	1
37	0	0	0		81	1	0	1
38	0	0	0		82	1	1	0
39	0	0	0		83	0	1	0
40	0	0	0		84	0	0	0
41	0	0	0		85	0	1	0
42	0	0	0		86	0	0	0
43	0	0	0		87	0	1	0
44	0	0	0		88	0	0	0
					89	0	1	0

 

 

This method included data collection, molecular descriptor selection, correlation model development, and finally model evaluation.

TIBO (Fig.-1) (Tetrahydroimidazole Benzodiazepine- 1-one derivatives,27 are one of the important classes of non-nucleoside reverse transcriptase inhibitors which inhibit the replication of HIV-1.^9-10 TIBO derivatives, like most of the other non-nucleoside inhibitors, on binding to the binding pocket, adapt conformation which resemble the wings of a butterfly, and hence called as butterfly like conformation. The specific conformation of the 7-membered ring of the TIBO derivatives is responsible for producing their butterfly like arrangement. Comparison of the different RT–NNI complexes suggests modifications to the TIBO group of inhibitors which might enhance their binding and hence, potentially, their therapeutic efficacy. ^11-13 TIBO blocks the chemical reaction, but does not interfere with nucleotide

 

binding or the nucleotide induced conformational   change (Spence et al., 1995).

 

Fig (1) Parent Structure of TIBO Derivatives

 

Table 3 observed and calculated log(1/C) TIBO of derivatives

 

MATERIAL AND METHODS:

Experimental

Data Set

The biological data used in this study are the antiHIV activity (log 1/C) of a series of TIBO derivatives. The data were collected from the review article^14-17. The structural substituent and biological activity of these compounds are listed in Tables 1. The antiHIV activity was taken as log(1/C50).

 

Physiochemical Parameter

The physiochemical parameter like Molecular Refractivity (MR), Molecular Volume (MV), Parachor (η), Index of Refraction (IR), Surface Tension (ST), Density (D) and otanol-water partition coefficient (logP) has been calculated by chemsketch freeware version 12.

 

 

Indicator Parameter

In QSAR study the role of indicator parameter is very important and has been widely used in various studies. In the present study, we have used six indicator parameter, (Table 2) they are as follows-

 

I_Cl        Presence of Chlorine atom at –X position on benzene ring is indicated by 1 and absence is indicate by 0

I_S         Presence of Sulphur atom at –Z position on five member ring is indicated by 1 and absence is indicate by 0

I_O         Presence of Oxygen atom –Z position on five member ring is indicated by 1 and absence is indicate by 0

I_DMA Presence of DMA at –R position on seven member ring is indicated by 1 and absence is indicate by 0

I_5Me    Presence of -5Me at –X’ position on seven member ring is indicated by 1 and absence is indicate by 0

I_2Me    Presence of -2Me at –X’ position on seven member ring is indicated by 1 and absence is indicate by 0

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 MSTAT, to predict anti HIV activities of TIBO derivatives.

 

RESULT AND DISCUSSION:

Initial statistical analysis from correlation matrix has indicated that no statistically significant monovariate regression expressions (models) are possible for modeling of the compounds (89) used.

 

Table 4 Correlation matrix between biological activity and indicator parameter

	BA	I_Cl	I_S	I_O	I_DMA
BA	1.0000
I_Cl	.17653	1.0000
I_S	.66772	.24041	1.0000
I_O	-.66772	- .24041	-1.0000	1.0000
I_DMA	.48068	.23245	.40072	-.40072	1.0000

 

From the correlation matrix, (Table 4) it is observed that the role of indicator parameter is dominance on the other physiochemical properties. In monovariate, the best mathematical model is as follows-

 

Log (1/C) = 1.9877 (± 0.2234) I_S + 4.8568    Eq. (1)

N= 89     R=0 .6896 Se= 1.0456         F=78.870

Log (1/C) = -1.9877 (± 0.2234) I_O + 4.8568  Eq. (2)

N= 89     R= -0.6896 Se= 1.0456        F=78.870

 

Equation (1) and (2) both are similar in different statistical parameter but in equation (2) the negative role of the indicator parameter I_O i. e. presence of oxygen atom at five member ring is not favorable, where as the presence of sulphur atom at the same position shows the positive relationship with the biological activity.  So, equation (1) is consider the best monovariate QSAR model and to know the other structural requirement for the biological activity, we were tested several bi-prametric combination and found the best result as follows-

 

Log (1/C) = 1.5013 (± 0.2080) I_S + 1.1953 (± 0.2066) I_DMA + 4.4504                                               Eq. (3)

N= 89     R=0.7890 Se= 0.8922 F=70.905

 

Equation (3) shows the role of indicator parameter (I_DMA) at the –R position on seven member ring. The positive correlation of the I_DMA with the biological activity is the measure of the greater binding affinity of the drug, so as the improvement in the R and fisher ratio is also suggested that this one is the best bi-parametric combination.

In order to know the better combination and the structural information several triparametric combinations is tested and the best result is as follows-

 

Log (1/C) = 1.4098 (± 0.2035) I_S + 1.1114 (± 0.2017) I_DMA +0.7105 (± 0.2612) I_Cl+ 4.4221        Eq. (4)

N= 89     R= 0.8079 Se= 0.8607 F=53.252

 

Equation (4) shows that the all three indicator parameter viz. I_S, I_DMA, I_Cl is very important and enhance the biological activity of the particular set of TIBO derivatives. Chlorine atom is the best sustituent on benzene ring, so as, five member ring favors the sulphur atom and dimethyl allyl (DMA) subsituent is important for the seven member ring. Fig (2), observed (log 1/C) and calculated (log 1/C) for all TIBO 89 derivatives.

 

Fig (2) Plot between observed (log1/C) and calculated (log 1/C)

 

The lower value of fisher ratio is not satisfy the equation (4) as the best model  and the limitation of the thumb rule not allow us to go for further combination testing. So, from here we started the removal of the compound from the data set on the basis of difference in their observed (log 1/C) and calculated (log 1/C).

 

After the deletion of the compound of higher residue, the best mathematical model obtained as follows-

 

Log (1/C) = 1.5283 (± 0.1774) I_S + 1.2298 (± 0.1750) I_DMA +0.6478 (± 0.2213) I_Cl+ 4.3071        Eq. (5)

N= 84 R= 0.8711 Se= 0.7211 F=83.930
Outlier Total No. of Compound 5

 

Log (1/C) = 1.5807 (± 0.1592) I_S + 1.2666 (± 0.1552) I_DMA +0.7426 (± 0.1967) I_Cl+ 4.2549        Eq. (6)

N= 78 R= 0.9079 Se= 0.6154 F=115.676
Outlier Total No. of Compound 6

 

Log (1/C) = 1.4627 (±0.1473) I_S + 1.2254 (± 0.1443) I_DMA +0.7485 (± 0.1798) I_Cl+ 4.3417          Eq. (7)

N= 74 R= 0.9170 Se= 0.5582 F=122.383
Outlier Total No. of Compound 4

 

There is gradual improvement in the value of correlation coefficient and fisher ratio, so as to decrease in the value of standard error of estimation from equation (5 to 7). The substitution of the chlorine atom on the benzene ring is increased, means the electron donating group is important in the binding affinity of the drug. At the same time the dimethyl allyl (DMA) group attachment on the seven member ring is decrease, means the role of DMA is getting decrease after the every deletion of the compound. So, from the above regression analysis it can be found that the all three indicator parameter is very important for enhancement of the biological activity and to understand the behavior of the drug receptor interaction.   Fig (3) shows the plot between observed (log 1/C) and calculated (log 1/C) after the deletion of misfit compound into the data set.

 

Fig (3) Plot between observed (log1/C) and calculated (log 1/C)

 

Table 5 Correlation matrix of outlier 15 TIBO derivatives

	BA	MR	Pol	LogP
BA	1.0000
MR	.55494	1.0000	.58974	.69743
Pol	.53329	.86702	1.0000	.58639
LogP	.51531	.69473	.59639	1.0000

 

From the correlation matrix (Table 5) of outlier 15 TIBO compounds it has been observed that the all MR, Pol and LogP are greatly correlated with the biological activity, but when we tested the bi and tri- parametric combination for the obtaining the information, no bi and tri- parametric combination gave the better result.

 

CONCLUSION:

So, from above discussion, we can say that the presence of sulfur atom (-S) leads to better activity than oxygen at five member ring, because of the higher electro negativity of the sulfur atom.  Another important aspect of the model is that the –DMA substitution in place of -2MA, on seven member ring is definitely enhance the binding affinity of the drug. Presence of –Cl atom on benzene ring of the TIBO derivatives is really important to drug receptor binding affinity. So, all the three indicator parameter out of six, provide structural evidence for the modeling for the set of compound in present study.

 

REFERENCE:

1.       Simon V, Ho D D, Karim QA. Lancet 2006; 368, 489.

2.       De Clercq E. Rev. Med. Virol. 2000; 10, 255.

3.       Trinajstic N. Chemical Graph Theory. 1992; 225-273.

4.       a) Bazoui H, Zahouily M, Boulaajaj S, Sebti S, Zakarya D. SAR QSAR Environ. Res. 2002; 13, 567.

b) Bazoui H, Zahouily M, Sebti S, Boulaajaj S, Zakarya DJ. Mol. Model. 2002; 8, 1-7.

c) Agrawala VK, Singha J, Mishraa KC, Khadikar PV, Jaliwalac YA. Arkivoc. 2006; 162.

5.       Kubinyi H. QSAR:Hansch analysis and related approaches. In: Mannhold R, Krogsgarrd Larsen P, Timmerman H (eds) Methods and principles in medicinal chemistry. Wiley, Weinheim; 1993.

6.       Kubinyi, H., (ed) 3D QSAR in drug design: theory, methods and applications. ESCOM, Leiden 1993.

7.       Klebe G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov. Today. 2006; 11, 580 - 594.

8.       Jorgensen WL. Efficient drug lead discovery and optimization. Accounts Chem. Res. 2009; 42, 724 - 733.

9.       Barre-Sinoussi F, Chermann JC, Rey F, Nugeyre MT, Chamaret S, Gruest J, Dauguet C, Axler-Blin C, Vezinet-Brun F, Rouzioux C, Rozenbaum W and Montagnier L. Science. 1983 ; 220, 868

10.     Gallo RC et al  Science  1984; 224,  500

11.     Ren J, Esnouf R, Hopkins A, Ross C, Jones Y, Stammers D and Stuart D. Structure. 1995; 3, 915

12.     Zhou Z and Madura J D 2004 J. Chem. Inf. Comput. Sci. 44 2167

13.     Bahram H, Hossein Tabaei MS and Fatemeh N. J. Mol. struct. Theochem. 2005; 39, 732

14.     Kukla MJ, Breslin HJ, Pauwels R, Fedde CL, Miranda M, Scott MK, Sherrill RG, Raeymackers A, Van Gelder J, Andries K, Janssen MAC, De Clercq E, Janssen PAJ. J. Med. Chem. 1991; 34, 746.

15.     15.Kukla MJ, Breslin HJ, Diamond CJ, Grous PP, Ho CY, Miranda M, Rodgers JD, Sherril RG, De Clercq E, Pauwels R, Andries K, Moens LJ, Janssen MAC, Janssen PAJ. J. Med. Chem. 1991; 34, 3187.

16.     Breslin HJ, Kukla MJ, Ludovici DW, Mohrbacher ROW, Miranda M, Rodgers JD, Hitchens TK, Leo G, Authier DA, Ho CY, Scott MK, De Clercq E, Pauwels R, Andries K, Janssen MAC, Janssen PAJ. J. Med. chem. 1995; 38, 771.

17.     (a) Ho W, Kukla MJ, Breslin HJ, Ludovici DW, Grous P, Diamond CJ, Miranda M, Rodgers JD, Ho CY, e Clercq E, Pauwels R, Andries K, Janssen MAC, Janssen PAJ. J. Med. Chem. 1995;  38, 794.

(b) Cramer R, Bunce JD, Patterson DE, Frank IE. Quant. Struct.- ct. Relat. 1988; 7, 18.

 

 

 

Received on 27.12.2011         Modified on 25.01.2012

Accepted on 12.02.2012         © AJRC All right reserved