INTRODUCTION:^1-8

Virally encoded HIV reverse transcriptase (RT) catalyses the replication of single stranded viral RNA to a double-stranded DNA. Inhibition of RT prevents the formation of this double-stranded DNA that can be integrated in the host DNA. Reverse transcriptase inhibitors can be divided into two categories, nucleoside (NRTI) and nonnucleoside reverse transcriptase inhibitors (NNRTI).The NNRTIs are a diverse group of compounds, which non-competitively interact with an allosteric site of HIV-1 RT and thereby inactivate the enzyme without need for preactivation of the drugs. NNRTIs bind in a highly hydrophobic pocket of the enzyme and exhibit grater affinity for the enzyme-substrate complex than for the free enzyme. The hydrophobic allosteric site is unique to HIV-1 RT and is not found in other RTs or DNA polymerases. This results in a high selectivity index and a low toxicity of the NNRTs. However, rapid eliciting resistance is a major problem with this type of inhibitor as well.3D-QSAR methodology was used for generating relationships between molecular interaction fields with the experimentally reported activity. We present here our 3D-QSAR studies on a training set of HIV Reserve Transcriptase by considering the steric and electrostatic interactions.

EXPERIMENTAL:

Data Set:

The data set are utilized from reported literature by Herschhorn A et.al².

Modelling studies:

The ligand geometries were optimized by energy minimization using MMFF94 forcefield and Gasteiger-Marsili charges for the atoms, till a gradient of 0.001 kcal/mol/A° was reached, maintaining the template structure rigid during the minimization. The hydrophillic, steric and electrostatic interaction energies are computed at the lattice points of the grid using a methyl probe of charge +1. The dataset was divided into a trainng set (13 molecules, Table 1) and a test set (04 molecules, Table 1) on the basis of chemical and biological diversity using the random selection method for generation of the training and test set data. The molar inhibitory concentration (pIC) values for antibacterial activity were used for the present 3D-QSAR study. A relationship between independent and dependent variables (3D fields and biological activities, respectively) were determined statistically using MLR analysis. The quality of fit for a regression equation was assessed relative to its correlation coefficient (r²), cross validation correlation coefficient (q²), predicted correlation coefficient for test set (pred_r²) and F test value.

RESULTS AND DISSUSION:

In order to derive a reasonable QSAR equation, the obtained equations were evaluated by the judging the external predictive results for the test set. The results of observed and predicted activity are shown in Table 1. On successful runs of PLS, different sets of equations were generated by keeping the chain length of equations to four, which were further analyzed statistically to select the best model. Two models were selected using a combination of different descriptors.

pIC = 0.0562+ 0.2847(±0.0599) E_1128 - 211.3790 (±42.4957) S_1731 Model A

r² = 0.8581, q² = 0.8326, F = 30.2340, pred_ r² = 0.5272

Table No 1: Molecules under study

Sr no	Molecules	Observed activity	Predicted activity
1.		1.66096	0.72502
2.		4.89908	4.93065
3.		2.3159	2.2558
4.		0.76862	0.83257
5.		1.12804	1.32329
6.		0.76862	0.92004
7.		2.0959	1.81794
8.		0.76862	1.45554
9. *		0.76862	0.82446
10.		0.76862	1.01767
11.		0.76862	1.57296
12. *		1.56673	1.28988
13.		0.76862	0.6747
14.		0.76862	1.19075
15. *		0.76862	2.40597
16. *		0.76862	0.7497
17.		2.4098	1.0529

* Indicates Test Set Molecules

Model A describes the structural features optimum for reserve transcriptase inhibitors activity against HIV. The r² value for model A was 0.8581. Cross validation correlation coefficient (q²) and F test value were considered for the selection of model. The external predictivity of model A (0.5272) was better than that of other models. The points that were found optimum for the activity after the QSAR study are shown in figure 2. The contribution of points E_1128, S_1731 i.e. electronic and steric interaction fields (blue and Green points) at lattice points 1128, 1731 imply that interactions at the selected points are indeed significant for the structure-activity relationship. The positive contribution of the field E_1128 indicates the addition of groups having electronic interaction at lattice point 1128 and groups having electrostatic interactions at lattice points required for amplified activity against reserve transcriptase. Along with this the field S_1731 which contribute negatively to the activity are needed to be modified. The steric interaction at lattice point (Green point in figure 1) are needed to be reduced. The correlation plot of the predicted and observed activity is shown in figure 2.

Fig No 1: Field Point of selected QSAR Model

Fig No 2: Correlation plot of observed activity and predicted activity

ACKNOWLEDGEMENT:

The authors are thankful to Dr. H. N. More., Principal, Bharati Vidyapeeth College of Pharmacy, and Kolhapur for providing facilities to carry out the research work

REFERENCES:

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Received on 03.06.2011 Modified on 23.06.2011

Asian J. Research Chem. 4(9): Sept, 2011; Page 1385-1387