Proposal and evaluation of a new norm-based QSAR model for HIV-1 reverse transcriptase inhibitors for 2-amino-6-arylsulfonylbenzonitriles and congeners

Kanwal Shahid a, Haicheng QIANa, Qingzhu JIAa, Qiang WANG a*, Xiangying X U a, Huifen JI a, Jingchen

YIN a, Wenxuan, WANG a, Peisheng Ma b

a. School of Material Science and Chemical Engineering, Tianjin University of Science and Technology, 13St. TEDA, Tianjin, 300457, Peopleâ??s Republic of China

b. School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, Peopleâ??s Republic of

China

* To whom correspondence should be addressed. E-mail: wang_q@tust.edu.cn

Abstract

Reverse transcriptase (RT) is one of the three enzymes, encoded by HIV-1 (human immunodeficiency syndrome type 1). The search and development of anti-HIV drugs is currently one of the most urgent tasks of pharmacological studies. The focus of this study is to present a new structure activity relationship model based on the norm indexes and use that model for the prediction of the anti-HIV-1 activity for 68 reverse transcriptase inhibitors (2-amino-6- arylsulfonylbenzonitriles and their thio and sulfinyl congeners). Results indicate that the new model is stable and provides satisfactory results. The prediction efficiency of the model is
evidenced by R2 (square correlation coefficient) values of 0.550 and 0.515, for the training set
and testing set, respectively.

Keywords: Reverse transcriptase inhibitors; RTIs; Anti HIV-1 activity; norm index; QSAR

1. Introduction

Human immune-deficiency virus (HIV) is a chronic viral infection, and is the main cause of the syndrome which causes the progressive destruction of the immune system called as â??acquired immunodeficiency syndrome (AIDS)â?. The syndrome was first identified in 1981 in western world, as rare form of pneumonia accompanied by infected immune systems. The deadly
syndrome claimed nearly 24.8 million lives by December 2001. The situation is much worse

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today. According to the UNAIDS report on global epidemic 2013, an estimated 35.3 million people were infected with HIV in 2012, globally. 1
HIV is a retrovirus, which targets the CD4+cells of the immune system for replication. Aids is defined as the presence of HIV infection with a CD4 cell count less than 200 cells/mm3 and/or the presence of an AIDS-defining clinical condition, which includes any number of opportunistic infections, malignancies, or other clinical syndromes as defined by the Centers for Disease Control and Prevention. HIV virus is classified into two different types: HI-1 and HIV-2. HIV-1 is easily transmitted, which is why this type of virus is predominant in the world and is the reason behind most of the HIV infections 2. HIV-2, on the other hand, is difficult to transmit, which is why it is relatively uncommon.
Reverse transcriptase (RT) is a key enzyme in the HIV-1 lifecycle. When the retrovirus infects a cell, RT copies the single stranded viral RNA genome into double stranded DNA. The viral DNA is then integrated into the host chromosomal DNA which then allows host cellular processes, such as transcription and translation to reproduce the virus. Due to its essential role in
HIV-1 replication, RT is a major target for the development of antiretroviral agents.3 Reverse
transcriptase inhibitors (RTIs) blocks the RTâ??s enzymatic function to prevent the completion of double stranded viral DNA synthesis. Hence, prevent the multiplication of HIV infection.
There is currently no proven cure of AIDS; however, combinations of antiretroviral drugs referred to as Highly Active Antiretroviral Therapy (HAART), are used to control the viral application. During the last two decades, researchers have reported number of different sets of compounds as possible anti-HIV agents. Those sets of compounds, after advanced clinical trials, were then classified into three classes 4: (i) NRTIs- nucleoside reverse transcriptase inhibitors, such as zidovudine, and NtRTIs - nucleotide reverse transcriptase; (ii) NNRTIs-non-nucleoside reverse transcriptase inhibitors, such as nevirapine; (iii) PIs - protease inhibitors, such as
saquinavir.
NNRTIs bind near the substrate binding site of RT and induce a conformational change that results in reduced enzymatic activity.5,6 Several different classes of NNRTIs have been discovered. The NNRTIs class used in this research is designed by Chan et al.7 and are described
in Fig.1.

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Recently, a set of norm indexes have been proposed by our group, based on which some QSAR modes had been developed and successfully used for prediction toxicity values (log(LC50), 96 h LC50 data for Poecilia reticulata) for 190 diverse narcotic pollutants,8 the aryl hydrocarbon receptor binding affinity (pEC50) of dibenzofurans and the mutagenic potency (lnR) of aromatic and heteroaromatic amines.9 Therefore, it is logical to further evaluate the performance of these norm indexes for prediction of other properties of chemical compounds, especially for drugs. Here, the objective of the present work is to establish a new QSAR model based on our norm indexes for prediction the anti-HIV-1 activity for 68 reverse transcriptase inhibitors.

Data Sets

68 molecules (2-amino-6-arylsulfonylbenzonitriles and congeners) were selected from literature to pursue this research.10 Out of these 68 molecules, 11 molecules were selected as the testing set, whereas the remaining 57 molecules were the training set. The general molecular structures of the compounds are shown in Fig.1.

Proposed method

Based on the chemical graphs, the distance matrix was introduced, from which the extended adjacency matrix, the extended interval matrix and the extended interval jump matrix were further deduced. To improve the predictive effect of this norm-indexes based QSAR model, a property matrix was defined and combination of different types of molecular descriptors, such as atomic weight, van der walls radius, electronegativity, branching degree and atom charge were specially included.
The matrices used in this research were:

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M1 = (ai j )

M 2 = (ai j )

M 3 = (ai j )

M 4 = (ai j )

ai j = i â?? j

1 if i and j are adjacent

ai j = { 0 otherwise

1 if the path length between atoms i and j is 2

ai j = { 0 otherwise

1 if the path length between atoms i and j is 3

ai j = { 0 otherwise

M 5 = (ai j ) distance matrix

â?¡ â?¤

Me = â?¢

1 ei 1

Atom chargeâ?¥

�� van der walls radius

n

j =1

atomic weight ��

Where eà­§ is atom iâ?? electronegativity.

MD1 = â?¡â?£M 2

MD2 = â?¡â?£M 3

MD3 = â?¡â?£M 3

MD4 = â?¡â?£M 4

MD5 = â?¡â?£M 4

MD6 = â?¡â?£M 4

MD7 = â?¡â?£M 5

Me [:, 4]�� Me [:,1]�� Me [:, 3]�� Me [:, 2]�� Me [:, 3]�� Me [:, 4]�� Me [:,1]��

T

MD8 = M 2 + Me [:, 2]Ã? Me [:, 2]

MD9 = M 3 + Me [:, 2]Ã? Me [:, 2]

The new QSAR model for anti HIV-1 activity predictions is expressed mathematically in eq. (1)

Anti HIV â??1 activity = 6.3825 â?? 8.9848 Ã? norm(MD1 ,1) + 7.7089 Ã? norm(MD3 , 2) â?? 6.6008 Ã? norm(MD3 , fro)

â??193.3471Ã? norm(MD4 ,1) +1.3531Ã? norm(MD4 , 2) â?? 6.5096 Ã? norm(MD5 , 2) +187.5646 Ã? norm(MD6 ,1)

+3.0894 Ã? norm(MD7 ,1) + 27.84 Ã? norm(MD8 , fro) + 0.5944 Ã? norm(MD9 , 2)

ï¼?1ï¼?

Where, norm(M, 1) means the largest column sum of matrix M, norm(M, 2) means the largest singular value of matrix M, norm(M, fro) is the frobenius-norm of matrix M.
The molecular structure building and modeling is carried out by using the free version of Hyperchem software.11 Energy minimization of the molecules was obtained by using ab-initio method. Among which, the charge distribution and the moleculesâ?? geometries were optimized by using ab initio methods at STO-3G level. In addition, descriptor values such as atomic weight,
van der walls radius, electronegativity etc. were referenced from CRC handbook.12

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Using all above methods, the most stable structure of each compound was produced and used to calculate several physicochemical descriptors. The research also includes the multiple linear regression analysis of activity coefficients and molecular descriptors. The approach is performed with the ordinary least squares (OLS) regression.

Results and Discussion

The predicted vs. experimental values are scatter plotted and shown in fig.2. Using eq.(1), R2 (square correlation coefficient) values for activity prediction of anti-HIV-1 data are 0.550 for the training set, and 0.515 for the testing set.

Fig.2. Scatter plot showing the correlation between the predicted by our model vs. experimental values of anti-HIV-1 activity

Conclusions

Based on the norm indexes proposed in this work, a new QSAR model has been developed to study the anti-HIV-1 effects of 68 reverse transcriptase inhibitors (2-amino-6- arylsulfonylbenzonitriles and their thio and sulfinyl congeners). The descriptors used in the research, involved topological descriptors, geometrical descriptors and quantum chemical descriptors. Results indicate that the anti-HIV-1 activity can be analyzed with this new norm indexes based QSAR model.

Acknowledgements

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Research reported in this work was supported by the National Natural Science Foundation of
China (No. 21306137, and No. U1162104).

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