
Research Article


QSAR Analysis of HIV1 Reverse Transcriptase Inhibitory 5Alkyl2 [(Aryl and Alkyloxylcarbonylmethyl) Thio]6 (1Napthylmethyl) Pyrimidin4 (3H)Ones


C. Karthikeyan ,
Dengale Santosh ,
N.S. Hari Narayana Moorthy
and
Piyush Trivedi


ABSTRACT

QSAR analysis of a novel set of HIV1 reverse transcriptase inhibitors of SDABO series was investigated by using QuaSAR descriptors of MOE. The MMFF94 force field with root mean square gradient of 0.01 kcal mol^{1} Å was used to energy minimize the compounds. Correlation between reported biological activity values and QuaSAR descriptors was established by multiple linear regression analysis method. The generated correlations were found to be statistically significant and exhibited good predictive power. The results obtained from the QSAR study reveal that substituents with a permanent dipole and electronreleasing capacity will increase the HIV1 RT binding affinity of SDABO derivatives. The findings of the study suggest that HIV1 inhibitory activity of SDABO derivatives is dependent on the electronic properties and shape of the molecules.




How
to cite this article:
C. Karthikeyan , Dengale Santosh , N.S. Hari Narayana Moorthy and Piyush Trivedi , 2007. QSAR Analysis of HIV1 Reverse Transcriptase Inhibitory 5Alkyl2 [(Aryl and Alkyloxylcarbonylmethyl) Thio]6 (1Napthylmethyl) Pyrimidin4 (3H)Ones. International Journal of Virology, 3: 1927. DOI: 10.3923/ijv.2007.19.27 URL: https://scialert.net/abstract/?doi=ijv.2007.19.27



Introduction Human Immunodeficiency Virus (HIV) infection, with its clinical progression to AIDS, is one of the leading causes of morbidity and mortality in the world (Piot et al., 2001). The delineation of the lifecycle of Human Immunodeficiency Virus has shown that the virus requires the catalytic activity of three unique enzymes namely protease, integrase and reverse transcriptase for its replication (Darke and Huff, 1994). Among them, Reverse Transcriptase (RT) is the key enzyme which plays an essential and multifunctional role in the replication of the Human Immunodeficiency Virus (HIV) (Jonckheere et al., 2000) and thus represents an attractive target for the development of new drugs useful in AIDS therapy (De Clercq, 2000). RT is responsible for the synthesis of double stranded viral DNA from proviral RNA for subsequent incorporation into the host cell chromosomes.
The currently available RT inhibitors can be classified into two groups; Nucleoside
reverse transcriptase inhibitors (NRTIs), which act as a chain terminators to
block the elongation of HIV1 viral DNA (De Clercq, 1995) strand, nonnucleoside
reverse transcriptase inhibitors (NNRTI), which directly inhibit reverse transcriptase
enzyme by binding to the allosteric site, near the polymerase active site (De
Clercq, 1998). Nonnucleoside inhibitors of this enzyme (NNRTIs) are especially
attractive drug candidates because they do not function as chain terminators
and do not bind at the dNTP site making them less likely to interfere with the
normal function of other DNA polymerases and therefore less toxic than nucleoside
inhibitors (NRTIs) such as AZT. Many structurally distinct families of NNRTIs
have been identified including TSAO (Balzarini et al., 1992), TIBO (Pauwels
et al., 1990), HEPT (Tanaka et al., 1992), αAPAs (Pauwels
et al., 1993), Pyridones (Goldman et al., 1991) and ITUs (Ludovici
et al., 2001). The currently approved NNRTIs are nevirapine delaviridine
and efavirenz (De Clercq, 2001) emirivine (MKC442) (De Clercq, 2001), GW420867X
(Prince et al., 1999) and AG1549 (Fujiwara et al., 1998) (S1153)
are currently being evaluated in clinical studies. Recently, it has been shown
that combination of NRTIs, NNRTIs and PIs have been found to decrease HIV viral
load, increase CD4 count, decrease mortality and delay disease progression,
particularly in AIDS patient with advanced immune suppression (Palella et
al., 1998). However, the efficacy of NNRTIs is seriously compromised by
the emergence of mutant viral strains (De Clercq, 2002a, b) Some mutations,
most notably K103N, are selected both in vitro and in vivo by
most currently available NNRTIs (Bacheler, 1999). K103N is also the most frequently
observed mutation among patients failing Highly Active Antiretroviral Therapy
(HAART) because it confers resistance to all of the clinically approved NNRTIs.
The look out for potent NNRTIs devoid of any resistanceassociated problems continues. QSAR is a powerful tool for the design of bioactive compounds and the prediction of corresponding activity with physical and chemical properties. QSAR studies have been successfully applied in many instances to guide the design of potent HIV RT inhibitors (Leonard and Roy, 2004; Gayen et al., 2004; Prabhakar et al., 2004). Related to the forgoing and in continuation of our efforts (Balaji et al., 2004) to develop potent HIV RT inhibitors, the present study strives to apply novel set of QuaSAR descriptors (Lin, 1997) programmed into molecular modeling software MOE (MOE, 2002) for modeling the HIV1 RT inhibitory by a novel set of SDABO derivatives reported by He et al. (2004). The rationale for selection of the series for QSAR analysis is based on the following • 
The SDABO derivatives exhibit potent activity against mutated
drug resistant HIV1 strains comparable to existing standard drugs such
as nevirapine and efavirenz. 
• 
Unlike the standard drugs, the SDABO analogs also exhibited
the capability to inhibit HIV2 multiplication. 
• 
Exact mechanism of action of these compounds is yet to be
known. 
The aforementioned clearly augur well for the application of QSAR analysis on these analogs and considerable efforts were also spared to discern the factors influencing the inhibitory potency of these molecules. Materials and Methods The 21 structurally diverse 5 alkyl 2[(aryl and alkyloxy carbonyl methyl) thio]6 (1napthyl methyl) pyrimidine 4 (3H)ones used in the present study were taken from the literature (He et al., 2004). The activity data have been reported as IC_{50} values where IC_{50} is the experimentally determined inhibitory concentration required to protect the cell against viral (HIV1 III_{B }strain) cytopathogenicity by 50% in MT4 cells (Table 1). The computing tools used for the present study were molecular operating environment (MOE, 2002), statistical software SYSTAT (Version 10.2) (SYSTAT 10.2, 2003) and our inhouse validation program VALSTAT (VALSTAT, 2004). All the computations were carried out on Compaq PIV workstation at Drug Design laboratory, S.G.S.I.T.S., Indore, India on March 2006. Structures of compounds in the series were sketched by using builder module of MOE software and sketched the structures were subsequently energy minimized upto root mean square gradient of 0.01 kcal mol^{1} Å using MMFF94 force field. The energyminimized structures were stored in MOE database for descriptor calculation.
Table 1: 
Structural variations in the SDABO and their HIV1 RT inhibitory
activity values 

Molecular descriptors were calculated for the lowest energy conformers of the compounds in the series using the QuaSAR module of the molecular modeling software MOE. The QuaSAR module of the MOE program provides a widely applicable set of classical molecular descriptors, which can be broadly, classified into two sets, 2D and internal 3D descriptors. The two dimensional descriptors include traditional physicochemical properties, (atom counts and bond counts, mr, logP and vdw_area etc), connectivitybased topological descriptors (Kier and Hall connectivity and Kappa Shape indices; adjacency and distance matrix descriptors), pharmacophore feature descriptors (e.g., donor, acceptor, polar, positive, negative, hydrophobic), partial charge descriptors based on partial equalization of orbital electronegativities method. Quantumchemical descriptors were also additionally chosen to account for the electronic properties of the molecules, calculated with the semiempirical PM3 Hamiltonian method as implemented by the Molecular Operating Environment program. Over 130 descriptors programmed in to MOE were calculated for each molecule in the series. However, many of the calculated descriptors such as descriptors in the PEOE_VSA6 to PEOE_VSA+6 series, SlogP_VSA (19) series and SMR_VSA (17) series were not interpretable hence they were not used for QSAR modeling. Only, those descriptors, which could be easily interpreted, were considered for formulation of QSAR models (Table 2).
Statistical processing of the generated data was performed using statistical
software SYSTAT (SYSTAT 10.2, 2003). QSAR models were constructed by multiple
linear regression method following a stepwise procedure that is, only one parameter
at a time was added to a model and always in the order of most significant to
least significant. Statistical parameters were calculated for each step in the
process so the significance of the added parameter could be verified. The quality
of the regression equations was adjudged by the statistical parameters such
as correlation coefficient R, squared correlation coefficient R^{2},
Fischer ratio values and standard error of the estimate SEE.
Table 2: 
Descriptors for quantitative models of HIVRT inhibitory activity
of SDABO series 

Guidelines for the acceptance of regressions were: The squared correlation
coefficient R^{2}, above 0.7 or higher (R>0.80), minimum intercorrelation
between the descriptors found in the same equation (<0.7), Fischer ratio
values indicating 99% level of significance.
Results and Discussion
Linear regression analysis of the using the biological activity parameter as
dependent variable and the reduced pool of descriptors as predictor variable
resulted in several correlations. The generated correlations were evaluated
for statistical significance and the most significant correlations were chosen
on the basis of standard test of significance and correlation coefficient. The
best correlations selected are summarized below.
LogIC_{50} =[4.40391(±
2.34397)] +PEOE_VSA_FPPOS [23.2418 (± 9.70254)] +Kier2 [0.271892
(± 0.189855)] 
(1) 
N = 21, R = 0.91, R^{2} = 0.82,
SEE = 0.316, F _{(2,18 (F = 5.85))} = 42.00, P>0.000LogIC_{50}
=[86.4896 (± 46.2168)] +Chi1_C [0.586258(± 0.135277)] +PetitjeanSC
[3.96893(± 2.74307)] +PM3_HOMO [10.2435(± 5.23658)] 
(2) 
N = 21, R = 0.92, R^{2} = 0.84,
SEE = 0.305, F_{(3,17 (F = 4.84))} = 30.83, , P>0.000.LogIC_{50}
=[114.351(± 33.7159)] +Weiner Path [0.00112734 (± 0.000185163)]+
PM3_HOMO [13.1177(± 3.73701)] + PM3_DIPOLE [0.340935 (±
0.184433)] 
(3) 
N = 20, R = 0.963789, R^{2} =
0.928889, SEE = 0.203038, F_{(3,16 (F = 4.94))} = 69.66, P>0.000 

In the equations, n is the number of molecules. R is the correlation coefficient, R^{2 }is the squared correlation coefficient, SEE is the standard error of estimate, F is the Fischer ratio values at 99% confidence levels and pvalue is the significance level. The figures within the parentheses are 95% confidence limits. Models 13 manifests good statistical quality and explains more than 80% of variance in the biological activity as established by high squared correlation coefficient (R^{2}>0.8). Further, low values of standard error of estimate indicate accuracy of the statistical fit. The Ftest values are significant at of the correlations exceeds the tabulated Fvalue (given in parentheses of the calculated F values) by a large margin as desired in linear regression. The pvalues less than 0.000 also indicate that there is indeed a significant relationship between the predictor variables and dependant variables in the selected correlations.
Absence of collinear descriptors in the selected correlations was established
by calculation of correlation matrix (Table 3) and Variance
Inflation Factor (VIF) values (Table 4). VIF value (Cho
et al., 2001) was calculated from 1/1R^{2}, where R^{2}
is the multiple correlation coefficient of one descriptor’s effect regressed
on the remaining molecular descriptors. VIF values larger than 5 indicates that
the information of the descriptors may be hidden by the correlation of the descriptors.
A perusal of the correlation matrix and VIF values of descriptors recorded in
Table 3 and 4, which shows that the descriptors
used in the regressions are reasonably orthogonal to each other.
The biparametric model (Eq. 1) includes the partial charge descriptor PEOE_VSA_FPPOS (Lin, 1997) and topological descriptor Kier2 (Hall and Kier, 1991). The partial charge descriptor PEOE_VSA_FPPOS represents the fractional positive polar vander Waals surface area of the molecule. Mathematically, it can be defined as the sum of vander Waals surface area (vi) such that partial charge (qi) is greater than 0.2 divided by the total surface area. The descriptor PEOE_VSA_FPPOS takes a negative weight in the correlation, which suggests that increase in the molecular surface area bearing a polar positive charge will decrease the HIV1 RT inhibitory potency of SDABO derivatives. Development of a fractional positive polar partial charge on an atom is always associated with electron withdrawal by electronegative atom in its immediate vicinity. The aforementioned fact point towards the alkoxy substituents in the R_{2} position of pyrimidine ring as they form ester linkage, which leaves partial positive polar charge on the alkyl groups because of electron withdrawing ability of the carboxyl moiety. The observation also leads to hypothesis that presence of polar positive partial charges on the alkyl groups somehow impedes the interaction of alkyl substituents with its complementary group in the enzyme. The topological descriptor Kier2 denotes Kier’s kappa shape index, which encodes information related to the degree of star graphlikeness and linear graph likeness of the molecule. The descriptor values are higher for a linear molecule and decreases with branching in the molecule. Thus, the positive coefficient of the descriptor in model 1 implies that nonbranched molecule will exhibit better HIV1 RT inhibitory activity than branched counterparts.
The triparametric model (Eq. 2) comprises of two topological
descriptors Chi1_C (Hall and Kier, 1991; Kier and Hall, 1977), petitjeanSC (Petitjean,
1992) and a quantum chemical descriptor PM3_HOMO (Mati and Lobanov, 1996). The
topological descriptor Chi1_C refers to Kier and Halls carbon connectivity index
of order 1. In general, the descriptor encodes information regarding degree
of branching, cyclization in the molecule. Mathematically, it can be defined
as
Chi1_C = Σ(δ_{i}δ_{j})^{1/2} 
^{}(4) 
Where δ_{i} and δ_{j} are the vertex connectivity
degree of carbon atoms i and j, respectively and the summation extends to all
bonded pairs of non hydrogen carbon atoms in the group or molecule.
The value of the δ_{i }increase with branching in the molecule.
Thus, the positive coefficient of the descriptor Chi1_C in Eq.
2 suggests that nonbranched S_DABO derivatives will have increased HIV1
RT inhibitory potency. The topological parameter Petitjean Shape coefficient
bears a negative weight in model 2 which suggest that molecular shape is an
important determinant in the binding of SDABO derivatives to HIV1 Reverse
transcriptase. The quantum chemical descriptor PM3_HOMO in the model 2 denotes
energy associated highest occupied molecular orbital and can be related to the
ionization potential of the molecule.
Table 3: 
Correlation matrix showing the intercorrelation of molecular
descriptors used in models 

Table 4: 
VIF values descriptors in generated correlations 

The coefficient of descriptor bears a negative sign in model 2, the negative
values of HOMO, which corresponds to more electron releasing group favors HIV1
reverse transcriptase inhibitory activity of S DABO derivatives.
Another triparametric model (Eq. 3) of good statistical quality was obtained with the following descriptors; Weiner Path (Wiener, 1947), PM3_HOMO (Mati and Lobanov, 1996) and PM3_DIPOLE (Mati and Lobanov, 1996). Weiner Path descriptor is contributing towards the activity in the first model. Wiener Path index is defined as the half the sum of all entries in a distance matrix.
Wiener path index is a global descriptor and has contributions from all the
atoms of the molecule. It is inversely related to the degree of compactness
of the molecule and decreases with increase in the branching and cyclicity of
the molecules. Thus, the positive coefficient of the descriptor Wiener Path
against colon carcinoma cells in model 1 suggest decreased branching in the
side chain and resultant increase in its flexibility is conducive HIV1 RT inhibitory
activity of SDABO derivatives. The negative coefficient of the quantum chemical
descriptor PM3_HOMO reinforces the conclusion drawn from Eq. 2.
Interestingly, another quantum chemical descriptor PM3_DIPOLE bears a positive
weight in the Eq. 3. The descriptor PM3_DIPOLE accounts for
dipoledipole interaction between functional groups at R2 and the receptor.
The positive coefficient associated with this descriptor suggests that charge
distribution in the molecule is related with binding affinity of the molecules
to the enzyme.
Compound number 13 was found to be an outlier in Eq. 3 on account of large deviation of calculated activity from the experimentally determined value (studentized residual = 3.08). The outlying behavior of the compound is not immediately apparent and merits further studies.
The predictive power of the generated correlations was evaluated by cross validation
method following a ‘leaveoneout’ scheme using inhouse program VALSAT.
The reliability of the correlations was tested in a cross validation with the
determination of r^{2}cv (cross validated r^{2}) or q^{2}.
In this method, one data point is removed systematically from the dataset and
a QSAR model is constructed on the basis of reduced dataset and subsequently
used to predict the activity of the removed data point. This procedure is repeated
until a complete set of predicted activities.
Table 5: 
Experimental (Log IC_{50}) and predicted activity
values (Model 13) for HIV1 RT inhibition 

Table 6: 
Comparison of cross validation parameters for generated QSAR
models 

^{a }= Squared correlation coefficient of prediction.
^{b} = Standard deviation of prediction. ^{c} = Standard
error of prediction 
The correlation coefficient between the experimentally determined activity
and activity predicted by LOO method is calculated (cross validated r^{2})
(Table 5). High values of q^{2} considered as proof
of high predictive ability of the models. It is worth mentioning that all the
generated correlations exhibits good predictive ability as established by high
q^{2} values (>0.6) and the best being recorded for biparametric
Eq. 3. The q^{2 }values for the obtained correlations
are given in the Table 5.
Further confirmation on predictive ability of the correlations was obtained by determining the uncertainty in the prediction (S_{PRESS}) and standard error due to prediction (SDEP). The S_{PRESS} and SDEP values should be low for a regression equation to have good predicivity. The S_{PRESS} and SDEP values for the correlations obtained are presented in the Table 6. Conclusions Finally to conclude, the QSAR analysis of a series of 21 HIV1 RT inhibitory SDABO derivatives using a novel set of QuaSAR descriptors resulted in quantitative models of good statistical significance. The generated QSAR models also showed good predictive potential as established by their high q^{2}values (>0.7) and hence can be used in the prediction of biological activity of novel molecules prior to their synthesis. Further, the QSAR study suggest that HIV1 inhibitory activity of SDABO derivatives is related to the electronic properties and topology of the molecule. From the results of the QSAR study, it appears that substituents with a permanent dipole and electronreleasing capacity will increase the HIV1 RT binding affinity of SDABO derivatives. Additionally, the study also indicates that molecular branching and increase in molecular surface area bearing a polar positive partial charge decreases the HIV1 inhibitory potency of SDABO derivatives Acknowledgements The authors gratefully acknowledge Prof. Lemont B. Kier, Emeritus Faculty of Medicinal Chemistry, Virginia Commonwealth University and Prof. Michael Petitjean, p for providing reprints of their research publications. The authors wish to thank TATA ELXSI for providing molecular modeling software MOE for the present study.

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