Prediction of HIV-1 Protease Inhibitory Activity of (4-Hydroxy-6-Phenyl-2-Oxo-2H-<WBR>Pyran-3-yl) Thiomethanes: QSAR Study

V. Ravichandran^a, Abhishek K. Jain, V.K. Mourya^b and R. K. Agrawal^a*

Department of Pharmaceutical Sciences, Dr. H. S. Gour University, Sagar (M.P.), India.

^a Govt. College of Pharmacy, Osmanpura, Aurangabad, Maharashtra, India.

*For Correspondence - phravi75@rediffmail.com

Originally Published in : Current Trends in Biotechnology and Pharmacy , Volume 3 (2) April - 2009

Abstract

In pursuit of better HIV-1 protease inhibitory agents, QSAR studies were performed on a series of (4-hydroxy-6-phenyl-2-oxo-2h-pyran-3-yl) thiomethanes using WIN CAChe 6.1. Stepwise multiple linear regression analysis was performed to derive QSAR models which were further evaluated for statistical significance and predictive power by internal and external validation. The best QSAR model was selected, having correlation coefficient (R) = 0.923 and cross-validated squared correlation coefficient (q²) = 0.743. The developed best QSAR model indicates that the hydrophobicity and ionization potential play an important role in the HIV-1 protease inhibitory activities.

Keywords: QSAR; HIV-1 protease inhibitory activity; multiple linear regressions; thiomethane.

Introduction

HIV- 1 (Human Immunodeficiency Virus Type-1) is the pathogenic retrovirus and causative agent of AIDS or AIDS- related complex (ARC) (1). When viral RNA is translated into a polypeptide sequence, it is assembled in a long polypeptide chain, which includes several individual proteins namely, reverse transcriptase, protease, integrase, etc. Before these enzymes become functional, they must be cut from the longer polypeptide chain.

Acquired immune deficiency syndrome (AIDS) is a formidable pandemic that is still wreaking havoc world wide. The catastrophic potential of this virally caused disease may not have been fully realized. The causative moiety of the disease is human immunodeficiency virus (HIV), which is a retrovirus of the lentivirus family (2). The three viral enzymes; reverse transcriptase, protease and integrase encoded by the group specific antigen and group specific antigen-polyprotein genes of HIV play an important role in the virus replication cycle. Among them, viral protease catalyzes the formation of viral functional enzymes and proteins necessary for its survival. The viral particles at this stage are called virions. The virus particles after the protease action have all the necessary constituents of mature virus and are capable of invading other T4 cells and repeating the life cycle of proviral DNA from viral RNA, the key stage in viral replication. Its central role in virus maturation makes protease is a prime target for anti-HIV-therapy.

Computational chemistry has developed into an important contributor to rational drug design. Quantitative structure activity relationship (QSAR) modeling results in a quantitative correlation between chemical structure and biological activity. QSAR analyses of HIV-1 reverse transcriptase inhibitors (3), HIV-1 protease inhibitors (4,5) and HIV-1 integrase inhibitors (6,7) and gp 120 envelope glycoprotein (8) were reported. Leonard et al. [A1]has developed a few QSAR models for anti-HIV activities of different group of compounds (9,10). The present group of authors has developed a few quantitative structure-activity relationship models to predict anti-HIV activity of different group of compounds (11-20). In continuation of such efforts, in this article, we have performed QSAR analysis to explore the correlation between physicochemical and biological activity of thiomethane derivatives using modeling software WIN CAChe 6.1 (molecular modeling software, a product of Fujitsu private limited, Japan) and statistical software STATISTICA version 6 (StatSoft, Inc., Tulsa, USA).

Materials and Methods

In the present work we have taken 16 thiomethane compounds (Table 1) and their HIV-1 protease inhibitory activity from the reported work (21). Many of these compounds inhibited wild type HIV-1 protease with IC₅₀ values between 0.058 mM and 7.82 mM. There is high structural diversity and a sufficient range of the biological activity in the selected series of thiomethane derivatives. It insists as to select these series of compounds for our QSAR studies. All the HIV-1 protease inhibitory activities used in the present study were expressed as pIC₅₀ = -log₁₀ IC₅₀. Where IC₅₀ is the micro molar concentration of the compounds producing 50% reduction in the HIV-1 protease activity is stated as the means of at least two experiments. The compounds which did not show confirmed HIV-1 protease inhibitory activity and the compounds having particular functional groups at a particular position once in the above cited literature have not been taken for our study. We carried out QSAR analysis and established a QSAR model to guide further structural optimization and predict the potency and physiochemical properties of clinical drug candidates.

All the sixteen compounds (13 compounds in training set and three in test set, training and test set selection has been done manually) were built on workspace of molecular modeling software WIN CAChe 6.1, which is a product of Fujitsu private limited, Japan. The energy minimization was done by geometry optimization of molecules using MM2 (Molecular Mechanics) followed by semi empirical PM3 method available in MOPAC module until the root mean square gradient value becomes smaller than 0.001 kcal/mol Å. The physicochemical properties were calculated on project leader file of the software. These properties were fed manually into statistical software named STATISTICA version 6 (StatSoft, Inc., Tulsa, USA) and a correlation matrix was made to select the parameters having very less inter-correlation and maximum correlation with activity. This was followed by multiple linear regression analysis to achieve best model.

Internal validation was carried out by Leave one out (LOO) method using statistical software STATISTICA. The cross-validated correlation coefficient, q², was calculated using the following equation:

  q2 = 1 –    PRESS/N
å (yi - ym)²
i=1      
N
PRESS =  å (ypred,i – yi)²                                                             i=1

Where y_i is the activity for training set compounds, y_m is the mean observed value, corresponding to the mean of the values for each cross-validation group, and y_pred,i is the predicted activity for y_i. The LOO predicted values are shown in table 1.

In present study the calculated descriptors were conformational minimum energies (CME), Zero-order connectivity index (CI0), First-order connectivity index (CI1), Second-order connectivity index (CI2), dipole moment (DM), total energy at its current geometry after optimization of structure (TE), heat of formation at its current geometry after optimization of structure (HF), ionization potential (IP), electron affinity (EA), octanol-water partition coefficient(logP), molar refractivity(MR), shape index order 1 (SI1), shape index order 2 (SI2), shape index order 3 (SI3), Zero-order valance connectivity index (VCI0), First-order valance connectivity index (VCI1), Second-order valance connectivity index (VCI2). (Physicochemical parameters data will be provided on request).

Results and Discussion

The QSAR studies of the thiomethane series resulted in several QSAR equations. Inter-correlation between the descriptors involved in the QSAR model is £ 0.57. The best equation when we considered only one parameter is Eq. 1.

pIC50 = 4.336 (± 0.596) - 1.004 (± 0.151) logP                                                          (1)

n =13, R= 0.895, R2 = 0.802, R2adj = 0.783, SEE = 0.264, F = 44.42, P < 0.001, q2 = 0.726,             SPRESS = 0.309, SDEP = 0.297.                       

The above equation is statistically significant one. The R2 and internal predictivity of the model is good. When we have considered the best equation containing two parameters is Eq. 2.

pIC50 = -5.993 (± 4.613) - 0.831 (± 0.165) logP + 1.075 (± 0.581) IP                                   (2)

n =13, R= 0.923, R2 = 0.852, R2adj = 0.823, SEE = 0.238, F = 28.80, P < 0.001, q2 = 0.743,             SPRESS = 0.299, SDEP = 0.287.   

When we considered three parameters for developing model, there was no significant improvement in R² and q². Eq. 2 was selected as the best model on the basis of high q² values and R² value. The values given in the parentheses are 95% confidence intervals of the regression coefficients. Eq. 2 could explain 85.2% and predict 74.3% of the variance of the HIV-1 protease inhibitory activity data. The calculated HIV-1 protease inhibitory activity values by Eq.2 are given in table1. This model showed good correlation coefficient (R) of 0.923 between descriptors [logP and IP] and HIV-1 protease inhibitory activity. This model also indicates statistical significance > 99.9% with F value F_(2,10) =28.80. The residual of the observed and calculated activities are shown in fig. 1. The predictive ability of the selected model was also confirmed by external r²_CVext method. According to Tropsha et al., [A2] the proposed QSAR model is predictive as it satisfies the conditions r²_CVext > 0.5 and R^{2 Pred > 0.6 (r2_CVext = 0.885, R2_Pred = 0.991) (22). The robustness of this model was checked by Y–randomization test. The low R2 and q2 values indicate that the good results in our original model are not due to a chance correlation or structural dependency of the training set.}

^{The QSAR [A3] shows a linear relationship of HIV-1 protease inhibitory activity with the logP. Its negative sign indicates that highly hydrophobic groups are not good for improving the activity of the series. The positive coefficient of IP showed that the presence of electron donating groups is favor for activity. Thus we conclude that the biological activity will be increased if substituents that bring about changes in the molecule as mentioned above are attached to it.}

^{Acknowledgments}

^{One of the authors V. Ravichandran is thankful to AICTE, New Delhi for providing (QIP) Senior Research Fellowship.}

^References

1. ^{Gallo, R. C., Salahuddin, S. Z, Popovic, M., Shearer, G. M, Kaplan, M., Haynes, B. F, Palker, T. J., Redfield, R., Oleske, J. and Safai, B. (1984) Frequent detection and isolation of cytopathic retroviruses (HTLV-III) from patients with AIDS and at risk for AIDS. Science, 224: 500-503.}
2. ^{De Clercq, E. (1995) Toward improved anti-HIV chemotherapy: therapeutic strategies for intervention with HIV infections. J. Med. Chem., 38: 2491-2517.}
3. ^{Pungpo, P. and Hannongbua, S. (2000) Three-dimensional quantitative structure-activity relationships study on HIV-1 reverse transcriptase inhibitors in the class of dipyridodiazepinone derivatives, using comparative molecular field analysis. J. Mol. Graphics & Modell., 18: 581-590.}
4. ^{Jayatilleke, P.R.N., Nair, A.C., Zauhar, R. and Welsh, W.J. (2000) Computational studies on HIV-1 protease inhibitors: Influence of calculated inhibitor-enzyme binding affinities on the statistical quality of 3D-QSAR CoMFA models. J. Med. Chem. , 43: 4446-4451.}
5. ^{Nair, A.C., Jayatilleke, P., Wang, X., Miertus, S. and Welsh, W.J. (2002) Computational studies on tetrahydropyrimidine-2-one HIV-1 protease inhibitors: improving three-dimensional quantitative structure-activity relationship comparative molecular field analysis models by inclusion of calculated inhibitor-and receptor based properties. J. Med. Chem., 45: 973-983.}
6. ^{Buolamwini, J.K. and Assefa, H. (2002) CoMFA and CoMSIA 3D QSAR and docking studies on conformationally-restrained cinnamoyl HIV-1 integrase inhibitors: Exploration of a binding mode at the active site. J. Med. Chem., 45: 841-852.}
7. ^{Raghavan, K., Buolamwini, J.K., Fesen, M.R., Pommier, Y. and Kohn, K.W. (1995) Three dimensional quantitative structure-activity relationships (QSAR) of HIV integrase inhibitors: A comparative molecular field analysis (CoMFA) study. J. Med. Chem., 38: 890-897.}
8. ^{Debnath, A.K., Jiang, S., Strick, N., Lin, K. and Haberfield, P. (1994) Three dimensional structure-activity analysis of a series of porphyrin derivatives with anti-HIV-1 activity targeted to the V3 loop of the gp 120 envelope glycoprotein of the human immunodeficiency virus type 1. J. Med. Chem., 37: 1099-1108.}
9. ^{Thomas, J.T. and Roy, K. (2006) QSAR by LFER model of HIV protease inhibitor mannitol derivatives using FA-MLR, PCRA and PLS techniques. Bioorg. Med. Chem., 14: 1039-1046.}
10. ^{Thomas, J.T. and Roy, K. (2006) Comparative QSAR modeling of CCR5 receptor binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas. Bioorg. Med .Chem. Lett., 16: 4467-4474.}
11. ^{Ravichandran, V., Jain, P.K., Mourya, V.K. and Agrawal, R.K. (2008) QSAR study on some arylsulfonamides as anti-HIV agents. Med. Chem. Res., 16: 342-351.}
12. ^{Ravichandran, V. and Agrawal, R. K. (2007) Predicting anti-HIV activity of PETT derivatives:CoMFA approach. Bioorg. Med. Chem. Lett., 17: 2197-2202.}
13. ^{Ravichandran, V., Mourya, V.K. and Agrawal, R.K. (2007) QSAR study of novel 1, 1, 3 – trioxo [1, 2, 4] - thiadiazine (TTDs) analogues as potent anti-HIV agents. Arkivoc, XIV: 204-212.}
14. ^{Ravichandran, V.; Prashanthakumar, B.R., Sankar, S. and Agrawal, R.K. (2008) Comparative molecular similarity indices analysis for predicting anti-HIV activity of phenyl ethyl thiourea (PET) derivatives. Med. Chem. Res., 17: 1-11.}
15. ^{Ravichandran, V., Mourya, V.K. and Agrawal, R.K. (2007) QSAR prediction of HIV–1 reverse transcriptase inhibitory activity of benzoxazinone derivatives. Internet Electron. J. Mol. Des., 6: 363-374.}
16. ^{Ravichandran V, Mourya VK, Agrawal RK (2008) QSAR modeling of hiv-1 reverse transcriptase inhibitory activity with PETT derivatives. Digest J. Nanomat. Biostruct., 3: 9-17.}
17. ^{Ravichandran, V., Mourya, V.K. and Agrawal, R.K. (2008) Prediction of HIV-1 protease inhibitory activity of 4-hydroxy-5,6-dihydropyran-2-ones: QSAR study. J. Enzyme Inhib. Med. Chem., (Inpress).}
18. ^{Ravichandran, V., Mourya, V.K. and Agrawal, R.K. (2008) QSAR analysis of 6-aryl-2,4-dioxo-5-hexenoic acids as HIV–1 integrase inhibitors. Indian J. Pharm. Edu. Res., 42: 40-47.}
19. ^{Sahu, K.K., Ravichandran, V., Jain, P.K., Sharma, S., Mourya, V.K. and Agrawal, R.K. (2008) QSAR analysis of chicoric acid derivatives as HIV–1 integrase inhibitors. Acta Chimi. Slov., 55:138-145.}
20. ^{Sahu, K.K., Ravichandran, V., Mourya, V.K. and Agrawal, R.K. (2007) QSAR analysis of caffeoyl naphthalene sulphonamide derivatives as HIV-1 Integrase inhibitors. Med. Chem. Res., 15: 418-430.}
21. ^{Prasad, J.V.N.V., Para, K.S., Tummino, P.J., Fergusion, D., McQuade, T.J., Lunney, E.A., Rapundalo, S.T., Bately, B.L., Hingoram, G., Domagala, J.M., Gracheck, S. J., Bhat, T.N., Liu, B.L., Baldwin, E.T., Erickson, J.W. and Sawyer, T.K. (1995) Nonpeptidic potent HIV-1 protease inhibitors: (4-hydroxy-6-phenyl-2-oxo-2h-pyran-3-yl) thiomethanes that span P₁ – P₂’ subsites in a unique mode of active site binding. J. Med. Chem., 38: 898-905.}
22. ^{Tropsha, A., Gramatica, P., Gombar, V.K. (2003) The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models. Quant. Struct. Act. R el., 22: 1-9.}

^{Table 1: Structure, selected parameters and their HIV-1 protease inhibitory activity of thiomethane analogues}

^{a - test set compounds, b - the experimental IC₅₀ values (in micro molar) were converted into –logIC₅₀ (pIC50, in micro molar), Cal.Act.- calculated activity by Eq.2 and Pred.Act. – predicted activity by Eq.2 (leave one out method).}

^Figures

^{Fig. 1. Residual value of observed and calculated HIV-1 protease inhibitory activity of thiomethane derivatives . [A4]}

^{Fig. 1}