April Journal-2009.p65 - Association of Biotechnology and Pharmacy

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Current Trends in Biotechnology and Pharmacy Vol. 3 (2) 149-154, April 2009. ISSN 0973-8916 (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. Intercorrelation between the descriptors involved in the QSAR model is ≤ 0.57. The best equation when we considered only one parameter is Eq. 1. pIC 50 = 4.336 (± 0.596) - 1.004 (± 0.151) logP (1) n =13, R= 0.895, R 2 = 0.802, R 2 = 0.783, SEE = adj 0.264, F = 44.42, P < 0.001, q 2 = 0.726, S PRESS = 0.309, SDEP = 0.297. The above equation is statistically significant one. The R 2 and internal predictivity of the model is good. When we have considered the best equation containing two parameters is Eq. 2. pIC 50 = -5.993 (± 4.613) - 0.831 (± 0.165) logP + 1.075 (± 0.581) IP (2) n =13, R= 0.923, R 2 = 0.852, R 2 = 0.823, SEE = adj 0.238, F = 28.80, P < 0.001, q 2 = 0.743, S PRESS = 0.299, SDEP = 0.287. When we considered three parameters for developing model, there was no significant improvement in R 2 and q 2 . Eq. 2 was selected as the best model on the basis of high q 2 values and 152 R 2 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 r 2 method. According to Tropsha et al., the CVext proposed QSAR model is predictive as it satisfies the conditions r 2 > 0.5 and CVext R2 > 0.6 Pred (r2 CVext = 0.885, R2 = 0.991) (22). The robustness of Pred this model was checked by Y–randomization test. The low R 2 and q 2 values indicate that the good results in our original model are not due to a chance correlation or structural dependency of the training set. Residual value 0.6 0.4 0.2 -0.2 -0.6 1 4 5 6 7 8 9 10 11 12 13 14 15 16 Compound number The QSAR 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. Ravichandran et al
Current Trends in Biotechnology and Pharmacy Vol. 3 (2) 149-154, April 2009. ISSN 0973-8916 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 structureactivity 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. 153 6. Buolamwini, J.K. and Assefa, H. (2002) CoMFA and CoMSIA 3D QSAR and docking studies on conformationallyrestrained 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 structureactivity 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. Prediction of HIV-1 Protease Inhibitory Activity
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