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QSAR study on diketo acid and carboxamide derivatives as potent HIV-1 integrase inhibitor Presented By Olayide Arodola (Master student Pharmaceutical Chemistry)

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Aim of this study The aim of this study is to find out how accurate the QSAR method predicted the activities of compounds in comparison to their experimental biological activities. Therefore, a 2-dimensional QSAR model was used to analyze 40 potential diketo acid and carboxamide-based compounds as HIV-1 integrase inhibitors.

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KEY WORDS: Diketo acid and Carboxamide derivatives 2D-QSAR (2-dimensional quantitative structural activity relationship) GFA (Genetic function algorithm) Integrase inhibitor SOFTWARES USED IN THIS STUDY Chemdraw ultra 10.0 (to draw 2D structures of the compounds) Discovery studio v3.5 (to perform QSAR analysis)

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The integration of HIV-1 DNA into the host chromosome contains a series of DNA cutting and joining reactions. The first step in the integration process is 3end processing. In the second step, termed DNA strand transfer, the viral DNA end is inserted into the target DNA. Thus, the integrase enzyme is crucial for viral replication and represents a potential target for antiretroviral drug. About HIV-1 integrase

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First, a quick reminder: what do you understand by drug A very broad definition of a drug would include all chemicals other than food that affect living processes. if it helps the body, its medicine, but if it causes a harmful effect on the body, its poison. Nowadays, we are facing a problem of screening a huge number of molecules in other to testify: If they are toxic to human If they have an effect on virus e.g HIV, HPV (cervical cancer), H1N1 (flu), ebola etc

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Such screenings are measured by laborious experiments. Researchers came up with a process to relate a series of molecular features with biological activities or chemical reactivities, which is expected to decrease a number of laborious and expensive experiments thereby selecting small number of good compounds for later synthesis.

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QSAR QSAR is a mathematical relationship between a biological activity of a molecular system and its physical and chemical characteristics i.e QSAR represents an attempt to develop correlations between biological activity and physicochemical properties of a set of molecules. In pharmacology, biological activity describes the beneficial or adverse effects of a drug on living matter.pharmacologydrugliving matter Physicochemical properties of a compound simply means both its physical and chemical property. The first application of QSAR is attributed to Hansch (1969), who developed an equation that related biological activity to certain physicochemical properties of a set of structures.

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WHY QSAR The number of compounds required for synthesis in order to place 10 different groups in 4 positions of benzene ring is 10 4 Solution: synthesize a small number of compounds and from their data derive rules to predict the biological activity of other compounds.

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Compounds + biological activity New compounds with improved biological activity QSAR Correlate chemical structure with activity using statistical approach QSAR and Drug Design

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BASIC PRINCIPLES BASIC PRINCIPLES A QSAR normally takes the general form of a linear equation: Biological activity Biological activity = Const + (C 1 P 1 ) + (C 2 P 2 ) + (C 3 P 3 ) +... where the parameters P 1 through p n are computed for each molecule in the series and the coefficients C 1 through c n are calculated by fitting variations in the parameters and the biological activity. A = k 1 d 1 + k 2 d 2 + k 3 d 3 + k n d n + Const A Biological activity D Structural properties (descriptors) K Regression coefficient

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There are a series of statistical model analysis that are used to develop a QSAR model, they include: Multiple linear regression (MLR) Principle component analysis (PCA) Partial least square (PLS) Genetic function algorithm (GFA)

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There are a series of statistical model analysis that are used to develop a QSAR model, they include: Multiple linear regression (MLR) Principle component analysis (PCA) Partial least square (PLS) Genetic function algorithm (GFA)

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Why GFA GFA was used to develop this QSAR models for variable selection. The purpose of variable selection is to select the variables significantly contributing to prediction and to discard other variables by fitness function. Ability to build multiple models rather than single model Ability to incorporate the lack of fit (LOF) error that resists over-fitting Automatic removal of outliers e.g 1, 3, 6, 9, 100 Provision of additional information not available from other statistical regression analysis

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CpdCoreR1R2R3IC 50 ( M) *pIC 50 ( M) Predicted pIC 50 ( M) 1APyrrole4'-F-0.170.7700.409 2AO-xylene--5.67-0.7540.105 3A1,2-(CH 3 )-1H- pyrrole --0.220.6580.377 4a4a A2,3-(CH 3 ) thiopene--0.180.7450.326 5A2,4-(CH 3 ) thiopene--0.160.7960.498 6A1,3-(CH 3 )-1H- pyrrole --0.50.3010.616 7A2,5-( CH 3 ) thiopene--0.50.3010.608 8a8a B4'-Cl--1.00.0000.485 9B3'-F--0.250.6020.463 10B-4'-OCH 3 -0.150.8240.505 11B-3'-OCH 3 -0.140.8540.591 12 a C4'-F--0.101.0001.178 13CH--0.230.6380.971 14C2'-Cl--0.370.4321.280 15C3'-Cl--0.041.3981.239 16 a C4'-Cl--0.380.4201.213 17C4'-F, 3'-Cl--0.041.3981.267 18C4'-FCN-0.021.6991.580 19C4'-FBr-0.031.5231.276 20 a C4'-FI-0.021.6991.482 21DN(CH 3 ) 3 tetrahydro -2H-pyran 1'- (CH 3 )- 4'-F benzene 0.0022.6992.495 22DNH-CO- CH 3 CH 3 1'- (CH 3 )- 4'-F benzene 0.0072.1551.681 23DNH-SO 2 - CH 3 CH 3 1'- (CH 3 )- 4'-F benzene 0.0082.0971.973 24 a DNH-CO-N(CH 3 ) 2 CH 3 1'- (CH 3 )- 4'-F benzene 0.0181.7451.580 25DNH-SO 2 -N(CH 3 ) 2 CH 3 1'- (CH 3 )- 4'-F benzene 0.0121.9211.957 26DNH-CO-CO- N(CH 3 ) 2 CH 3 1'- (CH 3 )- 4'-F benzene 0.012.0001.704 27DNH-CO-CO-OCH 3 CH 3 1'- (CH 3 )- 4'-F benzene 0.0151.8241.797 28 a DNH-CO-CO-OHCH 3 1'- (CH 3 )- 4'-F benzene 0.0042.3981.594 29DN(CH 3 )-CO-CO- N(CH 3 ) 2 CH 3 1'- (CH 3 )- 4'-F benzene 0.0151.8241.943 30DNH-CO-CO-1,4-( CH 3 ) morpholine CH 3 1'- (CH 3 )- 4'-F benzene 0.021.6991.970 31DNH-CO-CO-1,4-( CH 3 ) piperazine CH 3 1'- (CH 3 )- 4'-F benzene 0.0261.5851.391 32 a DNH-CO-CO- N(CH 3 ) 2 CH 3 1'- (C 2 H 5 )- 2',3'- (OCH 3 ) 0.0211.6781.937 33DNH-CO-CO- N(CH 3 ) 2 CH 3 1'- (C 2 H 5 )- 3'-Cl-4'- F benzene 0.0092.0461.739 34DNH-CO-pyridineCH 3 1'- (CH 3 )- 4'-F benzene 0.021.6992.020 35DNH-CO-pyridazineCH 3 1'- (CH 3 )- 4'-F benzene 0.0151.8241.931 36 a DNH-CO-pyrimidineCH 3 1'- (CH 3 )- 4'-F benzene 0.0072.1551.936 37DNH-CO-oxazoleCH 3 1'- (CH 3 )- 4'-F benzene 0.0072.1552.325 38DNH-CO-thiazoleCH 3 1'- (CH 3 )- 4'-F benzene 0.0082.0972.221 39DNH-CO-iH Imidazole CH 3 1'- (CH 3 )- 4'-F benzene 0.0062.2222.357 40 a DNH-CO-1,3,4- oxadiazole CH 3 1'- (CH 3 )- 4'-F benzene 0.0151.8242.656

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Methods Out of 40 compounds, 30 were used as a training set and 10 as a test set to evaluate the internal degree of predicitivity of the QSAR equation. Using Chemdraw ultra 10.0, different 2D structures were drawn, followed by the conversion to 3D structures of reasonable conformations using Discovery studio v3.5 software. A large number of descriptors were also calculated (e.g. ALogP, molecular weight, molar refractivity, dipole moment, heat of formation, Radius of gyration, Wiener index, Zagreb index etc.). 2D QSAR analysis was carried out using genetic function algorithm (GFA) analysis.

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RESULT A QSAR model was generated for integrase activity. In order to select the optimal set of descriptors, we used systematic variable selection leave one out (LOO) method in a stepwise forward manner for the selection of descriptors. Three best QSAR equations models generated for this study using the GFA approach and LOO method are shown in table below.

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EquationR2R2 Q2Q2 LOFP-value 1 Y= -11.65 0.0024929W + 0.088809Z + 0.01936M + 1.1879R 0.8200.5580.1935.174e-09 2 Y= -12.896 0.0028585W + 0.077907Z + 0.020068M + 0.015681Ms 0.8120.4700.2029.270e-09 3 Y= -9.6736 0.0020098W + 0.078883Z + 0.89779R 0.7900.6200.1905.641e-09 Y: pIC 50, set of descriptors (W, Z, M, R, Ms,), R 2 : correlation coefficient, Q 2 : cross-validated R squared, LOF: Lack of fit, P-value: significance level

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17 19 3034 35 0.040.03 0.02 0.015 pIC 50 = -11.65 0.0024W + 0.089Z + 0.019M + 1.187R

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Cmpds pIC 50 Predicted 1 Residual 1 Predicted 2 Residual 2 Predicted 3 Residual 3 1 0.770.4090.3610.3930.3770.2740.496 2-0.7540.105-0.8590.407-1.1610.335-1.089 30.6580.3770.2810.3970.261 0.397 50.7960.4980.2980.6180.1780.2280.568 60.3010.616-0.3150.536-0.2350.422-0.121 70.3010.608-0.3070.398-0.0970.512-0.211 90.6020.4630.1390.3300.2720.6020.000 100.8240.5050.3190.5630.2610.6920.132 110.8540.5910.2630.900-0.0460.7250.129 130.6380.971-0.3330.676-0.0381.017-0.379 140.4321.280-0.8481.316-0.8841.276-0.844 151.3981.2390.1591.1660.2321.2600.138 171.3981.2670.1311.401-0.0031.3400.058 181.6991.5800.1191.3110.3881.5590.139 191.5231.2760.2471.4640.0591.3620.160 212.6992.4950.2042.796-0.0972.3340.365

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222.1551.6810.4741.6720.4831.7130.442 232.0971.9730.1242.0340.0631.9890.108 251.9211.957-0.0361.998-0.0771.975-0.054 262.0001.7040.2961.7240.2761.7770.223 271.8241.7970.0271.7070.1171.867-0.043 291.8241.943-0.1191.851-0.0271.883-0.059 301.6991.970-0.2711.926-0.2271.929-0.230 311.5851.3910.1941.4990.0861.594-0.009 332.0461.7390.3071.8450.2011.8600.186 341.6992.020-0.3211.809-0.1102.154-0.455 351.8241.931-0.1071.7870.0372.017-0.193 372.1552.325-0.1702.302-0.1472.0900.065 382.0972.221-0.1242.243-0.1462.109-0.012 392.2222.357-0.1352.2190.0022.1330.089

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Cmpds pIC 50 Predicted 1 Residual 1 Predicted 2 Residual 2 Predicted 3 Residual 3 40.7450.3260.4190.2870.4580.2820.463 80.0000.485-0.4850.761-0.7610.587-0.587 121.0001.178-0.1780.8360.1641.215-0.215 160.4201.212-0.7921.259-0.8391.233-0.813 201.6991.4820.2171.784-0.0851.4730.226 241.7451.5800.1651.4710.2741.6340.111 282.3981.5940.8041.5000.8981.7060.692 321.6781.937-0.2601.877-0.1991.961-0.283 362.1551.9360.2191.7650.3902.0960.059 401.8242.656-0.8322.360-0.5362.371-0.547

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Conclusion From the above result, it can be concluded that Radius of gyration, Zagreb index, Weiner index and minimized energy are statistically important with the correlation coefficient value of 0.8209, which is highly significant. This QSAR method can be used to predict the activities of future HIV-1 integrase inhibitors.

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References 1.Summa, V., Petrocchi, A., Bonelli, F., Crescenzi, B., Donghi, M., Ferrara, M., Fiore, F., Gardelli, C., Paz, O. G., Hazuda, D. J., Jones, P., Kinzel, O., Laufer, R., Monteagudo, E., Muraglia, E., Nizi, E., Orvieto, F., Pace, P., Pescatore, G., Scarpelli, R., Stillmock, K., Witmer, M. V., and Rowley, M. (2008) Discovery of Raltegravir, a potent, selective orally bioavailable HIV- integrase inhibitor for the treatment of HIV-AIDS infection, J. Med. Chem. 51, 5843-5855. 2.Wai, J. S., Egbertson, M. S., Payne, L. S., Fisher, T. E., Embrey, M. W., Tran, L. O., Melamed, J. Y., Langford, H. M., Guare, J. P., Zhuang, L. G., Grey, V. E., Vacca, J. P., Holloway, M. K., Naylor-Olsen, A. M., Hazuda, D. J., Felock, P. J., Wolfe, A. L., Stillmock, K. A., Schleif, W. A., Gabryelski, L. J., and Young, S. D. (2000) 4-aryl-2,4-dioxobutanoic acid inhibitors of HIV-1 integrase and viral replication in cells, J. Med. Chem. 43, 4923-4926. 3.Wai, J. S., Kim, B., Fisher, T. E., Zhuang, L., Embrey, M. W., Williams, P. D., Staas, D. D., Culberson, C., Lyle, T. A., Vacca, J. P., Hazuda, D. J., Felock, P. J., Schleif, W. A., Gabryelski, L. J., Jin, L., Chen, I. W., Ellis, J. D., Mallai, R., and Young, S. D. (2007) Dihydroxypyridopyrazine-1,6-dione HIV-1 integrase inhibitors, Bioorg. Med. Chem. Lett. 17, 5595-5599.

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My Current Research Could the FDA-approved anti-HIV drugs be promising anti- cancer agents? An answer from extensive molecular dynamic analyses

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Acknowledgement Dr Mahmoud Soliman ( my supervisor ) & the lab members CHPC (Technical support) UKZN School of health sciences (Financial support)

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Thank you