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

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.

 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)

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 3”end 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

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

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.

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.

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.

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

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

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)

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)

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

CpdCoreR1R2R3IC 50 ( μ M) *pIC 50 ( μ M) Predicted pIC 50 ( μ M) 1APyrrole4'-F AO-xylene A1,2-(CH 3 )-1H- pyrrole a4a A2,3-(CH 3 ) thiopene A2,4-(CH 3 ) thiopene A1,3-(CH 3 )-1H- pyrrole A2,5-( CH 3 ) thiopene a8a B4'-Cl B3'-F B-4'-OCH B-3'-OCH a C4'-F CH C2'-Cl C3'-Cl a C4'-Cl C4'-F, 3'-Cl C4'-FCN C4'-FBr a C4'-FI DN(CH 3 ) 3 tetrahydro -2H-pyran 1'- (CH 3 )- 4'-F benzene DNH-CO- CH 3 CH 3 1'- (CH 3 )- 4'-F benzene DNH-SO 2 - CH 3 CH 3 1'- (CH 3 )- 4'-F benzene a DNH-CO-N(CH 3 ) 2 CH 3 1'- (CH 3 )- 4'-F benzene DNH-SO 2 -N(CH 3 ) 2 CH 3 1'- (CH 3 )- 4'-F benzene DNH-CO-CO- N(CH 3 ) 2 CH 3 1'- (CH 3 )- 4'-F benzene DNH-CO-CO-OCH 3 CH 3 1'- (CH 3 )- 4'-F benzene a DNH-CO-CO-OHCH 3 1'- (CH 3 )- 4'-F benzene DN(CH 3 )-CO-CO- N(CH 3 ) 2 CH 3 1'- (CH 3 )- 4'-F benzene DNH-CO-CO-1,4-( CH 3 ) morpholine CH 3 1'- (CH 3 )- 4'-F benzene DNH-CO-CO-1,4-( CH 3 ) piperazine CH 3 1'- (CH 3 )- 4'-F benzene a DNH-CO-CO- N(CH 3 ) 2 CH 3 1'- (C 2 H 5 )- 2',3'- (OCH 3 ) DNH-CO-CO- N(CH 3 ) 2 CH 3 1'- (C 2 H 5 )- 3'-Cl-4'- F benzene DNH-CO-pyridineCH 3 1'- (CH 3 )- 4'-F benzene DNH-CO-pyridazineCH 3 1'- (CH 3 )- 4'-F benzene a DNH-CO-pyrimidineCH 3 1'- (CH 3 )- 4'-F benzene DNH-CO-oxazoleCH 3 1'- (CH 3 )- 4'-F benzene DNH-CO-thiazoleCH 3 1'- (CH 3 )- 4'-F benzene DNH-CO-iH Imidazole CH 3 1'- (CH 3 )- 4'-F benzene a DNH-CO-1,3,4- oxadiazole CH 3 1'- (CH 3 )- 4'-F benzene

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.

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.

EquationR2R2 Q2Q2 LOFP-value 1 Y= − W Z M R e-09 2 Y= − W Z M Ms e-09 3 Y= − W Z R e-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

pIC 50 = − W Z M R

Cmpds pIC 50 Predicted 1 Residual 1 Predicted 2 Residual 2 Predicted 3 Residual

Cmpds pIC 50 Predicted 1 Residual 1 Predicted 2 Residual 2 Predicted 3 Residual

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 , which is highly significant. This QSAR method can be used to predict the activities of future HIV-1 integrase inhibitors.

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, 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, 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,

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

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

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