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.

• 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 QSARThe number of compounds required for synthesis in order to place 10

different groups in 4 positions of benzene ring is 104

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

A QSAR normally takes the general form of a linear equation:

Biological activity = Const + (C1×P1) + (C2×P2) + (C3×P3) +...

where the parameters

P1 through pn are computed for each molecule in the series and the coefficients C1 through cn

are calculated by fitting variations in the parameters and the biological activity.

• A = k1d1 + k2d2 + k3d3 + kndn + 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

Cpd Core

R1 R2 R3 IC50(μM)

*pIC50(μM)

Predicted pIC50(μM)

1 A Pyrrole 4'-F - 0.17 0.770 0.409

2 A O-xylene - - 5.67 -0.754 0.105

3 A 1,2-(CH3)-1H-pyrrole

- - 0.22 0.658 0.377

4a A 2,3-(CH3) thiopene

- - 0.18 0.745 0.326

5 A 2,4-(CH3) thiopene

- - 0.16 0.796 0.498

6 A 1,3-(CH3)-1H- pyrrole

- - 0.5 0.301 0.616

7 A 2,5-( CH3) thiopene

- - 0.5 0.301 0.608

8a B 4'-Cl - - 1.0 0.000 0.485

9 B 3'-F - - 0.25 0.602 0.463

10 B - 4'-OCH3 - 0.15 0.824 0.505

11 B - 3'-OCH3 - 0.14 0.854 0.591

12a C 4'-F - - 0.10 1.000 1.178

13 C H - - 0.23 0.638 0.971

14 C 2'-Cl - - 0.37 0.432 1.280

15 C 3'-Cl - - 0.04 1.398 1.239

16a C 4'-Cl - - 0.38 0.420 1.213

17 C 4'-F, 3'-Cl - - 0.04 1.398 1.267

18 C 4'-F CN - 0.02 1.699 1.580

19 C 4'-F Br - 0.03 1.523 1.276

20a C 4'-F I - 0.02 1.699 1.482

21 D N(CH3)3 tetrahydro-2H-pyran

1'-(CH3)-4'-F benzene

0.002 2.699 2.495

22 D NH-CO- CH3 CH3 1'-(CH3)-4'-F benzene

0.007 2.155 1.681

23 D NH-SO2- CH3 CH3 1'-(CH3)-4'-F benzene

0.008 2.097 1.973

24a D NH-CO-N(CH3)2 CH3 1'-(CH3)-4'-F benzene

0.018 1.745 1.580

25 D NH-SO2-N(CH3)2 CH3 1'-(CH3)-4'-F benzene

0.012 1.921 1.957

26 D NH-CO-CO-N(CH3)2

CH3 1'-(CH3)-4'-F benzene

0.01 2.000 1.704

27 D NH-CO-CO-OCH3

CH3 1'-(CH3)-4'-F benzene

0.015 1.824 1.797

28a D NH-CO-CO-OH CH3 1'-(CH3)-4'-F benzene

0.004 2.398 1.594

29 D N(CH3)-CO-CO-N(CH3)2

CH3 1'-(CH3)-4'-F benzene

0.015 1.824 1.943

30 D NH-CO-CO-1,4-( CH3) morpholine

CH3 1'-(CH3)-4'-F benzene

0.02 1.699 1.970

31 D NH-CO-CO-1,4-( CH3) piperazine

CH3 1'-(CH3)-4'-F benzene

0.026 1.585 1.391

32a D NH-CO-CO-N(CH3)2

CH3 1'-(C2H5)-2',3'-(OCH3)

0.021 1.678 1.937

33 D NH-CO-CO-N(CH3)2

CH3 1'-(C2H5)-3'-Cl-4'-F benzene

0.009 2.046 1.739

34 D NH-CO-pyridine CH3 1'-(CH3)-4'-F benzene

0.02 1.699 2.020

35 D NH-CO-pyridazine

CH3 1'-(CH3)-4'-F benzene

0.015 1.824 1.931

36a D NH-CO-pyrimidine

CH3 1'-(CH3)-4'-F benzene

0.007 2.155 1.936

37 D NH-CO-oxazole CH3 1'-(CH3)-4'-F benzene

0.007 2.155 2.325

38 D NH-CO-thiazole CH3 1'-(CH3)-4'-F benzene

0.008 2.097 2.221

39 D NH-CO-iH Imidazole

CH3 1'-(CH3)-4'-F benzene

0.006 2.222 2.357

40a D NH-CO-1,3,4- oxadiazole

CH3 1'- (CH3)-4'-F benzene

0.015 1.824 2.656

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.

  Equation R2 Q2 LOF P-value

1  

Y=   -11.65 − 0.0024929W + 0.088809Z + 0.01936M + 1.1879R 

0.820 0.558 0.193 5.174e-09

2   

Y=   -12.896 − 0.0028585W + 0.077907Z + 0.020068M + 0.015681Ms 

0.812 0.470 0.202 9.270e-09

3 

Y=  -9.6736 − 0.0020098W + 0.078883Z + 0.89779R

0.790 0.620 0.190 5.641e-09

Y: pIC50, set of descriptors (W, Z, M, R, Ms,), R2: correlation coefficient, Q2: cross-validated R squared, LOF: Lack of fit, P-value: significance level

N

N

O OH

OH

O

F

Cl

17

N

N

O OH

Br

OH

O

F

19

HN

N

HN

OH

O

HN

OF

O

N

O

30

HN

N

HN

OH

O

HN

OF

O

N

34

HN

N

HN

OH

O

HN

OF

O

NN

350.04 0.03

0.02 0.02

0.015

pIC50 =   -11.65 − 0.0024W + 0.089Z + 0.019M + 1.187R

Cmpds pIC50 Predicted1 Residual1 Predicted2

Residual2 Predicted3 Residual3

1 0.77 0.409 0.361 0.393 0.377 0.274 0.4962 -0.754 0.105 -0.859 0.407 -1.161 0.335 -1.0893 0.658 0.377 0.281 0.397 0.261 0.261 0.3975 0.796 0.498 0.298 0.618 0.178 0.228 0.5686 0.301 0.616 -0.315 0.536 -0.235 0.422 -0.1217 0.301 0.608 -0.307 0.398 -0.097 0.512 -0.2119 0.602 0.463 0.139 0.330 0.272 0.602 0.00010 0.824 0.505 0.319 0.563 0.261 0.692 0.13211 0.854 0.591 0.263 0.900 -0.046 0.725 0.12913 0.638 0.971 -0.333 0.676 -0.038 1.017 -0.37914 0.432 1.280 -0.848 1.316 -0.884 1.276 -0.84415 1.398 1.239 0.159 1.166 0.232 1.260 0.13817 1.398 1.267 0.131 1.401 -0.003 1.340 0.05818 1.699 1.580 0.119 1.311 0.388 1.559 0.13919 1.523 1.276 0.247 1.464 0.059 1.362 0.16021 2.699 2.495 0.204 2.796 -0.097 2.334 0.365

22 2.155 1.681 0.474 1.672 0.483 1.713 0.44223 2.097 1.973 0.124 2.034 0.063 1.989 0.10825 1.921 1.957 -0.036 1.998 -0.077 1.975 -0.05426 2.000 1.704 0.296 1.724 0.276 1.777 0.22327 1.824 1.797 0.027 1.707 0.117 1.867 -0.04329 1.824 1.943 -0.119 1.851 -0.027 1.883 -0.05930 1.699 1.970 -0.271 1.926 -0.227 1.929 -0.23031 1.585 1.391 0.194 1.499 0.086 1.594 -0.00933 2.046 1.739 0.307 1.845 0.201 1.860 0.18634 1.699 2.020 -0.321 1.809 -0.110 2.154 -0.45535 1.824 1.931 -0.107 1.787 0.037 2.017 -0.19337 2.155 2.325 -0.170 2.302 -0.147 2.090 0.06538 2.097 2.221 -0.124 2.243 -0.146 2.109 -0.01239 2.222 2.357 -0.135 2.219 0.002 2.133 0.089

Cmpds pIC50

Predicted1

Residual1

Predicted2

Residual2

Predicted3

Residual3

4 0.745 0.326 0.419 0.287 0.458 0.282 0.4638 0.000 0.485 -0.485 0.761 -0.761 0.587 -0.58712 1.000 1.178 -0.178 0.836 0.164 1.215 -0.21516 0.420 1.212 -0.792 1.259 -0.839 1.233 -0.81320 1.699 1.482 0.217 1.784 -0.085 1.473 0.22624 1.745 1.580 0.165 1.471 0.274 1.634 0.11128 2.398 1.594 0.804 1.500 0.898 1.706 0.69232 1.678 1.937 -0.260 1.877 -0.199 1.961 -0.28336 2.155 1.936 0.219 1.765 0.390 2.096 0.05940 1.824 2.656 -0.832 2.360 -0.536 2.371 -0.547

ConclusionFrom 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. 

References1. 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.

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)

Thank you