2D QSAR study of HL-60 cytotoxic Pyrazole derivative

Avinash Kumar Singh, Ethiraj K. R.

Pharmaceutical Chemistry Division, School of advanced sciences,

VIT UNIVERSITY– VELLORE

krethiraj@vit.ac.in, +91-8608444266

Abstract

A molecular library of 90 compounds having cytotoxic potential to HL-60 cell line

was created through an exhaustive search from different virtual library and journals.

The IC50 of those compounds were taken as biological end point. More than1895

molecular descriptors belonging to different class were generated using, P client, an

online server from VCC lab. . A 2D QSAR correlation between biological end point

and descriptor is calculated using Sarchitect 2.5. The QSAR study showed that suc-

cessful correlation can be achieved for cytotoxic activity of poly substituted py-

razzole using descriptors (R2= 0.773, AR2= 0.716, Q2=0.821). The 2-D QSAR equa-

tion has 18 variables.

Introduction

Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia, is a com-

plex disease. APL is challenging both clinical and a generic perspective. Induction

therapy for APL typically consists of empirically derived cytotoxic chemotherapy, com-

posed of an Anthracycline and all-tans-retinoic acid. There is clearly a compelling need

to develop more effective and less toxic therapies for this disease.

Pyrazole, as a prominent structural motif, is found in numerous pharmaceutically active

compounds. Studied have also suggested that pyrazole compounds had a potent cytotox-

icity against HL-60.

Materials and Methods

Materials

P client (online server from VCC lab )

Sarchitect 2.5

QSAR Methodology

Model Building (Multi Linear Regression, MLR)

Here

Y = Biological end point

ωi = Coefficient of variable I

Xi = Input vector

C = Constant

IC50 = 1331.988+6.902(nBNZ)-36.218(BEHV3)+107.688(BELV8)- 134.435

(BEHe4)+299.33(BELe7)-256.089(BEHp1)-328.582(BELp7)-88.052

(MATS6e)-22.467(GATS6m)-44.166(GATS3P)+801.402(X1A)+0.078

(piPC08)+5.883(RDF080u)- 20.705(Mor25u)+37.497(Mor20m)-0.230

(Vm)+0.693(Au)-62.325(H3P)

R2 = 0.77, AR2 = 0.71, Q2 = 0.821, SE = 5.208, F– statistics = 13.49

Here,

R2 = Multiple R2, AR2 = Adjusted R2, SE = Standard Error, F = Fisher’s statistic

Predicted (IC50)vs. Actual (IC50)

Model Validation

Best Model

Identifier IC50 Predicted

(IC50)

Molecule 62 33.9 33.258835

1-(5-methyl-1-(pyrimidin-2-yl)-1H-pyrazol-4-yl)-3-(4-phenylpiperazin-1-yl)propan-1-

one

nBNZ BEHv3 BELv8 BELe7 BEHe4 BEHp1 BELP7 MATS6e GATS6m GATS3p PiPe08 RDF0804 Mor25u Vm Au H3P X1A45 Mor20m

1 3.565 1.11 1.019 3.638 3.808 1.237 -0.07 2.226 1.963 917.5 3.101 -0.177 154 80.34 1.068 45 -0.156

Descriptor Parameters of best Model

References

Sharma, S.; Bagchi, B.; Mukhopadhyay, S.; Bothra, A., 2D QSAR studies of several po-

tent aminopyridine, anilinopyrimidine and pyridine carboxamide-based JNK inhibitors.

Indian Journal of Pharmaceutical Sciences 2011, 73 (2), 165

Vyas, V. K.; Ghate, M.; Katariya, H., 2D and 3D-QSAR study on 4-anilinoquinozaline

derivatives as potent apoptosis inducer and efficacious anticancer agent. Organic and

Medicinal Chemistry Letters 2011, 1 (1), 13.

P Verma, R.; Hansch, C., A QSAR Study on the Cytotoxicity of Podophyllotoxin Ana-

logues Against Various Cancer Cell Lines. Medicinal Chemistry 2010, 6 (2), 79-86.

Results and Discussions

Work Flow

Experimental section