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QSAR study of human epidermal growth factor receptor (EGFR) inhibitors: conformation-independent models

  • Silvina E. FioressiEmail author
  • Daniel E. Bacelo
  • Pablo R. DuchowiczEmail author
Original Research
  • 12 Downloads

Abstract

Many compounds have been proposed and tested as human epidermal growth factor receptor (EGFR) inhibitors for cancer treatment. Recently, new survival mechanisms of cancer cells have been discovered with the consequent resistance to therapy, which makes it necessary to search for new anticancer drugs. Here we perform a quantitative structure-activity relationship (QSAR) study on 290 compounds reported in the literature as EGFR inhibitors to analyze the molecular properties that may influence their activity. A large number of nonconformational descriptors (17,974) were explored including molecular descriptors, flexible molecular descriptors, and combination of both. To avoid ambiguities derived from the existence of several conformational states, only constitutional and topological molecular descriptors have been considered. The models were validated through Y-randomization, cross-validation, and mean absolute error criteria. A simple model involving flexible descriptors shows the best predictive performance and suggests that the presence of multiple aromatic rings and amino groups in a compound structure may increase its EGFR inhibitory activity.

Keywords

Cancer EGFR QSAR Tyrosine kinase protein HER1 Drug design 

Abbreviations

EGFR

Epidermal growth factor receptor

QSAR

Quantitative structure-activity relationship

U.S. FDA

United State Food and Drug Administration

IC50

The inhibitory activity was expressed as the concentration of the test compound that inhibited the activity of EGFR by 50%

pIC50

The logarithmic molar IC50 values

SMILES

Molecular-input line-entry system

SR

Structural representation

HSG

Hydrogen-suppressed graph

HFG

Hydrogen-filled graph

GAO

Graph of atomic orbitals

SA

Structural attributes

DCW

Defined flexible descriptor

CW

Correlation weights

MC

Monte Carlo simulation

T

Threshold value

BSM

Balanced subsets method (BSM)

k-MCA

k-means cluster analysis

RM

Replacement method

MLR

Multivariable linear regression

Loo

Leave-one-out cross-validation

\(R_{{\rm{Loo}}}^2\)

Loo variance

MAE

Mean absolute error

AD

Applicability domain

hi

Calculated leverage value

h*

Warning leverage value

SVal

Standard deviation in the validation set

F

Fisher parameter

o(2.5S)

Number of outlier compounds in the training set

Notes

Acknowledgements

We are grateful for financial support provided by the National Research Council of Argentina (CONICET) project PIP11220130100311 and to the Ministerio de Ciencia, Tecnología e Innovación Productiva for access to electronic library facilities. SEF, DEB, and PRD are members of the scientific researcher career of CONICET.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

44_2019_2437_MOESM1_ESM.pdf (838 kb)
Supplementary Information

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Facultad de Ciencias Exactas y NaturalesUniversidad de BelgranoBuenos AiresArgentina
  2. 2.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP, Diag. 113y 64La PlataArgentina

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