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

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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.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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

h i :

Calculated leverage value

h * :

Warning leverage value

S Val :

Standard deviation in the validation set

F:

Fisher parameter

o(2.5S) :

Number of outlier compounds in the training set

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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.

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  1. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Facultad de Ciencias Exactas y Naturales, Universidad de Belgrano, Villanueva 1324 CP 1426, Buenos Aires, Argentina

    Silvina E. Fioressi & Daniel E. Bacelo

  2. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP, Diag. 113y 64, Sucursal 4, C.C. 16, 1900, La Plata, Argentina

    Pablo R. Duchowicz

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  1. Silvina E. Fioressi
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Fioressi, S.E., Bacelo, D.E. & Duchowicz, P.R. QSAR study of human epidermal growth factor receptor (EGFR) inhibitors: conformation-independent models. Med Chem Res 28, 2079–2087 (2019). https://doi.org/10.1007/s00044-019-02437-y

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