Title:Looking for New Inhibitors for the Epidermal Growth Factor Receptor
Volume: 18
Issue: 3
Author(s): Riccardo Concu*M. Natalia D.S. Cordeiro
Affiliation:
- REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 687, 4169-007 Porto,Portugal
Keywords:
Head and Neck Squamous Cell Carcinoma, Epidermal Growth Factor Receptor, Tyrosine Kinase Inhibitors, Drug
Design, Quantitative Structure Activity Relationships (QSAR), Classification and Regression Techniques, Machine Learning.
Abstract: Epidermal Growth Factor Receptor (EGFR) is still the main target of the Head and Neck
Squamous Cell Cancer (HNSCC) because its overexpression has been detected in more than 90% of this
type of cancer. This overexpression is usually linked with more aggressive disease, increased resistance
to chemotherapy and radiotherapy, increased metastasis, inhibition of apoptosis, promotion of neoplastic
angiogenesis, and, finally, poor prognosis and decreased survival. Due to this reason, the main target in
the search of new drugs and inhibitors candidates is to downturn this overexpression. Quantitative
Structure-Activity Relationship (QSAR) is one of the most widely used approaches while looking for
new and more active inhibitors drugs. In this contest, a lot of authors used this technique, combined with
others, to find new drugs or enhance the activity of well-known inhibitors. In this paper, on one hand,
we will review the most important QSAR approaches developed in the last fifteen years, spacing from
classical 1D approaches until more sophisticated 3D; the first paper is dated 2003 while the last one is
from 2017. On the other hand, we will present a completely new QSAR approach aimed at the
prediction of new EGFR inhibitors drugs. The model presented here has been developed over a dataset
consisting of more than 1000 compounds using various molecular descriptors calculated with the
DRAGON 7.0© software.
