Machine Learning-Based Approach to Developing Potent EGFR Inhibitors for Breast Cancer-Design, Synthesis, and In Vitro Evaluation.

Abstract

The epidermal growth factor receptor (EGFR) is vital for regulating cellular functions, including cell division, migration, survival, apoptosis, angiogenesis, and cancer. EGFR overexpression is an ideal target for anticancer drug development as it is absent from normal tissues, marking it as tumor-specific. Unfortunately, the development of medication resistance limits the therapeutic efficacy of the currently approved EGFR inhibitors, indicating the need for further development. Herein, a machine learning-based application that predicts the bioactivity of novel EGFR inhibitors is presented. Clustering of the EGFR small-molecule inhibitor (∼9000 compounds) library showed that N-substituted quinazolin-4-amine-based compounds made up the largest cluster of EGFR inhibitors (∼2500 compounds). Taking advantage of this finding, rational drug design was used to design a novel series of 4-anilinoquinazoline-based EGFR inhibitors, which were first tested by the developed artificial intelligence application, and only the compounds which were predicted to be active were then chosen to be synthesized. This led to the synthesis of 18 novel compounds, which were subsequently evaluated for cytotoxicity and EGFR inhibitory activity. Among the tested compounds, compound 9 demonstrated the most potent antiproliferative activity, with 2.50 and 1.96 μM activity over MCF-7 and MDA-MB-231 cancer cell lines, respectively. Moreover, compound 9 displayed an EGFR inhibitory activity of 2.53 nM and promising apoptotic results, marking it a potential candidate for breast cancer therapy.