Exploring 4th generation EGFR inhibitors: A review of clinical outcomes and structural binding insights

Abstract

Epidermal growth factor receptor (EGFR) is a potential target for anticancer therapies and plays a crucial role in cell growth, survival, and metastasis. EGFR gene mutations trigger aberrant signaling, leading to non-small cell lung cancer (NSCLC). Tyrosine kinase inhibitors (TKIs) effectively target these mutations to treat NSCLC. While the first three generations of EGFR TKIs have been proven effective, the emergence of the EGFR-C797S resistance mutation poses a new challenge. To address this, various synthetic EGFR TKIs have been developed. In this review, we have summarized the EGFR TKIs reported in the past five years, focusing on their clinical outcomes and structure-activity relationship analysis. We have also explored binding modes and interactions between the binding pocket and ligands to provide insights into the mechanisms of these inhibitors, which contribute to advancements in targeted cancer therapy. Additionally, artificial Intelligence-driven methods, including recursive neural networks and reinforcement learning, have revolutionized EGFR inhibitor design by facilitating rapid screening, predicting EGFR mutations, and novel compound generation.