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Life sciences · Journal article
Journal of Oncology Pharmacy Practice · September 22, 2026
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Objective To critically review the current pharmacologic treatment landscape for epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC), focusing on clinical pharmacology, therapeutic evolution, and emerging challenges of EGFR-targeted therapies. Data Sources Peer-reviewed literature in PubMed, Web of Science, and Embase databases (January 2000 to June 2026) was searched using keywords including “EGFR-mutant NSCLC,” “EGFR-TKIs,” “drug resistance,” “artificial intelligence,” and “precision oncology.” Reference lists and clinical trial registries were also screened. Data Summary This review systematically examines clinical misconceptions surrounding EGFR-targeted therapies, focusing on three cognitive biases that undermine precision implementation: overgeneralization of trial efficacy to unselected populations; oversimplified sequencing that ignores clonal selection trade-offs (second-generation TKIs reduce subsequent osimertinib Progression-free survival (PFS) by ∼2 months); and inflated efficacy perceptions for Ex20ins inhibitors (aggregate objective response rate (ORR) masks far-loop responses of only 22%) and MET combinations (true benefit confined to high-expression subgroup). We also critically evaluate artificial intelligence's translational potential in molecular subtyping, resistance prediction, and drug discovery, alongside the data, modeling, and validation barriers limiting its clinical deployment. Conclusions Despite the expanding therapeutic armamentarium, clinically meaningful precision remains constrained by persistent misconceptions and translational bottlenecks. Artificial intelligence (AI) offers enabling potential, but its integration requires standardized data infrastructures, dynamic modeling, and prospective validation. AI outputs should currently be interpreted as hypothesis-generating tools, not replacements for clinical judgment. Continued collaborative efforts are warranted to refine treatment sequencing and translate emerging technologies into measurable patient benefit.