Biomarker-driven and AI-assisted nanomedicine for breast cancer: advancing precision drug delivery from bench to bedside.

INTRODUCTION

Breast cancer remains the most prevalent malignancy among women worldwide, with its pronounced molecular heterogeneity demanding therapeutic strategies that transcend conventional systemic chemotherapy toward tumor-selective, molecularly precise interventions. The convergence of biomarker science, engineered nanomedicine, and artificial intelligence (AI) offers a transformative framework for realizing this goal.

AREAS COVERED: This review examines how molecular biomarkers (e.g. mucin glycoproteins, human epidermal growth factor receptor 2 (HER2), circulating tumor cells, and circulating tumor DNA) serve dual roles as diagnostic identifiers and active targeting ligands directing engineered nanocarriers. We critically evaluate biomarker-guided nanoplatforms (liposomal, polymeric, metallic, and protein-based architectures), stimuli-responsive drug release strategies, and AI-driven microfluidic manufacturing for scalable, good manufacturing practice-compliant nanoparticle production. Literature was retrieved from PubMed/MEDLINE, Scopus, and Web of Science (2000-2026), supplemented by ClinicalTrials.gov data.

EXPERT OPINION: Near-term clinical impact is most realistically achieved through AI-assisted biomarker interpretation and multi-analyte liquid biopsy integration rather than novel nanoformulations, whose translational attrition remains high. Bridging the bench-to-bedside gap requires prospective biomarker-stratified clinical trials, co-developed companion diagnostics, and federated AI architectures as advances that will progressively replace static histological subtype classification with a continuously updated, molecularly individualized treatment paradigm for breast cancer.