Volume 10 Number 2 2026
AI-Guided Nanoparticle Design for Pancreatic Cancer Drug Delivery in Precision Oncology
Most Farhana Akter1*, Md. Robiul Islam1, Md Abu Bakar Siddique2, Muhammad Asif 3, Hafiza Sidra Yaseen 4
Integrative Biomedical Research 10 (2) 1-8 https://doi.org/10.25163/biomedical.10210951
Submitted: 15 August 2026 Revised: 04 October 2026 Accepted: 11 October 2026 Published: 13 October 2026
Pancreatic ductal adenocarcinoma remains, even now, one of oncology's most stubborn problems: five-year survival still hovers near 10-13%, a figure that has barely moved despite genuinely impressive gains elsewhere in cancer care. Much of that stubbornness traces back to biology rather than to any single missing drug - a dense, fibrotic stroma, collapsed microvasculature, and an immunologically "cold" microenvironment conspire to keep even well-designed therapeutics from ever reaching their target. This review reveals, how far artificial intelligence has actually moved the needle on nanoparticle-based delivery for this disease - not just what is theoretically possible, but what has been demonstrated. We trace the field across four interlocking layers: the physiological and genomic barriers that necessitate smart delivery in the first place; the stimuli-responsive and cell-based platforms engineered to overcome them; the machine learning and deep learning architectures now used to predict nanoparticle behavior before a single batch is synthesized; and the translational bottlenecks - fragmented datasets, black-box interpretability, immunogenicity, manufacturing variability - that still stand between a promising algorithm and a patient. Drawing on the peer-reviewed and preprint literature synthesized here, we find that AI-guided design has matured well beyond proof-of-concept for narrow prediction tasks (particle size, encapsulation efficiency, biodistribution) but remains far short of an integrated, clinically validated pipeline. We close with a pragmatic roadmap - FAIR data infrastructures, explainable AI, organoid-based validation, and quality-by-design manufacturing - that we believe represents the most realistic path toward translating computationally designed nanotherapeutics into precision pancreatic oncology.
Keywords: pancreatic ductal adenocarcinoma; nanomedicine; artificial intelligence; machine learning; stimuli-responsive nanoparticles; precision oncology
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