Engineering strategies to address immune and delivery barriers in pancreatic ductal adenocarcinoma: a barrier-matched translational framework

Abstract

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, largely owing to profound therapeutic resistance driven by tumor heterogeneity, a highly immunosuppressive tumor microenvironment (TME), dense desmoplastic stroma, immune exclusion, and impaired antitumor immune surveillance. Although conventional chemotherapy provides limited clinical benefit, immune-based therapies have shown modest efficacy in unselected PDAC, highlighting the need for strategies that overcome the biological barriers underlying immune resistance. Recent advances in precision medicine and bioengineering have generated a diverse range of therapeutic platforms aimed at remodeling the PDAC ecosystem. This review evaluates oncolytic virotherapy, gene-editing technologies, engineered immune-cell therapies, nanotechnology-enabled delivery systems, and artificial intelligence (AI)-assisted precision oncology according to the PDAC barriers they are intended to address and the maturity of the supporting evidence. Oncolytic viruses may enhance tumor immunogenicity and reshape suppressive immune niches, whereas gene editing and engineered cellular therapies provide opportunities to target oncogenic vulnerabilities, improve immune-cell function, and overcome antigenic and stromal constraints. Nanotechnology-based platforms can modify tissue access, payload exposure, and local immune modulation in selected models, whereas AI approaches support molecular and spatial stratification and generate treatment-prioritization hypotheses. Most supporting evidence remains preclinical or early phase, and no platform class has established broad comparative clinical benefit in unselected PDAC. Translation remains constrained by intratumoral heterogeneity, delivery limitations, safety, manufacturing complexity, and insufficient predictive biomarkers. Translation will depend on biomarker-defined enrollment and on linking administered dose to tumor exposure, target engagement, biological activity, safety, and the added benefit of the investigational component. We therefore present a barrier-matched development framework rather than a validated treatment-assignment algorithm.