AI-Driven Rational Design of Nanotherapeutics for Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains a formidable malignancy characterized by late diagnosis, high recurrence rates, and pronounced chemoresistance. While nanoparticle-based drug delivery systems (NDDS) offer theoretical advantages over conventional therapies, their clinical translation in PDAC has been severely limited. The dense desmoplastic stroma, elevated interstitial fluid pressure, and hypovascularity effectively neutralize the enhanced permeability and retention (EPR) effect, restricting passive nanoparticle penetration. Consequently, the therapeutic paradigm is shifting from indiscriminate stromal depletion-which paradoxically accelerates metastasis-to stromal normalization and immune microenvironment reprogramming. This review comprehensively evaluates advanced NDDS platforms, highlighting transcytosis-triggering active targeting, stimuli-responsive carriers, and multimodal theranostic systems designed to bypass physical barriers and convert immunologically "cold" tumors to "hot." Furthermore, we analyze the expanding role of artificial intelligence (AI) and machine learning in predicting synergistic drug combinations and optimizing nanoparticle physicochemical properties. Crucially, we critique current epistemic limitations in AI-driven nanomedicine, particularly data sparsity and the reliance on 2D preclinical models, emphasizing the necessity of 3D patient-derived organoid validation. By integrating rational NDDS design with AI and biomarker-driven strategies, this review maps the essential pathways toward personalized nanotherapeutics for PDAC.