Biofabrication and artificial intelligence strategies for investigating solid- and fluid-pressure mechanobiology in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is shaped by a mechanically abnormal tumor microenvironment (TME) in which dysregulated mechanotransduction promotes malignant progression and therapeutic resistance. Two coupled but distinct pressure states dominate this landscape: solid stress and interstitial fluid pressure (IFP). Solid stress arises from constrained tumor growth, stromal contractility, and extracellular matrix remodeling. By contrast, IFP reflects hydrostatic pressure within the interstitial fluid compartment and is elevated by vascular leakage, impaired drainage, and low tissue hydraulic conductivity. Together, these abnormalities compress vessels, disrupt transport, and activate mechanotransduction programs that reinforce malignant adaptation. Here we integrate solid stress and IFP within a unified pressure-state framework for PDAC. We examine how these forces shape tumor progression, drug transport, and therapeutic response. We then evaluate spheroid, organoid, hydrogel, bioprinted, and microfluidic models according to what they truly control, directly measure, or merely infer. This distinction separates pressure-relevant systems from pressure-reconstructing models. We also discuss stromal normalization and the emerging role of artificial intelligence and machine learning (AI/ML) in model engineering and patient stratification. Current computational approaches can optimize mechanically defined models and infer pressure-related tumor states from multimodal data. However, they still rely largely on surrogates rather than direct measurements of solid stress or IFP. Our framework defines the biomechanical validation required to develop clinically predictive models of PDAC mechanobiology.