TME-informed bioprinted 3D tissue model as used to characterize architecture-associated drug uptake and response in PDAC.

Abstract

4220 Background: Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest solid tumors despite extensive drug development efforts. Beyond tumor cell-intrinsic factors, therapeutic failure in PDAC is strongly influenced by the tumor microenvironment (TME), which is dominated by a collagen-rich desmoplastic extracellular matrix (ECM). This architecture alters tissue mechanics, restricts diffusion, and limits effective drug delivery to tumor cells. However, most preclinical platforms rely on 2D models that fail to capture human-relevant TME architecture, potentially leading to overestimation of drug uptake and potency. Amid growing regulatory and scientific momentum toward New Approach Methods (NAMs), there is an urgent need for human-relevant, architecture-aware models that improve translational predictivity in PDAC. Methods: Primary human PDAC specimens were analyzed using quantitative, spatially resolved TME mapping to characterize extracellular matrix (ECM) composition, collagen density, and intratumoral versus peripheral distribution. From our prior PDAC analysis, human tumors exhibited collagen-dominant ECM with diffuse intratumoral organization rather than peripheral confinement. These architectural features were used to configure a 3D bioprinted ex vivo slice tissue (BEST) PDAC model using PANC02 cells. Drug uptake kinetics and therapeutic response were evaluated in TME-informed 3D BEST models and directly compared with conventional 2D monolayer cultures using matched compounds and exposure paradigms. Results: Spatial TME mapping confirmed that human PDAC tumors harbor dense, heterogeneously distributed collagen spanning the tumor parenchyma, a feature not captured by simplified culture systems. Incorporation of these intratumoral collagen features into the 3D BEST model resulted in delayed drug uptake and attenuated response dynamics consistent with diffusion-limited transport. In contrast, 2D models demonstrated accelerated growth kinetics, more rapid drug uptake, and higher apparent drug potency over shorter exposure durations. These findings indicate that architecture-associated transport barriers intrinsic to PDAC substantially modulate therapeutic response and that conventional 2D systems may overestimate drug efficacy by failing to account for collagen-mediated resistance mechanisms. Conclusions: By directly linking quantitative TME mapping to 3D model configuration, this NAMs-aligned bioprinted PDAC platform captures architecture-associated constraints on drug uptake that are absent from conventional models. This approach provides a human-relevant framework for evaluating therapeutic response in desmoplastic tumors and supports improved translational fidelity and therapeutic prioritization in PDAC.