Inkjet-printed morphogenesis of tumor-stroma interface using bi-cellular bioinks of collagen-poly(N-isopropyl acrylamide-co-methyl methacrylate) mixture.

Recent advances in biomaterials and 3D printing/culture methods enable various tissue-engineered tumor models. However, it is still challenging to achieve native tumor-like characteristics due to lower cell density than native tissues and prolonged culture duration for maturation. Here, we report a new method to create tumoroids with a mechanically active tumor-stroma interface at extremely high cell density. This method, named "inkjet-printed morphogenesis" (iPM) of the tumor-stroma interface, is based on a hypothesis that cellular contractile force can significantly remodel the cell-laden polymer matrix to form densely-packed tissue-like constructs. Thus, differential cell-derived compaction of tumor cells and cancer-associated fibroblasts (CAFs) can be used to build a mechanically active tumor-stroma interface. In this methods, two kinds of bioinks are prepared, in which tumor cells and CAFs are suspended respectively in the mixture of collagen and poly (N-isopropyl acrylamide-co-methyl methacrylate) solution. These two cellular inks are inkjet-printed in multi-line or multi-layer patterns. As a result of cell-derived compaction, the resulting structure forms tumoroids with mechanically active tumor-stroma interface at extremely high cell density. We further test our working hypothesis that the morphogenesis can be controlled by manipulating the force balance between cellular contractile force and matrix stiffness. Furthermore, this new concept of "morphogenetic printing" is demonstrated to create more complex structures beyond current 3D bioprinting techniques.

Predictive Design and Analysis of Drug Transport by Multiscale Computational Models Under Uncertainty.

Computational modeling of drug delivery is becoming an indispensable tool for advancing drug development pipeline, particularly in nanomedicine where a rational design strategy is ultimately sought. While numerous in silico models have been developed that can accurately describe nanoparticle interactions with the bioenvironment within prescribed length and time scales, predictive design of these drug carriers, dosages and treatment schemes will require advanced models that can simulate transport processes across multiple length and time scales from genomic to population levels. In order to address this problem, multiscale modeling efforts that integrate existing discrete and continuum modeling strategies have recently emerged. These multiscale approaches provide a promising direction for bottom-up in silico pipelines of drug design for delivery. However, there are remaining challenges in terms of model parametrization and validation in the presence of variability, introduced by multiple levels of heterogeneities in disease state. Parametrization based on physiologically relevant in vitro data from microphysiological systems as well as widespread adoption of uncertainty quantification and sensitivity analysis will help address these challenges.

Engineering of biomaterials for tumor modeling.

Development of biomaterials mimicking tumor and its microenvironment has recently emerged for the use of drug discovery, precision medicine, and cancer biology. These biomimetic models have developed by reconstituting tumor and stroma cells within the 3D extracellular matrix. The models are recently extended to recapitulate the in vivo tumor microenvironment, including biological, chemical, and mechanical conditions tailored for specific cancer type and its microenvironment. In spite of the recent emergence of various innovative engineered tumor models, many of these models are still early stage to be adapted for cancer research. In this article, we review the current status of biomaterials engineering for tumor models considering three main aspects - cellular engineering, matrix engineering, and engineering for microenvironmental conditions. Considering cancer-specific variability in these aspects, our discussion is focused on pancreatic cancer, specifically pancreatic ductal adenocarcinoma (PDAC). In addition, we further discussed the current challenges and future opportunities to create reliable and relevant tumor models.