All layers matter: Innovative three-dimensional epithelium-stroma-endothelium intestinal model for reliable permeability outcomes

Drug development is an ever-growing field, increasingly requesting reliable in vitro tools to speed up early screening phases, reducing the need for animal experiments. In oral delivery, understanding the absorption pattern of a new drug in the small intestine is paramount. Classical two-dimensional (2D) in vitro models are generally too simplistic and do not accurately represent native tissues. The main goal of this work was to develop an advanced three-dimensional (3D) in vitro intestinal model to test absorption in a more reliable manner, by better mimicking the native environment. The 3D model is composed of a collagen-based stromal layer with embedded fibroblasts mimicking the intestinal lamina propria and providing support for the epithelium, composed of enterocytes and mucus-secreting cells. An endothelial layer, surrogating the absorptive capillary network, is also present. The cellular crosstalk between the different cells present in the model is unveiled, disclosing key players, namely those involved in the contraction of collagen by fibroblasts. The developed 3D model presents lower levels of P-glycoprotein (P-gp) and Multidrug Resistance Protein 2 (MRP2) efflux transporters, which are normally overexpressed in traditional Caco-2 models, and are paramount in the absorption of many compounds. This, allied with transepithelial electrical resistance (TEER) values closer to physiological ranges, leads to improved and more reliable permeability outcomes, which are observed when comparing our results with in vivo data.

Automated summary

The study aimed to develop an advanced 3D intestinal model for more reliable absorption testing and better mimicking the native environment, unveiling cellular crosstalk between different cells. This model showed lower levels of efflux transporters and improved permeability outcomes compared to traditional models, leading to more accurate results in line with in vivo data.