Tiboni, M. | bioRxiv Science

bioRxiv2025

Shear Stress Modulates Endothelial Ca2+ Signaling and Barrier Integrity in a Microfluidic Organ-on-a-Chip Platform

Endothelial cells (ECs) line the blood vessels and form the primary barrier between the bloodstream and the brain. Blood flow exerts a modulatory effect on the endothelial phenotype, and evidence indicates that capillary-like fluid shear stress enhances endothelial tight junctions and transporters. ECs possess mechanosensitive channels that activate endothelial responses and modulate their functions. Among the various responses to shear stress, morphological adaptations have been the most extensively studied, while functional live responses remain mostly unexplored. Calcium has been identified as key modulators that translates mechanical stimuli into biological processes, regulating endothelial activity. In this study we investigate the effect of acute and long-term shear stress on endothelial cells through live calcium imaging and immunocytochemistry, by using a modular 3D printed organ-on-a-chip, capable of simulating in-vivo capillary and enabling the possibility to study cellular crosstalk.

physiology↗

bioRxiv2024

Leveraging machine learning to streamline the development of liposomal drug delivery systems

Drug delivery systems efficiently and safely administer therapeutic agents to specific body sites. Liposomes, spherical vesicles made of phospholipid bilayers, have become a powerful tool in this field, especially with the rise of microfluidic manufacturing during the COVID-19 pandemic. Despite its efficiency, microfluidic liposomal production poses challenges, often requiring laborious, optimization on a case-by-case basis. This is due to a lack of comprehensive understanding and robust methodologies, compounded by limited data on microfluidic production with varying lipids. Artificial intelligence offers promise in predicting lipid behaviour during microfluidic production, with the still unexploited potential of streamlining development. Herein we employ machine learning to predict critical quality attributes and process parameters for microfluidic-based liposome production. Validated models predict liposome formation, size, and production parameters, significantly advancing our understanding of lipid behaviour. Extensive model analysis enhanced interpretability and investigated underlying mechanisms, supporting the transition to microfluidic production. Unlocking the potential of machine learning in drug development can accelerate pharmaceutical innovation, making drug delivery systems more adaptable and accessible.

pharmacology and toxicology↗