Design and development of organ-on-a-chip devices as in vitro models to reproduce the organ pathophysiology and validate new anticancer therapies

In recent years, traditional 2D biological models have proven to be not sufficiently suitable to mimic the complexity of the human cellular environment. Research is increasingly transitioning toward 3D culture models, which enhance the relevance and predictive value of in vitro data. In this context, microphysiological systems, i.e., organs- on-a-chip (OoC), have emerged as powerful platforms for studying cell-cell and cell- extracellular matrix (ECM) interactions in a controlled, scalable, and dynamic environment. The aim of this work was to examine the potential of organ-on-a-chip technology as a tool for identifying biological targets that could provide more information on the diagnosis and treatment of diseases such as muscular dystrophy, osteoporosis and cancer. The present work is divided into three different fields: • Tumor-on-a-chip: In collaboration with Takis S.r.l. and CNR-IFN, a platform simulating cancer-immune system interactions in the tumor microenvironment (TME) was developed to evaluate innovative therapies. This includes testing anti-ErbB3 antibodies and bispecific T-cell engagers (BiTEs) alone or combined with anti-PD-L1 and anti-CTLA-4 immune checkpoint inhibitors. Imaging assays were automated for high-throughput analysis using Nikon JOBS software. • Muscle-on-a-chip: In collaboration with CNR Nanotec (Lecce), two devices enabling non-invasive contractile force measurements were studied, focusing on the development and maturation of muscle fibers from myoblasts at micro- and mesoscale. Fabrication processes were optimized for each device, and a force assessment model was created to correlate pillar deflection with contractile forces. • Bone-on-a-chip: In collaboration with the Unit of Food Science and Nutrition of Università Campus Bio-Medico di Roma (UCBM), a high-content screening platform was developed to create 3D bone micro-tissues from bone marrow mesenchymal stromal cells and evaluate bioactive molecules, including spirulina, resveratrol, and quercetin, for their osteogenic potential. Artificial Intelligence (AI) models using the Arivis AI toolkit were implemented for automated image segmentation and analysis. The present Ph.D. work highlights the potentiality of on-chip methodologies combined with high-content microscopy to measure relevant biological processes in a dynamic manner, with the possibility to exploit Artificial Intelligence-enabled methods for processing high-content data.