My cells, my model: immune-competent autologous organ-on-chip systems as a new paradigm in precision medicine

Francesco Bisconti¹Hugo Abreu²Thuy Duong Nguyen²Fabiola Stolfi²Noemi Corbezzolo¹Giuseppe Gigli¹Davide Raineri²Giuseppe Cappellano^3*Alessandro Polini¹Francesca Gervaso¹Annalisa Chiocchetti²

• ¹Istituto di Nanotecnologia Consiglio Nazionale delle Ricerche, Lecce, Italy
• ²Universita degli Studi del Piemonte Orientale Amedeo Avogadro - Sede di Novara, Novara, Italy
• ³Scuola di Medicina, Università degli Studi del Piemonte Orientale, Novara, Italy

Organ-on-chip (OoC) technology aims to replicate key physiological functions of one or more tissues within sophisticated three-dimensional microfluidic platforms. Beyond their engineering advances, OoC systems are increasingly recognized for their potential to bring preclinical research closer to clinical reality, especially when incorporating patient-derived cells. This autologous dimension represents a new frontier, as it enables the faithful modeling of individual immune processes in a physiologically relevant and truly personalized context. Importantly, if the immune system itself is to be incorporated on-chip, the requirement for autologous integration extends to all tissues involved, ensuring consistency and fidelity of patient-specific responses. Academic and industrial efforts have progressively advanced from single-tissue to multi-tissue and multi-organ OoC systems, converging toward autologous OoC (aOoC) platforms that can (i) capture patient-specific immunopathophysiology with higher fidelity, (ii) potentially complement and, in specific contexts, reduce reliance on animal models, and (iii) directly inform immunotherapy development and therapeutic decision-making within precision medicine. In this review, we first summarize the principles and fabrication strategies underlying OoC technology, then trace their evolution toward autologous systems capable of modeling autoimmune diseases and assessing drug efficacy and safety in a translationally relevant manner. Finally, we discuss the current limitations of these platforms and outline the major challenges that must be addressed to advance their translational potential.