3D (Bio)printed In Vitro Infection Models: Tools to Investigate Host–Pathogen Interactions and Accelerate the Discovery of Therapies

High Resolution Image Download MS PowerPoint Slide Humanity has always been challenged by infectious diseases, which remain a major burden on global health. The discovery of antibiotics and the rise of modern medicine in the last century represented a major breakthrough in the fight against infections. However, the adaptive nature of pathogens, together with the widespread misuse of antibiotics, has led to the emergence of strains resistant to previously successful therapies. The increasing prevalence of antimicrobial resistance is now undermining our ability to treat infectious diseases, creating an urgent need for therapeutic approaches. The discovery of new treatments begins in the laboratory, where reliable infection models are essential. Conventional two-dimensional (2D) in vitro models often fail to reproduce the structural and functional complexity of human tissues, thereby limiting the translational relevance of their findings. In this context, three-dimensional (3D) in vitro infection models have emerged as powerful platforms to bridge this gap, offering more physiologically relevant systems that better recapitulate in vivo conditions. Among these, 3D (bio)printing technologies provide a reproducible and versatile approach for fabricating cellular models with controlled spatial architecture, enabling the design of complex tissue-like structures. These advances facilitate the creation of biomimetic in vitro environments that support more realistic host–pathogen interactions, offering valuable opportunities to accelerate the development of new therapeutics and to deepen our understanding of infectious mechanisms. This review summarizes recent progress in 3D (bio)printed in vitro infection models, with a particular focus on bacterial and viral infections, which remain the most extensively studied. Finally, we discuss current limitations, future perspectives, and regulatory considerations needed to translate these models into clinically relevant tools.