부산대학교 도서관

Simple Summary: Because of the limitations of current in vivo and in vitro models, this review considers a human relevant approach: modeling colorectal radiotoxicity through human-derived organoids and microfluidics. This system can offer a closer representation of the microenvironment. Co-culturing bacteria and patient derived tumor organoids under a radiotherapy setup, can enable an understanding of the interplay between radiotherapy, the gut microbiota, patient outcomes and assess radioprotective agents of interest. However, challenges in model development highlight the necessity for refinement, questioning their potential to bridge the gap between preclinical research and clinical applications in CRC treatment. Radiotherapy is a commonly employed treatment for colorectal cancer, yet its radiotoxicity-related impact on healthy tissues raises significant health concerns. This highlights the need to use radioprotective agents to mitigate these side effects. This review presents the current landscape of human translational radiobiology, outlining the limitations of existing models and proposing engineering solutions. We delve into radiotherapy principles, encompassing mechanisms of radiation-induced cell death and its influence on normal and cancerous colorectal cells. Furthermore, we explore the engineering aspects of microphysiological systems to represent radiotherapy-induced gastrointestinal toxicity and how to include the gut microbiota to study its role in treatment failure and success. This review ultimately highlights the main challenges and future pathways in translational research for pelvic radiotherapy-induced toxicity. This is achieved by developing a humanized in vitro model that mimics radiotherapy treatment conditions. An in vitro model should provide in-depth analyses of host-gut microbiota interactions and a deeper understanding of the underlying biological mechanisms of radioprotective food supplements. Additionally, it would be of great value if these models could produce high-throughput data using patient-derived samples to address the lack of human representability to complete clinical trials and improve patients' quality of life. [ABSTRACT FROM AUTHOR]