Tumor organoids on-a-chip and the role of AI in predictive oncology and personalized cancer medicine

Abstract

The drug development process in cancer faces significant challenges due to high failure rates in translational studies despite promising in vitro results. Additionally, conventional animal models exhibit inherent limitations and ethical concerns, constraining their relevance to cancer studies. Recognizing the pivotal role of the tumor microenvironment (TME) on cancer development and treatment outcome, recent advancements in 3D microfluidic devices and tumor-on-a-chip models enabled researchers to explore the TME with enhanced accuracy and reliability, yielding novel insights. Notably, the emergence of physiological tumor models, particularly 3D models such as organoids derived from human tissues, provides a more accurate representation of in vivo tumor features. Moreover, 3D tumor models hold promise for diverse applications, including highthroughput drug testing, disease modeling, and regenerative medicine. Meanwhile, combining artificial intelligence (AI) with patient-derived tumor organoids has become a key strategy in predictive oncology and personalized cancer treatment. Furthermore, incorporating quantitative systems pharmacology (QSP) and physiologically based pharmacokinetic (PBPK) modeling, and pharmacokinetics/pharmacodynamics (PK/PD) analysis with generative artificial intelligence (Gen-AI) has revolutionized predictive oncology by enabling precise simulations of drug interactions and patient-specific responses, thereby enhancing the predictive accuracy of personalized cancer treatments. These advanced methodologies harness the power of AI algorithms to analyze intricate datasets derived from patient-specific tumor organoids. Moreover, the predictive modeling capabilities of generative AI facilitate the development of personalized treatment strategies customized for each patient, thereby revolutionizing oncology practice. This review explores the synergistic impact of tumor-on-a-chip models, organoids derived from patient tumors, and generative AI. Together, these technologies mark a significant advancement in precision medicine, offering promising chances to improve therapeutic effectiveness and treatment outcomes in cancer care.

Keywords: Artificial Intelligence; Personalized Cancer Medicine; Predictive Oncology Graphical; Tumor Organoid; Tumor Organoid Personalized Cancer Medicine Artificial Intelligence Predictive Oncology Graphical.