A Patient-Derived Organoid Xenograft (PDOX) model is an innovative cancer research tool that combines the strengths of both patient-derived organoids (PDOs) and xenograft models. In this model, 3D tumor organoids derived from a patient’s cancer are implanted into immunodeficient mice, enabling in vivo studies of tumor biology and drug response. This hybrid model allows for the replication of patient-specific tumor characteristics in a living organism, providing an advanced system for studying cancer progression, metastasis, and treatment efficacy.

Multimodal machine learning is a powerful tool for improving the precision and personalization of anticancer drug selection. By integrating multiple data modalities, it helps to provide more accurate and clinically relevant insights, potentially leading to better patient outcomes and more effective treatments. However, careful attention to data quality, interpretability, and validation in clinical settings is essential to ensure the successful application of these models.

AI systems are transforming drug discovery by speeding up the process, reducing costs, and improving success rates through advanced data analysis, pattern recognition, and predictive modeling.