Abstract
The limited translatability of animal models can be significantly amplified by integration of Artificial Intelligence (AI) and Machine Learning (ML). This Viewpoint represents a fresh paradigm in pharmacology and translational science, one that accelerates hypothesis testing, reduces resource burden, and improves clinical predictability. By aligning computational precision with experimental rigor, this integrated approach provides more ethical, scalable, and personalized cancer therapeutics.