Abstract
Cancer is a heterogeneous disease, and its management is increasingly shifting from generalized treatments to individualized therapeutic strategies. Personalized drug therapy involves tailoring medical treatment to the individual characteristics of each patient, including their genetic profile, tumor microenvironment, and biomarker expression. This article explores the key developments, technological advances, challenges, and clinical applications of personalized drug therapy for cancer patients. It highlights the role of genomics, proteomics, and data-driven modeling in improving therapeutic efficacy and minimizing adverse effects. Emphasis is also placed on the integration of AI, patient stratification techniques, and real-world applications in oncology.
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