Advances and Challenges in Drug Screening for Cancer Therapy: A Comprehensive Review

Cancer drug screening is shifting from low-predictive, reductionist assays to human-relevant, data-integrated platforms. This review synthesizes preclinical strategies using a unified lens-Principle, Advantages, Limitations, and Clinical Application-to enable like-for-like comparison. We first appraise traditional two-dimensional (2D) monolayers and animal models, noting scalability and historical utility alongside constrained translational fidelity. We then evaluate advanced systems-patient-derived organoids (PDOs), patient-derived xenografts (PDXs), and organ-on-a-chip-that better recapitulate architecture, microenvironmental cues, and pharmacodynamics (PD), yet face trade-offs in throughput, timelines, costs, and standardization. Functional genomic screens (CRISPR/RNAi) and large-scale pharmacogenomics are summarized as engines for mechanism-based target discovery and resistance mapping, while AI-enabled modeling supports response prediction, biomarker development, and rational combinations. Finally, we discuss trial designs (basket/umbrella), drug repurposing lessons, and regulatory momentum for new approach methodologies. Across platforms, we emphasize cross-model validation, dataset harmonization, and clinically anchored endpoints as prerequisites for real-world impact. We conclude with pragmatic guidance for matching screening modality to study goals, sample constraints, and decision timelines to accelerate precision oncology.