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

Area of Science:

• Oncology
• Translational Medicine
• Drug Discovery

Background:

• Traditional cancer drug screening assays (2D monolayers, animal models) have limited translational fidelity.
• There is a growing need for human-relevant, data-integrated preclinical platforms in oncology.
• Advancements are shifting towards more predictive models for cancer therapeutics.

Purpose of the Study:

• To synthesize and compare preclinical cancer drug screening strategies.
• To evaluate traditional and advanced models using a unified framework (Principle, Advantages, Limitations, Clinical Application).
• To provide guidance for selecting appropriate screening modalities in precision oncology.

Main Methods:

• Review and appraisal of traditional assays (2D monolayers, animal models).
• Evaluation of advanced preclinical systems: patient-derived organoids (PDOs), patient-derived xenografts (PDXs), organ-on-a-chip.
• Summary of functional genomic screens (CRISPR/RNAi), pharmacogenomics, and AI-enabled modeling.

Main Results:

• Traditional models offer scalability but lack translational fidelity.
• Advanced models (PDOs, PDXs, organ-on-a-chip) better recapitulate human physiology but face challenges in throughput, cost, and standardization.
• Genomic screens and AI modeling are crucial for target discovery, resistance mapping, and response prediction.

Conclusions:

• Cross-model validation, data harmonization, and clinically anchored endpoints are essential for real-world impact.
• Selecting the right screening modality depends on study goals, sample availability, and timelines.
• Pragmatic guidance is provided to accelerate precision oncology through optimized preclinical screening.