PanDrugs

PanDrugs prioritizes anticancer therapies by integrating drug–target interaction datasets, genomic alteration data, and pathway context to produce evidence-based, patient-specific lists of potential treatments.

Key Features:

• Data Integration: Consolidates datasets spanning targeted therapies to preclinical drugs and provides a large database of drug–target interactions in cancer research.
• Genomic Interpretation: Interprets genomic alterations to generate a prioritized, evidence-based list of potential anticancer therapies tailored to individual patients using a novel scoring system that assesses gene relevance and drug feasibility.
• Pathway Contextualization: Expands analysis beyond single cancer driver genes to druggable pathway contexts, incorporating multi-gene markers and functional experimental data.
• Clinical Decision Support: Applies its methodology to The Cancer Genome Atlas (TCGA) patients and has been validated in a xenograft mouse model to identify clinically actionable genomic alterations for personalized treatment strategies.

Scientific Applications:

• Genomic profile interpretation: Distinguishes relevant biomarkers from extensive lists of molecular alterations to focus on alterations with therapeutic relevance.
• Therapeutic prioritization within pathways: Prioritizes potentially druggable alterations within a pathway context to broaden therapeutic options beyond known cancer genes.
• Clinical decision-making support: Facilitates evidence-based selection of candidate anticancer therapies for personalized treatment strategies.

Methodology:

Consolidates drug–target interaction datasets from targeted therapies to preclinical drugs; applies a novel scoring system that assesses gene relevance and drug feasibility to prioritize therapies; expands analysis to druggable pathway contexts and applies the method to TCGA patient datasets.