PharmOmics

PharmOmics integrates species- and tissue-specific transcriptome signatures to characterize drug functions, predict tissue-specific toxicities (e.g., hepatotoxicity and nephrotoxicity), and support drug repurposing across human, mouse, and rat datasets.

Key Features:

• Species- and Tissue-Specific Drug Signatures: Integrates transcriptome data from human, mouse, and rat tissues sourced from GEO, ArrayExpress, TG-GATEs, and DrugMatrix to derive drug signatures.
• Multi-Dimensional Data Integration: Constructs multi-species and multi-tissue gene expression signatures as molecular indicators of drug function and interspecies response differences.
• Gene Network-Based Approaches: Applies gene network-based methodologies to analyze interactions and prioritize drug repositioning candidates.
• Toxicity Prediction: Identifies tissue-specific in vivo drug signatures associated with toxicities such as hepatotoxicity and nephrotoxicity.
• Drug Repurposing Validation: Predicts and validates drug repurposing candidates, including identification of two candidate drugs for non-alcoholic fatty liver disease in mice.

Scientific Applications:

• Translational Medicine: Characterizes drug actions across species and tissues to inform interspecies extrapolation and translational decisions.
• Drug Discovery and Repurposing: Prioritizes existing drugs for new indications and supports validation of candidate therapeutics such as those for non-alcoholic fatty liver disease.
• Preclinical Safety Assessment: Guides prediction of tissue-specific adverse effects to inform preclinical toxicity evaluation and reduce clinical attrition.

Methodology:

Integrates transcriptome datasets from GEO, ArrayExpress, TG-GATEs, and DrugMatrix for human, mouse, and rat tissues; generates multi-species and multi-tissue gene expression signatures; applies gene network-based analyses for repositioning and toxicity signal identification; implemented using Rshiny and JavaScript.