Aim 2: To identify and validate candidate drug targets for PAH.

Objective

To identify and validate candidate drug targets for PAH by combining literature review, data mining, and experimental validation to develop effective therapeutic strategies.

Sub-Aim 2.1: To obtain reversing gene expression signatures.

• Objective: Obtain gene expression signatures that indicate therapeutics capable of reversing disease-associated gene expression profiles.
• References:
  • [Reference 1] (e.g., Author, Year, Title, Journal, DOI)
  • [Reference 2]
• Methodology:
  • [Gene Expression Analysis] (e.g., Identification of reversing signatures)
  • [Drug Signature Matching] (e.g., Connectivity Map, L1000)
• Tools:
  • [Connectivity Map] (Database for drug-induced gene expression profiles)
  • [L1000] (Platform for gene expression signature analysis)
• Datasets:
  • [Disease-associated Gene List] (e.g., Compiled from literature and databases)
  • [Drug Response Data] (e.g., Connectivity Map signatures)
• Preliminary Data:
  • Reversing gene expression signatures have been identified for X number of candidate drugs.
  • [Figures, tables, or other data visuals can be linked or described here.]
• Progress:
  • What Worked: Successful identification of reversing signatures for specific drugs.
  • What Didn’t Work: Limitations in the coverage of drug response datasets.

Sub-Aim 2.2: To implement knowledge graph-based drug repositioning methods.

• Objective: Enhance understanding of therapeutic mechanisms and identify drugs for repositioning using knowledge graph-based approaches.
• References:
  • [Reference 1] (e.g., Author, Year, Title, Journal, DOI)
  • [Reference 2]
• Methodology:
  • [Knowledge Graph Construction] (e.g., Integration of gene-disease-drug data)
  • [Drug Repositioning] (e.g., Knowledge graph algorithms)
• Tools:
  • [RENET2] (Relation extraction tool for gene-disease associations)
  • [Neo4j] (Graph database for knowledge graph construction)
• Datasets:
  • [Gene-Disease-Drug Associations] (e.g., Compiled from DisGeNET, literature)
  • [Knowledge Graph Data] (e.g., Data from RENET2 and other sources)
• Preliminary Data:
  • Initial knowledge graph-based analysis has identified X potential drug candidates.
  • [Figures, tables, or other data visuals can be linked or described here.]
• Progress:
  • What Worked: Identified novel drug candidates through knowledge graph-based analysis.
  • What Didn’t Work: Complexity in integrating diverse data sources into the knowledge graph.

Sub-Aim 2.3: To validate the efficacy of identified drug candidates in PAH models.

• Objective: Collaborate with partners to validate the efficacy of identified drug candidates in vitro using PAH cellular models.
• References:
  • [Reference 1] (e.g., Author, Year, Title, Journal, DOI)
  • [Reference 2]
• Methodology:
  • [In Vitro Validation] (e.g., Experimental design, efficacy assessment metrics)
  • [Toxicity Assessment] (e.g., In vitro assays for drug toxicity)
• Tools:
  • [Cellular Models] (e.g., PAH cell lines)
  • [Drug Screening Platforms] (e.g., High-throughput screening technologies)
• Datasets:
  • [Experimental Data] (e.g., Data from in vitro validation assays)
  • [Toxicity Data] (e.g., Results from toxicity assessment)
• Preliminary Data:
  • Preliminary in vitro experiments show promising results for X drug candidates.
  • [Figures, tables, or other data visuals can be linked or described here.]
• Progress:
  • What Worked: Successfully identified drug candidates with potential efficacy in PAH models.
  • What Didn’t Work: Variability in cellular response across different PAH models.