TICTAC: target illumination clinical trial analytics with cheminformatics

Area of Science:

• Computational Biology
• Drug Discovery
• Bioinformatics

Background:

• Identifying disease-target associations is crucial for drug discovery and therapeutic development.
• Clinical trial data offers valuable insights but suffers from quality and interpretability issues.
• An integrated approach is needed to consolidate diverse evidence for prioritizing biological targets.

Purpose of the Study:

• To develop a data integration and visualization pipeline for inferring and evaluating disease-target associations.
• To enable exploration of diseases linked to drug targets and vice versa.
• To provide a scalable, open-source solution for hypothesis generation in drug discovery.

Main Methods:

• Integrated clinical trial data with standardized metadata.
• Employed robust aggregation techniques to consolidate multivariate evidence from multiple studies.
• Developed a scoring framework using aggregated statistical metrics (e.g., meanRank) to rank and filter associations.

Main Results:

• Successfully evaluated disease-target associations by linking protein-coding genes to diseases.
• Incorporated a confidence assessment method based on aggregated evidence.
• Systematically ranked associations to streamline the identification and prioritization of biological targets.

Conclusions:

• The developed pipeline offers a scalable solution for hypothesis generation, scoring, and ranking in drug discovery.
• As an open-source tool with publicly available datasets, it is designed for ease of use.
• Empowers scientists to make data-driven decisions for prioritizing biological targets and discovering novel therapeutics.