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. 2025 Jun 9:5:1579865.
doi: 10.3389/fbinf.2025.1579865. eCollection 2025.

TICTAC: target illumination clinical trial analytics with cheminformatics

Jeremiah I Abok  1 , Jeremy S Edwards  1 , Jeremy J Yang  2
Affiliations

TICTAC: target illumination clinical trial analytics with cheminformatics

Jeremiah I Abok et al. Front Bioinform. .

Abstract

Introduction: Identifying disease-target associations is a pivotal step in drug discovery, offering insights that guide the development and optimization of therapeutic interventions. Clinical trial data serves as a valuable source for inferring these associations. However, issues such as inconsistent data quality and limited interpretability pose significant challenges. To overcome these limitations, an integrated approach is required that consolidates evidence from diverse data sources to support the effective prioritization of biological targets for further research.

Methods: We developed a comprehensive data integration and visualization pipeline to infer and evaluate associations between diseases and known and potential drug targets. This pipeline integrates clinical trial data with standardized metadata, providing an analytical workflow that enables the exploration of diseases linked to specific drug targets as well as facilitating the discovery of drug targets associated with specific diseases. The pipeline employs robust aggregation techniques to consolidate multivariate evidence from multiple studies, leveraging harmonized datasets to ensure consistency and reliability. Disease-target associations are systematically ranked and filtered using a rational scoring framework that assigns confidence scores derived from aggregated statistical metrics.

Results: Our pipeline evaluates disease-target associations by linking protein-coding genes to diseases and incorporates a confidence assessment method based on aggregated evidence. Metrics such as meanRank scores are employed to prioritize associations, enabling researchers to focus on the most promising hypotheses. This systematic approach streamlines the identification and prioritization of biological targets, enhancing hypothesis generation and evidence-based decision-making.

Discussion: This innovative pipeline provides a scalable solution for hypothesis generation, scoring, and ranking in drug discovery. As an open-source tool, it is equipped with publicly available datasets and designed for ease of use by researchers. The platform empowers scientists to make data-driven decisions in the prioritization of biological targets, facilitating the discovery of novel therapeutic opportunities.

Keywords: clinical trial data; disease-target; drug discovery; hypothesis generation; inference.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

FIGURE 1
FIGURE 1
Clinical Trial study counts by year indicating growth and trends.
FIGURE 2
FIGURE 2
TICTAC dashboard. The image displays genes currently associated with the disease “type 2 diabetes/insulin resistance” (DOID:9352).
FIGURE 3
FIGURE 3
Examples of understudied (Tbio) genes for disease “type 2 diabetes/insulin resistance” in TICTAC.
FIGURE 4
FIGURE 4
Provenance for association between gene MCR4 (Melanocortin receptor 4) and disease “type 2 diabetes/insulin resistance”.
FIGURE 5
FIGURE 5
TICTAC dashboard, displaying a plot of genes associated with disease “lung cancer” (DOID:1324).
FIGURE 6
FIGURE 6
Provenance for association between PGR gene (Uniprot: P06401) “Progesterone receptor” and disease “lung cancer”.
FIGURE 7
FIGURE 7
TICTAC data sources and interfaces. TICTAC integrates clinical trial data from the AACT db and cross referenced other sources to rank disease–target associations. These associations can be accessed through the TICTAC github repository.
See this image and copyright information in PMC

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