Drug Targets Illumination: An Evidence-Based Data Analytics & Informatics Approach to Hypothesis Generation Through Clinical Trials and Genomic Variants

DRUG TARGETS ILLUMINATION: AN EVIDENCE-BASED DATA ANALYTICS & INFORMATICS APPROACH TO HYPOTHESIS GENERATION THROUGH CLINICAL TRIALS AND GENOMIC VARIANTS Target Illumination Clinical Trial Analytics with Cheminformatics (TICTAC) introduces a scalable data integration pipeline that aggregates multivariate clinical trial evidence to systematically rank disease–target associations. By harmonizing metadata from AACT and employing confidence-scoring techniques, TICTAC facilitates hypothesis-driven target selection for drug discovery, enabling a data-driven approach to therapeutic development. In parallel, a bioinformatics-driven analysis of gnomAD exome mutations in the ZPR1 gene highlights the broader role of genetic variants in disease association. By employing variant effect predictors, protein stability modeling, and ACMG/AMP guidelines, this study identifies high-impact ZPR1 mutations and reveals significant ethnic disparities in their prevalence. These findings emphasize that genetic variants contribute to disease mechanisms beyond traditional biological targets and merit further exploration in drug discovery workflows. While these two approaches operate independently, they collectively reinforce the need for a multifaceted strategy in drug discovery and development. Researchers can enhance disease modeling, refine biomarker discovery, and optimize therapeutic target selection by integrating insights from clinical trial analytics and genomic variant interpretation. Future efforts should explore deeper incorporation of genetic insights into cheminformatics-driven analytics to advance targeted therapeutic interventions and improve precision medicine strategies.