Computational Theoretic Framework for Systematic Discovery of Druggable Targets in Trypanosoma cruzi

Abstract The persistent therapeutic gap in Chagas disease stems from inadequate target validation frameworks that fail to systematically integrate biological essentiality with pharmacological tractability. We developed a structured, criteria-based "onto-epistemic" methodology that separates target definition from evidence validation through a quantitative, fail-fast pipeline. This approach establishes vulnerability through four ontological criteria while validating candidates through progressive epistemological testing. Applied to the Trypanosoma cruzi proteome, the framework achieves an 1,800-fold reduction from ~22,000 proteins to ten high-confidence targets, including TcP21, TcAkt_PH, and GP63. Statistical analysis confirms significant stage-dependent expression differences (F(3,116) = 130.15, p < 0.001, η² = 0.771), while sensitivity analysis demonstrates ranking robustness across diverse weighting schemes (mean Spearman correlation = 0.983 ± 0.016). The modular architecture enables adaptation across kinetoplastid species, providing a reproducible and transparent foundation for rational therapeutic development in neglected diseases.