Bridging the translational gap: an operational classification and quantitative framework for accelerating enteric vaccine research using animal models.

Enteric bacterial infections, notably shigellosis and salmonellosis, represent a staggering global health burden. Vaccine development is severely hampered by host-restriction, creating a persistent "translational gap" where preclinical data fails to correlate with human outcomes. Traditionally, model selection has been largely qualitative. This review provides a comprehensive analysis of the research landscape, evaluating the physiological fidelity of non-human primates, the surgical precision of rabbit and guinea pig models, and specialized induced rodent systems, including the streptomycin‑iron-overload mouse model. We further discuss emerging alternatives, including translucent zebrafish for real-time imaging and Human Intestinal Enteroids (HIE) for human-specific mechanistic insights. Crucially, we move beyond qualitative descriptions by introducing the Model Utility Index (MUI), a novel quantitative framework designed to objectively balance Normalized Clinical Fidelity (F) against the Geometric Efficiency Quotient (E). By applying constrained optimization logic, we propose the Context-Specific Model Optimization (CSMO) selection matrix to simplify the logistical burden of model choice, focusing on the unique translational hurdles of host-restricted invasive pathogens such as Shigella and typhoidal Salmonella. This strategy aligns specific research bottlenecks - from high-throughput screening to final regulatory validation, with mathematically optimized research systems. In light of the FDA Modernization Act 2.0, this integrated approach provides a robust roadmap for utilizing New Approach Methodologies (NAMs) to de-risk clinical transitions and accelerate the licensure of next-generation enteric vaccines.