A Rigorously Formalized Conceptual Framework for Neural-Targeted Therapeutics to Mitigate Hypersensitivity Reactions: Advancing Neuroimmunology through Extended Pharmacokinetic-Pharmacodynamic Modeling and Stochastic Extensions

Hypersensitivity reactions, emblematic of allergic pathologies, impose a significant global health burden, with CDC data indicating 31.8% prevalence among U.S. adults (cdc2023). While standard pharmacotherapies target immune effectors, the neuro-immune axis—where neuropeptides amplify mast cell degranulation—represents a key therapeutic target (konstantinou2022, talbot2017). This paper presents a pharmacokinetically informed conceptual model: neural-selective inhibitors that suppress neurogenic inflammation without immunosuppression. We develop an extended ODE system modeling allergen-mast-neuron-drug-histamine-inhibitor interactions, including equilibria derivations, stability analysis, and Hill-type pharmacodynamics. Simulations using Python, calibrated to WHO/CDC data, confirm model accuracy. Rigorous validation employs local and global sensitivity analysis (PRCC and Sobol indices for all parameters), MCMC-based Bayesian inference, Monte Carlo uncertainty propagation, and falsifiability criteria. A stochastic SDE extension, solved via Euler-Maruyama, enhances translational relevance. This framework provides verifiable, reproducible tools for neuro-targeted hypersensitivity prevention.