(M-063) Quantitative Model of the Ocular Immune Response to Seasonal Allergic Conjunctivitis

Narshini Gunputh – School of Mathematics and Statistics – Rochester Institute of Technology; Collynn Woeller – Department of Ophthalmology (SMD) – University of Rochester; Lucia Carichino – School of Mathematics and Statistics – Rochester Institute of Technology; Kara Maki – School of Mathematics and Statistics – Rochester Institute of Technology

Objectives: Seasonal allergic conjunctivitis (SAC) is caused by airborne pollen exposure and affects 15-40% of the population. Despite its prevalence, its immunogenicity is not fully understood. The aim of this work is to mathematically model the cellular-level ocular immune response to SAC. Quantifying the cascade signaling of certain inflammatory markers can guide the development of alternative and more effective treatment routes.

Methods: The proposed model investigates the ocular immune response. A system of 23 differential equations is developed to characterize the interactions between the ocular immune cells and the inflammatory markers (e.g., cytokines, antibodies, and chemokines). The model is validated by comparing its predictions to experimental data on the average levels of inflammatory cytokines for individuals exposed to ragweed pollen for 24 hours (Leonardi et al. (2006)). Correlations are studied between predicted histamine concentrations and clinical symptom score data reported by Bonini et al. (2022). Local and global sensitivity analyses are also performed to identify key parameters for calibrating the cytokines to experimental data. A structural identifiability analysis using GENSSI 2.0 and a practical identifiability analysis using the Markov-Chain Monte-Carlo method are performed to determine the identifiability of the key parameters and support model optimization.

Results: The model accurately captured the cytokine concentrations, with predictions falling into the range of observed data. Among the cytokines investigated, IL-6 emerges as the cytokine with the highest concentration, consistent with its primary effect in the cascade signaling of the immune response. We found that the level of IL-4 directly influences the activation of the humoral response, resulting in the release of histamine. The predicted histamine levels are correlated with the clinical symptom score data (Spearman’s rank correlation coefficient, ρ = 0.76), indicating that the mechanistic model may be used to extract relevant clinical insights. The sensitivity analysis highlighted 7 key parameters as the most significant to the model output. Furthermore, the structural identifiability analysis showed that all the parameters are globally identifiable, and the practical identifiability analysis explored the fit of the model to experimental data.

Conclusions: The current model results align with experimental data and suggest that IL-6 and IL-4 are the key inflammatory biomarkers in SAC. These findings have the potential to inform future therapeutic strategies and dosing regimens for SAC. Future work includes accounting for the effects of allergy drugs on the immune response and exploring the efficacy of different ocular drug delivery.

Citations: [1] Elieh Ali Komi D, Rambasek T, Bielory L. Clinical implications of mast cell involvement in allergic conjunctivitis. Allergy. 2018; 73: 528–539. 
[2] Gadkar K, Feigelman J, Sukumaran S, Rodrigo MC, Staton T, Cai F, Bauer RN, Choy DF, Stokes CL, Scheerens H, Ramanujan S. Integrated systems modeling of severe asthma: Exploration of IL-33/ST2 antagonism. CPT Pharmacometrics Syst Pharmacol. 2022
[3] Bonini, M., Monti, G.S., Pelagatti, M.M. et al. Ragweed pollen concentration predicts seasonal rhino-conjunctivitis and asthma severity in patients allergic to ragweed. Sci Rep 12, 15921 (2022). https://doi.org/10.1038/s41598-022-20069-y
[4] Leonardi, A., Curnow, S.J., Zhan, H. and Calder, V.L. (2006), Multiple cytokines in human tear specimens in seasonal and chronic allergic eye disease and in conjunctival fibroblast cultures. Clinical & Experimental Allergy, 36: 777-784

Keywords: ocular immune response, mathematical modeling, therapeutic strategies