Abstract

Seasonal allergic conjunctivitis (SAC) is one of the most common ocular disorders caused by exposure to airborne allergens such as pollen. It is typically treated with eye drops, but only about 5% of the drug in an eye drop reaches the target tissue. Drug-eluting soft contact lenses have been proposed as an alternative delivery method, but one limitation of a contact lens is the burst-release. In this thesis, we develop multiple mathematical models to support the design and evaluation of drug-eluting contact-lenses for the treatment of SAC. In the first part of the thesis, we build a diffusion-based model to describe the release of therapeutics from pre-soaked contact lenses. We apply the model to experimental release data from 11 published studies covering 14 commercial and experimental contact lenses and multiple therapeutics. Using a two-step parameter estimation algorithm, we estimate the diffusion coefficient and the 50% release time or each lens-drug pair. Our statistical analysis shows that lens water content is positively correlated with the diffusion coefficient across all combinations, and that drug molecular mass, density, and molecular volume are significant predictors of the release time. We then build a multiple linear regression model that predicts the 50% release time from lens and drug properties, which can be used as an early-stage design tool for new drug-eluting contact lenses. In the second part of the thesis, we develop a mechanistic model of the ocular immune system response during SAC. To our knowledge, this is the first mechanistic model of the ocular immune response to SAC. The model couples real ragweed pollen exposure data in Milan, Italy, over a 63-day pollen season with the dynamics of mast cells, cytokines (IL-4, IL-5, IL-13, IL-33, IL-10), histamine, and immunoglobulin E (IgE) in the conjunctiva. We perform local and global sensitivity analysis, structural identifiability analysis, and calibrate the model against clinical cytokine data from the literature. The model reproduces key features of disease progression and aligns with experimental cytokine measurements. Our sensitivity analysis, identified IL-4, IL-5, and IL-8 signaling as the most influential drivers of inflammation, consistent with established clinical targets for allergic diseases. In the third part of the thesis, we couple the drug release model and the immune response model with a pharmacokinetic-pharmacodynamics model of SAC treatment. We use this framework to compare the effectiveness of ketotifen delivered via eye drop and contact lens during peak pollen season. Our results show that contact lens delivery suppresses histamine production more effectively than eye drops, mainly because of the sustained release profile of the drug over the wear period. The model also reveals that targeting histamine production alone is not enough to fully suppress the allergic response, suggesting that combination therapies targeting both mast cell activation and histamine production could be more effective. Together, these models provide a quantitative framework that can help guide the design of new drug-eluting contact lenses and the selection of treatment routes for SAC.

Publication Date

5-2026

Document Type

Dissertation

Student Type

Graduate

Degree Name

Mathematical Modeling (Ph.D)

Department, Program, or Center

Mathematics and Statistics, School of

College

College of Science

Advisor

Kara L. Maki

Advisor/Committee Member

Lucia Carichino

Campus

RIT – Main Campus

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