Multiscale mathematical modeling of aqueous humor dynamics: Applications for precision care in glaucoma

Authors

  • Matthew Lang Ophthalmology, Indiana University School of Medicine, Indianapolis, IN, United States
  • Alon Harris Ophthalmology, Indiana University School of Medicine, Indianapolis, IN, United States.
  • Alice Chandra Verticchio Vercellin Ophthalmology, University of Pavia, Italy
  • Sunu Mathew Ophthalmology, Indiana University School of Medicine, Indianapolis, IN, United States
  • Giovanna Guidoboni University of Missouri, Columbia, MO, United States

DOI:

https://doi.org/10.18060/23522

Abstract

Background and Hypothesis: Reduction of intraocular pressure (IOP) resulting from overproduction or impaired outflow of aqueous humor is, to date, the only approved medical treatment for the disease. IOP is determined by the balance between aqueous humor production at the ciliary body and drainage at the anterior chamber angle. A mathematical model describing aqueous humor (AH) flow was employed in order to study the effects of glaucoma medications and risk factors on IOP.

Experimental Design or Project Methods: IOP can be calculated as the unknown variable in the mathematical balance between AH inflow (Jin) and outflow (Jout). Model simulations using MATLAB were used to calculate IOP in conditions of varied trabecular meshwork resistance (R) over changes in episcleral venous pressure (EVP), uveosclearal outflow facility (Cuv), and ciliary capillary blood pressure (cBP) in order to simulate the effects of EVP reducing medications, prostaglandin analogs, and systemic BP, respectively.

Results: The simulated effects of EVP reducing medications and prostaglandin analogs led to IOP decrease of 13.12%, 5.51%, and 2.88% for conditions of R0, 3R0, and 6R0, respectively, where R0 is equal to the trabecular meshwork resistance when IOP = EVP. The simulated effects of prostaglandin analogs resulted an IOP decrease of 13.73%, 18.34%, and 19.83%, for conditions of R0, 3R0, and 6R0, respectively. The simulated effects of increasing systemic BP resulted in an IOP increase of 49.88%, 58.51%, and 60.71% for conditions of R0, 3R0, and 6R0, respectively.

Conclusion and Potential Impact: The model simulations predict differential efficacy in IOP reducing medications in patients of varied R as well as the differential impact of systemic BP on IOP in patients of varied R. This model has the potential to predict the IOP reducing effect of medications in an individual as well as the impact of risk factors such as systemic BP on IOP.

Downloads

Published

2019-10-08

Issue

Vol. 2 No. 1 (2019)

Section

Abstracts