Glaucoma is a major cause of blindness. The inability to predict a patient's IOP response to medications is a critical barrier for the clinician to consistently provide highly effective IOP-based treatments. Current trial-and- error approaches to glaucoma management are inefficient and have not addressed this barrier as there are no predictive factors for drug response. Our long-term goal is to improve outcomes by identifying biomarkers and environmental factors that profile a patient at risk for glaucoma by age-of-onset, rate of disease progression, poor response to treatment, and large IOP fluctuation. Our objective is to address this critical barrier by focusing on physiological factors that predict IOP response to drugs. Our central hypothesis is that individual aqueous humor dynamic components predict IOP response to medications. We will achieve our objective and work toward our goal by testing the central hypothesis through two aims.
Aim 1 : Test the hypothesis that aqueous humor inflow is a physiological marker of variation in timolol-mediated IOP response between individuals.
Aim 2 : Test the hypothesis that aqueous humor outflow is a physiological marker of variation in latanoprost-mediated IOP response between individuals. The overall experimental design will be a comprehensive comparison of physiological components of IOP in the same individual under control conditions without treatment and under experimental conditions with treatment using the two most commonly used drug classes, beta-blockers (Aim 1) and prostaglandins (Aim 2). The main outcome measures are the physiological components of IOP, namely, aqueous humor inflow, outflow facility, episcleral venous pressure, and uveoscleral outflow. The relationships among these four physiological components to IOP will be analyzed by generalized linear models. These results will provide a critically needed database on physiological components of IOP under baseline and treated conditions in the same cohort. Our team is committed to build upon these extensive physiology data from controls to the next phase by studying patients with ocular hypertension and early stages of open-angle glaucoma. Such a model would form the basis for future studies to investigate molecular and environmental interactions on IOP-based treatment outcomes and new therapeutic targets. Our results will advance understanding of IOP response variance to medications by dissecting the physiological components of drug response variations between individuals. This knowledge will bring us closer to predicting therapeutic efficacy, and decreasing treatment failures by identifying patients who are poor responders a priori. Prescribing medications based on a patient's profile of drug response will eliminate time wasted on ineffective drug prescriptions and result in more efficient medical management with fewer follow-up office visits to assess poor efficacy.
Our current trial-and-error approach to glaucoma medical treatment is inefficient and ineffective in impacting glaucoma-related blindness. Our studies will bring us closer to predicting glaucoma drug response, and decreasing treatment failures by recognizing in advance patients who are poor responders to treatment. Personalized medicine based on a patient's profile of drug responsiveness will eliminate time wasted on ineffective drugs and result in more efficient medical management.
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