We’ve spent years evaluating each intricate detail of dry eye disease in order to find treatments for those plagued by the gritty, burning, stinging symptoms. As novel treatments advance at a rapid pace through the pipeline, it’s no doubt a tribute to the development of both our enhanced understanding of the disease and the improved technologies that, used in conjunction, will help to complete the dry eye therapeutic story.
While each dry eye story is different, we’ve learned for most dry eye sufferers, their symptoms tend to fluctuate as the seasons change, which makes conducting environmental studies especially challenging. One way to minimize the environmental factors is to conduct a clinical trial during a single season. Even doing so, however, does not account for additional situational factors that may influence an individual patient’s dry eye symptoms, such as extended visual tasking or certain medications that cause ocular drying.
The Controlled Adverse Environment (CAE) model was designed to reduce both the environmental and situational factors by controlling humidity, temperature, air flow, lighting conditions, and visual tasking.1 Using a clinical model that reproduces a standard ocular challenge equally for all patients is a valuable tool for investigating treatments for dry eye. One key aspect of the CAE is its utility in distinguishing subpopulations of patients with dry eye, making it especially useful for screening and enriching patient populations.
Our understanding of dry eye and the way it is assessed in the clinic has become more precise, yielding information essential to advancing potential treatments. What was once thought to be a condition due solely to insufficient tear production is now recognized as a multifactorial disease with a variety of therapeutic approaches.
This is a result of significant strides in ocular surface research and improvements in clinical assessment techniques. The concept of tear-film breakup time (TFBUT) in particular has come a long way since the pioneering work of Lemp in the 1970s. No longer are large or varying amounts of sodium fluorescein used, artificially lengthening breakup time, but rather very precise microquantities in order to obtain more accurate values.2
While TFBUT remains a common measure used in clinical research for measuring the properties of the tear film, we’ve also learned that it does not say enough about the overall ocular tear film dynamics. Recognizing that blink also plays a role in the health and stability of the ocular surface, the Ocular Protection Index (OPI) was developed. As a ratio of the interblink interval (IBI) and tear-film break-up time (TFBUT/IBI), lower OPI values, particularly those less than one, are associated with increased risk of signs and symptoms of dry eye, since it is likely that corneal exposure occurs prior to the next blink.
While the OPI took the concept of TFBUT one step further in assessing ocular surface health, the tool did not account for a more dynamic ocular surface assessment, that of tear-film breakup area. The development of the OPI 2.0 System provides clinicians with a fully automated, real-time measurement of corneal surface exposure, as the system is designed to evaluate ocular surface protection under a normal blink pattern and normal visual conditions, two key concepts necessary for accurate, realistic measurements.
In an initial study, the OPI 2.0 system distinguished between dry eye and normal by way of the added area metrics (corneal surface exposure). 3 This method was validated in a second study that demonstrated that the fully automated system was able to provide accurate, reliable measures of corneal surface exposure while distinguishing between normal subjects and subjects with dry eye.4 In a study published late last year utilizing the CAE, the corneal surface exposure metric of mean breakup area (MBA) was able to detect changes in the ocular surface induced by the CAE.5 Following CAE exposure, subjects experienced a decrease in MBA, demonstrating a possible compensatory mechanism.
Do you know how many times you’ve blinked since you started reading this article? Wouldn’t it be interesting if you did?
Despite appearing to be an incredibly simple action, blinking is actually quite complicated, as are the implications of blink physiology for patients with dry eye. Continuing research on blink patterns is essential in order to explore fully the impact blinking has on dry eye, and moving forward, what benefits therapies may have by considering the effect blink has on the tear film of patients with dry eye.
In a recent study, for example, IBI was significantly shorter for patients with dry eye performing a visual task compared with normal subjects.6
Utilizing digital imaging to track natural blink patterns offers an advantage over the more invasive, traditionally used measures. In a study examining lid contact time, or lid closures, of up to multiple seconds, termed “extended blinks,” a concept heavily studied in fatigue research, subjects with dry eye had longer lid contact times than normal subjects.7
In addition to having longer durations of extended blinks, IBIs were significantly longer after an extended blink for dry eye subjects (potentially indicating a compensatory response), and blinks of longer than 1 second occurred almost exclusively in subjects with dry eye. Other research considering extended blinks has found that subjects with dry eye are more than 10 times more likely than normal subjects to exhibit blinks of 1 second duration or longer (Lafond A, et al. IOVS 2013;54:ARVO E-Abstract 962).
Blink, while perhaps appearing inconsequential, should be carefully considered when studying dry eye. By gaining a more comprehensive understanding of patterns, we may be better able to diagnose subjects with dry eye, and determine duration of disease, underlying ocular discomfort, and tear film stability, as well as assess other therapeutic effects. Further, examining variations of blink patterns may help to categorize subgroups of patients who may have a different response to treatment.
The integration of automated analyses has been quite useful in enhancing our understanding of dry eye. Numerical scales are the mainstay used by most practitioners and clinical researchers to quantify and classify the extent and quality of ocular redness in patients with dry eye. In order to supplement the subjective evaluation of a clinical grader, however, automated detection of hyperemia has been utilized to provide increased repeatability and sensitivity. Dry eye is particularly well suited for automated detection of redness because hyperemia occurs as horizontal banding over the conjunctiva.8 Computerized technologies are also showing their utility in assessing lissamine green staining severity (Lane K, et al. IOVS 2013;54: ARVO E-Abstract 6045).
As of late, there has also been buzz within the industry regarding mucins and their impact on the tear film of the ocular surface. Being able to measure levels of mucin in the tear film may give way to additional therapies targeted at stimulating mucin secretion. Any defect in the aqueous, lipid, and/or mucin layers of the tear film can cause and/or exacerbate dry eye symptoms, so targeting these layers for potential treatments is of utmost importance.
Advancements in technology and our evergrowing understanding of dry eye will surely help generate future therapies for those plagued by the symptoms of the disease. What we don’t know, we will seek to learn, and what we do know, we will capitalize on in the hopes of coming full circle with our attempts to combat dry eye.
