At this year's ARVO conference in Seattle, WA, Eye Care Network spoke with Noel Brennan, MS Optom, Ph.D. Johnson & Johnson clinical researchers shared highlights from their presentations on myopia control and predictive modeling.
Video transcript:
Note: The transcript below has been lightly edited for clarity.
Noel Brennan, MSc Optom, PhD:
Hello, my name is Noel Brennan. I am a clinical researcher at Johnson & Johnson. I'm coming to his ARVO 2024 in Seattle. Here I am giving a presentation on how we can predict his three-year myopia control effect from one year's data. The reason for this is that his three-year study is the gold standard for myopia control research. However, this has some problems. Because keeping children in a control group for three years is probably no longer ethically acceptable. Additionally, some long-term studies have noticed that the control group appears to be possibly becoming contaminated. Parents may find that their children are progressing too quickly and may withdraw them from, or perhaps even seek research. Treatment outside the study protocol. So we wanted to see if we could predict these long-term efficacy numbers from short-term data.
And our first attempt at doing this kind of thing failed. We couldn't find enough studies that included a three-year control and treatment group. But first all we could do was watch the progress. So we looked at his one-year axial growth. Axial elongation is actually the benchmark you use to check your progress. And a lot of the studies on that have three years of data on him, and they found that the correlation was very good. From this we can infer that you will get twice as much in 3 years as you would in 1 year. So what the presentation here has done is built on that.
I found some more studies published and went back and looked at some of the earlier studies. In the end, we found 15 studies that were able to correlate 1-year effectiveness with 3-year effectiveness. And, quite unbelievably, even though the quality of the studies wasn't necessarily that high, the correlation had an R-squared value of 0.97, which is quite remarkable. So this further supports the concept that his 3-year effectiveness is double his 1-year effectiveness. Some may ask, why not triple? This is because the initial treatment effect is very large and strong in the first year, and the effect subsides in subsequent years.
And how do we make sense of this? With all this information, what kind of news can we report? First of all, you know from a regulatory perspective, he probably doesn't need to do a three-year study and probably needs to reevaluate what he should do. However, his second point for clinicians is that once you have his robust one-year clinical data from a trial, you can use it to determine how well a particular intervention works over time. It's predictable. And certainly now, from our perspective, this is the basis of our understanding of effectiveness, the one-year axial reduction that we're seeing. With these lists, you can see where you rank. And we think that's really the path we should take for the future. So the next step in this research is really to build a complete model of effectiveness, a comprehensive model, so that we can predict what's going to happen beyond three years, six years, 12 years. It is to do. We are well on our way there. What matters to ophthalmologists is what the data looks like over the course of a year. Therefore, a robust one-year exam is necessary. When you look at the data and rank it, the rankings are maintained over time so you can determine which treatments are most effective. However, in reality, the 1-year data should be a multicenter randomized controlled trial with a large sample size. Because we're seeing some data where the trial design isn't that strong, and we're getting some weird numbers. that. Therefore, a high-quality 1-year trial is required.