Post Hoc Analysis of FILLY Trial Using AI

Background

Geographic atrophy (GA) is a devastating form of age-related macular degeneration (AMD) that can slowly lead to irreversible vision loss. There is currently one approved treatment for GA, pegcetacoplan, with several others in the pipeline and many trials ongoing. Pegcetacoplan targets a component of the complement system (C3), which is thought to cause inflammation and tissue damage downstream. Fu et al conducted a post hoc analysis of the FILLY trial to assess the efficacy of pegcetacoplan for GA using a fully automated deep-learning approach for analyzing optical coherence tomography (OCT) scans. The researchers used this technique on OCT images of patients with GA who received pegcetacoplan or a placebo. They extracted structural data and used the AI to segment images according to the constituent features of retinal pigment epithelium (RPE) and outer retinal atrophy (RORA), including hypertransmission, photoreceptor degeneration (PRD), and RPE loss (which were all secondary endpoints). The primary endpoint was the square root change in GA area defined as complete RORA (cRORA).  

Results

In total, 197 eyes were included in the analysis, with 71 in the pegcetacoplan monthly group, 61 in the every other month (EOM) group, and 65 in the sham group. Monthly treated eyes demonstrated significantly slower progression of cRORA and RPE loss. EOM treated eyes showed only significantly slower RPE loss, while not demonstrating significant differences in other endpoints. As compared to sham, the monthly group had a 45% reduction in cRORA growth rate in contrast with 27% reduction for the EOM group. Intact macular area was preserved in monthly treatment compared with sham; however, not in EOM treatment. 

Using a regression model, the researchers identified two predictive markers of reduced GA growth at 12 months, PRD in isolation (without the other two constituents of RORA) (coefficient 0.0195, P=.01), and intact macular area (coefficient 0.00752, P=.02). These may be predictors of GA progression, and may serve as clinical endpoints in future studies. 

Overall, the results of this study suggest that pegcetacoplan, especially monthly dosing is effective in reducing GA lesion size and preserving macular integrity in patients with GA. The deep learning algorithm provided accurate and efficient analysis of GA lesion subtypes, which could be useful in assessing the efficacy of other emerging therapeutics for GA.

Strengths

One of the strengths of this study is its use of a fully automated deep-learning approach for analyzing spectral domain-OCT (SD-OCT) images. This approach allows for efficient and accurate extraction of structural data from retinal imaging, which is essential for assessing the efficacy of emerging therapeutics for GA. Given that OCT-based analysis captures the morphology of various retinal layers, this modality is preferred to fundus autofluorescence-based quantification of GA. Without deep learning algorithms like these, manual segmentation would be slow, and suffer from inter-grader variability.  

The researchers also incorporated topographical and volumetric analysis, which provided greater insight into the effect of treatment beyond binary presence or absence of features.

Limitations

One limitation of this study is its small sample size (197 eyes). This post hoc analysis included only a subset of patients (those with imaging on Heidelberg OCT) from the original clinical trial, which may limit the generalizability of the findings. Additionally, the study did not assess the long-term safety and efficacy of pegcetacoplan, which will be important to evaluate in future studies.

Conclusion and Takeaway Points

Overall, this study provides further evidence of the efficacy of pegcetacoplan for GA and demonstrates the value of a fully automated deep-learning approach for rapid and repeatable analysis of SD-OCT images. The identification of two novel clinical endpoints—PRD in isolation and intact macula—may facilitate more efficient assessment of emerging therapeutics for GA. If used in clinical trials, AI algorithms such as these may provide improved quantification as compared to human graders. If used in clinic, algorithms like these may actually provide quantitative tracking of treatment results, which is currently lacking.

This study brings up several areas of concern for routine clinical use of pegcetacoplan. No statistically significant differences were found for EOM treatment groups with regards to intact macula, photoreceptor degeneration, or GA growth rate. This is worrisome, as most clinicians plan to use the treatment EOM in order to limit treatment burden, while balancing similar efficacy to monthly treatment as was observed in the pivotal trials. Furthermore, the novel analysis by ETDRS region demonstrated that the effect of growth rate reduction was greatest further from the fovea, although this analysis may have been affected by a baseline foveal involvement rate of 59.7%. This is concerning for real-world clinical practice treatment patterns, as clinicians are more likely to treat patients with fovea-threatening GA, where it appears this medication has least effect. The researchers did not find a protective effect of the drug on the foveal region.

Further research is needed to confirm these findings and to evaluate the long-term safety and efficacy of pegcetacoplan, especially in routine clinical use, where it will undoubtedly be injected less than monthly, and may be used primarily for fovea-threatening disease.