Date Published: March 29, 2017
Publisher: Public Library of Science
Author(s): Stanley Lambertus, Nathalie M. Bax, Ana Fakin, Joannes M. M. Groenewoud, B. Jeroen Klevering, Anthony T. Moore, Michel Michaelides, Andrew R. Webster, Gert Jan van der Wilt, Carel B. Hoyng, Andreas Wedrich.
Each inherited retinal disorder is rare, but together, they affect millions of people worldwide. No treatment is currently available for these blinding diseases, but promising new options—including gene therapy—are emerging. Arguably, the most prevalent retinal dystrophy is Stargardt disease. In each case, the specific combination of ABCA4 variants (> 900 identified to date) and modifying factors is virtually unique. It accounts for the vast phenotypic heterogeneity including variable rates of functional and structural progression, thereby potentially limiting the ability of phase I/II clinical trials to assess efficacy of novel therapies with few patients. To accommodate this problem, we developed and validated a sensitive and reliable composite clinical trial endpoint for disease progression based on structural measurements of retinal degeneration.
We used longitudinal data from early-onset Stargardt patients from the Netherlands (development cohort, n = 14) and the United Kingdom (external validation cohort, n = 18). The composite endpoint was derived from best-corrected visual acuity, fundus autofluorescence, and spectral-domain optical coherence tomography. Weighting optimization techniques excluded visual acuity from the composite endpoint. After optimization, the endpoint outperformed each univariable outcome, and showed an average progression of 0.41° retinal eccentricity per year (95% confidence interval, 0.30–0.52). Comparing with actual longitudinal values, the model accurately predicted progression (R2, 0.904). These properties were largely preserved in the validation cohort (0.43°/year [0.33–0.53]; prediction: R2, 0.872). We subsequently ran a two-year trial simulation with the composite endpoint, which detected a 25% decrease in disease progression with 80% statistical power using only 14 patients.
These results suggest that a multimodal endpoint, reflecting structural macular changes, provides a sensitive measurement of disease progression in Stargardt disease. It can be very useful in the evaluation of novel therapeutic modalities in rare disorders.
Inherited blindness affects millions of people worldwide—the majority suffering from retinal disease . Inherited retinal disorders now represent the primary cause of blindness in the working age population in the UK, and secondary in childhood . They are clinically and genetically heterogeneous, caused by sequence variants in more than 300 distinct genes (RetNet) http://www.sph.uth.tmc.edu)/. Mutations in the ATP-binding cassette, subfamily A, member 4 (ABCA4) gene are linked to arguably the most common retinal dystrophy: autosomal recessive Stargardt disease (STGD1) . Each case of STGD1 is, in a sense, unique by specific combinations of pathogenic ABCA4 variants (> 900 variants identified to date) and modifying factors. Consequently, the natural course is highly variable, ranging from severe early-onset rapid degeneration [4, 5] to relatively mild late-onset disease [6, 7]. The eventual vision loss results from progressive impairment and degeneration of photoreceptors and their supporting retinal pigment epithelium (RPE) .
The genotypic and phenotypic heterogeneity of rare diseases are a challenge for designing therapeutic clinical trials using conventional parameters. This affects many patients; current estimates are between 6.5 and 9.9 million inhabitants of the EU28 countries (1.3–2.0%). Jointly, these diseases represent a relevant public health issue , and to evaluate novel treatments, better strategies are needed. Current strategies use biomarkers, which are often insufficient to provide appropriate sample size calculations, or require long-term follow-up (S1 Table). However, an integrated approach of these individual biomarkers can result in a reliable and sensitive marker for disease progression. In this paper, we showed that such markers can be developed using composite endpoints and weighting optimization techniques.