Highly sensitive measurements of disease progression in rare disorders: Developing and validating a multimodal model of retinal degeneration in Stargardt disease

Background
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.
Methods and findings
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.
Conclusions
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.

背景
每一种遗传性视网膜疾病均属罕见病种，但整体而言，全球受其影响的人群数以百万计。目前尚无针对这类致盲性疾病的有效治疗方案，但包括基因治疗在内的多款极具潜力的新型疗法正逐步涌现。公认的最常见视网膜营养不良类型为斯塔加特病（Stargardt disease）。每一例患者的ABCA4变体（ABCA4 variants，目前已鉴定出超过900种）与修饰因子的特定组合几乎独一无二，这造就了广泛的表型异质性——包括功能与结构进展速率的差异，进而可能限制了I/II期临床试验利用少量受试者评估新型疗法疗效的能力。为解决这一难题，我们基于视网膜变性的结构测量结果，开发并验证了一种灵敏可靠的疾病进展复合临床试验终点。

方法与结果
我们纳入了来自荷兰（开发队列，n=14）与英国（外部验证队列，n=18）的早发型斯塔加特病受试者的纵向临床数据。该复合终点由最佳矫正视力、眼底自发荧光及光谱域光学相干断层扫描指标整合构建。通过加权优化技术，我们将最佳矫正视力从复合终点中剔除。优化完成后，该复合终点的表现优于所有单变量结局指标，其平均年进展速率为0.41°视网膜偏心度（95%置信区间：0.30~0.52）。与实际纵向观测值对比，该模型可精准预测疾病进展（决定系数R²=0.904）。上述表现在验证队列中基本得以保留（年进展速率0.43°/年，95%置信区间0.33~0.53；预测R²=0.872）。随后我们利用该复合终点开展了为期两年的试验模拟，仅需14名受试者即可检测出25%的疾病进展降幅，统计效力达80%。

结论
上述结果表明，反映黄斑结构改变的多模态终点可灵敏衡量斯塔加特病的疾病进展情况，在罕见疾病新型治疗手段的评估中具备极高应用价值。