Three month FVC change: a potential trial endpoint for IPF based on individual participant data meta-analysis

Rationale Novel therapies for idiopathic pulmonary fibrosis (IPF) are in development, but there remains uncertainty about the optimal trial endpoint. An earlier endpoint would enable assessment of a greater number of therapies in adaptive trial designs. Objectives Individual participant data (IPD) from placebo arms of interventional trials were sought to determine whether short-term changes in forced vital capacity (FVC), gas transfer for carbon monoxide (DLCO) and six-minute walk distance (6MWD) could act as surrogate endpoints to accelerate early-phase trials in IPF. Methods Electronic databases were searched on 1st December 2020, and IPD were sought and meta-analysed. The primary outcome was overall mortality according to baseline and/or three-month change in either FVC, DLCO or 6MWD, with a secondary outcome of disease progression at 12 months, adjusted for age, sex, smoking status and baseline physiology. Measurements and main results IPD was available for 10/23 eligible studies totalling 1819 participants. Baseline and three-month change in all physiological variables were independently associated with disease outcomes. A 2.5% relative decline in FVC over three months was associated with mortality (adjusted hazard ratio 1.14, 95%CI 1.06;1.24, I2 = 59.9%) and disease progression (adjusted odds ratio 1.29; 95%CI 1.18;1.40, I2=67%). Optimal thresholds for three-month change in FVC for distinguishing disease outcomes were identified. Conclusions IPD meta-analysis of trial placebo arms demonstrated three-month change in physiological variables, particularly FVC, were associated with mortality and disease progression among individuals with untreated IPF. FVC change over three months may hold potential as a surrogate endpoint in IPF interventional adaptive trials.

Plasma biomarkers of Alzheimer’s disease improve prediction of cognitive decline in cognitively unimpaired elderly populations

Plasma biomarkers of amyloid, tau, and neurodegeneration (ATN) need to be characterized in cognitively unimpaired (CU) elderly individuals. We therefore tested if plasma measurements of amyloid-β (Aβ)42/40, phospho-tau217 (P-tau217), and neurofilament light (NfL) together predict clinical deterioration in 435 CU individuals followed for an average of 4.8 ± 1.7 years in the BioFINDER study. A combination of all three plasma biomarkers and basic demographics best predicted change in cognition (Pre-Alzheimer’s Clinical Composite; R2 = 0.14, 95% CI [0.12–0.17]; P < 0.0001) and subsequent AD dementia (AUC = 0.82, 95% CI [0.77–0.91], P < 0.0001). In a simulated clinical trial, a screening algorithm combining all three plasma biomarkers would reduce the required sample size by 70% (95% CI [54–81]; P < 0.001) with cognition as trial endpoint, and by 63% (95% CI [53–70], P < 0.001) with subsequent AD dementia as trial endpoint. Plasma ATN biomarkers show usefulness in cognitively unimpaired populations and could make large clinical trials more feasible and cost-effective.

Clinical Perspectives and Trends: Microperimetry as a trial endpoint in retinal disease

Endpoint development trials are underway across the spectrum of retinal disease. New validated endpoints are urgently required for the assessment of emerging gene therapies and in preparation for the arrival of novel therapeutics targeting early stages of common sight-threatening conditions such as age-related macular degeneration. Visual function measures are likely to be key candidates in this search. Over the last two decades, microperimetry has been used extensively to characterize functional vision in a wide range of retinal conditions, detecting subtle defects in retinal sensitivity that precede visual acuity loss and tracking disease progression over relatively short periods. Given these appealing features, microperimetry has already been adopted as an endpoint in interventional studies, including multicenter trials, on a modest scale. A review of its use to date shows a concurrent lack of consensus in test strategy and a wealth of innovative disease and treatment-specific metrics which may show promise as clinical trial endpoints. There are practical issues to consider, but these have not held back its popularity and it remains a widely used psychophysical test in research. Endpoint development trials will undoubtedly be key in understanding the validity of microperimetry as a clinical trial endpoint, but existing signs are promising.

Plasma biomarkers of Alzheimer’s disease predict cognitive decline and could improve clinical trials in the cognitively unimpaired elderly

Plasma biomarkers of amyloid, tau, and neurodegeneration (ATN) need to be characterized in cognitively unimpaired (CU) elderly indviduals. We therefore tested if plasma measurements of amyloid-β (Aβ)42/40, phospho-tau217 (P-tau217), and neurofilament light (NfL) together predict clinical deterioration in 435 CU individuals followed for an average of 4.8 ±1.7 years in the BioFINDER study. A combination of all three plasma biomarkers and basic demographics best predicted change in the cognition (Pre-Alzheimer’s Clinical Composite; R2=0.14, 95% CI [0.12-0.17]; P<0.0001) and subsequent AD dementia (AUC=0.82, 95% CI [0.77-0.91], P<0.0001). In a simulated clinical trial, a screening algorithm combining all three plasma biomarkers would reduce the required sample size by 70% (95% CI [54-81]; P<0.001) with cognition as trial endpoint, and by 63% (95% CI [53-70], P<0.001) with subsequent AD dementia as trial endpoint. Plasma ATN biomarkers show usefulness in cognitively unimpaired populations and could make large clinical trials more feasible and cost-effective.

Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease

Stargardt disease is one of the most common forms of inherited retinal disease and leads to permanent vision loss. A diagnostic feature of the disease is retinal flecks, which appear hyperautofluorescent in fundus autofluorescence (FAF) imaging. The size and number of these flecks increase with disease progression. Manual segmentation of flecks allows monitoring of disease, but is time-consuming. Herein, we have developed and validated a deep learning approach for segmenting these Stargardt flecks (1750 training and 100 validation FAF patches from 37 eyes with Stargardt disease). Testing was done in 10 separate Stargardt FAF images and we observed a good overall agreement between manual and deep learning in both fleck count and fleck area. Longitudinal data were available in both eyes from 6 patients (average total follow-up time 4.2 years), with both manual and deep learning segmentation performed on all (n = 82) images. Both methods detected a similar upward trend in fleck number and area over time. In conclusion, we demonstrated the feasibility of utilizing deep learning to segment and quantify FAF lesions, laying the foundation for future studies using fleck parameters as a trial endpoint.