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Clinical study protocol for a low-interventional study in intermediate age-related macular degeneration developing novel clinical endpoints for interventional clinical trials with a regulatory and patient access intention-MACUSTAR.

Trials

Authors: Jan H Terheyden, Frank G Holz, Steffen Schmitz-Valckenberg, Anna Lüning, Matthias Schmid, Gary S Rubin, Hannah Dunbar, Adnan Tufail, David P Crabb, Alison Binns, Clara I Sánchez, Carel Hoyng, Philippe Margaron, Nadia Zakaria, Mary Durbin, Ulrich Luhmann, Parisa Zamiri, José Cunha-Vaz, Cecília Martinho, Sergio Leal, Robert P Finger

BACKGROUND: There is an unmet need for treatment options in intermediate age-related macular degeneration (iAMD). However, for any new interventions to be tested in clinical trials, novel currently unavailable clinical endpoints need to be developed. Thus, the MACUSTAR study aims to develop and evaluate functional, structural, and patient-reported candidate endpoints for use in future iAMD trials.

METHODS: The protocol describes a low-interventional clinical multicenter study employing a novel two-part design. The cross-sectional part (total duration, 1 month) and the longitudinal part (total duration, 36 months) include participants with iAMD and control groups with early/late/no AMD. The cross-sectional part's primary objective is a technical evaluation of functional, structural, and patient-reported candidate outcomes. The longitudinal part's primary objective is to assess the prognostic power of changes in functional, structural, and patient-reported outcomes for progression from iAMD to late AMD. All data will be used to support a biomarker qualification procedure by regulatory authorities.

DISCUSSION: The MACUSTAR study characterizes and evaluates much needed novel functional, structural, and patient-reported endpoints for future clinical trials in iAMD and will improve our understanding of the natural history and prognostic markers of this condition.

TRIAL REGISTRATION: ClinicalTrials.gov NCT03349801 . Registered on 22 November 2017.

PMID: 32682441

Participating cluster members