GM1 Gangliosidosis

An 18-month double-blind, randomized, placebo-controlled, multicenter, Phase 3 study to evaluate the safety and efficacy of oral nizubaglustat (AZ-3102) in late-infantile and juvenile forms of Niemann-Pick type C disease and in late-infantile and juvenile-onset forms of GM1 gangliosidosis or GM2 gangliosidosis

Hypothesis: To characterize and describe disease progression and heterogeneity of the gangliosidosis diseases. This research study seeks to develop a quantitative method to delineate disease progression for the gangliosidosis diseases (Tay-Sachs disease, Sandhoff disease, and GM1 gangliosidosis) in order to better understand the natural history and heterogeneity of these diseases. Such a quantitative method will also be essential for evaluating any treatments that may become available in the future, such as gene therapy. The data from this study will be necessary to provide end-points for future therapies, guide medical decisions about treatment, provide objective measurement of treatment outcomes, and accurately inform parents regarding potential outcomes.

FLOWER is a completely virtual, nationwide, real-world observational study to collect, annotate, standardize, and report clinical data for rare diseases. Patients participate in the study by electronic consent (eConsent) and sign a medical records release to permit data collection. Medical records are accessed from institutions directly via eFax or paper fax, online from patient electronic medical record (EMR) portals, direct from DNA/RNA sequencing and molecular profiling vendors, and via electronic health information exchanges. Patients and their treating physicians may also optionally provide medical records. Medical records are received in or converted to electronic/digitized formats (CCDA, FHIR, PDF), sorted by medical record type (clinic visit, in-patient hospital, out-patient clinic, infusion and out-patient pharmacies, etc.) and made machine-readable to support data annotation, full text searches, and natural language processing (NLP) algorithms to further facilitate feature identification.