Natural history of infantile-onset spinal muscular atrophy.

TLDR: Clinical trials in infantile‐onset spinal muscular atrophy require an understanding of disease progression and identification of meaningful biomarkers to hasten therapeutic development and predict outcomes.

Abstract: OBJECTIVE Infantile-onset spinal muscular atrophy (SMA) is the most common genetic cause of infant mortality, typically resulting in death preceding age 2. Clinical trials in this population require an understanding of disease progression and identification of meaningful biomarkers to hasten therapeutic development and predict outcomes. METHODS A longitudinal, multicenter, prospective natural history study enrolled 26 SMA infants and 27 control infants aged <6 months. Recruitment occurred at 14 centers over 21 months within the NINDS-sponsored NeuroNEXT (National Network for Excellence in Neuroscience Clinical Trials) Network. Infant motor function scales (Test of Infant Motor Performance Screening Items [TIMPSI], The Children's Hospital of Philadelphia Infant Test for Neuromuscular Disorders, and Alberta Infant Motor Score) and putative physiological and molecular biomarkers were assessed preceding age 6 months and at 6, 9, 12, 18, and 24 months with progression, correlations between motor function and biomarkers, and hazard ratios analyzed. RESULTS Motor function scores (MFS) and compound muscle action potential (CMAP) decreased rapidly in SMA infants, whereas MFS in all healthy infants rapidly increased. Correlations were identified between TIMPSI and CMAP in SMA infants. TIMPSI at first study visit was associated with risk of combined endpoint of death or permanent invasive ventilation in SMA infants. Post-hoc analysis of survival to combined endpoint in SMA infants with 2 copies of SMN2 indicated a median age of 8 months at death (95% confidence interval, 6, 17). INTERPRETATION These data of SMA and control outcome measures delineates meaningful change in clinical trials in infantile-onset SMA. The power and utility of NeuroNEXT to provide "real-world," prospective natural history data sets to accelerate public and private drug development programs for rare disease is demonstrated. Ann Neurol 2017;82:883-891.