Alterity Therapeutics reports new data from ATH434 Phase 2 trial at AAN meeting

Alterity Therapeutics (ATHE) announced that new presentations related to its Multiple System Atrophy program were delivered at the American Academy of Neurology 2025 Annual Meeting, one of the premier global neurology meetings. Notably, new data from the ATH434-201 trial was prominently featured via an oral presentation during a Scientific Platform Session on Movement Disorders. Title: Topline Data from a Randomized, Double Blind, Placebo Controlled Phase 2 Study of ATH434 in Multiple System Atrophy. Summary: The oral presentation produced additional data on Alterity’s ATH434-201 Phase 2 clinical trial. Overall, the study results support continued advancement of ATH434 for the treatment of MSA. The imaging outcomes in n=61 participants indicate a heterogeneous localization of pathology and evidence that ATH434 reduces the iron signal in MSA affected brain regions. The clinical analysis included 71 patients who had at least one-post baseline UMSARS I assessment. The data showed that on the modified UMSARS I rating scale, ATH434 demonstrated a clinically significant treatment effect versus placebo with a 48% decrease in clinical progression at the 50 mg dose and a 30% decrease in clinical progression at the 75 mg dose at 52 weeks. Additional assessments showed improvement: the Clinical Global Impression of Severity Scale, including a nominally significant difference at the 50 mg dose at 52 weeks. The Orthostatic Hypotension Symptom Assessment showed trends favoring benefit in both groups. Increased activity in the outpatient setting was seen on wearable sensors at both dose levels as compared to placebo, with observed improvements in step count, bouts of walking, total walking time, and total standing time. The benefit on step count at 50 mg was nominally significant. Title: Association Between Wearable Sensor Data and Clinical Scores in Individuals with Early-stage Multiple System Atrophy. Summary: The poster described the n=18 patients with clinically probable MSA who wore sensors for continuous monitoring of physical activity during our bioMUSE natural history study. The study determined the association between gold-standard clinical measures and sensor-derived parameters of locomotion, posture, and postural transitions at baseline. From this, machine learning models were developed to investigate whether sensor-derived measures could predict scores and performance on clinical evaluations. The study found that sensor-derived metrics, specifically those measuring walking and postural transitions, may increase the understanding of impairments associated with MSA. Across several tests, the participants who had increased walking time, walking episodes, and higher step counts had better scores on the tandem walk and Timed Up and Go. Additionally, positive relationships were identified between the average sit-to-stand and stand-to-sit durations with motor rating scales such as the MSA Rating Scale, the motor section of the Parkinson’s Plus Rating Scale, and the TUG. Importantly, regression models established successful prediction of clinical scores, with TUG demonstrating the highest explained variance.