Results from the long-term extension of PRIME: A randomized Phase 1b trial of aducanumab.

INTRODUCTION: Aducanumab selectively targets aggregated forms of amyloid beta (Aß), a neuropathological hallmark of Alzheimer's disease (AD). METHODS: PRIME was a Phase 1b, double-blind, randomized clinical trial of aducanumab. During the 12-month placebo-controlled period, participants with prodromal AD or mild AD dementia were randomized to receive aducanumab or placebo. At week 56, participants could enroll in a long-term extension (LTE), in which all participants received aducanumab. The primary endpoint was safety and tolerability. RESULTS: Amyloid-related imaging abnormalities-edema (ARIA-E) were the most common adverse event. Dose titration was associated with a decrease in the incidence of ARIA-E. Over 48 months, aducanumab decreased brain amyloid levels in a dose- and time-dependent manner. Exploratory endpoints suggested a continued benefit in the reduction of clinical decline over 48 months. DISCUSSION: The safety profile of aducanumab remained unchanged in the LTE of PRIME. Amyloid plaque levels continued to decrease in participants treated with aducanumab. HIGHLIGHTS: PRIME was a Phase 1b, double-blind, randomized clinical trial of aducanumab. We report cumulative safety and 48-month efficacy results from PRIME. Amyloid-related imaging abnormalities-edema (ARIA-E) were the most common adverse event (AE); 61% of participants with ARIA-E were asymptomatic. Dose titration was associated with a decrease in the incidence of ARIA-E. Aducanumab decreased levels of amyloid beta (Aß) in a dose- and time-dependent manner.

Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities.

Importance: Amyloid-related imaging abnormalities (ARIA) are brain magnetic resonance imaging (MRI) findings associated with the use of amyloid-ß-directed monoclonal antibody therapies in Alzheimer disease (AD). ARIA monitoring is important to inform treatment dosing decisions and might be improved through assistive software. Objective: To assess the clinical performance of an artificial intelligence (AI)-based software tool for assisting radiological interpretation of brain MRI scans in patients monitored for ARIA. Design, Setting, and Participants: This diagnostic study used a multiple-reader multiple-case design to evaluate the diagnostic performance of radiologists assisted by the software vs unassisted. The study enrolled 16 US Board of Radiology-certified radiologists to perform radiological reading with (assisted) and without the software (unassisted). The study encompassed 199 retrospective cases, where each case consisted of a predosing baseline and a postdosing follow-up MRI of patients from aducanumab clinical trials PRIME, EMERGE, and ENGAGE. Statistical analysis was performed from April to July 2023. Exposures: Use of icobrain aria, an AI-based assistive software for ARIA detection and quantification. Main Outcomes and Measures: Coprimary end points were the difference in diagnostic accuracy between assisted and unassisted detection of ARIA-E (edema and/or sulcal effusion) and ARIA-H (microhemorrhage and/or superficial siderosis) independently, assessed with the area under the receiver operating characteristic curve (AUC). Results: Among the 199 participants included in this study of radiological reading performance, mean (SD) age was 70.4 (7.2) years; 105 (52.8%) were female; 23 (11.6%) were Asian, 1 (0.5%) was Black, 157 (78.9%) were White, and 18 (9.0%) were other or unreported race and ethnicity. Among the 16 radiological readers included, 2 were specialized neuroradiologists (12.5%), 11 were male individuals (68.8%), 7 were individuals working in academic hospitals (43.8%), and they had a mean (SD) of 9.5 (5.1) years of experience. Radiologists assisted by the software were significantly superior in detecting ARIA than unassisted radiologists, with a mean assisted AUC of 0.87 (95% CI, 0.84-0.91) for ARIA-E detection (AUC improvement of 0.05 [95% CI, 0.02-0.08]; P = .001]) and 0.83 (95% CI, 0.78-0.87) for ARIA-H detection (AUC improvement of 0.04 [95% CI, 0.02-0.07]; P = .001). Sensitivity was significantly higher in assisted reading compared with unassisted reading (87% vs 71% for ARIA-E detection; 79% vs 69% for ARIA-H detection), while specificity remained above 80% for the detection of both ARIA types. Conclusions and Relevance: This diagnostic study found that radiological reading performance for ARIA detection and diagnosis was significantly better when using the AI-based assistive software. Hence, the software has the potential to be a clinically important tool to improve safety monitoring and management of patients with AD treated with amyloid-ß-directed monoclonal antibody therapies.