Background: Amyloid-β plaques (Aβ) are associated with Alzheimer's disease (AD). Pooled assessment of amyloid reduction in transgenic AD mice is critical for expediting anti-amyloid AD therapeutic research.
Objective: The mean threshold of Aβ reduction necessary to achieve cognitive improvement was measured via pooled assessment (n = 594 mice) of Morris water maze (MWM) escape latency of transgenic AD mice treated with substances intended to reduce Aβ via reduction of beta-secretase cleaving enzyme (BACE).
Methods: Machine learning and statistical methods identified necessary amyloid reduction levels using mouse data (e.g., APP/PS1, LPS, Tg2576, 3xTg-AD, control, wild type, treated, untreated) curated from 22 published studies.
Results: K-means clustering identified 4 clusters that primarily corresponded with level of Aβ: untreated transgenic AD control mice, wild type mice, and two clusters of transgenic AD mice treated with BACE inhibitors that had either an average 25% "medium reduction" of Aβ or 50% "high reduction" of Aβ compared to untreated control. A 25% Aβ reduction achieved a 28% cognitive improvement, and a 50% Aβ reduction resulted in a significant 32% improvement compared to untreated transgenic mice (p < 0.05). Comparatively, wild type mice had a mean 41% MWM latency improvement over untreated transgenic mice (p < 0.05). BACE reduction had a lesser impact on the ratio of Aβ42 to Aβ40. Supervised learning with an 80% -20% train-test split confirmed Aβ reduction was a key feature for predicting MWM escape latency (R2 = 0.8 to 0.95).
Conclusions: Results suggest a 25% reduction in Aβ as a meaningful treatment threshold for improving transgenic AD mouse cognition.
Keywords: Alzheimer’s disease; BACE inhibitor; anti-amyloid therapy; artificial intelligence; effect size; machine learning; transgenic mice.
© 2024 – The authors. Published by IOS Press.