Development of a novel anti‐amyloid therapeutic: an orally administrable anti‐abeta‐oligomer compound in early preventative intervention
Bibliographic record
Abstract
BACKGROUND: Over the past decade, the Multhaup lab have evaluated the safety and efficacy of a protease resistant D-amino acid Aβ-Interacting Peptide (D-AIP) as a novel anti-amyloid preventive strategy, which targets Aβ accumulation as the primary event in Alzheimer disease (AD) pathogenesis. D-AIP selectively binds to soluble oligomers of Aβ42 in vitro (Barucker et al. 2015), neutralizes Aβ42 oligomer toxicity in Drospohila models (Zhong et al. 2019), and crosses the blood-brain barrier in wild-type mice (Shobo et al. 2022). Here, we investigated effects of D-AIP on early Aβ pathogenesis in 3xTg-AD mice. METHOD: 3xTg mice were orally treated with D-AIP from 4 to 6-months-old. Liquid chromatography mass spectrometry was used to detect and quantify D-AIP in brain homogenates and plasma. D-AIP and Aβ42 oligomers were localized in 3xTg brains by matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI). Immunohistochemistry was used to track amyloid pathology, microglia and astrocyte reactivity in brain sections. An ultrasensitive Meso Scale Discovery immunoassay was used to quantify Aβ species and follow the progression of amyloid deposition. RESULT: Orally dosed D-AIP possessed favourable biostability, pharmacokinetics, and brain region distribution. Notably, (i) D-AIP forms heteromeric complexes with toxic Aβ oligomers in the brains of 3xTg-AD animals, (ii) attenuated plaque amyloid pathology and (iii) attenuated neuroinflammation at the lag-phase of amyloid aggregation in male and female 3xTg mice. Additionally, behaviour and structural analysis showed that D-AIP treatment had no adverse effects on memory and cognition. CONCLUSION: Our findings demonstrate that oral administrated D-AIP effectively targeted Aβ oligomers to prevent AD-associated deposition and neurotoxicity in an AD mouse model at very early stages of amyloid deposition. Since orally delivered D-AIP had no observable adverse effect it presents great promise as a next-generation AD therapeutic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".