D-amino acid-based peptide inhibitors as early or preventative therapy in Alzheimer disease
Bibliographic record
Abstract
Beta amyloid (Aβ) accumulation is recognized as a hallmark of Alzheimer disease (AD) pathology and the aggregation of Aβ peptide is hypothesized to drive pathogenesis. As such, Aβ is a logical target for therapeutic intervention and there have been many studies looking at diverse classes of drugs that target Aβ. Of concern is the recent failure of several clinical trials, highlighting the need for earlier, possibly preventative intervention, and raising the question of what form of Aβ is the best target. The Aβ oligomers are considered to be the toxic species, but many therapies, such as antibody therapies, target monomers, removing them as substrates for aggregation. Peptide inhibitors, in contrast, are able to interfere with the aggregation process itself. Designing peptide inhibitors requires some knowledge of Aβ structure; while there is structural information about the amyloid core of Aβ fibrils, the transient nature of oligomers makes them difficult to characterize. Fortunately, some interaction sites have been identified between monomers and oligomers of Aβ and these, plus known aggregation-prone sequences in Aβ, can serve as a basis for inhibitor design. In this mini-review we focus on D-amino acid based peptide inhibitors and discuss how their non-toxic and stable nature can be beneficial, while they specifically target aggregation-prone sequences within the Aβ peptide. Many peptide inhibitors have been designed using the LVFFA domain within Aβ to disrupt the self-assembly of Aβ peptide. While this may be sufficient to stop aggregation in vitro, other aggregation sites at the C-terminus may promote aggregation independently and the flexible N terminus may be a good target to induce clearance of aggregates. Ultimately, it may be a combination of targets that provides the best therapeutic strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".