The amyloid-β1–42-oligomer interacting peptide D-AIP possesses favorable biostability, pharmacokinetics, and brain region distribution
Bibliographic record
Abstract
We have previously developed a unique 8-amino acid Aβ42 oligomer-Interacting Peptide (AIP) as a novel anti-amyloid strategy for the treatment of Alzheimer's disease. Our lead candidate has successfully progressed from test tubes (i.e., in vitro characterization of protease-resistant D-AIP) to transgenic flies (i.e., in vivo rescue of human Aβ42-mediated toxicity via D-AIP-supplemented food). In the present study, we examined D-AIP in terms of its stability in multiple biological matrices (i.e., ex-vivo mouse plasma, whole blood, and liver S9 fractions) using MALDI mass spectrometry, pharmacokinetics using a rapid and sensitive LC-MS method, and blood brain barrier (BBB) penetrance in WT C57LB/6 mice. D-AIP was found to be relatively stable over 3 h at 37 °C in all matrices tested. Finally, label-free MALDI imaging showed that orally administered D-AIP can readily penetrate the intact BBB in both male and female WT mice. Based upon the favorable stability, pharmacokinetics, and BBB penetration outcomes for orally administered D-AIP in WT mice, we then examined the effect of D-AIP on amyloid "seeding" in vitro (i.e., freshly monomerized versus preaggregated Aβ42). Complementary biophysical assays (ThT, TEM, and MALDI-TOF MS) showed that D-AIP can directly interact with synthetic Aβ42 aggregates to disrupt primary and/or secondary seeding events. Taken together, the unique mechanistic and desired therapeutic potential of our lead D-AIP candidate warrants further investigation, that is, testing of D-AIP efficacy on the altered amyloid/tau pathology in transgenic mouse models of Alzheimer's disease.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".