The Proteolytic Stability and Cytotoxicity Studies of <scp>l</scp>‐Aspartic Acid and <scp>l</scp>‐Diaminopropionic Acid derived β‐Peptides and a Mixed α/β‐Peptide
Bibliographic record
Abstract
The use of peptides as drugs in pharmaceutical applications is hindered by their susceptibility to proteolysis and therefore low bioavailability. beta-Peptides that contain an additional methylene group in the backbone, are gaining recognition from a pharmaceutical stand point as they are considerably more resilient to proteolysis and metabolism. Recently, we reported two new classes of beta-peptides, beta(3)- and beta(2)-peptides derived from L-aspartic acid and L-diaminopropionic acid, respectively. Here, we report the proteolytic stability of these beta-peptidic compounds and a mixed alpha /beta-peptide against three enzymes (pronase, trypsin and elastase), as well as, human serum. The stability of these peptides was compared to an alpha-peptide. Peptides containing beta-linkages were resistant to all conditions. The mixed alpha /beta-peptide, however, exhibited proteolysis in the presence of trypsin and pronase but not elastase. The rate of degradation of the mixed alpha /beta-peptide was slower than that would be expected for an alpha-peptide. In addition, these beta-peptides were not toxic to HeLa and COS-1 cell lines as observed by MTT cytotoxicity assay. These results expand the scope of mixed alpha /beta-peptides containing beta-amino acids or small beta-peptide fragments as therapeutic peptides.
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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".