Strengthening peptide-based drug activity with novel glyconanoparticle
Bibliographic record
Abstract
The therapeutic application of peptide-based drugs is significantly limited by the rapid proteolytic degradation that occurs when in blood. Encapsulation of these peptide structures within a delivery system, such as liposomes, can greatly improve both stability and target delivery. As part of our work focused on novel ambiphilic mannosylated neoglycolipids as targeted drug delivery systems, we have developed a C14-alkyl-mannopyranoside that forms self-assembled monodisperse liposomes. Herein, these glycoliposomes are investigated as a potential method to improve the plasma stability of peptide-based drugs. Reversed phase high-performance liquid chromatography (RP-HPLC) and mass spectrometry (MS) methods were developed to assess the in vitro plasma stability of two structurally diverse peptides, including the kappa opioid receptor selective antagonist dynantin, and the NOD2 innate immune receptor ligand muramyl dipeptide (MDP). The RP-HPLC methods developed were able to resolve the peptides from background plasma contaminants and provided suitable response levels and linearity over an appropriate concentration range. Both compounds were found to be significantly degraded in rat plasma. Increasing degrees of both entrapment and stabilization were noted when dynantin was combined with the C14-alkyl-mannopyranoside in increasing peptide:glycoside ratios. The combination of MDP with the glycolipid also led to peptide entrapment, which greatly improved the plasma stability of the peptide. Overall, the results clearly indicate that the stability of peptide-based structures, which are subject to degradation in plasma, can be greatly improved via entrapment within C14-alkyl-mannopyranoside-bearing glycoliposomes.
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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".