Identification of an IDR peptide formulation candidate that prevents peptide aggregation and retains immunomodulatory activity
Bibliographic record
Abstract
Abstract Synthetic peptides derived from naturally occurring host defence peptides (HDPs) have garnered significant attention as novel pharmaceuticals, particularly as alternatives to antibiotics and for immunomodulatory applications. One of the barriers to advancing synthetic peptides for therapeutic applications is their tendency to aggregate under specific ionic conditions similar to those they would encounter in vivo. Formulating peptides with biocompatible excipients that prevent solvent‐induced peptide aggregation represents a possible solution to this aggregation issue; however, this strategy has not been systematically explored. In the present work, we describe the screening of various polymeric substances (including hyaluronic acid, carboxymethyl cellulose and hydroxypropyl methyl cellulose) as formulation candidates for HDPs and identified derivatized hyperbranched polyglycerols (dHPGs) as a biocompatible excipient that limits peptide aggregation. Notably, the immunomodulatory activity of a synthetic innate defence regulator peptide, IDR‐1018, formulated with dHPG was retained when evaluated against both peripheral blood mononuclear cells and human bronchiolar epithelial cells in vitro. Further characterization of dHPG polymers revealed that decreasing the negative charge density on the polymer surface potentiated the cytotoxic effects of IDR‐1018, emphasizing the need to optimize this parameter for future peptide delivery formulations. Importantly, the dHPG formulated IDR‐1018 inhibited peptide aggregation in vitro in the presence of mucin as well as in vivo when injected subcutaneously into CD1 mice. This study highlights the potential use of dHPGs as formulation candidates for synthetic HDPs and identifies important considerations regarding their physico‐chemical properties and relevance to immunomodulatory applications.
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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) | 0.001 | 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".