Short peptide analogs as alternatives to collagen in pro-regenerative corneal implants
Bibliographic record
Abstract
Short collagen-like peptides (CLPs) are being proposed as alternatives to full-length collagen for use in tissue engineering, on their own as soft hydrogels, or conjugated to synthetic polymer for mechanical strength. However, despite intended clinical use, little is known about their safety and efficacy, mechanism of action or degree of similarity to the full-length counterparts they mimic. Here, we show the functional equivalence of a CLP conjugated to polyethylene glycol (CLP-PEG) to full-length recombinant human collagen in vitro and in promoting stable regeneration of corneal tissue and nerves in a pre-clinical mini-pig model. We also show that these peptide analogs exerted their pro-regeneration effects through stimulating extracellular vesicle production by host cells. Our results support future use of CLP-PEG implants for corneal regeneration, suggesting the feasibility of these or similar peptide analogs in clinical application in the eye and other tissues. STATEMENT OF SIGNIFICANCE: Although biomaterials comprising full-length recombinant human collagen and extracted animal collagen have been evaluated and used clinically, these macromolecules provide only a limited number of functional groups amenable to chemical modification or crosslinking and are demanding to process. Synthetic, customizable analogs that are functionally equivalent, and can be readily scaled-up are therefore very desirable for pre-clinical to clinical translation. Here, we demonstrate, using cornea regeneration as our test bed, that collagen-like-peptides conjugated to multifunctional polyethylene glycol (CLP-PEG) when grafted into mini-pigs as corneal implants were functionally equivalent to recombinant human collagen-based implants that were successfully tested in patients. We also show for the first time that these materials affected regeneration through stimulation of extracellular vesicle production by endogenous host cells that have migrated into the CLP-PEG scaffolds.
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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".