Optimized TPL6 Peptide Gel Exhibits Broad‐Spectrum Antimicrobial Activity and Effectively Treats Drug‐Resistant Wound Infections in Diabetic Mice
Bibliographic record
Abstract
Antibiotics are the primary treatments for diabetic foot infections (DFIs) but are often ineffective against severe, polymicrobial, or drug-resistant microbial strains. Antimicrobial peptides (AMPs) offer broad-spectrum activity, yet most fail in clinical translation due to known limitations. Here, TPL6 is developed, a TP4-derived AMP with glycine-rich segments deleted, yielding a variant with potent, robust antimicrobial activity and reduced cytotoxicity. TPL6 retains its α-helical structure and activity under high glucose and salt, and after heat stress. It also does not induce microbial resistance after prolonged exposure. Formulated with adjuvants into a hydrogel, TPL6 gel restores activity in serum-rich environments and outperforms Bacineocin ointment in simulated wound fluid. The gel further prevents formation and eradicates biofilms of multidrug-resistant (MDR) pathogens. In ex vivo porcine skin and diabetic mouse wound models infected with MDR Staphylococcus aureus or Candida albicans, TPL6 gel shows superior therapeutic efficacy versus Bacineocin ointment and Canesten 1% cream. Thus, this study shows that structural modification and adjuvant-based formulation of TPL6 can overcome key translational barriers that have hindered clinical application of AMPs, supporting its potential as an antibiotic-free treatment for drug-resistant wound infections.
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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".