Replacing antibiotics in poultry : the potential of antimicrobial peptides
Bibliographic record
Abstract
The use of antibiotic growth promoters (AGPs) in poultry began in the mid-20th century and became widespread. However, concerns over the emergence of antibiotic resistance have led to increasingly strict regulations in the EU, US, and Canada, and have driven a search for alternative solutions. Antimicrobial peptides (AMPs) are short and mostly cationic biomolecules. Some AMPs show antimicrobial activity and are promising alternatives to conventional antibiotics in the poultry industry. Yet their safety, delivery, and efficacy under commercial production environments remain underexplored. In this study, I aimed to evaluate the safety, growth promoting effects, delivery feasibility, stability, and selective antimicrobial activity of several AMPs through in vivo and in vitro experiments. Selected AMPs (TeRu4, TeBi1, PeNi4, and CLIB_denovo6) were administered via in ovo injection at 10 or 20 µg/egg and tested in pen trials, assessing hatchability, survival probability, and production parameters. Moreover, I assessed the stability of these AMPs in drinking water conditions of farms and assessed their antimicrobial spectra. All AMPs were well-tolerated, with hatchability, survival, and production parameters for most AMPs comparable to controls. There were some AMP- and sex-specific effects, suggesting context-dependent responses. Notably, TeRu4 and CLIB_denovo6, both at 20 µg/egg, significantly increased the survival probability of female birds compared to the controls by approximately 4.9 and 5.7%, respectively, by day 35. Moreover, TeBi1 at 20 µg/egg significantly increased the survival probability of male birds compared to the control by approximately 6.6% by day 35. All AMPs demonstrated high stability in tap water and TeRu4 had selective activity against Gram-negative pathogens, supporting its potential for targeted use. These findings support the feasibility of AMP-based interventions in poultry but highlight the need for standardized protocols and mechanistic studies. Next steps should focus on validating these outcomes in larger, production-scale settings.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".