Towards Robust Delivery of Antimicrobial Peptides to Combat Bacterial Resistance
Bibliographic record
Abstract
Antimicrobial peptides (AMPs), otherwise known as host defence peptides (HDPs), are naturally occurring biomolecules expressed by a large array of species across the phylogenetic kingdoms. They have great potential to combat microbial infections by directly killing or inhibiting bacterial activity and/or by modulating the immune response of the host. Due to their multimodal properties, broad spectrum activity, and minimal resistance generation, these peptides have emerged as a promising response to the rapidly concerning problem of multidrug resistance (MDR). However, their therapeutic efficacy is limited by a number of factors, including rapid degradation, systemic toxicity, and low bioavailability. As such, many strategies have been developed to mitigate these limitations, such as peptide modification and delivery vehicle conjugation/encapsulation. Oftentimes, however, particularly in the case of the latter, this can hinder the activity of the parent AMP. Here, we review current delivery strategies used for AMP formulation, focusing on methodologies utilized for targeted infection site release of AMPs. This specificity unites the improved biocompatibility of the delivery vehicle with the unhindered activity of the free AMP, providing a promising means to effectively translate AMP therapy into clinical practice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".