Unraveling the mechanisms behind the enhanced efficacy of β-lactam-based sideromycins
Bibliographic record
Abstract
Previous studies have explored combining β-lactams with siderophores to create Trojan horse molecules that can penetrate the outer membrane of Gram-negative bacteria via TonB-dependent transporter (TBDT). While the main advantage explaining their enhanced antibiotic activity is believed to be improved membrane permeability, other factors remain underexplored. This study evaluates three siderophore-β-lactam compounds: a bis-catechol siderophore linked to ampicillin or loracarbef, and a mixed bis-catechol-mono-hydroxamate siderophore linked to cefaclor. Minimal inhibitory concentrations showed that siderophore conjugation could enhance β-lactam efficacy by over 8000-fold. Comparison with unconjugated β-lactams revealed a complex interplay between β-lactamase susceptibility, competition with endogenous siderophore, membrane uptake, and binding to penicillin-binding proteins (PBPs). Enhanced PBP binding, particularly in Escherichia coli, emerged as a key factor contributing to improved bacterial inhibition by siderophore-β-lactam conjugates. Overall, the study provides insights into how siderophore conjugation enhances β-lactam activity and the therapeutic potential of the conjugates as narrow or broad-spectrum antibiotics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".