Zinc Chelation by a Small-Molecule Adjuvant Potentiates Meropenem Activity in Vivo against NDM-1-Producing <i>Klebsiella pneumoniae</i>
Bibliographic record
Abstract
The widespread emergence of antibiotic drug resistance has resulted in a worldwide healthcare crisis. In particular, the extensive use of β-lactams, a highly effective class of antibiotics, has been a driver for pervasive β-lactam resistance. Among the most important resistance determinants are the metallo-β-lactamases (MBL), which are zinc-requiring enzymes that inactivate nearly all classes of β-lactams, including the last-resort carbapenem antibiotics. The urgent need for new compounds targeting MBL resistance mechanisms has been widely acknowledged; however, the development of certain types of compounds-namely metal chelators-is actively avoided due to host toxicity concerns. The work herein reports the identification of a series of zinc-selective spiro-indoline-thiadiazole analogues that, in vitro, potentiate β-lactam antibiotics against an MBL-carrying pathogen by withholding zinc availability. This study demonstrates the ability of one such analogue to inhibit NDM-1 in vitro and, using a mouse model of infection, shows that combination treatment of the respective analogue with meropenem results in a significant decrease in bacterial burden in contrast to animals that received antibiotic treatment alone. These results support the therapeutic potential of these chelators in overcoming antibiotic resistance.
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 | 0.000 |
| 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".