Antibiotic Hybrids: the Next Generation of Agents and Adjuvants against Gram-Negative Pathogens?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The global incidence of drug-resistant Gram-negative bacillary infections has been increasing, and there is a dire need to develop novel strategies to overcome this problem. Intrinsic resistance in Gram-negative bacteria, such as their protective outer membrane and constitutively overexpressed efflux pumps, is a major survival weapon that renders them refractory to current antibiotics. Several potential avenues to overcome this problem have been at the heart of antibiotic drug discovery in the past few decades. We review some of these strategies, with emphasis on antibiotic hybrids either as stand-alone antibacterial agents or as adjuvants that potentiate a primary antibiotic in Gram-negative bacteria. Antibiotic hybrid is defined in this review as a synthetic construct of two or more pharmacophores belonging to an established agent known to elicit a desired antimicrobial effect. The concepts, advances, and challenges of antibiotic hybrids are elaborated in this article. Moreover, we discuss several antibiotic hybrids that were or are in clinical evaluation. Mechanistic insights into how tobramycin-based antibiotic hybrids are able to potentiate legacy antibiotics in multidrug-resistant Gram-negative bacilli are also highlighted. Antibiotic hybrids indeed have a promising future as a therapeutic strategy to overcome drug resistance in Gram-negative pathogens and/or expand the usefulness of our current antibiotic arsenal.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it