Antibiotic adjuvants: multicomponent anti-infective strategies
Bibliographic record
Abstract
The unremitting emergence of multidrug-resistant bacterial pathogens highlights a matching need for new therapeutic options. For example, new carbapenemases such as KPC (class A Klebsiella pneumoniae) and NDM-1 (New Delhi metallo-β-lactamase 1) are surfacing, resulting in almost total resistance to β-lactam antibiotics. Furthermore, resistance is quickly disseminated, not only in the healthcare sector, but also within the community at large, because many resistance determinants are carried on mobile genetic elements readily shared among pathogens. The absence of new antibiotics has led to a growing reliance on older, more toxic drugs such as colistin, but resistance to these is already arising. One approach to combat this growing problem is the use of combination drug antibiotic adjuvant therapy, which potentiates the activity of antibiotics. Here, we review the current situation and discuss potential drug combinations that may increase the potency of antibiotics in the future. Adjuvant therapies include antibiotic combinations, synergy between antibiotics and nonantibiotics, inhibition of resistance and molecules that alter the physiology of antibiotic-insensitive cells, such as those in biofilms. We provide a rationale for these multicomponent strategies, highlighting current research and important considerations for their clinical use and pharmacological properties.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".