Overcoming problems of poor drug penetration into bacteria: challenges and strategies for medicinal chemists
Bibliographic record
Abstract
INTRODUCTION: Bacterial cell walls and membranes provide essential protection for bacteria against environmental influences. Different bacteria possess different cell envelopes and understanding each of these structures is crucial for the design of effective antibacterial drugs whose targets are intracellular. Optimal properties of drugs that are required for their entry into bacteria are still hard to predict. The guidelines that are suitable and well established for the penetration of a drug into eukaryotic cells are poorly adaptable to the complex world of pathogens. Areas covered: The factors that govern the penetration of anti-infection drugs into bacteria are examined and the available strategies to overcome this therapeutically very important barrier are reviewed. The areas covered include optimization of the physicochemical properties of compounds, utilization of iron-chelating compounds, i.e. siderophores, the use of efflux pump inhibitors, and of carriers such as liposomes. Expert opinion: Although several rules governing permeation have recently been proposed for effective antibacterial drugs, none of them has been so far established as the 'golden' rule. Thus, new research is needed to find a more general approach on how to increase the concentration of antibacterial compounds in bacterial cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".