Structure-activity relationships of niclosamide to overcome colistin resistance
Bibliographic record
Abstract
Antibacterial resistance poses a significant threat to global healthcare systems, particularly against Gram-negative bacteria (GNB). Addressing this challenge requires urgent development of new therapies, especially given the emergence of resistance even to last-resort antibiotics like colistin. However, rising rates of colistin resistance highlight the need for alternative strategies. One promising approach involves repurposing existing drugs, such as the anthelmintic niclosamide, known to enhance colistin activity in combination therapy. Despite its potential, niclosamide faces limitations due to poor solubility, bioavailability, and off-target toxicity. Thus, repurposing it as an antibacterial agent necessitates the synthesis of new derivatives capable of overcoming these challenges. Additionally, a comprehensive exploration of niclosamide's structure–activity relationship (SAR) against GNB remains unexplored. We synthesized a series of niclosamide analogs to address these gaps, focusing on three main objectives: mitigating toxicity by replacing the nitro group, modifying the central amide moiety, and designing niclosamide-based hybrid antibiotics. Our SAR investigation led to the discovery of several lead compounds with comparable colistin-potentiating activity to niclosamide but with reduced cytotoxicity. Notably, we also identified synergy with antibiotics beyond colistin, including cefiderocol and bacitracin. Overall, our work provides critical insights into synthetic strategies for developing new niclosamide derivatives. We also demonstrate that toxicity to mammalian cells can be minimized while maintaining colistin potentiation and reveal promising avenues for repurposing niclosamide in combating antibacterial 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".