Retargeting Gram-Positive-Only Adarotene-Derived Antibacterials to Broad-Spectrum Antibiotics
Bibliographic record
Abstract
Background: Bacterial resistance to antibiotics continues to rise globally, posing a significant public health challenge and incurring substantial social and economic burdens. In response, the World Health Organization (WHO) has published a list of priority pathogens for which effective treatment options are critically limited. Several antibiotics are categorized as Gram-positive-only (GPO) agents due to their lack of activity against Gram-negative species. Although these compounds often target conserved bacterial processes, their limited spectrum is largely attributed to poor penetration of the Gram-negative outer membrane (OM). Results: In this study, we designed and synthesized a series of adarotene-derived compounds to evaluate the impact of introducing positively charged groups on their interaction with the Gram-negative OM. One of the newly synthesized derivatives, SPL 207, displayed minimum inhibitory concentration (MIC) values ranging from 8 to 64 µM against a panel of Gram-positive and Gram-negative bacteria. The ability of SPL207 to disrupt outer and inner membrane permeability was evaluated using fluorescence assays and confocal microscopy, revealing that the compound compromises membrane integrity across all tested Gram-negative bacteria. Strong synergistic activity was observed in combination with colistin against three P. aeruginosa colistin-resistant strains. Atomistic details of membrane interference were elucidated by molecular dynamics (MD) simulations, with SPL207 clearly acting as a membrane destabilizer by enhancing Ca2+ ions diffusion and lipids destabilization. Conclusions: Although the observed MIC values remain above clinically acceptable thresholds, these findings provide a promising proof of concept. The further structural optimization of adarotene derivatives may yield novel broad-spectrum agents with improved antimicrobial potency against MDR pathogens.
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".