Comparative efficacy of dalbavancin alone and with rifampicin against in vitro biofilms in a pharmacodynamic model with methicillin-resistant Staphylococcus aureus
Bibliographic record
Abstract
BACKGROUND: The anti-biofilm efficacy of dalbavancin (DAL) has been evaluated in static models. The comparative activity of DAL alone and with rifampicin (RIF) against biofilm-embedded methicillin-resistant Staphylococcus aureus (MRSA) was evaluated using an in vitro pharmacokinetic/pharmacodynamic (PK/PD) model. METHODS: Two MRSA strains (HUB-4, HUB-5) were evaluated with the Calgary Device System and the dynamic CDC-Biofilm Reactor over 144 h. Dosage regimens simulated the human PK of DAL (1500 mg, single dose), vancomycin (VAN) (1000 mg/12 h) and linezolid (LZD) (600 mg/12 h), alone and with RIF (600 mg/24 h). Efficacy was evaluated by assessing log10CFU/mL changes (ΔlogCFU/mL) and screening for resistance was conducted. RESULTS: The minimal biofilm inhibitory/eradication concentrations of DAL were 0.25/16 mg/L (HUB-4) and 0.25/8 mg/L (HUB-5). In the PK/PD analysis, DAL alone showed limited efficacy but no development of resistance. Adding RIF improved the activities of DAL, VAN, and LZD, but RIF-resistant strains appeared over time in all cases. DAL-RIF was bactericidal against HUB-4 in the absence of resistance at 72 h and 144 h (ΔlogCFU/mL: -3.54±0.83, -4.32±0.12, respectively), an effect that was only achieved by LZD-RIF at 144 h (-3.33 ± 0.66). DAL-RIF activity against HUB-5 was impaired by RIF resistance to a greater extent than other combinations and this combination had no bactericidal effect. CONCLUSIONS: The anti-biofilm efficacy of DAL was improved significantly by adding RIF. Although DAL resistance did not occur, RIF resistance appeared in all combination therapies and decreased their efficacy over time. DAL-RIF in vitro treatment appears to be a promising anti-biofilm therapy, but further studies are needed to evaluate the efficacy and risk of resistance in vivo.
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".