Pharmacodynamic target attainment potential of azithromycin, clarithromycin, and telithromycin in serum and epithelial lining fluid of community-acquired pneumonia patients with penicillin-susceptible, intermediate, and resistant Streptococcus pneumoniae
Bibliographic record
Abstract
OBJECTIVE: To compare the probability of target attainment (PTA) for macrolides and ketolides against penicillin-susceptible, intermediate, and resistant Streptococcus pneumoniae in both serum and epithelial lining fluid (ELF) of patients with community-acquired pneumonia (CAP). METHODS: Monte Carlo simulations were used to assess the attainment of the bacterial eradication-linked pharmacodynamic index of the free drug area under the concentration-time curve over 24 hours to minimum inhibitory concentration (fAUC(0-24)/MIC90) by azithromycin, clarithromycin, and telithromycin, at therapeutic doses, against penicillin-susceptible, intermediate, and resistant S. pneumoniae. RESULTS: In serum, azithromycin and clarithromycin were found to have a probability of attaining the recommended fAUC(0-24)/MIC90 ratio of 30 in 50.2% and 74.6%, respectively, of CAP patients with penicillin-intermediate strains, and a probability of 36.9% and 60.7%, respectively, in cases of penicillin-resistant strains. Telithromycin maintained a probability of reaching the fAUC(0-24)/MIC90 ratio of 30 in serum and ELF in 89.1% of CAP patients, regardless of the penicillin resistance of the strain. CONCLUSIONS: Clarithromycin results in a higher PTA than azithromycin in the treatment of penicillin-susceptible S. pneumoniae, but both of these agents exhibit a decreasing efficacy as S. pneumoniae penicillin resistance increases. When compared to clarithromycin and azithromycin, telithromycin maintains higher PTA in CAP patients with penicillin-resistant strains of S. pneumoniae.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".