Fluoroquinolone AUIC Break Points and the Link to Bacterial Killing Rates: In Vitro Models
Bibliographic record
Abstract
2003 September, Volume 37 ■ 1331 Schentag et al. 1 vigorously advocate the use of AUC24 / minimum inhibitory concentration (MIC) >125 for fluoroquinolones in order to achieve bacteriologic eradication against all target organisms and in all hosts. Their argument faces opposition from other groups in the field of pharmacodynamics.2 However, we welcome debate in the scientific arena because such dialogue opens the door for better understanding of fluoroquinolone pharmacokinetics/pharmacodynamics. Schentag et al.’s arguments attract attention to some important issues that necessitate further investigation and discussion concerning the way we design our in vitro modeling experiments, set study endpoints, define antibiotic break points, interpret results, and consider how antibiotic-resistant mutants develop within a bacterial population. Examination of and reflection on pharmacodynamic concepts may provide better approaches to designing in vitro and animal studies, as well as clinical trials, so that fluoroquinolones can be modeled to achieve maximum bacterial eradication (hopefully with acceptable toxicity) and optimal prevention of the development of bacterial resistance. We all agree that for fluoroquinolones, AUC24/MIC, and maximum concentration (Cmax )/MIC are the best predictors of microbiologic and, less so, clinical outcome. However, some important issues need to be considered to arrive at an optimal approach and draw conclusions from our in vitro modeling studies. First, it is time to realize that different fluoroquinolone AUC24/MIC (not simply AUC24/MIC >125) ratios are required for eradication and prevention of resistance in different organisms (e.g., Streptococcus pneumoniae, Pseudomonas aeruginosa). This has been well described previously3,4 and confirmed by MacGowan et al.5 As well, we need to realize that the immune system in an immunocompetent host may directly or indirectly lower the AUC24/MIC ratios required for bacterial eradication, potentially to varied degrees, depending on the organism. Second, since only the unbound fraction of the drug is biologically active,6,7 only the unbound (non-protein bound) fraction of the drug should be used in pharmacodynamic calculations. Thus, we should consider only the unbound drug AUC24/MIC and unbound drug Cmax/MIC pharmacodynamic predictors. This issue was made clear by Mouton et al.8 in their efforts to standardize pharmacokinetic/pharmacodynamic terminology for antiinfective drugs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".