Population pharmacokinetics and exposure‐response analysis of tigecycline in patients with hospital‐acquired pneumonia
Bibliographic record
Abstract
Background Tigecycline has been widely used to treat hospital‐acquired pneumonia (HAP) off‐label since it is effective against a wide range of multidrug‐resistant bacteria. However, no recommended dosage for this indication has been evaluated, resulting in possible inadequate treatment. Aims The aims of this study are to establish the population pharmacokinetic (PPK) model of tigecycline in Chinese patients with HAP, as well as to evaluate the exposure‐response relationship for the treatment of HAP with multidrug‐resistant gram‐negative bacteria. Methods A PPK analysis of tigecycline was conducted on pooled data from 328 blood samples obtained from 89 patients with HAP. Tigecycline plasma concentrations were measured by a two‐dimensional liquid chromatographic system and the data were analysed using Phoenix NLMETM software. Exposure‐response analyses for efficacy were performed based on the data from 79 HAP patients with multidrug‐resistant gram‐negative infections. Classification and regression tree and logistic regression analyses were employed to identify which pharmacokinetic‐pharmacodynamic (PK‐PD) indices and magnitudes were the significant predictors of tigecycline efficacy. Results A two‐compartment model with zero‐order absorption and first‐order elimination adequately described the data. A larger body weight was associated with increased central volume of distribution and clearance ( P < .005), and increased age, baseline creatinine concentration and aspertate aminotransferase were associated with decreased clearance ( P < .005). The AUC 0‐12h × V/MIC ratio, APACHEII score and combined Pseudomonas aeruginosa infection are the strong predictors for tigecycline clinical response. Classification and regression tree analyses indicated that the combination of APACHEII score < 24 and AUC 0‐12h × V/MIC ratio ≥ 100 was associated with clinical success. Conclusions The proposed PPK model may serve as the basis for estimating tigecycline exposure for PK‐PD analyses, and the PK‐PD index and magnitude found in this study could be used for designing proper dosage regimens of tigecycline.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".