Consideration of height‐based tobramycin dosing regimens for the treatment of adult cystic fibrosis pulmonary exacerbations
Bibliographic record
Abstract
Aims Some population pharmacokinetic models have been developed using height to explain some of the interindividual variability in tobramycin pharmacokinetics in cystic fibrosis patients. However, their predictive performance when extrapolated to other clinical centres is unclear. Therefore, the aim of this study was to externally evaluate the predictability of tobramycin population pharmacokinetic models with an independent dataset and perform simulations using previously recommended height‐based dosing regimens. Methods A literature search was conducted through the PubMed database to identify relevant population pharmacokinetic models. Tobramycin plasma concentration data from April 2014 to November 2019 were retrospectively collected from the Institut universitaire de cardiologie et de pneumologie de Québec, Canada. External evaluations were performed using NONMEM® v7.5 and RStudio® v1.3.1073. Monte Carlo simulations were performed to evaluate the probability of target attainment of Cmax/MIC ratios for several dosing regimens. Results The validation dataset included 27 patients and 143 concentration samples. Three models were evaluated. Only the ones by Crass et al. and Alghanem et al. performed satisfactorily in terms of prediction‐based diagnostics with MDPE values of −3.4% and 29.3% and MDAPE values of 19.0 and 29.5%, respectively. In simulation‐based evaluations, both pcVPC and NPDE showed no evidence of model misspecification. Our simulations suggest that patients treated with a once‐daily dose of 3.4 mg/cm should produce peak and trough levels consistent with current guidelines. Conclusion Our results show that the models by Crass et al. and Alghanem et al. are appropriate for simulation‐based applications to aid individualized dosing in our population and that height‐based dosing regimens could be considered in cystic fibrosis patients.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".