Development of a Physiologically Based Pharmacokinetic Model of Ethionamide in the Pediatric Population by Integrating Flavin‐Containing Monooxygenase 3 Maturational Changes Over Time
Bibliographic record
Abstract
Currently, ethionamide is the most frequently prescribed second-line antituberculosis drug in children. After extensive metabolism by flavin-containing monooxygenase (FMO) isoform 3 in the liver, the drug may exert cytotoxic effects. The comparison of children in different age groups revealed a significant age-related increase in ethionamide elimination in vivo. However, to date, the exact mechanism underlying this dynamic increase in ethionamide elimination has not been elucidated. We hypothesized that the age-dependent changes in ethionamide elimination were predominantly a result of the progressive increases in the expression and metabolic capacity of FMO3 during childhood. To test this hypothesis, a full physiologically based pharmacokinetic (PBPK) model of ethionamide was established and validated in adults through incorporation of comprehensive metabolism and transporter profiles, then expanded to the pediatric population through integration of FMO3 maturational changes over time. Thus, a good prediction PBPK model was validated successfully both in adults and children and applied to demonstrate the critical contribution of FMO3 in the mechanistic elimination pathway of ethionamide. In addition, a significant correlation between clearance and age was observed in children by accounting for ethionamide maturation, which could offer a mechanistic understanding of the age-associated changes in ethionamide elimination. In conclusion, this study underlines the benefits of in vitro-in vivo extrapolation and a quantitative PBPK approach for the investigation of transporter-enzyme interplay in ethionamide disposition and the demonstration of FMO3 ontogeny in children.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".