Pharmacokinetic modeling and simulation support for age‐ and weight‐adjusted dosing of dabigatran etexilate in children with venous thromboembolism
Bibliographic record
Abstract
BACKGROUND: Dabigatran etexilate (DE), a direct oral thrombin inhibitor, has been evaluated in children with venous thromboembolism (VTE) using oral solution, pellets, or capsules. OBJECTIVES: This study evaluated DE pharmacokinetics (PK) in children with VTE and the appropriateness of a DE pediatric age- and weight-based dosing algorithm. PATIENTS/METHODS: A population PK model was fitted to data from four single-arm and one randomized, comparative pediatric VTE studies (358 children aged birth to <18 years; 2748 PK observations) and one healthy-adult study (32 males aged <40 years; 1523 PK observations) using nonlinear mixed-effects modeling. A stepwise, covariate, model-building procedure evaluated the influence of covariates (e.g., age, body weight, body surface area [BSA]-normalized renal function, and sex). The final model was used to evaluate the pediatric dosing algorithm, with simulations comparing pediatric trough exposure with reference exposure defined for the pediatric studies. RESULTS: The population PK of dabigatran was adequately described by a two-compartment model with first-order elimination and absorption. Age, weight, BSA-normalized renal function, and sex were statistically significant covariates (all P < .05). Apparent clearance increased with age (independently of body weight), diminished with decreasing BSA-normalized renal function, and was lower in females than males. All disposition parameters increased with body weight escalation (allometric scaling). Simulations confirmed that for all DE formulations, the final pediatric dosing algorithms achieved reference exposure without dose adjustment. CONCLUSIONS: Using a population PK model of DE for children with VTE, simulations showed that the final dosing algorithms were appropriate for all DE formulations; no dose titration was needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".