Population pharmacokinetics of thrombomodulin alfa in pediatric patients with hematological malignancy and disseminated intravascular coagulation
Bibliographic record
Abstract
Abstract Background Thrombomodulin alfa (TM‐α) is a new class of anticoagulant drug for patients with disseminated intravascular coagulation (DIC). This study aimed to determine the pharmacokinetics of TM‐α and determine the optimal dose in pediatric patients with hematological malignancy and DIC. Procedure Pediatric patients with hematological malignancy and DIC were administered TM‐α at a dose of 0.06 mg/kg (380 U/kg) over 30 min every 24 hr. Blood samples were taken at steady state before the start, immediately after the end, and 24 hr after the start of the sixth administration. Population pharmacokinetic analysis was performed using sparse samples with the nonlinear mixed‐effect modeling program NONMEM®, version 7.3. Results The actual and predicted plasma concentrations of TM‐α based on the final population pharmacokinetic model showed a good linear correlation. Clearance and volume of distribution of TM‐α were affected by body weight. The clearance of TM‐α in pediatric patients with hematological malignancy and DIC was higher than that in adults as previously reported. Six of eight patients did not achieve the target trough concentration at steady state. Furthermore, the pharmacokinetic simulation based on the estimated pharmacokinetic parameters from the final model demonstrated that TM‐α administered at a dose of 0.06 mg/kg every 24 hr also failed to achieve the target trough concentration at steady state in the majority of pediatric patients. Conclusions Our study shows that further dose adjustment of TM‐α is necessary considering the higher clearance per body weight in pediatric patients with hematological malignancy and DIC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".