Pharmacokinetic implications of dosing emicizumab based on vial size: A simulation study
Bibliographic record
Abstract
INTRODUCTION: Emicizumab is dosed as mg/kg and, according to the label, any unused drug left in the vial(s) must be discarded, thereby wasting expensive resources. The aim of this study was to use population pharmacokinetics to illustrate the implications of changing the dosing interval to avoid wastage. METHODS: We used a previously published emicizumab PopPK model after extending its validation to children. We simulated PK parameters for labelled dosing regimens and for regimens using full vials with infusion frequency varied to keep the steady-state drug concentration unchanged. Cost and drug savings were calculated. RESULTS: The model evaluation was successful. When rounding up, the average individual below 53, 47 and 39 has a time-to-trough increase of up to 5.7, 7.9 and 5.8 days for the QW, Q2 W and Q4 W regimen, respectively. This resulted in an annual cost reduction of up to $173,136, $75,747 and $61,319 USD per patient. At higher body weights, rounding down the dose to the nearest vial resulted in negligible changes in the steady state concentration and cost savings of up to $93,781, $46,891 and $23,446 USD per patient, respectively. CONCLUSION: Individuals with a lower body weight may benefit from increasing dose intervals and rounding up dose up to the nearest vial, and individuals with a higher body weight from maintaining the injection frequency and rounding dose down to the nearest vial without significant change in emicizumab levels. Administering the entire vial may result in a reduction of vials used annually and potential cost savings.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| 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".