Limited sampling strategies for accurate determination of extended half‐life factor VIII pharmacokinetics in severe haemophilia A patients
Bibliographic record
Abstract
BACKGROUND: Extended half-life (EHL) factor VIII (FVIII) products may decrease the burden of prophylactic treatment in haemophilia A by reducing infusion frequency. However, these products still exhibit wide inter-patient variability and benefit from pharmacokinetic (PK) tailoring. OBJECTIVE: Identify limited sampling strategies for rFVIIIFc, an EHL FVIII product, that produce accurate estimates of PK parameters and relevant troughs. METHODS: We performed a limited sampling analysis on simulated populations of adults, adolescents, and children based on published population PK data. Sampling strategies were evaluated by comparing the error in estimates of half-life, clearance, and trough levels, to a full 6-sample design. Furthermore, we assessed the impact of incorporating knowledge about prior doses, and the day of the PK study within the regimen. We also evaluated the potential inappropriate dose adjustment rate (IDAR) among the modelled sampling strategies. RESULTS: Many sampling strategies, including several 2-sample designs, accurately predicted the PK and exposure measures (median absolute error <10%). When samples are only collected during a single visit (i.e., predose + peak), inclusion of prior dose information reduces median half-life error from >20% to ~5% for adults/adolescents. In this same scenario, appropriate scheduling of the PK study decreases likelihood of unmeasurable predose samples, reducing median error on the 72-h trough from 25% to <12% in the youngest population. CONCLUSIONS: The PK of rFVIIIFc can be accurately estimated using only peak and trough samples, provided that knowledge of prior doses is incorporated and the PK study is planned on an appropriate day within the dosing regimen.
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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".