Pharmacokinetic modelling and validation of the half‐life extension needed to reduce the burden of infusions compared with standard factor <scp>VIII</scp>
Bibliographic record
Abstract
INTRODUCTION: Currently, no universally accepted definition of extended half-life (EHL) recombinant FVIII (rFVIII) exists. Identifying the minimum half-life extension ratio required for a reduction in dosing frequency compared with standard rFVIII could enable a more practical approach to decisions around prophylaxis with EHL rFVIII. AIM: To identify the half-life extension ratio required to decrease rFVIII dosing frequency by at least 1 day while maintaining the proportion of patients with plasma rFVIII levels above 1 IU/dL and without increasing the total weekly dose. METHODS: A previously published population pharmacokinetic model for standard rFVIII was used to estimate the percentage of patients with factor VIII (FVIII) levels always >1 IU/dL using various benchmark regimens. Using modelling, dosing frequency was reduced while rFVIII half-life was extended until the percentage of patients with FVIII >1 IU/dL equalled that of the benchmark regimen. RESULTS: Benchmark 3×/wk dosing totalling 100 IU/kg/wk of rFVIII resulted in 56.6% of patients with FVIII levels always >1 IU/dL. With 2×/wk dosing, totalling 80 or 90 IU/kg/wk, half-life extensions required to maintain 56.6% of patients at FVIII levels >1 IU/dL were 1.30 and 1.26, respectively. A half-life extension ratio of 1.33 was required to change dosing from every 48 hours to every 72 hours (both at 105 IU/kg/wk) while maintaining 92.8% of patients with FVIII >1 IU/dL. CONCLUSION: Based on this investigation, EHL rFVIII products should have a minimum half-life extension ratio of 1.3 to provide a reduction in dosing frequency from 3× to 2×/wk compared with standard rFVIII products while maintaining the same minimum FVIII trough level.
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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 | 0.002 | 0.004 |
| 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".