Extended half‐life factor VIII concentrates in adults with hemophilia A: Comparative pharmacokinetics of two products
Bibliographic record
Abstract
BACKGROUND: The use of pharmacokinetic (PK) studies to help design personalized prophylaxis regimens for factor VIII (FVIII) concentrate in individuals with hemophilia A has been recognized for many years but only became practical for routine clinical use with the availability of web-accessible population PK applications based on Bayesian analysis. OBJECTIVE: To compare PK variables using population PK studies done on 2 extended half-life recombinant FVIII concentrates in 23 individuals with hemophilia A after switching from one product to the other. METHODS: We retrospectively analyzed PK parameters derived from the Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-HEMO) application on 23 individuals with severe or moderately severe hemophilia A who were required to switch from recombinant FVIII Fc (Eloctate; Biogen, Cambridge, MA, USA) to recombinant antihemophilic factor PEGylated (Adynovate; Takeda Pharmaceutical Company, Osaka, Japan) between 2016 and 2017. RESULTS: There were minor PK differences between Eloctate and Adynovate, but some parameters did reach statistical significance, namely in vivo recovery (mean, 2.73 IU/dL per IU/kg vs 2.41 IU/dL per IU/kg), clearance (mean, 0.163 mL/h vs 0.194 mL/h), and volume of distribution at steady state (mean, 42.5 ml/kg vs 49.8 mL/kg). Smaller nonsignificant trends toward higher values for Adynovate were seen in terminal half-life, area under the curve, and predicted times to 5% and 1% residual FVIII after infusion. CONCLUSION: Population PK analysis revealed differences between the two extended half-life FVIII concentrates, reaching significance for in vivo recovery, clearance, and volume of distribution.
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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 | 0.002 |
| 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) | 0.001 | 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".