Favorable Pharmacokinetic Characteristics of Extended-Half-Life Recombinant Factor VIII BAY 94-9027 Enable Robust Individual Profiling Using a Population Pharmacokinetic Approach
Bibliographic record
Abstract
BACKGROUND: Prophylaxis with factor VIII (FVIII) should be individualized based on patient characteristics, including FVIII pharmacokinetics. Population pharmacokinetic (popPK) modeling simplifies pharmacokinetic studies by obviating the need for multiple samples. OBJECTIVE: The objective of this study was to characterize the pharmacokinetics and inter-individual variability (IIV) of BAY 94-9027 in relation to patient characteristics in support of a popPK-tailored approach, including identifying the optimal number and timing of pharmacokinetic samples. METHODS: Pharmacokinetic samples from 198 males (aged 2‒62 years) with severe hemophilia A, enrolled in BAY 94-9027 clinical trials, were analyzed. Baseline age, height, weight, body mass index, lean body weight (LBW), von Willebrand factor (VWF) level, and race were evaluated. A popPK model was developed and used to simulate pharmacokinetic endpoints difficult to observe from measured FVIII levels, including time to maintain FVIII levels above 1, 3, and 5 IU/dL after different BAY 94-9027 doses. RESULTS: A one-compartment model adequately described BAY 94-9027 pharmacokinetics. Clearance and central volume of distribution were significantly associated with LBW; clearance was inversely correlated with VWF. Due to the monophasic pharmacokinetics and well-understood IIV sources, identification of patient pharmacokinetics was achievable with sparse blood sampling. Median predicted time to maintain FVIII levels > 1 IU/dL in patients aged ≥ 12 years ranged from 120.1 to 127.2 h after single BAY 94-9027 doses of 45‒60 IU/kg. CONCLUSIONS: This analysis evaluated the pharmacokinetics of BAY 94-9027 and its sources of IIV. Using the model, determination of individual patient pharmacokinetics was possible with few FVIII samples, and a sparse sampling design to support pharmacokinetic-guided dosing was identified.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".