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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".