Comparison of single subject and population‐based pharmacokinetics for optimizing prophylaxis with simoctocog alfa in patients with haemophilia A
Bibliographic record
Abstract
Abstract Introduction The use of pharmacokinetic assessment for optimal prophylactic dosing of factor concentrates in haemophilia has gained increasing enthusiasm over the last decade. However, blood sampling on several occasions is burdensome and limited sampling using population‐based PK is appealing. Aim To compare the pharmacokinetics and dosing recommendations for prophylaxis using six‐point single subject versus population‐based method (WAPPS‐Hemo) for simoctocog alfa (Nuwiq®). Methods Twelve adult patients with severe haemophilia A received a factor VIII (FVIII) dose of ≈50 IU/kg, and the activity was measured pre‐infusion and at 30 min, 6, 9, 24 and 48 h post‐infusion. Half‐life (t1/2), weight‐normalized AUC and time to troughs of 5%, 3% and 1% were calculated. The correlation between the PK algorithms was assessed using intraclass correlations (ICC) and dosing estimations were provided. Results WAPPS‐Hemo yielded a slightly longer mean t1/2, but the overall correlation between the methods was good (ICC ≥0.79) The time to troughs of 5%, 3% and 1% showed ICCs ≥0.86. For all variables, the most converging limited time point was 6+48 h. Additional time points did not improve the correlation. Despite similar pharmacokinetics, the mean estimated dose for a specific trough level varied from 60% less to 20% more using the population‐based approach. The time to 1% and the corresponding dose was sensitive to the baseline assumption. Conclusion Our data support the use of population‐based PK for patients on simoctocog alfa prophylaxis but also indicates differences, stressing the importance of the sampling scheme and monitoring actual FVIII levels achieved.
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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 | 0.003 | 0.005 |
| 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 | 0.000 |
| 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".