Pharmacokinetics of factors IX, recombinant human activated factor VII and factor XIII
Bibliographic record
Abstract
Summary. There is now a volume of literature on the pharmacokinetics (PK) of coagulation factor concentrates, although the majority is on factor VIII (FVIII) and factor IX (FIX). PK of FIX and FVIII are different with FIX having a larger volume of distribution (Vdss), higher elimination clearance (CL), longer mean resident time (MRT) and longer terminal half‐life (T1/2,β). Factor IX in vivo recovery (IVR) is also much shorter possibly due to reversible binding of FIX to the endothelium and possibly to platelets. There is considerable FIX PK variability between products (particularly between plasma‐derived FIX and recombinant FIX), and between individuals. Important inter‐individual factors leading to PK variability include age and body weight because plasma volume as a fraction of body weight decreases with increasing weight and hence age. Thus, IVR increases with body weight and hence age and is consequently lower in children than in adults. Absolute Vdss and CL increase linearly with body weight and age in children and adolescents, becoming stable in adults with more stable weight. Inter‐individual variability also likely applies to other clotting factors, particularly to recombinant activated FVII (rFVIIa) but likely also to the less well studied factor XIII (FXIII). The former is known to have an extremely short T1/2,β, large Vdss, high CL, short MRT, whereas the latter has an extremely long T1/2,β, large Vdss, short CL and long MRT. Both are discussed in this article. Understanding of PK of specific clotting factors in individual patients is important in order to make decisions regarding appropriate dosage and dosage intervals to treat patients, and to allow by means of computer modelling the determination of dosage to achieve target trough level at various dosing intervals for patients undergoing prophylaxis.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".