Pharmacokinetics in routine haemophilia clinical practice: rationale and modalities—a practical review
Bibliographic record
Abstract
Prophylactic therapy with exogenous clotting factor concentrates in haemophilia A and B aims to achieve levels of circulating FVIII or FIX that are adequate for the prevention or reduction of spontaneous joint bleeding. Historically, a minimum trough level of at least 1% of the normal levels of circulating clotting factor has been targeted using standardised protocols. However, clearance of clotting factor varies between products and patients, and other pharmacokinetic (PK) parameters such as the frequency and magnitude of peaks may be important for ensuring optimal coverage. Thus, it is increasingly recognised that an individualised, PK-based approach to prophylaxis is necessary to achieve optimal protection. This review focuses on the clinical implications of using PK-guided, individualised prophylaxis in haemophilia to improve patient outcomes and considers practical methods of establishing patients' PK parameters. The most useful PK parameters will depend on the aim of the specific treatment (e.g. preventing activity-related and traumatic bleeds or addressing subclinical bleeding). In clinical practice, lengthy and frequent post-infusion sampling for PK analysis is costly and a significant burden for patients. However, a Bayesian analysis allows for the estimation of different PK parameters (e.g. half-life, factor concentrations over time, etc.) with only a minimum number of samples (e.g. 4, 24 and 48 h for haemophilia A), by using the patient's data to adjust a relevant population PK value towards the actual value. Numerous tools are available to aid in the practical use of Bayesian PK-guided dosing in the clinic, including the Web-based Application for the Population Pharmacokinetic Service hosted by McMaster University, Canada. The PK data can be used to determine the appropriate prophylaxis regimen for the individual patient, which can be monitored by assessment of the trough level at each clinic visit. Collection of PK data and subsequent PK-guided dosing should become standard practice when determining treatment strategies for people with haemophilia.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".