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Enregistrement W2985324256 · doi:10.1182/blood-2019-127046

Correlations between von Willebrand Factor Antigen Levels and Factor VIII Pharmacokinetics Are Similar across Different FVIII Products in Patients with Severe Hemophilia A

2019· article· en· W2985324256 sur OpenAlexaff
Alfonso Iorio, Monika Maas Enriquez, Heinz Delesen, Alexander Solms, Stacy E. Croteau, Erik Berntorp

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueHemophilia Treatment and Research
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésMedicineVon Willebrand factorRecombinant DNAPharmacokineticsInternal medicineFactor IXGastroenterologyImmunologyPharmacologyPlateletChemistry

Résumé

récupéré en direct d'OpenAlex

Background: Prophylactic treatment with replacement factor VIII (FVIII) is the standard of care for patients with severe hemophilia A to prevent bleeding and subsequent joint damage and chronic arthropathy. Because of variability in patient characteristics and clinical needs, it is recommended that prophylaxis is tailored to individual patient needs based on their baseline characteristics, bleeding history, and FVIII pharmacokinetic (PK) parameters. Endogenous levels of von Willebrand factor antigen (VWF:Ag) are known to influence the PK of FVIII. This analysis investigates the relationship between FVIII PK parameters and VWF:Ag levels, as well as between VWF:Ag levels and age, in patients with severe hemophilia A treated with various recombinant FVIII products using data obtained from multiple clinical trials. Patients/Methods: This integrated regression analysis is based on data with five FVIII products from six clinical trials. The products were: PEGylated recombinant FVIII BAY 94-9027 (damoctocog alfa pegol, Jivi®); sucrose-formulated recombinant FVIII (rFVIII-FS; Kogenate®-FS); BAY 79-4980 (liposomal version of Kogenate®-FS); unmodified full-length recombinant FVIII BAY 81-8973 (Kovaltry); and recombinant antihemophilic factor plasma/albumin-free method rAHF-PFM (octocog alfa, Advate®). The trials were: NCT01184820, NCT01029340, NCT00623727, NCT01233258, NCT02483208, and NCT01580293. PK parameters were estimated by means of a non-compartmental analysis based on densely sampled single-dose profiles measured by one-stage and chromogenic assays, and following a dose of 25-60 IU/kg. The primary regression analysis was performed using the log-transformed PK parameters (half-life [t1/2], clearance [CL], and dose-normalized area under the curve [AUCnorm]) as dependent variables and the VWF:Ag levels as the independent variable in a mixed model including fixed treatment effects and random patient effects. In addition, the impact of age on VWF:Ag levels was investigated. One-stage assay results are reported. Results based on the chromogenic assay were in line with those reported for the one-stage assay. Results: Data from 104 patients from six studies were used to investigate the relationship of the dose-independent PK-parameter, t1/2 vs VWF:Ag levels, and of VWF:Ag levels vs age. Median age was 31 years (range 12-64 years). For the analyses of the PK parameters AUCnorm and CL, data from 77 patients from five studies were analyzed. Median age was 32 years (range 12-64 years). Some patients participated in multiple studies. The primary regression analyses across all products and studies on the log-transformed PK parameters showed significant associations between PK parameters and VWF:Ag levels. Significant positive correlations vs VWF:Ag levels were observed for t1/2 (n = 155 observations in 104 patients, P < 0.001) and AUCnorm (n = 117 observations in 77 patients, P < 0.001), resulting in a negative correlation with CL (Figure 1). Other factors, such as age and the interaction of treatment with the VWF:Ag-PK effect, were found to be redundant and were not included in the final model. Analysis of VWF:Ag levels vs age using a simple linear regression that did not incorporate the dependencies of multiple observations in patients, revealed a positive correlation between age and VWF:Ag levels (Spearman coefficient , r = 0.290 [P = 0.0029]; Pearson coefficient, r = 0.334 [P = 0.0005]). Comparable results were observed in a mixed model with random patient effects. Conclusions: In this combined analysis across five FVIII products, FVIII PK parameters (t1/2, AUCnorm, and CL) were significantly correlated with endogenous VWF:Ag levels which were also correlated with age. Moreover, while there were differences in the average PK parameters between products (represented by the intercept for treatment), all five FVIII products demonstrated similar VWF-PK relationships. It is well known that a patient's VWF:Ag level influences the PK of rFVIII, but this analysis suggests that the quantitative relationship between FVIII PK and VWF:Ag is similar for modified and unmodified FVIII products. This finding can be used, for example, to support selection of adequate treatment regimens when a patient is switching rFVIII product. Disclosures Maas Enriquez: Bayer: Employment. Delesen:Bayer: Employment. Solms:Bayer: Employment, Equity Ownership. Croteau:Novo Nordisk: Consultancy, Honoraria, Research Funding; Shire: Consultancy, Honoraria; Spark Therapeutics: Research Funding; Pfizer: Research Funding; Genentech: Consultancy, Honoraria; Octapharma: Honoraria; CSL Behring: Consultancy, Honoraria; Bayer: Consultancy, Honoraria. Berntorp:Bayer: Consultancy, Honoraria, Research Funding, Speakers Bureau; CSL Behring: Research Funding; Shire: Consultancy, Honoraria, Research Funding; Sobi/Bioverativ: Research Funding; Octapharma: Consultancy, Honoraria.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,029
Tête enseignante GPT0,288
Écart entre enseignants0,258 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2019
Routes d'admission1
Résumé présentoui

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