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Systematic Review of the Published Evidence on the Pharmacokinetic Characteristics of Factor VIII and IX Concentrates

2014· article· en· W2600984278 on OpenAlexaff
Menchen Xi, Tamara Navarro, Sunil Mammen, Victor S. Blanchette, Cédric Hermans, Massimo Morfini, Peter W. Collins, Kathelijn Fischer, Ellis J. Neufeld, Guy Young, Kaan Kavaklı, Paolo Radossi, Amy L. Dunn, Lehana Thabane, Alfonso Iorio

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsHospital for Sick ChildrenMcMaster University
Fundersnot available
KeywordsFactor IXMedicinePharmacokineticsPopulationMEDLINEPopulation pharmacokineticsPharmacologyInternal medicineChemistry

Abstract

fetched live from OpenAlex

Abstract Introduction: The efficacy of factor VIII and IX concentrates administered to prevent bleeding episodes in patients with hemophilia A and B is correlated with the plasma levels measured over time after the infusion. The inter-patient variability of pharmacokinetic (PK) parameters is large, and it is difficult to assess individual PK profiles due to the need for multiple time points. This is often not feasible, particularly for pediatric patients. Population PK modeling potentially provides a practical solution to this problem. The successful modelling of PK parameters at the population level requires knowledge of disposal characteristics and relevant covariates. We performed a systematic review of the available evidence in order to identify available PK data for factor VIII and IX concentrates to facilitate the implementation of a population PK approach. Methods: We conducted a literature search in MEDLINE and EMBASE from January 1997 to May 2014, using the keywords "hemophilia" and "pharmacokinetic". We included only articles that published original PK data for factor VIII and IX concentrates in humans and published in English. Two authors independently screened the studies and extracted the relevant data. Results: We retrieved 237 unique articles published between 1998 and 2013. We excluded 185 articles that did not meet our research criteria. We included 52 articles, with a total of 1365 patients included in PK analyses. 26 articles reported PK data on factor VIII concentrates, 18 articles report PK data on factor IX concentrates, and one article reported on both factor VIII and IX concentrates. Seven articles reported pharmacokinetic data on both factor VIII and Von Willebrand factor concentrates. We extracted the following data: number of patients, type and severity of hemophilia, patient age, factor concentrate infused, dose infused, sampling data points, half-life, clearance, recovery and the model used for pharmacokinetics, and inclusion of patients undergoing surgery or with inhibitors. The main results are summarized in table 1. Conclusions: This review provides the first systematic appraisal of the methods and results of published papers in the field. The data gathered confirms the intra-patient variability of factor concentrate PK and provides useful information on which to build population based PK models. *3 FIX articles and 2 FVIII articles did not report lab test; one article reported PK data for both FIX and FVIII †11 articles reported FVIII PK data for both one-stage clotting and chromogenic assays ǂPapers reporting on long-acting FVIII and FIX were included in the review, but not summarized in the table. For this reason, not all 1365 patients are accounted for in the table §Estimate of the range of the means found in the papers Disclosures Xi: Baxter: Research Funding. Navarro-Ruan:Baxter: Research Funding. Mammen:Baxter: Research Funding. Collins:Baxter: Consultancy, Honoraria, Research Funding, Speakers Bureau; CSL: Consultancy, Honoraria, Research Funding, Speakers Bureau; NovoNordisk: Consultancy, Honoraria, Research Funding, Speakers Bureau; Bayer: Consultancy, Honoraria, Research Funding, Speakers Bureau. Neufeld:Baxter: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer: data safety monitoring board, data safety monitoring board Other; Biogen IDEC: Membership on an entity's Board of Directors or advisory committees; NovoNordisk: Membership on an entity's Board of Directors or advisory committees; Pfiser: consultancy, data and safety monitoring board Other; Octapharma: Research Funding. Dunn:CSL Behring,: Membership on an entity's Board of Directors or advisory committees; Bayer: Membership on an entity's Board of Directors or advisory committees; Baxter: Membership on an entity's Board of Directors or advisory committees; Biogen: Membership on an entity's Board of Directors or advisory committees; Pfiser: Membership on an entity's Board of Directors or advisory committees. Iorio:Baxter: Honoraria, Research Funding; Bayer: Honoraria, Research Funding; NovoNordisk: Honoraria, Research Funding; Biogen: Honoraria, Research Funding; Pfiser: Honoraria, Research Funding.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.056
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.006
Bibliometrics0.0170.019
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.034
GPT teacher head0.310
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

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Citations1
Published2014
Admission routes1
Has abstractyes

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