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Population Pharmacokinetic Model of ADVATE in Pediatric and Adult Patients with Hemophilia A.

2009· article· en· W2584399332 on OpenAlexaff
Myungshin Oh, Sven Björkman, Phillip Schroth, Sandor Fritsch, Peter W. Collins, Kathelijn Fischer, Victor S. Blanchette, Gerald Spotts, Bruce M. Ewenstein

Bibliographic record

VenueBlood · 2009
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicinePopulationCovariatePharmacokineticsVolume of distributionMixed modelInternal medicineStatisticsMathematics

Abstract

fetched live from OpenAlex

Abstract Abstract 3492 Poster Board III-429 Introduction The objective of this analysis was to characterize the population pharmacokinetic (PK) model of ADVATE® (Antihemophilic Factor (Recombinant), Plasma/Albumin-Free Method) in hemophilia A patients. This included estimation of typical population pharmacokinetic parameters and inter-individual and residual variability and identification of covariates that are significant predictors of variability in a pooled population of children and adults. Patients and Methods Plasma FVIII activity PK data were collected for 3 ADVATE® clinical trials in previously treated patients: 184 full PK data sets for 100 adults/adolescents, aged 10 to 65 years, and from 52 reduced sample PK data sets for 52 children, aged 1 to 6 years. Population PK analysis was conducted using non-linear mixed effects modeling with the first-order integral approximation method in SAS® software (NLMIXED procedure). A two-compartment model was used as the base model and the influence of age and weight were explored. Results Two-compartment PK models with additive plus proportional residual variability model and exponential inter-individual variability model adequately described the data. Clearance (CL) is significantly correlated with age and body weight and central volume of distribution (V1) is also related with body weight. The estimated population PK parameters were (mean parameter, (inter-individual variability %)): CL (2.92 mL/kg·h, 22%), V1 (0.46 dL/kg, 5.2%), peripheral volume V2 (0.09 dL/kg) and inter-compartmental clearance Q (2.07 mL/ kg·h). Conclusions A population PK model that describes the combined PK data from adults and pediatric studies has been constructed. A significant portion of inter-individual variability in both volume and clearance can be explained by subject weight. An additional smaller effect of age on clearance but not volume was observed. A population PK model for Factor VIII could provide the clinician with advantages in designing a patient specific treatment regimen. It could provide more relevant guidance in individual patient pharmacokinetics than just incremental recovery without the burden of a full PK assessment of the patient. Disclosures: Oh: Baxter: Employment. Off Label Use: Prophylaxis is not indicated in the US. Björkman:Baxter: Consultancy; Octapharma: Consultancy. Schroth:Baxter: Employment. Fritsch:Baxter: Employment. Collins:Bayer: Consultancy; Novo Nordisk: Consultancy; Baxter: Consultancy. Fischer:Bayer: Consultancy; Wyeth: Consultancy; Baxter: Consultancy. Blanchette:Bayer: Consultancy; Baxter: Consultancy. Casey:Baxter: Employment. Spotts:Baxter: Employment. Ewenstein:Baxter: Employment.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.266
Teacher spread0.255 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2009
Admission routes1
Has abstractyes

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