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Use of a Population Pharmacokinetic Model of ADVATE In Pediatric and Adult Patients with Hemophilia A Permits Limited Blood Sampling for Individual Dose Tailoring.

2010· article· en· W2478766160 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 · 2010
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicinePharmacokineticsCovariatePopulationBlood samplingSampling (signal processing)NONMEMInternal medicineStatisticsMathematics

Abstract

fetched live from OpenAlex

Abstract Abstract 1416 Introduction: A population pharmacokinetic (PK) model of a recombinant FVIII (rFVIII) was established on ADVATE® (Antihemophilic Factor (Recombinant), Plasma/Albumin-Free Method) studies in pediatric and adult patients with hemophilia A. The objective of this analysis was to evaluate the effect of reduced PK sampling time points on the estimated PK parameters in the population PK model. Patients and Methods: Plasma FVIII activity PK data were collected for 3 ADVATE® clinical trials in previously treated patients: 184 full PK data sets (11 time points) for 100 adults/adolescents, aged 10 to 65 years, and from 52 reduced sample PK data sets (5 time points) for 52 children, aged 1 to 6 years. A population PK analysis was conducted on a two-compartment structure model and the covariate effect of age and weight was explored. Four reduced sampling scenarios from the full 10 post-infusion sampling time points, were investigated: 1) Reduced to 4 (1 hr, 9 hr, 24 hr, and 48 hr), 2) Reduced to 3 (6 hr, 24 hr, and 48 hr), 3) Reduced to 2 (6 hr and 24 hr), and 4) Reduced to 1 sampling time points (24 hr post-infusion). After applying the reduced sampling on a random 10% of sampling set at a time in the population PK model, the differences in model estimates and individual PK estimates between full and reduced sampling, were evaluated. Results: The two-compartment population PK model adequately described the data. Clearance (CL) was significantly correlated with age and body weight and central volume of distribution was also related with body weight. Absolute deviations (%) from the estimates using full PK sampling in the Individual PK estimates (CL, Vss, and Half-life) using each of the reduced sampling time points were showed in the below table. Conclusions: It appears that PK parameters estimated using population PK model are robust to reduced sampling time points. Accurate measurement of PK on reduced samples gives patients and clinicians the opportunity to design treatment regimens that are better tailored to individuals. Disclosures: Oh: Baxter: Employment. Björkman:Baxter: Consultancy; Octapharma: Consultancy. Schroth:Baxter: Employment. Fritsch:Baxter: Employment. Collins:NovoNordisk: Consultancy, Honoraria, The EACH2 registry was funded by Novonordisk; Baxter Healthcare: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Fischer:Baxter: Consultancy; NovoNordisk: Consultancy. Blanchette:Bayer: Consultancy; Baxter: Research Support. Casey:Baxter: Employment. Spotts:Baxter: Employment. Ewenstein:Baxter Bioscience: 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)0.002
metaresearch head score (Gemma)0.007
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.300
Teacher spread0.249 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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