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Record W2945839580 · doi:10.1002/psp4.12414

Physiologically‐Based Pharmacokinetic Modeling of Fluconazole Using Plasma and Cerebrospinal Fluid Samples From Preterm and Term Infants

2019· article· en· W2945839580 on OpenAlexaff
Jacqueline G. Gerhart, Kevin Watt, Andrea N. Edginton, Kelly C. Wade, Sara Salerno, Daniel K. Benjamin, P. Brian Smith, Christoph P. Hornik, Michael Cohen‐Wolkowiez, Shahnaz Duara, Ashley Ross, Karen E. Shattuck, Dan L. Stewart, Natalie Neu, Daniel González

Bibliographic record

VenueCPT Pharmacometrics & Systems Pharmacology · 2019
Typearticle
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsUniversity of Waterloo
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Advancing Translational SciencesNational Institute of Allergy and Infectious DiseasesNational Institute of Child Health and Human DevelopmentNational Institute of General Medical SciencesNational Institutes of Health
KeywordsPharmacokineticsCerebrospinal fluidDosingFluconazoleMedicinePhysiologically based pharmacokinetic modellingPlasma concentrationPharmacologyInternal medicineAntifungal

Abstract

fetched live from OpenAlex

Fluconazole is used to treat hematogenous Candida meningoencephalitis in preterm and term infants. To characterize plasma and central nervous system exposure, an adult fluconazole physiologically-based pharmacokinetic (PBPK) model was scaled to infants, accounting for age dependencies in glomerular filtration and metabolism. The model was optimized using 760 plasma samples from 166 infants (median postmenstrual age (range) 28 weeks (24-50)) and 27 cerebrospinal fluid (CSF) samples from 22 infants (postmenstrual age 28 weeks (24-33)). Simulations evaluated achievement of the surrogate efficacy target of area under the unbound concentration-time curve ≥ 400 mg • hour/L over the dosing interval in plasma and CSF using dosing guidelines. Average fold error of predicted concentrations was 0.73 and 1.14 for plasma and CSF, respectively. Target attainment in plasma and CSF was reached faster after incorporating a loading dose of 25 mg/kg. PBPK modeling can be useful in exploring CNS kinetics of drugs in children.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.332
Teacher spread0.279 · 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

Citations25
Published2019
Admission routes1
Has abstractyes

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