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Application of pharmacokinetic (PK)-guided 5-fluorouracil (FU) in clinical practice.

2013· article· en· W2603540908 on OpenAlexaff
Jai N. Patel, Allison M. Deal, Bert H. O’Neil, Joe Ibrahim, Gary B. Sherrill, Janine M. Davies, Stephen A. Bernard, Richard M. Goldberg, Oludamilola Olajide, Prashanti Atluri, John J. Inzerillo, Howard L. McLeod, Christine M. Walko

Bibliographic record

VenueJournal of Clinical Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineBevacizumabDosingPharmacokineticsFluorouracilBody surface areaArea under the curveInternal medicineUrologyGastroenterologyToxicityColorectal cancerNomogramClinical trialPharmacologyOncologyCancerChemotherapy

Abstract

fetched live from OpenAlex

2595 Background: Body surface area (BSA)-based dosing of FU results in up to 100-fold inter-individual PK variability. PK-guided FU compared to BSA-based dosing resulted in higher response rates and decreased rate of toxicities in two randomized clinical trials. A paucity of data exists on PK-guided FU dosing in the clinical setting. Methods: A total of 70 colorectal cancer (CRC) patients (pts) from 6 academic and community sites received mFOLFOX6 (FU 2,400 mg/m2over 46 h every 2 wks) +/- bevacizumab. Peripheral blood was obtained 2-44 h after start of FU infusion and AUCs were estimated using an immunoassay at Myriad Genetics. FU doses for cycles 2-4 (C2-4) were adjusted algorithmically to target an area under the concentration-time curve (AUC) of 20-25 mg*h/L. The primary outcome was the % of pts within target AUC by C4, with a secondary outcome of toxicity rates compared to historical data. Comparisons between cycles were made using generalized linear models, accounting for repeated observations within pt. Results: The % of pts within target AUC post C1 and C4 was 30% (17/57, 95%CI: 18-43%) and 46% (24/52, 95%CI: 32-61%), respectively (OR=2.16, p=0.05). For each subsequent cycle, the odds of a pt being within range increases by 28% (p=0.04) (Table). The median dose needed to achieve target AUC at C4 was 2,580 (range 1,920-3,484) mg/m2. The median AUC post C1 and C4 was 19 and 21 mg*h/L, respectively. Less grade 3/4 mucositis and diarrhea were seen compared to historical data (3 v 15% and 6 v 12%, respectively); however, no difference in grade 3/4 neutropenia was noted (27 v 33%). Nine pts were non-evaluable by protocol for PK analysis, largely due to sampling/processing errors. Conclusions: PK-guided FU resulted in a greater number of pts achieving the targeted AUC and fewer pts under-dosed at C4 compared to C1. Individualization of FU dosing in the front-line, community and academic, setting is achievable for the treatment of CRC; however, larger clinical trials are needed to define the clinical utility of PK-guided FU. Clinical trial information: NCT01164215. [Table: see text]

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Other designmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.013
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
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 head0.158
GPT teacher head0.550
Teacher spread0.391 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
Domainnot available
GenreEmpirical · Other

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

Citations3
Published2013
Admission routes1
Has abstractyes

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