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Record W2123762312 · doi:10.1002/cpdd.35

Pharmacokinetics of Irinotecan With and Without Panitumumab Coadministration in Patients With Metastatic Colorectal Cancer

2013· article· en· W2123762312 on OpenAlexaff
Bingbing Yang, Chi‐Yuan Wu, Eric X. Chen, Jeffrey R. Infante, Alin Chen, Bing Gao, Brian P. Smith, Jason B. Litten, Hagen F. Kennecke

Bibliographic record

VenueClinical Pharmacology in Drug Development · 2013
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsBC Cancer AgencyPrincess Margaret Cancer Centre
FundersPfizerAmgen
KeywordsMedicinePanitumumabIrinotecanPharmacokineticsColorectal cancerOncologyPharmacologyInternal medicineCancerCetuximab

Abstract

fetched live from OpenAlex

This study examined the effects of panitumumab, a human monoclonal antibody against epidermal growth factor receptor (EGFR), on irinotecan pharmacokinetics. This phase I, open-label, multicenter, single-arm study enrolled patients with metastatic colorectal cancer (mCRC) without prior exposure to an EGFR inhibitor. In cycle 1, patients received irinotecan (180 mg/m(2) intravenously [IV]) on day 1 and panitumumab (6 mg/kg IV) on Day 4. In cycle 2 (2 weeks after cycle 1 panitumumab administration) and subsequent every-2-week cycles, patients received panitumumab followed immediately by irinotecan until disease progression or intolerability. Primary and secondary endpoints included Cmax and AUC of irinotecan after irinotecan infusion in cycles 1 and 2, and adverse events, respectively. Nineteen of 27 treated patients were eligible for pharmacokinetic analysis. Pharmacokinetic profiles of irinotecan with or without panitumumab coadministration were nearly identical. The 90% confidence intervals for ratios of geometric means for irinotecan Cmax and AUC with or without panitumumab were within the 80-125% interval, indicating that panitumumab had no apparent effects on irinotecan pharmacokinetics. Adverse events were as expected for irinotecan plus panitumumab combination therapy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.367
Teacher spread0.337 · 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 teacher head, not a consensus.

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

Citations3
Published2013
Admission routes1
Has abstractyes

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