MétaCan
Menu
Back to cohort
Record W2068549807 · doi:10.2217/fon.14.208

Dovitinib (CHIR258, TKI258): Structure, Development and Preclinical and Clinical Activity

2015· article· en· W2068549807 on OpenAlexaff
Camillo Porta, Palma Giglione, Wanda Liguigli, Chiara Paglino

Bibliographic record

VenueFuture Oncology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFibroblast Growth Factor Research
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsMedicineSorafenibPlatelet-derived growth factor receptorTyrosine-kinase inhibitorReceptorPharmacologyVEGF receptorsReceptor tyrosine kinaseCancer researchTyrosine kinaseOncologyCancerInternal medicineGrowth factorHepatocellular carcinoma

Abstract

fetched live from OpenAlex

Dovitinib is an oral multikinase inhibitor targeting FGF receptors, PDGF receptors and VEGF receptors. Its activity against FGF receptors suggests its usefulness in treating cancers after the failure of VEGF/VEGF receptor-targeting agents. The identified dose and schedule to be used in further studies was 500 mg orally for 5 days on and 2 days off. Biological considerations and the results achieved in a Phase I/II trial suggested its activity in advanced renal cell carcinoma patients pretreated with a tyrosine kinase inhibitor and an mTOR inhibitor. Surprisingly, in a randomized controlled Phase III trial versus sorafenib in the same setting, dovitinib failed to demonstrate any superiority. At present, dovitinib is being tested in different tumor types. However, molecular-based patient selection seems to be key to fully exploit the activity of this drug.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.062
GPT teacher head0.405
Teacher spread0.342 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations64
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueFuture OncologySame topicFibroblast Growth Factor ResearchFrench-language works237,207