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Record W2549137967 · doi:10.1002/ijc.30533

Development of crizotinib, a rationally designed tyrosine kinase inhibitor for non‐small cell lung cancer

2016· review· en· W2549137967 on OpenAlexaff
Candice C. Poon, John J. Kelly

Bibliographic record

VenueInternational Journal of Cancer · 2016
Typereview
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCrizotinibMedicineLung cancerClinical trialTyrosine-kinase inhibitorOncologyInternal medicineAnaplastic lymphoma kinaseDrug developmentTyrosine kinaseTargeted therapyALK inhibitorCancerDrugPharmacologyReceptor

Abstract

fetched live from OpenAlex

Non-small cell lung cancer (NSCLC) is the number one cause of global mortality. Despite aggressive treatment, the prognosis is dismal. Patients with advanced NSCLC have a median survival of 4 months from the time of diagnosis. Fortunately, molecularly based approaches to drug discovery have yielded a tyrosine kinase inhibitor, crizotinib, which significantly prolongs median progression-free survival in a subset of patients. Although initial clinical trial results demonstrate crizotinib has a promising role to play in NSCLC treatment, development of resistance leaves much to be elucidated about how to effectively combat this deadly disease. In this review, we follow the discovery and development of crizotinib from bench to bedside and provide an example of successful bottom-up drug design. Then, we explore the clinical trial results that fast-tracked its eventual use as a frontline therapy for sensitive NSCLC patients and the development of resistance. Lastly, we discuss the potential for future uses of crizotinib both within and beyond NSCLC.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.036
GPT teacher head0.413
Teacher spread0.376 · 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 designOther design
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

Citations43
Published2016
Admission routes1
Has abstractyes

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