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Record W1993341051 · doi:10.1517/13543776.12.12.1795

Developments in mitogen-induced extracellular kinase 1 inhibitors and their use in the treatment of disease

2002· article· en· W1993341051 on OpenAlexaff
Joan C. Krepinsky, Dongcheng Wu, Alistair J. Ingram, James W. Scholey, Damu Tang

Bibliographic record

VenueExpert Opinion on Therapeutic Patents · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversitySt. Joseph's HospitalUniversity of Toronto
Fundersnot available
KeywordsMAPK/ERK pathwayKinaseSignal transductionCancer researchExtracellularProtein kinase AMitogen-activated protein kinaseImmune systemASK1BiologyCell biologyMedicineImmunologyMitogen-activated protein kinase kinase

Abstract

fetched live from OpenAlex

Multiple signal transduction pathways converge on the Raf-mitogen-induced extracellular kinase (MEK)-extracellular signal-regulated kinase (Erk) cascade to effect diverse cellular processes, including proliferation, differentiation, survival, apoptosis and organ functions such as memory consolidation. Improper activation of this pathway contributes significantly to numerous diseases, including cancer and various immune disorders. Specific inhibition of this signalling cascade thus offers great therapeutic potential for many diseases. Since the discovery of the first MEK1 inhibitor in 1995, several novel classes of inhibitors, with varying selectivity for MEK1, have been developed. Clinical applications for some of these have been investigated, with the majority focusing on proliferative diseases in which abnormally increased Erk activity plays a major role, most notably cancer, or immunological and inflammatory conditions such as arthritis and organ transplant rejection. To a lesser extent, ischaemia/reperfusion (I/R) injury and chronic pain disorders have also been targeted.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.085
GPT teacher head0.274
Teacher spread0.189 · 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 designBench or experimental
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

Citations9
Published2002
Admission routes1
Has abstractyes

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