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Record W4398778278 · doi:10.1080/13543776.2024.2358818

Type II & III inhibitors of tropomyosin receptor kinase (Trk): a 2020–2022 patent update

2024· article· en· W4398778278 on OpenAlexaff
Petar Iliev, Carolin Jaworski, Carmen Wängler, Björn Wängler, Brent D. G. Page, Ralf Schirrmacher, Justin J. Bailey

Bibliographic record

VenueExpert Opinion on Therapeutic Patents · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Kinase Regulation and GTPase Signaling
Canadian institutionsTRIUMFUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsTrk receptorTropomyosinReceptorTropomyosin receptor kinase AChemistryTropomyosin receptor kinase CKinaseBiochemistryPharmacologyCell biologyBiologyPlatelet-derived growth factor receptorNeurotrophin

Abstract

fetched live from OpenAlex

INTRODUCTION: The Trk family proteins are membrane-bound kinases predominantly expressed in neuronal tissues. Activated by neurotrophins, they regulate critical cellular processes through downstream signaling pathways. Dysregulation of Trk signaling can drive a range of diseases, making the design and study of Trk inhibitors a vital area of research. This review explores recent advances in the development of type II and III Trk inhibitors, with implications for various therapeutic applications. AREAS COVERED: . Relevant patents were identified using the Web of Science database, Google, and Google Patents. EXPERT OPINION: While type II and III Trk inhibitor development has advanced more gradually compared to their type I counterparts, they hold significant promise in overcoming resistance mutations and achieving enhanced subtype selectivity - a critical factor in reducing adverse effects associated with pan-Trk inhibition. Recent interdisciplinary endeavors have marked substantial progress in the design of subtype selective Trk inhibitors, with impressive success heralded by the type III inhibitors. Notably, the emergence of mutant-selective Trk inhibitors introduces an intriguing dimension to the field, offering precise treatment possibilities.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.874

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)0.0010.000

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 head0.040
GPT teacher head0.300
Teacher spread0.260 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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