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Record W2779627475 · doi:10.1021/acs.jmedchem.7b01607

Identification of [<sup>18</sup>F]TRACK, a Fluorine-18-Labeled Tropomyosin Receptor Kinase (Trk) Inhibitor for PET Imaging

2017· article· en· W2779627475 on OpenAlexafffund
Vadim Bernard‐Gauthier, Andrew V. Mossine, Anne Mahringer, Arturo Aliaga, Justin J. Bailey, Xia Shao, Jenelle Stauff, Janna Arteaga, Phillip Sherman, Marilyn Grand’Maison, Pierre-Luc Rochon, Björn Wängler, Carmen Wängler, Peter Bartenstein, Alexey Kostikov, David R. Kaplan, Gert Fricker, Pedro Rosa‐Neto, Peter J. H. Scott, Ralf Schirrmacher

Bibliographic record

VenueJournal of Medicinal Chemistry · 2017
Typearticle
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsHospital for Sick ChildrenMontreal Neurological Institute and HospitalMcGill UniversityDouglas Mental Health University InstituteUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCancer Research SocietyCanada Foundation for InnovationNational Institute of Biomedical Imaging and BioengineeringWeston Brain InstituteU.S. Department of Energy
KeywordsTrk receptorChemistryTropomyosin receptor kinase APharmacophoreIn vivoRadioligandReceptorTropomyosinHEK 293 cellsBiochemistryNeurotrophin

Abstract

fetched live from OpenAlex

Changes in expression and dysfunctional signaling of TrkA/B/C receptors and oncogenic Trk fusion proteins are found in neurological diseases and cancers. Here, we describe the development of a first 18 F-labeled optimized lead suitable for in vivo imaging of Trk, [ 18 F]TRACK, which is radiosynthesized with ease from a nonactivated aryl precursor concurrently combining largely reduced P-gp liability and improved brain kinetics compared to previous leads while displaying high on-target affinity and human kinome selectivity.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
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 head0.039
GPT teacher head0.380
Teacher spread0.341 · 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 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

Citations47
Published2017
Admission routes2
Has abstractyes

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