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Record W2914439358 · doi:10.1016/j.bmcl.2019.02.014

Improved radiosynthesis and preliminary in vivo evaluation of the 11C-labeled tetrazine [11C]AE-1 for pretargeted PET imaging

2019· article· en· W2914439358 on OpenAlexaff
E. Johanna L. Stéen, Jesper Tranekjær Jørgensen, Ida Nymann Petersen, Kamilla Nørregaard, Szabolcs Lehel, Vladimir Shalgunov, Alexander Birke, Patricia E. Edem, Elina Tampio L’Estrade, Hanne D. Hansen, Jonas Villadsen, Maria Erlandsson, Tomas Ohlsson, Abdolreza Yazdani, John F. Valliant, Jesper L. Kristensen, Gitte M. Knudsen, Andreas Kjær, Matthias M. Herth

Bibliographic record

VenueBioorganic & Medicinal Chemistry Letters · 2019
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsMcMaster University
FundersHorizon 2020 Framework ProgrammeRigshospitalet
KeywordsRadiosynthesisChemistryTetrazineRadiochemistryIn vivoPet imagingPretargetingPositron emission tomographyNuclear medicineRadioimmunotherapyOrganic chemistry

Abstract

fetched live from OpenAlex

Pretargeted nuclear imaging based on the ligation between tetrazines and nano-sized targeting agents functionalized with trans-cyclooctene (TCO) has recently been shown to improve both imaging contrast and dosimetry in nuclear imaging of nanomedicines. Herein, we describe the improved radiosynthesis of a 11C-labeled tetrazine ([11C]AE-1) and its preliminary evaluation in both mice and pigs. Pretargeted imaging in mice was carried out using both a new TCO-functionalized polyglutamic acid and a previously reported TCO-functionalized bisphosphonate system as targeting agents. Unfortunately, pretargeted imaging was not successful using these targeting agents in pair with [11C]AE-1. However, brain imaging in pig indicated that the tracer crossed the blood-brain-barrier. Hence, we suggest that this tetrazine scaffold could be used as a starting point for the development of pretargeted brain imaging, which has so far been a challenging task.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.021
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.013
GPT teacher head0.274
Teacher spread0.261 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

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