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Record W4412278168

Direct Aromatic F-18-labeling of Tetrazines:A Rapid and Convenient Entry to Tetrazines for Pretargeted PET Imaging [Meeting Abstract]

2020· article· en· W4412278168 on OpenAlexaff
Rocio Garcia Vazquez, Umberto Maria Battisti, Daniel L. Stares, Ida Nymann Petersen, François Crestey, Dennis Svatunek, Hannes Mikula, Jesper L. Kristensen, Andreas Kjær, Matthias M. Herth

Bibliographic record

VenueResearch at the University of Copenhagen (University of Copenhagen) · 2020
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicChemical Reactions and Isotopes
Canadian institutionsInnovation Cluster (Canada)Nickel Institute
Fundersnot available
KeywordsChemistryRadiochemistry
DOInot available

Abstract

fetched live from OpenAlex

<b>134</b> <h3><b>Introduction:</b></h3> Recently, tetrazines have been successfully developed as pretargeted imaging agents. Particularly, they have been of interest when used with trans-cyclooctene (TCO) modified mAbs for pretargeted immunoimaging due to their fast reaction kinetics <i>in vivo</i> as well as their high specificity. (1) Pretargeting is based on a two-step process in which a TCO-tagged nanomedicine, for example an antibody, is administered and allowed to accumulate followed by a short-lived radiolabeled tetrazine. The latter reagent is able to form a stable covalent conjugate with the TCO through a bioorthogonal reaction. (2,3) This approach results in an enhanced safety profile as well as a higher image quality when compared to conventional radioimmunoimaging. (4) Many attempts to label highly reactive tetrazines with <sup>18</sup>F, the most ideal nuclide for PET imaging, have been attempted. However, no successful <sup>18</sup>F-direct labeling approach has been reported so far with enough radiochemical yields (RCYs). (5) In this work, we successfully developed a direct aromatic <sup>18</sup>F-labeling approach of tetrazines. <h3><b>Methods:</b></h3> Initially, a low reactive methyl-tetrazine was selected as a model compound to evaluate the feasibility of the synthesis and radiolabelling of a set of precursors through direct aromatic fluorinations (<i>Figure 1</i>). Thereafter, the best labeling approach was optimized in respect to labeling conditions varying temperature, time and base amount. In a next step, we studied the substrate scope of the reaction on a set of tetrazines including methyl-, H-, phenyl- and pyridyl- tetrazines (<i>Figure 2</i>). The effect of electron withdrawing and electron donating groups on the aromatic ring was evaluated in terms of synthetic accessibility, radiochemical conversion (RCC) and RCY (<i>Figure 3</i>). <h3><b>Results:</b></h3> Out of all tested labeling reactions, radiolabeling only succeeded through Cu-mediated <sup>18</sup>F-fluorination using stannate precursors (<i>Figure 1)</i>. Moderate to good RCYs (10-24%) were obtained for methyl-, phenyl- and H-tetrazines. 2-Pyridyl structures were prohibitive, most likely due to a chelation effect of copper with the respective pyridyl moieties of the tetrazines <i>(Figure 2</i>). Subsequently, we investigated which substitution pattern of the H-tetrazine yielded in highest RCYs. Best results were obtained with a 3,5-substitution pattern (<i>Figure 3</i>). <h3><b>Conclusions:</b></h3> This work showed the first <sup>18</sup>F-direct labeling strategy of highly reactive tetrazines, starting from organotin precursors via a Cu-mediated approach. The developed procedure is simple, short, reproducible as well as scalable and as such, superior to previously used <sup>18</sup>F-multistep labeling strategies with regard to clinical applications. Currently, we are working on the first directly <sup>18</sup>F-radiolabeled tetrazine structures, which will be used for pretargeted brain and cancer imaging. <b>Acknowledgments:</b> This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 813528. This project has also received funding from the European Union9s Horizon 2020 research and innovation program under grant agreement No 668532: Click-it.<b><i>Figure 1</i></b><i>. Radiolabeling strategies using different methyl-tetrazine precursors to probe the fluorination.</i><b><i>Figure 2</i></b><i>. Radiolabeling with Cu-mediated fluorination reaction of higher reactive tetrazines. *The radiolabeled compound was never isolated. †Preliminary studies based on n=2.</i><b><i>Figure 3</i></b><i>. Study of the RCCs when including in the aromatic ring different linkers. *The stannate precursor was never isolated. **The radiolabeled compound was never isolated.</i><i>†Preliminary studies based on n=2.</i>

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0650.001

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 head0.140
GPT teacher head0.389
Teacher spread0.249 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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