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Record W2971187092 · doi:10.1055/s-0039-1690194

A Multifunctional HBED-Type Chelator with Dual Conjugation Capabilities for Radiopharmaceutical Development

2019· article· en· W2971187092 on OpenAlexaff
Ata Makarem, Mohammadreza Kamali Sarvestani, Karel D. Klika, Klaus Kopka

Bibliographic record

VenueSynlett · 2019
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsInstitute of Cancer Research
FundersDeutsche KrebshilfeDeutscher Akademischer Austauschdienst
KeywordsChemistryBioconjugationCombinatorial chemistryBifunctionalChelationAzideDOTAMoleculeBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Bifunctional HBED chelators are hexadentate complexing ligands (chelators) that tightly coordinate to trivalent gallium and, additionally, are able to bind to bioactive molecules. In nuclear medicine, HBED-based radiopharmaceuticals are used as powerful radiotracers for tumor imaging. Among variants of bifunctional HBED chelators, HBED-CC is the most well-known; it possesses two terminal carboxylic acid groups that are able to undergo bioconjugation by amide-bond formation. However, to permit bioconjugation through click coupling, we previously modified the structure of HBED-CC and introduced HBED-NN chelator bearing two azide functions. We have now combined the conjugation capabilities of HBED-CC and HBED-NN chelators in one molecule and have created HBED-NC, which possesses both azide and carboxylic acid groups. The advantage of HBED-NC is that it provides options for constructing either monomeric or heterodimeric radiolabeling precursors. This work describes the synthesis of HBED-NC by either of two pathways.

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 score0.003

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.312
Teacher spread0.283 · 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

Citations16
Published2019
Admission routes1
Has abstractyes

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