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Record W4387041708 · doi:10.1021/acs.inorgchem.3c02714

DFO-Km: A Modular Chelator as a New Chemical Tool for the Construction of Zirconium-89-Based Radiopharmaceuticals

2023· article· en· W4387041708 on OpenAlexafffund
Akam K. Salih, Moralba Dominguez Garcia, Shvan J. Raheem, William K. Ahiahonu, Eric W. Price

Bibliographic record

VenueInorganic Chemistry · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsChemistryBifunctionalChelationMoietyLinkerCombinatorial chemistryZirconiumBioconjugationStereochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Zirconium-89-labeled monoclonal antibodies and other large macromolecules such as nanoparticles hold great promise as positron emission tomography imaging agents. In general, zirconium-89 is an ideal radionuclide for long-circulating vectors such as antibodies or nanoparticles. It is also a promising radionuclide for theranostic radiopharmaceuticals due to its suitable match in half-life with actinium-225, thorium-227, lutetium-177, and others. As such, demand for new and optimized bifunctional chelators for zirconium-89 continues to grow. Herein, we present the modular chelator DFO-Km, which is octadentate and features lysine as a modular amino acid linker. The modular amino acid linker can be changed to other natural or unnatural amino acids to access different bioconjugation chemistries, while the chelating portion is unchanged thus retaining identical metal ion coordination properties to DFO-Km. The epsilon-amine in the DFO-Km linker (lysine) was used to complete synthesis of a bifunctional derivative bearing a p -SCN-Ph moiety. The chelator DFO-Km includes a redesigned hydroxamic acid, which provides more flexibility for metal ion coordination relative to the monomer used in the previously published DFO-Em. Moreover, a set of comprehensive DFT calculations were performed to model and evaluate 16 geometric isomers of Zr-(DFO-Km), which suggested the complex would form the optimum cic–cis–trans–trans octadentate Zr(IV) coordination geometry with no aqua or hydroxide ligands present. The bifunctional derivative p -SCN-Ph-DFO-Km was compared directly with the commercially available p -SCN-Ph-DFO, and both underwent efficient conjugation to a nonspecific human serum antibody (IgG) to yield two model immunoconjugates. The behavior of [ 89 Zr]Zr-DFO-Km-IgG was studied in healthy mice for 2 weeks and compared to an equivalent cohort injected with [ 89 Zr]Zr-DFO-IgG as a clinical “gold standard” control. PET-CT and biodistribution results revealed higher stability of [ 89 Zr]Zr-(DFO-Km)-IgG in vivo over [ 89 Zr]Zr-DFO-IgG, as demonstrated by the significant reduction of zirconium-89 in the whole skeleton as visualized and quantified by PET-CT at 1, 3, 7, and 14 days post-injection. Using CT-gated regions of interest over these PET-CT images, the whole skeleton was selected and uptake values were measured at 14 days post-injection of 3.6 ± 0.9 (DFO) vs 1.9 ± 0.1 (DFO-Km) %ID/g ( n = 4, * p = 0.02), which represents a ∼48% reduction in bone uptake with DFO-Km relative to DFO. Biodistribution experiments performed on these same mice following the 14 day imaging time point revealed bone (both tibia) uptake values of 3.7 ± 1.3 (DFO) vs 2.0 ± 0.6 (DFO-Km) %ID/g ( n = 6, * p < 0.05), with the tibia uptake values in close agreement with whole-skeleton ROI PET-CT data. These results indicate that DFO-Km is an improved chelator for [ 89 Zr]Zr 4+ applications relative to DFO. The bifunctional chelator p -SCN-Ph-DFO-Km shows potential as a new chemical tool for creating bioconjugates using targeting vectors such as antibodies, peptides, and nanoparticles.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.332
Teacher spread0.304 · 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.

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

Citations15
Published2023
Admission routes2
Has abstractyes

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