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Record W4403096183 · doi:10.1016/j.ejmcr.2024.100223

Truncated NPY-based NPY(Y1)R-specific radiopeptides: Improved in vivo PET tumor imaging by application of peptidase inhibitors

2024· article· en· W4403096183 on OpenAlexaff
Benedikt Judmann, Nils.F. Baier, Henning Rudolf, Güllü Davarci, Björn Wängler, Ralf Schirrmacher, Gert Fricker, Carmen Wängler

Bibliographic record

VenueEuropean Journal of Medicinal Chemistry Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsUniversity of Alberta
FundersBundesministerium für Bildung und ForschungDeutsche Forschungsgemeinschaft
KeywordsIn vivoChemistryEndocrinologyInternal medicineMedicineBiologyBiotechnology

Abstract

fetched live from OpenAlex

The neuropeptide Y receptor subtype 1 (NPY(Y 1 )R) exhibits high expression rates on human breast cancer and is therefore an important target structure for the sensitive and specific visualization and characterization of the disease by Positron Emission Tomography (PET). However, imaging of this receptor type has been of limited success so far due to the low stability of peptide-based NPY-derived NPY(Y 1 )R-specific radiotracer candidates due to in vivo degradation. Given the challenges in stabilizing these agents, our study sought to explore whether the stability of NPY(Y 1 )R-specific radiopeptides could be enhanced. We aimed to achieve this by either modifying the peptide structure with various molecular scaffolds or by applying peptidase inhibitors. This evaluation aimed to identify the optimal approach for achieving effective NPY(Y 1 )R-specific imaging in the following. To validate our approach, we systematically investigated four new truncated 68 Ga-labeled analogs of NPY and the reference compound [ 68 Ga]Ga-[Lys 4 (N ε -DOTA)]BVD15, bearing different molecular scaffolds such as a DOTA or NODA-GA chelator, a 4-APipAc linker, a Bip unit, and an N -terminal Lys(lauryl) group. The four new radiotracers as well as the reference compound were obtained in high chemical and radiochemical yields with molar activities of 33–39 GBq/μmol. The radiopeptides exhibited varying log D 7.4 values, ranging from −3.37 ± 0.09 to +0.35 ± 0.11, and showed different levels of stability in human serum and liver microsomes, depending on their molecular structure. Subsequently, the influence of the peptidase inhibitors actinonin, phosphoramidon, captopril and E−64 on the in vitro stability of the radiotracers was investigated. In these studies, only actinonin demonstrated a positive effect on the stability of all radiopeptides. In contrast, phosphoramidon yielded variable results, and neither captopril nor E−64 showed a significant stabilizing effect. Consequently, the effect of actinonin administration on the in vivo PET/CT imaging results of the most promising ligand [ 68 Ga]Ga-[Lys 4 (N ε -NODA-GA)]BVD15 ([ 68 Ga]Ga- 2 ) was investigated in a T47D tumor-bearing xenograft mouse model, followed by ex vivo biodistribution studies. In these experiments, the administration of 250 μg actinonin resulted in a significantly increased uptake of [ 68 Ga]Ga- 2 in the T47D tumor (5.9 ± 1.0 % ID/g (with actinonin) instead of 3.1 ± 0.9 % ID/g (without actinonin) at 2h p.i.), and an increase in tumor-to-muscle ratios from 1.8 to 4.0 upon co-administration of the inhibitor. The results impressively demonstrate the positive influence of actinonin on the in vivo stability of the NPY(Y 1 )R-specific radiopeptide [ 68 Ga]Ga- 2 , resulting in an increased tumor accumulation and improved tumor-to-background ratios. These findings thus provide important incentive for further advancement of NPY(Y 1 )R-specific tumor imaging using PET. • Peptidic NPY(Y 1 )R-specific radioligands usually exhibit a limited in vivo stability. • Structural modifications of the peptide do not sufficiently solve the problem. • Peptidase inhibitors can decelerate radiopeptide degradation. • Actinonin is a suitable inhibitor for stabilizing NPY(Y 1 )R-specific radiopeptides. • Actinonin demonstrated to decelerate radiopeptide degradation in vitro and in vivo .

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.264
Teacher spread0.256 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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