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Record W3168529792 · doi:10.3390/ph14060547

Design, Synthesis, In Vitro and In Vivo Evaluation of Heterobivalent SiFAlin-Modified Peptidic Radioligands Targeting Both Integrin αvβ3 and the MC1 Receptor—Suitable for the Specific Visualization of Melanomas?

2021· article· en· W3168529792 on OpenAlexaff
Xia Cheng, Ralph Hübner, Valeska von Kiedrowski, Gert Fricker, Ralf Schirrmacher, Carmen Wängler, Björn Wängler

Bibliographic record

VenuePharmaceuticals · 2021
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsUniversity of Alberta
FundersBundesministerium für Bildung und ForschungWilhelm Sander-Stiftung
KeywordsIn vivoReceptorIn vitroIntegrinMelanocortin 1 receptorChemistryMelanomaPeptideCancer researchLigand (biochemistry)Molecular biologyBiophysicsBiochemistryMedicineBiologyPhenotype

Abstract

fetched live from OpenAlex

Combining two peptides addressing two different receptors to a heterobivalent peptidic ligand (HBPL) is thought to enable an improved tumor-targeting sensitivity and thus tumor visualization, compared to monovalent peptide ligands. In the case of melanoma, the Melanocortin-1 receptor (MC1R), which is stably overexpressed in the majority of primary malignant melanomas, and integrin αvβ3, which is involved in lymph node metastasis and therefore has an important role in the transition from local to metastatic disease, are important target receptors. Thus, if a radiolabeled HBPL could be developed that was able to bind to both receptor types, the early diagnosis and correct staging of the disease would be significantly increased. Here, we report on the design, synthesis, radiolabeling and in vitro and in vivo testing of different SiFAlin-modified HBPLs (SiFA = silicon fluoride acceptor), consisting of an MC1R-targeting (GG-Nle-c(DHfRWK)) and an integrin αvβ3-affine peptide (c(RGDfK)), being connected by a symmetrically branching framework including linkers of differing length and composition. Kit-like 18F-radiolabeling of the HBPLs 1–6 provided the labeled products [18F]1–[18F]6 in radiochemical yields of 27–50%, radiochemical purities of ≥95% and non-optimized molar activities of 17–51 GBq/μmol within short preparation times of 25 min. Besides the evaluation of radiotracers regarding logD(7.4) and stability in human serum, the receptor affinities of the HBPLs were investigated in vitro on cell lines overexpressing integrin αvβ3 (U87MG cells) or the MC1R (B16F10). Based on these results, the most promising compounds [18F]2, showing the highest affinity to both target receptors (IC50 (B16F10) = 0.99 ± 0.11 nM, IC50 (U87MG) = 1300 ± 288 nM), and [18F]4, exhibiting the highest hydrophilicity (logD(7.4) = −1.39 ± 0.03), were further investigated in vivo and ex vivo in a xenograft mouse model bearing both tumors. For both HBPLs, clear visualization of B16F10, as well as U87MG tumors, was feasible. Blocking studies using the respective monospecific peptides demonstrated both peptide binders of the HBPLs contributing to tumor uptake. Despite the somewhat lower target receptor affinities (IC50 (B16F10) = 6.00 ± 0.47 nM and IC50 (U87MG) = 2034 ± 323 nM) of [18F]4, the tracer showed higher absolute tumor uptakes ([18F]4: 2.58 ± 0.86% ID/g in B16F10 tumors and 3.92 ± 1.31% ID/g in U87MG tumors; [18F]2: 2.32 ± 0.49% ID/g in B16F10 tumors and 2.33 ± 0.46% ID/g in U87MG tumors) as well as higher tumor-to-background ratios than [18F]2. Thus, [18F]4 demonstrates to be a highly potent radiotracer for the sensitive and bispecific imaging of malignant melanoma by PET/CT imaging and impressively illustrates the suitability of the underlying concept to develop heterobivalent integrin αvβ3- and MC1R-bispecific radioligands for the sensitive and specific imaging of malignant melanoma by PET/CT.

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)0.007
metaresearch head score (Gemma)0.002
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 score0.122
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
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 head0.115
GPT teacher head0.401
Teacher spread0.287 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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