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Record W4320496059 · doi:10.3390/ph16020273

Towards Radiolabeled EGFR-Specific Peptides: Alternatives to GE11

2023· article· en· W4320496059 on OpenAlexaff
Benedikt Judmann, Björn Wängler, Ralf Schirrmacher, Gert Fricker, Carmen Wängler

Bibliographic record

VenuePharmaceuticals · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsUniversity of Alberta
FundersBundesministerium für Bildung und Forschung
KeywordsComputational biologyMedicineBiology

Abstract

fetched live from OpenAlex

The human epidermal growth factor receptor (EGFR) is closely related to several cancer-promoting processes and overexpressed on a variety of tumor types, rendering it an important target structure for the imaging and therapy of several malignancies. To date, approaches to develop peptidic radioligands able to specifically address and visualize EGFR-positive tumors have been of limited success. Most of the attempts were based on the lead GE11, as this peptide was previously described to be a highly potent EGFR-specific agent. However, since it has recently been shown that GE11 exhibits an insufficient affinity to the EGFR in monomeric form to be suitable as a basis for the development of tracers based on it, in the present work we investigated which other peptides might be suitable as lead structures for the development of EGFR-specific peptidic radiotracers. For this purpose, we developed 68Ga-labeled radioligands based on the peptides D4, P1, P2, CPP, QRH, EGBP and Pep11, having been described before as EGFR-specific. In addition, we also tested three truncated versions of the endogenous EGFR ligand hEGF (human epidermal growth factor) with respect to their ability to specifically target the EGFR with high affinity. Therefore, chelator-modified labeling precursors of the mentioned peptides were synthesized, radiolabeled with 68Ga and the obtained radioligands were evaluated for their hydrophilicity/lipophilicity, stability against degradation by human serum peptidases, in vitro tumor cell uptake, and receptor affinity in competitive displacement experiments on EGFR-positive A431 cells. Although all NODA-GA-modified (NODA-GA: (1,4,7-triazacyclononane-4,7-diyl)diacetic acid-1-glutaric acid) labeling precursors could be obtained more or less efficient in yields between 5 and 74%, the 68Ga-radiolabeling proved to be unsuccessful for two of the three truncated versions of hEGF ([68Ga]Ga-8 and [68Ga]Ga-9), producing several side-products. For the other agents [68Ga]Ga-1–[68Ga]Ga-7, [68Ga]Ga-10 and [68Ga]Ga-11, high radiochemical yields and purities of ≥98% and molar activities of up to 114 GBq/µmol were obtained. In the assay investigating the radiopeptide susceptibilities against serum peptidase degradation, the EGBP-based agent demonstrated a limited stability with a half-life of only 66.4 ± 3.0 min, whereas the other tracers showed considerably higher stabilities of up to an 8000 min half-life. Finally, all radiotracer candidates were evaluated in terms of tumor cell internalization and receptor binding potential on EGFR-positive A431 cell. In these experiments, all developed agents failed to show an EGFR-specific tumor cell uptake or a relevant EGFR-affinity. By contrast, the positive controls tested under identical conditions, [125I]I-hEGF and hEGF demonstrated the expected high EGFR-specific tumor cell uptake (33.6% after 1 h, being reduced to 1.9% under blocking conditions) and affinity (IC50 value of 15.2 ± 3.3 nM). Thus, these results indicate that none of the previously described peptidic agents developed for EGFR targeting appears to be a reasonable choice as a lead structure for the development of radiopeptides for targeting of EGFR-positive tumors. Likewise, the tested truncated variants of the endogenous hEGF do not seem to be promising alternatives for this purpose.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.005

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 head0.124
GPT teacher head0.445
Teacher spread0.321 · 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 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

Citations5
Published2023
Admission routes1
Has abstractyes

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