<sup>68</sup> Ga-Labeled Peptides Targeting Oxytocin Receptor in Breast Cancer Using Linchpin Chemistry for Tandem Peptide Cyclization and Radiometal Chelator Incorporation
Bibliographic record
Abstract
Breast cancer remains a leading cause of cancer-related death worldwide, partly due to disease heterogeneity and the lack of reliable biomarkers. The G protein-coupled oxytocin receptor (OTR) has emerged as a potential biomarker and therapeutic target in breast cancer, as its overexpression correlates with tumor growth and metastasis. OTR thus presents new opportunities for molecular imaging and targeted therapy in breast cancer. This study explores three novel 68 Ga-labeled peptides as potential OTR-specific imaging agents. Their preclinical evaluation includes in vitro assays and positron emission tomography (PET) studies in breast cancer models. The work also introduces the application of linchpin chemistry with LP1 and LP2 as a novel strategy for attaching bifunctional chelating agents. This tandem approach not only enables efficient peptide cyclization but also facilitates radiometal incorporation, representing a versatile platform for the design of next-generation radiopharmaceuticals. Binding studies using an aequorin-based assay in CHO cells expressing human OTR revealed the following EC 50 values: nat Ga-LP1-oxytocin (376 nM), nat Ga-DOTA-Lys 8 -oxytocin (1.38 nM), and nat Ga-LP2-oxytocin (123 nM). Radiolabeling with 68 Ga was efficient and reproducible, consistently yielding high decay-corrected radiochemical yields of 52–74% and high radiochemical purity >98%. PET imaging demonstrated maximum MCF-7 tumor uptake for 68 Ga-LP1-oxytocin (SUV max 0.64 ± 0.10; n = 3) and 68 Ga-LP2-oxytocin (SUV max 0.64 ± 0.05; n = 7) at 10 min postinjection, whereas 68 Ga-DOTA-Lys 8 -oxytocin reached comparable uptake (SUV max 0.64 ± 0.12; n = 3) at 30 min. Notably, 68 Ga-LP2-oxytocin showed superior background clearance and faster blood pool washout. Tumor uptake specificity was verified through competitive inhibition studies: predosing with oxytocin reduced tracer accumulation in a concentration-dependent manner at 10 min postinjection, with decreases of 33% at 50 μM and 68% at 300 μM, confirming selective OTR-mediated binding in vivo. Among the evaluated tracers, the novel 68 Ga-LP2-oxytocin peptide demonstrated efficient radiolabeling, strong binding potency, and favorable in vivo characteristics, including uptake in estrogen receptor-positive MCF-7 tumors and superior background and clearance profiles. With further structural optimization, 68 Ga-LP2-oxytocin holds promise as a PET radioligand for targeting OTR in breast cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".