Exploring macrocyclization strategies to design novel octreotate-based radioconjugates
Bibliographic record
Abstract
Peptides derived from the cyclic tetradecapeptide somastostatin exhibit a strong affinity primarily towards the G-protein coupled somatostatin receptor subtype 2 (SSTR2), which is overexpressed in neuroendocrine tumors. These somatostatin analogs, such as octreotide (TOC) or octreotate (TATE), are typically cyclized through a disulfide bridge. To address the potential fragility of this linkage in vivo, four distinct stapling strategies were explored to develop novel TATE derivatives with improved stability. Each approach induced a different distance between the two sulfhydryl groups involved into the macrocyclization. Additionally, the stapling linkers were designed to present a third functional group required for the regioselective insertion of a metal chelate. Ultimately, six stapled octreotate derivatives (stTATE-01/06), possessing 3 to 6 chemical bonds between the two cysteine residues, were synthesized and radiolabeled with indium-111. Evaluation of their affinity to SSTR2, conducted through a competitive binding assay, aimed to identify the most effective stapling strategy. However, a significant loss of affinity was observed for all stapled peptides compared to the gold standard DOTA-TATE, confirming that these macrocyclization approaches were detrimental to the biological activity of the new SSTR2 ligands.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".