Unnatural amino acid substitutions to improve in vivo stability and tumor uptake of 68Ga-labeled GRPR-targeted TacBOMB2 derivatives for cancer imaging with positron emission tomography
Bibliographic record
Abstract
Abstract Background Overexpressed in various solid tumors, gastrin-releasing peptide receptor (GRPR) is a promising cancer imaging marker and therapeutic target. Although antagonists are preferable for the development of GRPR-targeted radiopharmaceuticals due to potentially fewer side effects, internalization of agonists may lead to longer tumor retention and better treatment efficacy. In this study, we systematically investigated unnatural amino acid substitutions to improve in vivo stability and tumor uptake of a previously reported GRPR-targeted agonist tracer, [ 68 Ga]Ga-TacBOMB2 ( 68 Ga-DOTA-Pip-D-Phe 6 -Gln 7 -Trp 8 -Ala 9 -Val 10 -Gly 11 -His 12 -Leu 13 -Thz 14 -NH 2 ). Results Unnatural amino acid substitutions were conducted for Gln 7 , Trp 8 , Ala 9 , Val 10 , Gly 11 and His 12 , either alone or in combination. Out of 25 unnatural amino acid substitutions, tert -Leu 10 (Tle 10 ) and NMe-His 12 substitutions were identified to be preferable modifications especially in combination. Compared with the previously reported [ 68 Ga]Ga-TacBOMB2, the Tle 10 and NMe-His 12 derived [ 68 Ga]Ga-LW01110 showed retained agonist characteristics and improved GRPR binding affinity (K i = 7.62 vs 1.39 nM), in vivo stability (12.7 vs 89.0% intact tracer in mouse plasma at 15 min post-injection) and tumor uptake (5.95 vs 16.6 %ID/g at 1 h post-injection). Conclusions Unnatural amino acid substitution is an effective strategy to improve in vivo stability and tumor uptake of peptide-based radiopharmaceuticals. With excellent tumor uptake and tumor-to-background contrast, [ 68 Ga]Ga-LW01110 is promising for detecting GRPR-expressing cancer lesions with PET. Since agonists can lead to internalization upon binding to receptors and foreseeable long tumor retention, our optimized GRPR-targeted sequence, [Tle 10 ,NMe-His 12 ,Thz 14 ]Bombesin(7–14), is a promising template for use for the design of GRPR-targeted radiotherapeutic agents.
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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) | 0.000 | 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 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".