Comparison of biological properties of [<sup>177</sup>Lu]Lu‐ProBOMB1 and [<sup>177</sup>Lu]Lu‐NeoBOMB1 for GRPR targeting
Bibliographic record
Abstract
Abstract The gastrin‐releasing peptide receptor (GRPR) is overexpressed in prostate cancer and other solid malignancies. Following up on our work on [68Ga]Ga‐ProBOMB1 that had better imaging characteristics than [68Ga]Ga‐NeoBOMB1, we investigated the effects of substituting 68Ga for 177Lu to determine if the resulting radiopharmaceuticals could be used with a therapeutic aim. We radiolabeled the bombesin antagonist ProBOMB1 (DOTA‐pABzA‐DIG‐D‐Phe‐Gln‐Trp‐Ala‐Val‐Gly‐His‐Leu‐ψ‐Pro‐NH2) with lutetium‐177 and compared it with [177Lu]Lu‐NeoBOMB1 (obtained in 54.2 ± 16.5% isolated radiochemical yield with >96% radiochemical purity and 440.8 ± 165.1 GBq/μmol molar activity) for GRPR targeting. Lu‐NeoBOMB1 had better binding affinity for GRPR than Lu‐ProBOMB1 (Ki values: 2.26 ± 0.24 and 30.2 ± 3.23nM). [177Lu]Lu‐ProBOMB1 was obtained in 53.7 ± 5.4% decay‐corrected radiochemical yield with 444.2 ± 193.2 GBq/μmol molar activity and >95% radiochemical purity. In PC‐3 prostate cancer xenograft mice, tumor uptake of [177Lu]Lu‐ProBOMB1 was 3.38 ± 1.00, 1.32 ± 0.24, and 0.31 ± 0.04%ID/g at 1, 4, and 24 hours pi. However, the uptake in tumor was lower than [177Lu]Lu‐NeoBOMB1 at all time points. [177Lu]Lu‐ProBOMB1 was inferior to [177Lu]Lu‐NeoBOMB1, which had better therapeutic index for the organs receiving the highest doses.
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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 | 0.001 |
| 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".