Combination of terbium-161 with somatostatin receptor antagonists—a potential paradigm shift for the treatment of neuroendocrine neoplasms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Purpose The β ¯ -emitting terbium-161 also emits conversion and Auger electrons, which are believed to be effective in killing single cancer cells. Terbium-161 was applied with somatostatin receptor (SSTR) agonists that localize in the cytoplasm (DOTATOC) and cellular nucleus (DOTATOC-NLS) or with a SSTR antagonist that localizes at the cell membrane (DOTA-LM3). The aim was to identify the most favorable peptide/terbium-161 combination for the treatment of neuroendocrine neoplasms (NENs). Methods The capability of the 161 Tb- and 177 Lu-labeled somatostatin (SST) analogues to reduce viability and survival of SSTR-positive AR42J tumor cells was investigated in vitro. The radiopeptides’ tissue distribution profiles were assessed in tumor-bearing mice. The efficacy of terbium-161 compared to lutetium-177 was investigated in therapy studies in mice using DOTATOC or DOTA-LM3, respectively. Results In vitro, [ 161 Tb]Tb-DOTA-LM3 was 102-fold more potent than [ 177 Lu]Lu-DOTA-LM3; however, 161 Tb-labeled DOTATOC and DOTATOC-NLS were only 4- to fivefold more effective inhibiting tumor cell viability than their 177 Lu-labeled counterparts. This result was confirmed in vivo and demonstrated that [ 161 Tb]Tb-DOTA-LM3 was significantly more effective in delaying tumor growth than [ 177 Lu]Lu-DOTA-LM3, thereby, prolonging survival of the mice. A therapeutic advantage of terbium-161 over lutetium-177 was also manifest when applied with DOTATOC. Since the nuclear localizing sequence (NLS) compromised the in vivo tissue distribution of DOTATOC-NLS, it was not used for therapy. Conclusion The use of membrane-localizing DOTA-LM3 was beneficial and profited from the short-ranged electrons emitted by terbium-161. Based on these preclinical data, [ 161 Tb]Tb-DOTA-LM3 may outperform the clinically employed [ 177 Lu]Lu-DOTATOC for the treatment of patients with NENs.
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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 | 0.000 |
| 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 it