Combination of terbium-161 with somatostatin receptor antagonists—a potential paradigm shift for the treatment of neuroendocrine neoplasms
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 161Tb- and 177Lu-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, [161Tb]Tb-DOTA-LM3 was 102-fold more potent than [177Lu]Lu-DOTA-LM3; however, 161Tb-labeled DOTATOC and DOTATOC-NLS were only 4- to fivefold more effective inhibiting tumor cell viability than their 177Lu-labeled counterparts. This result was confirmed in vivo and demonstrated that [161Tb]Tb-DOTA-LM3 was significantly more effective in delaying tumor growth than [177Lu]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, [161Tb]Tb-DOTA-LM3 may outperform the clinically employed [177Lu]Lu-DOTATOC for the treatment of patients with NENs.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".