[212Pb]Pb-eSOMA-01: A Promising Radioligand for Targeted Alpha Therapy of Neuroendocrine Tumors
Bibliographic record
Abstract
Peptide receptor radionuclide therapy (PRRT) has been applied to the treatment of neuroendocrine tumors (NETs) for over two decades. However, improvement is still needed, and targeted alpha therapy (TAT) with alpha emitters such as lead-212 (212Pb) represents a promising avenue. A series of ligands based on octreotate was developed. Lead-203 was used as an imaging surrogate for the selection of the best candidate for the studies with lead-212. 203/212Pb radiolabeling and in vitro assays were carried out, followed by SPECT/CT imaging and ex vivo biodistribution in NCI-H69 tumor-bearing mice. High radiochemical yields (≥99%) and purity (≥96%) were obtained for all ligands. [203Pb]Pb-eSOMA-01 and [203Pb]Pb-eSOMA-02 showed high stability in PBS and mouse serum up to 24 h, whereas [203Pb]Pb-eSOMA-03 was unstable in those conditions. All compounds exhibited a nanomolar affinity (2.5–3.1 nM) for SSTR2. SPECT/CT images revealed high tumor uptake at 1, 4, and 24 h post-injection of [203Pb]Pb-eSOMA-01/02. Ex vivo biodistribution studies confirmed that the highest uptake in tumors was observed with [212Pb]Pb-eSOMA-01. [212Pb]Pb-eESOMA-01 displayed the highest absorbed dose in the tumor (35.49 Gy/MBq) and the lowest absorbed dose in the kidneys (121.73 Gy/MBq) among the three tested radioligands. [212Pb]Pb-eSOMA-01 is a promising candidate for targeted alpha therapy of NETs. Further investigations are required to confirm its potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
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".