Radiochemical and Biological Evaluation of 3p-C-NETA-ePSMA-16, a Promising PSMA-Targeting Agent for Radiotheranostics
Bibliographic record
Abstract
Bifunctional chelators (BFCs) are a key element in the design of radiopharmaceuticals. By selecting a BFC that efficiently complexes diagnostic and therapeutic radionuclides, a theranostic pair possessing almost similar biodistribution and pharmacokinetic properties can be developed. We have previously reported 3p-C-NETA as a promising theranostic BFC, and the encouraging preclinical outcomes obtained with [18F]AlF-3p-C-NETA-TATE led us to conjugate this chelator to a PSMA-targeting vector for imaging and treatment of prostate cancer. In this study, we synthesized 3p-C-NETA-ePSMA-16 and radiolabeled it with different diagnostic (111In, 18F) and therapeutic (177Lu, 213Bi) radionuclides. 3p-C-NETA-ePSMA-16 showed high affinity to PSMA (IC50 = 4.61 ± 1.33 nM), and [111In]In-3p-C-NETA-ePSMA-16 showed specific cell uptake (1.41 ± 0.20% ID/106 cells) in PSMA expressing LS174T cells. Specific tumor uptake of [111In]In-3p-C-NETA-ePSMA-16 was observed up to 4 h p.i. (1.62 ± 0.55% ID/g at 1 h p.i.; 0.89 ± 0.58% ID/g at 4 h p.i.) in LS174T tumor-bearing mice. Only a faint signal could be seen at 1 h p.i. in the SPECT/CT scans, whereas dynamic PET/CT scans performed after administration of [18F]AlF-3p-C-NETA-ePSMA-16 in PC3-Pip tumor xenografted mice resulted in a better tumor visualization and imaging contrast. Therapy studies with short-lived radionuclides such as 213Bi could further elucidate the therapeutic potential of 3p-C-NETA-ePSMA-16 as a radiotheranostic.
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) | 0.000 | 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".