Preclinical Evaluation of a PSMA-Targeting Homodimer with an Optimized Linker for Imaging of Prostate Cancer
Bibliographic record
Abstract
Prostate-specific membrane antigen (PSMA) targeting radiopharmaceuticals have been successfully used for diagnosis and therapy of prostate cancer. Optimization of the available agents is desirable to improve tumor uptake and reduce side effects to non-target organs. This can be achieved, for instance, via linker modifications or multimerization approaches. In this study, we evaluated a small library of PSMA-targeting derivatives with modified linker residues, and selected the best candidate based on its binding affinity to PSMA. The lead compound was coupled to a chelator for radiolabeling, and subject to dimerization. The resulting molecules, 22 and 30, were highly PSMA specific (IC50 = 1.0–1.6 nM) and stable when radiolabeled with indium-111 (>90% stable in PBS and mouse serum up to 24 h). Moreover, [111In]In-30 presented a high uptake in PSMA expressing LS174T cells, with 92.6% internalization compared to 34.1% for PSMA-617. Biodistribution studies in LS174T mice xenograft models showed that [111In]In-30 had a higher tumor and kidney uptake compared to [111In]In-PSMA-617, but increasing T/K and T/M ratios at 24 h p.i. Tumors could be clearly visualized at 1 h p.i. by SPECT/CT after administration of [111In]In-22 and [111In]In-PSMA-617, while [111In]In-30 showed a clear signal at later time-points (e.g., 24 h p.i.).
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 | 0.001 |
| 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".