Synthesis and Evaluation of ePSMA-DM1: A New Theranostic Small-Molecule Drug Conjugate (T-SMDC) for Prostate Cancer
Bibliographic record
Abstract
Small-molecule drug conjugates (SMDCs) are compounds in which a therapeutic payload is conjugated to a targeting vector, for specific delivery to the tumor site. This promising approach can be translated to the treatment of prostate cancer by selecting a targeting vector which binds to the prostate-specific membrane antigen (PSMA). Moreover, the addition of a bifunctional chelator to the molecule allows for the use of both diagnostic and therapeutic radionuclides. In this way, the distribution of the SMDC in the body can be monitored, and combination therapy regimes can be implemented. We combined a glutamate-urea-lysine vector to the cytotoxic agent DM1 and a DOTA chelator via an optimized linker to obtain the theranostic SMDC (T-SMDC) ePSMA-DM1. ePSMA-DM1 retained a high binding affinity to PSMA and demonstrated PSMA-specific uptake in cells. Glutathione stability assays showed that the half-life of the T-SMDC in a reducing environment was 2 h, and full drug release was obtained after 6 h. Moreover, 100 nM of ePSMA-DM1 reduced the cell viability of the human PSMA-positive LS174T cells by >85% after 72 h of incubation, which was comparable to a 10-fold higher dose of free DM1. [111In]In-ePSMA-DM1 and [177Lu]Lu-ePSMA-DM1 were both obtained in high radiochemical yields and purities (>95%), with >90% stability in PBS and >80% stability in mouse serum for up to 24 h post incubation at 37 °C. SPECT/CT imaging studies allowed for a faint tumor visualization of [111In]In-ePSMA-DM1 at 1 h p.i., and the ex vivo biodistribution showed tumor uptake (2.39 ± 0.29% ID/g) at 1 h p.i., with the compound retained in the tumor for up to 24 h. Therefore, ePSMA-DM1 is a promising T-SMDC candidate for prostate cancer, and the data obtained so far warrant further investigations, such as therapeutic experiments, after further optimization.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".