Baited SELEX: Drug-Directed Selection of Aptamers to PSMA for <i>In Vivo</i> Targeting of Prostate Cancer Xenografts in Mice
Bibliographic record
Abstract
We report a selection strategy for linking a drug-pharmacophore to a degenerate DNA library for use in cell- and whole-animal systematic evolution of ligands by exponential enrichment (SELEX) to direct the selection of aptamers to a specific target. This approach enables the discovery of aptamers with both high affinity and high tissue specificity, guided by a tethered small molecule and enhanced by an aptamer. We applied this approach to a critical application in prostate cancer (PCa) by conjugating a fluorescent analogue of the FDA-approved drug Pluvicto to a degenerate N40-DNA library to direct the selection of aptamers against the prostate-specific membrane antigen (PSMA). Seeking antibody-like functionality, we introduce two modified dNTPs─phenolic-dT and naphthyl-dC─to enhance the affinity and serum stability of selected aptamers. After 31 rounds of cell SELEX, followed by one round in a mouse bearing an LNCaP xenograft, next-generation sequencing informed the selection of several aptamers, of which an exemplar shows very high affinity for PSMA ( K d ∼ 0.8 nM). Its affinity depends on both the small-molecule drug and the modified nucleosides. Appreciating the outstanding challenge of identifying agents that differentiate PSMA on tumors from salivary glands, we identify aptamers that selectively bind PSMA-expressing tumors while sparing salivary glands. Use of a PSMA-targeting pharmacophore as a molecular bait represents the first example of SELEX against specific targets expressed on tumors while avoiding binding to the same target expressed on normal tissues. The resulting aptamers represent hybrid biologics that enhance the affinity and tumor specificity of the small-molecule drug-pharmacophore.
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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) | 0.001 | 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) | 0.002 | 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".