Development of prostate specific membrane antigen targeted ultrasound microbubbles using bioorthogonal chemistry
Bibliographic record
Abstract
Prostate specific membrane antigen (PSMA) targeted microbubbles (MBs) were developed using bioorthogonal chemistry. Streptavidin-labeled MBs were treated with a biotinylated tetrazine (MBTz) and targeted to PSMA expressing cells using trans-cyclooctene (TCO)-functionalized anti-PSMA antibodies (TCO-anti-PSMA). The extent of MB binding to PSMA positive cells for two different targeting strategies was determined using an in vitro flow chamber. The initial approach involved pretargeting, where TCO-anti-PSMA was first incubated with PSMA expressing cells and followed by MBTz, which subsequently showed a 2.8 fold increase in the number of bound MBs compared to experiments performed in the absence of TCO-anti-PSMA. Using direct targeting, where TCO-anti-PSMA was linked to MBTz prior to initiation of the assay, a 5-fold increase in binding compared to controls was observed. The direct targeting approach was subsequently evaluated in vivo using a human xenograft tumor model and two different PSMA-targeting antibodies. The US signal enhancements observed were 1.6- and 5.9-fold greater than that for non-targeted MBs. The lead construct was also evaluated in a head-to-head study using mice bearing both PSMA positive or negative tumors in separate limbs. The human PSMA expressing tumors exhibited a 2-fold higher US signal compared to those tumors deficient in human PSMA. The results demonstrate both the feasibility of preparing PSMA-targeted MBs and the benefits of using bioorthogonal chemistry to create targeted US probes.
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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) | 0.000 | 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 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".