Dose model for stent-based delivery of a radioactive compound for the treatment of restenosis in coronary arteries
Bibliographic record
Abstract
Radiolabeled drug-eluting stents have been proposed recently as a novel method to potentially reduce restenosis in coronary arteries. A P-32 labeled oligonucleotide (ODN) loaded on a polymer coated stent is slowly released in the arterial wall to deliver a therapeutic dose to the target tissue. However, the relatively low proportion of drugs transferred to the arterial wall (<2%-5% typically) raises questions about the degree to which radiolabeled drugs eluted from the stent can contribute to the total radiation dose delivered to tissues. A three-dimensional diffusion-convection transport model is used to model the transport of a hydrophilic drug released from the surface of a stent to the arterial media. Large drug concentration gradients are observed near the stent struts giving rise to a nonuniform radiation activity distribution for the drug in the tissues as a function of time. A voxel-based kernel convolution method is used to calculate the radiation dose rate resulting from this activity build-up in the arterial wall based on the medical internal radiation dose formalism. Measured residence time for the P-32 ODN in the arterial wall and at the stent surface obtained from animal studies are used to normalize the results in terms of absolute dose to tissue. The results indicate that radiation due to drug eluted from the stent contributes only a small fraction of the total radiation delivered to the arterial wall, the main contribution coming from the activity that remains embedded in the stent coating. For hydrophilic compounds with rapid transit times in arterial tissue and minimal binding interactions, the activity build-up in the arterial wall contributes only a small fraction to the total dose delivered by the P-32 ODN stent. For these compounds, it is concluded that radiolabeled drug-eluting stent will not likely improve the performance of radioactive stents for the treatment of restenosis. Also, variability in the delivery efficacy of drug delivery devices makes accurate dosimetry difficult and the drug washout in the systemic circulatory system may yield an unnecessary activity build-up and dose to healthy organs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".