Abstract 5518: Development of bio-affinity nanoparticles: Applications for cancer imaging and therapeutics
Bibliographic record
Abstract
Abstract Treatment of advanced cancer, is an extremely challenging proposition. Surgery is curative at early stages, but with substantial complications. Molecular treatments in early disease are of questionable benefit, coupled with the lack of therapeutic options in advanced disease, make treatment extremely challenging. Early cancers evolve through many somatic mutations involving many selective processes, induced in part by classical drug therapies themselves initiating drug resistance. Genetic cancers studies show extensive single missense mutations, copy number variations, splicing variants, genetic rearrangements and short DNA alterations in a large number of genes. However, physical agent therapies are immune to genetic alterations, a targeting the cancer cell and adjacent cells. By investigating cell surface markers that are correlated with disease progression, we have coupled bio-affinity agents (antibody, ligand, aptamer) to functionalized carbon nanoparticles and developed a platform of delivering highly-localized “energy” in the immediate proximity to the cells causing significant cellular damage. Similarly, these agents can be simultaneously used as imaging agents, creates a theranostic treatment agent. We have used papillary thyroid (PTC) and prostate cancer (CaP) cancer cells and selectively targeted THSR and PSMA, respectively in our models. Using a 2W laser light for 30 seconds, we are able to achieve near 100% cell killing of targeted cells, whereas receptor-null cells remain unharmed. Electron microscopy was used as a direct analysis to answer the mechanistic question of cell necrosis and cell death that cannot be approached via light microscopy. Altogether these results suggest that significant and specific cell killing can be achieved with small amounts of power over short periods of time. In conclusion, whereas in the past, simultaneous multiple drugs therapies have been limited by systemic toxicities, this approach represents a single delivery platform that can carry several small molecule drugs, in conjunction with new genetic modalities, in a targeted fashion with the added benefit of self-imaging so as to document successful targeting. Citation Format: Idit Dotan, Philip J.R. Roche, Elliot Mitmaker, Mark A. Trifiro, Miltiadis Paliouras. Development of bio-affinity nanoparticles: Applications for cancer imaging and therapeutics. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5518. doi:10.1158/1538-7445.AM2015-5518
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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) | 0.002 | 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".