Efficient Drug Delivery and Induction of Apoptosis in Colorectal Tumors Using a Death Receptor 5-Targeted Nanomedicine
Bibliographic record
Abstract
Death Receptor 5 (DR5) is a pro-apoptotic cell-surface receptor that is a potential therapeutic target in cancer. Despite the potency of DR5-targeting agents in preclinical models, the translation of these effects into the clinic remains disappointing. Herein, we report an alternative approach to exploiting DR5 tumor expression using antibody-targeted, chemotherapy-loaded nanoparticles. We describe the development of an optimized polymer-based nanotherapeutic incorporating both a functionalized polyethylene glycol (PEG) layer and targeting antibodies to limit premature phagocytic clearance whilst enabling targeting of DR5-expressing tumor cells. Using the HCT116 colorectal cancer model, we show that following binding to DR5, the nanoparticles activate caspase 8, enhancing the anti-tumor activity of the camptothecin payload both in vitro and in vivo. Importantly, the combination of nanoparticle-induced DR5 clustering with camptothecin delivery overcomes resistance to DR5-induced apoptosis caused by loss of BAX or overexpression of anti-apoptotic FLIP. This novel approach may improve the clinical activity of DR5-targeted therapeutics while increasing tumor-specific delivery of systemically toxic chemotherapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".