Quantifying the Ligand-Coated Nanoparticle Delivery to Cancer Cells in Solid Tumors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Coating the nanoparticle surface with cancer cell recognizing ligands is expected to facilitate specific delivery of nanoparticles to diseased cells in vivo. While this targeting strategy is appealing, no nanoparticle-based active targeting formulation for solid tumor treatment had made it past phase III clinical trials. Here, we quantified the cancer cell-targeting efficiencies of Trastuzumab (Herceptin) and folic acid coated gold and silica nanoparticles in multiple mouse tumor models. Surprisingly, we showed that less than 14 out of 1 million (0.0014% injected dose) intravenously administrated nanoparticles were delivered to targeted cancer cells, and that only 2 out of 100 cancer cells interacted with the nanoparticles. The majority of the intratumoral nanoparticles were either trapped in the extracellular matrix or taken up by perivascular tumor associated macrophages. The low cancer cell targeting efficiency and significant uptake by noncancer cells suggest the need to re-evaluate the active targeting process and therapeutic mechanisms using quantitative methods. This will be important for developing strategies to deliver emerging therapeutics such as genome editing, nucleic acid therapy, and immunotherapy for cancer treatment using nanocarriers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it