Quantifying the Ligand-Coated Nanoparticle Delivery to Cancer Cells in Solid Tumors
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".