Improving the Delivery of Drugs and Nucleic Acids to T Cells Using Nanotechnology
Bibliographic record
Abstract
T cells play several roles in antitumor immunity, including mediating cytotoxicity, generating immune memory, and promoting humoral immunity. Given these critical roles, T cells are the therapeutic target of immunotherapies that have achieved clinical success, notably immune checkpoint inhibitors and chimeric antigen receptor T‐cell therapy. However, a fraction of patients benefits from these treatments due to intolerable toxicities and limited efficacy. These issues stem in part from inefficient and nonselective drug delivery to T cells. Nanotechnology may help resolve these delivery issues, as nanoparticles can serve as modular drug delivery vehicles with targeting abilities that can be applied for ex vivo and in vivo delivery. Herein, applications of nanotechnology in improving extracellular delivery of cytokines and small molecule drugs and intracellular delivery of siRNA to T cells are described. An overview of nanoparticle‐mediated delivery of nucleic acids for chimeric antigen receptor T‐cell therapy and CRISPR/Cas9 genome editing is provided. Finally, an outlook on the challenges and opportunities for the advancement of nanoparticle‐mediated drug delivery to T cells is shared.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".