Lipid nanoparticles outperform electroporation in mRNA-based CAR T cell engineering
Bibliographic record
Abstract
Engineered T cells expressing chimeric antigen receptors (CARs) have been proven as efficacious therapies against selected hematological malignancies. However, the approved CAR T cell therapeutics strictly rely on viral transduction, a time- and cost-intensive procedure with possible safety issues. Therefore, the direct transfer of in vitro transcribed CAR-mRNA into T cells is pursued as a promising strategy for CAR T cell engineering. Electroporation (EP) is currently used as mRNA delivery method for the generation of CAR T cells in clinical trials but achieving only poor anti-tumor responses. Here, lipid nanoparticles (LNPs) were examined for ex vivo CAR-mRNA delivery and compared with EP. LNP-CAR T cells showed a significantly prolonged efficacy in vitro in comparison with EP-CAR T cells as a result of extended CAR-mRNA persistence and CAR expression, attributed to a different delivery mechanism with less cytotoxicity and slower CAR T cell proliferation. Moreover, CAR expression and in vitro functionality of mRNA-LNP-derived CAR T cells were comparable to stably transduced CAR T cells but were less exhausted. These results show that LNPs outperform EP and underline the great potential of mRNA-LNP delivery for ex vivo CAR T cell modification as next-generation transient approach for clinical studies.
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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) | 0.001 | 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".