Tailoring capsid-directed evolution technology for improved AAV-mediated CAR-T generation
Bibliographic record
Abstract
Chimeric antigen receptor (CAR) T cell (CAR-T) therapies present options for patients diagnosed with certain leukemias. Recent advances of the technology included a method to integrate the CAR into the T cell receptor alpha constant (TRAC) locus to take advantage of the endogenous promoter and regulatory elements for CAR expression. This method used adeno-associated viral (AAV) vectors based on AAV6 to deliver the donor template encoding the CAR construct. Since the original publication, improvements have been made to this targeted CAR integration technique; however, none of those techniques focused on improving the AAV vector used to deliver the therapeutic cargo. The herein presented study developed a novel AAV capsid directed evolution platform that allows for specifically selecting for novel AAV capsid variants that enable more efficient targeted gene editing-mediated CAR construct integration into the TRAC locus in primary T cells. Using this new platform, we selected several novel AAVs that enable more efficient editing in T cells than AAV6. Two novel capsids, AAV-T1 and AAV-T2, were able to mediate 5-fold improvement for on-target knockin, which resulted in 5-fold reduction of the vector dose to produce highly cytolytic T cells against a brain tumor cell line.
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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 | 0.001 |
| 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".