State of the art in CAR-based therapy: In vivo CAR production as a revolution in cell-based cancer treatment
Bibliographic record
Abstract
Chimeric antigen receptor (CAR) therapy has successfully treated relapsed/refractory hematological cancers. This strategy can effectively target tumor cells. However, despite positive outcomes in clinical applications, challenges remain to overcome. These hurdles pertain to the production of the drugs, solid tumor resistance, and side effects related to the treatment. Some cases have been missed during the drug preparation due to manufacturing issues, prolonged production times, and high costs. These challenges mainly arise from the in vitro manufacturing process, so reevaluating this process could minimize the number of missed patients. The immune cells are traditionally collected and sent to the laboratory; after several steps, the cells are modified to express the CAR gene before being injected back into the patient's body. During the in vivo method, the CAR gene is introduced to the immune cells inside the body. This allows for treatment to begin sooner, avoiding potential failures in drug preparation and the associated high costs. In this review, we will elaborate on the production and treatment process using in vivo CAR, examine the benefits and challenges of this approach, and ultimately present the available solutions for incorporating this treatment into clinical practice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".