Management and Prevention of Cellular-Therapy-Related Toxicity: Early and Late Complications
Bibliographic record
Abstract
Chimeric Antigen Receptor T (CAR-T) cell therapy has dramatically changed prognosis and treatment of relapsed and refractory hematologic malignancies. Currently the 6 FDA approved products target various surface antigens. While CAR-T therapy achieves good response, life-threatening toxicities have been reported. Mechanistically, can be divided into two categories: (1) toxicities related to T-cell activation and release of high levels of cytokines: or (2) toxicities resulting from interaction between CAR and CAR targeted antigen expressed on non-malignant cells (i.e., on-target, off-tumor effects). Variations in conditioning therapies, co-stimulatory domains, CAR T-cell dose and anti-cytokine administration, pose a challenge in distinguishing cytokine mediated related toxicities from on-target, off-tumor toxicities. Timing, frequency, severity, as well as optimal management of CAR T-cell-related toxicities vary significantly between products and are likely to change as newer therapies become available. Currently the FDA approved CARs are targeted towards the B-cell malignancies however the future holds promise of expanding the target to solid tumor malignancies. Further highlighting the importance of early recognition and intervention for early and late onset CAR-T related toxicity. This contemporary review aims to describe presentation, grading and management of commonly encountered toxicities, short- and long-term complications, discuss preventive strategies and resource utilization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".