Quantifying the Limits of Immunotherapies in Mice and Men
Bibliographic record
Abstract
Abstract Background CART-cells have demonstrated clinical success for the treatment of multiple different lymphomas and leukaemias. However, comparable clinical efficacy has not been observed for various solid tumours in human clinical trials, despite promising data from murine cancer models. Lower effective CART-cell delivery rates to human solid tumours compared to haematological malignancies in humans and solid tumours in mice might partially explain these divergent clinical outcomes, requiring the development of quantitative methods to calculate effective dosing in the clinic and models. Methods We used anatomical and physiological data for human and rodent circulatory systems to calculate the typical value and expected variation of the perfusion of healthy and tumour tissues, and used these to estimate the upper limits of immune cell delivery rates (maximum possible under optimal conditions) across different organs, tumours types and species. Results Estimated maximum delivery rates were up to order 10,000-fold greater in mice than humans yet reported CART-cell doses are typically only 10-100-fold lower in mice, suggesting that the effective delivery rates of CART-cells into tumours in clinical trials are far lower than in corresponding mouse models. Estimated delivery rates were compared to published PET data and found to be consistent. Conclusions The results suggest that higher effective human doses may be needed to drive efficacy comparable to mouse solid tumour models. These increased doses raise safety and manufacturing concerns. We posit that quantitation of species and organ-specific delivery and homing of engineered T-cells will be key to unlocking their potential for solid tumours.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".