Simulation-Based Software Modeling of CAR T Cell Therapy Efficacy Against Solid Malignant Tumors
Bibliographic record
Abstract
Genetically engineered T cells with Chimeric Antigen Receptors, CAR T cells, are a revolutionary immunotherapy used to treat advanced blood cancers. The purpose of this experiment was to model the destruction process of tumor cells with CAR T cell therapy using Complexity and Organized Behaviour Within Environmental Bounds (COBWEB), an agent-based simulation software. We designated parameter values for abiotic factors, agents (i.e. tumor cells, T cells) and the general environment in our immunotherapy simulation model to illustrate the interactions between tumor cells and cytotoxic components, which described the binding of innate CD8+ T cells or CAR T cells to tumor antigens. The models were used to observe and comparatively analyze the rate of destruction of a solid tumor by CAR T cells and innate CD8+ T cells. The solid tumor developed in a circular island for 60 ticks, representing days; innate CD8+ or CAR T cells were then able to infiltrate the island and the tumor cell population was monitored over 500 days. The CAR T cells exhibited a significantly powerful, efficient immune response against a general solid tumor relative to the innate CD8+ T cells, yet relapse occurred in both models albeit to a lesser extent with CAR T cells. However, further investigations are required to adequately simulate the side effects and realistically-limiting factors of CAR T cell therapy. Similar comparative analyses may help measure and compare the potency of the immune response of CAR T cells compared to standard, or lack of, treatments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".