Immunogenic cell death (ICD) enhancers—Drugs that enhance the perception of ICD by dendritic cells
Bibliographic record
Abstract
Summary The search for immunostimulatory drugs applicable to cancer immunotherapy may profit from target‐agnostic methods in which agents are screened for their functional impact on immune cells cultured in vitro without any preconceived idea on their mode of action. We have built a synthetic mini‐immune system in which stressed and dying cancer cells (derived from standardized cell lines) are confronted with dendritic cells (DCs, derived from immortalized precursors) and CD8 + T‐cell hybridoma cells expressing a defined T‐cell receptor. Using this system, we can identify three types of immunostimulatory drugs: (i) pharmacological agents that stimulate immunogenic cell death (ICD) of malignant cells; (ii) drugs that act on DCs to enhance their response to ICD; and (iii) drugs that act on T cells to increase their effector function. Here, we focus on strategies to develop drugs that enhance the perception of ICD by DCs and to which we refer as “ICD enhancers.” We discuss examples of ICD enhancers, including ligands of pattern recognition receptors (exemplified by TLR3 ligands that correct the deficient function of DCs lacking FPR1) and immunometabolic modifiers (exemplified by hexokinase‐2 inhibitors), as well as methods for target deconvolution applicable to the mechanistic characterization of ICD enhancers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".