Antibody‐Conjugated Nanogel with Two Immune Checkpoint Inhibitors for Enhanced Cancer Immunotherapy
Bibliographic record
Abstract
Cancer immunotherapy by immune checkpoint inhibitors (ICIs) acts on antitumor responses by stimulating the immune system to attack cancer cells. However, this powerful therapy is hampered by its high treatment cost and limited efficacy. Here, it is shown that the development of an antibody-conjugated nanogel (ANGel), consisting of N-isopropylacrylamide-co-acrylic acid and antibody-binding protein (protein A), potentiates the efficacy of two ICI monoclonal antibodies (mAbs) (cytotoxic-T-lymphocyte-associated antigen 4 and programmed death ligand-1 mAbs). Compared with mAb treatment alone, treatment with a bispecific ANGel surface-conjugated with the mAbs significantly decreases both the survival of Michigan Cancer Foundation-7 (MCF-7) and M D Anderson-Metastatic Breast-231 (MDA-MB-231) breast cancer cells in vitro and the burden of 4T1-luciferase-2-derived orthotopic syngeneic tumors in vivo. The bispecific ANGel is also more potent than the conventional treatment at prolonging survival in animals with triple-negative breast cancer. The advantage of the bispecific ANGel over other engineered bispecific antibodies arises not only from the adaptability to link multiple antibodies quickly and easily, but also from the capability to maintain the anticancer effect steadily at subcutaneously delivered tumor site. This finding has an important implication for cancer immunotherapy, opening a new paradigm to treat solid tumors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".