A novel anti‐DR5 chimeric antibody and epirubicin synergistically suppress tumor growth
Bibliographic record
Abstract
Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) induces apoptosis in a variety of tumor cells. TRAIL receptor 2 (DR5) expression is high in tumor cells, transformed cells, and clinical tumor specimens and is low in most normal cells and tissues; therefore, DR5 is considered an attractive target for cancer therapy. In this study, HMCAZ5, a novel mouse-human chimeric antibody based on AD5-10, was generated and stably expressed in CHO-dhfr(-) cells. Highly purified HMCAZ5 exhibits a high affinity for the receptor that is equal to the parental mouse antibody, induces apoptosis in various cancer cells but not in normal hepatocytes, and elicits both antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity in various human cancer cells. The anthracycline anticancer drug epirubicin (EPB) synergizes the cytotoxicity of HMCAZ5 in cancer cells by upregulating DR5 expression on the cell surfaces, enhancing p53 expression, Bid cleavage, and JNK phosphorylation and downregulating c-FLIP expression and Akt phosphorylation. Moreover, HMCAZ5 alone suppresses tumor growth, and EPB augments the tumoricidal activity in human colorectal and hepatocellular tumor xenografts in athymic nude mice. These data suggest that the anti-DR5 chimeric antibody HMCAZ5 may have a clinical use and represents a useful immunological strategy, in combination with chemotherapy, for the treatment of cancer.
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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".