Preclinical assessment of anti-cancer drugs by using RP215 monoclonal antibody
Bibliographic record
Abstract
RP215 monoclonal antibody was originally generated against OC-3-VGH ovarian cancer cells. It was shown to recognize specifically a carbohydrate-associated epitope(s) of cancer cell-expressed immunoglobulin heavy chains designated as CA215. Previous studies suggest that CA215 is expressed by all human cancer cell lines and tissues in both membrane bound and secreted forms. It may be an ideal target for therapeutic treatments of human cancers with humanized RP215-related antibodies. Based on the results of large scale immunohistochemical studies (50-100 cases each), the following types of cancers revealed high percentage(s) of positive staining with RP215: esophagus (76%), stomach (50%), colon (44%), ovary (64%), breast (32%), lung (31%), cervix (84%) and endometrium (78%). Nude mouse experiments were performed to determine if RP215 has any inhibitory effect on the growth of cancer cells in vivo. Following injections of a single dose (10 mg/kg) of I(131)-labeled RP215 (specific activity, 12.5 muCi/mg), the tumor size (OC-3-VGH ovarian cancer cells) was reduced to 30% of the untreated control within two weeks. By injecting the same dose, the unlabeled RP215 also reduced the tumor size to 50% of the control during the same period. The antibody treatments were found to have little effect on the body weight as well as apparent toxicity of these animals. To proceed with clinical trial studies of RP215-based anti-cancer drugs, chimeric form of this monoclonal antibody was generated and characterized. Through our effort, the "proof of concept" of anti-cancer drugs development was clearly established for the next stages of clinical trial studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".