Generation of the therapeutic monoclonal antibody NEO-201, derived from a cancer vaccine, which targets human malignancies and immune suppressor cells
Bibliographic record
Abstract
INTRODUCTION: Cancer vaccines stimulate the activation of specific humoral and cellular adaptive responses against cancer cells.Antibodies generated post vaccination can be isolated and further selected to develop highly specific and potent monoclonal antibodies (mAbs) against tumor-associated antigens. AREAS COVERED: This review describes different types of cancer vaccines, the process of the generation of the mAb NEO-201 from the Hollinshead cancer vaccine platform, the characterization of the antigen recognized by NEO-201, the ability of NEO-201 to bind and mediate the killing of cancer cells and immunosuppressive cells (gMDSCs and Tregs) through ADCC and CDC, NEO-201 preclinical and clinical toxicity and efficacy. EXPERT OPINION: To overcome the problem of poor clinical efficacy of cancer vaccines, due to the activity of immunosuppressive cells, cancer vaccines could be combined with other immunotherapeutics able to deplete immunosuppressive cells. Results from clinical trials, employing NEO-201 alone or in combination with pembrolizumab, showed that durable stabilization of disease after treatment was due to the ability of NEO-201 to target and reduce the percentage of circulating Tregs and gMDSCs.These findings provide compelling support to combine NEO-201 with cancer vaccines to reintegrate their ability to elicit a robust and durable immune adaptive response against cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".