A Rapid and Efficient <i>In Vivo</i> Method for Determining the Biologic Efficacy of Monoclonal Antibodies in Animal Models of Cancer
Why this work is in the frame
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Bibliographic record
Abstract
The selection of efficacious anti-tumor monoclonal antibodies (MAbs) for biological applications is a lengthy and labor-intensive process. In vitro characterization of one hybridoma fusion may reveal large numbers of tumor antigen-specific hybridomas. Very often, many of these tumor-specific antibodies need to be assessed in vivo using several different murine xenograft tumor prevention models to determine biological efficacy. The production and purification of sufficient quantities of many antigen-specific hybridomas is time-consuming, and several months can pass between initial determination of MAb specificity and bioactivity. Moreover, many tumor-specific MAbs selected using in vitro binding studies have no in vivo anti-tumor efficacy. These studies describe an in vivo screening method either to eliminate non-efficacious MAbs or to rank-order several tumor-specific MAbs in an expeditious manner. Proof-of-concept studies were conducted using two hybridomas secreting fully characterized neutralizing human anti-tumor MAbs (CNTO MAbs). Nu-/nu- mice were injected with CNTO MAb-secreting hybridoma cells in Matrigel cell matrix, followed by injection of target human tumor cells 4 days later (when circulating CNTO MAbs were detected in serum). Both the tumor take-rate and the mean tumor volumes were reduced significantly in mice treated with CNTO MAbsecreting hybridomas compared with mice treated with non-antibody-secreting cells. A panel of human antitumor antigen-specific MAbs with unknown biological efficacy was then evaluated by this method. The hybridomas exhibited a varied pattern of anti-tumor protection, indicating that some hybrids were secreting neutralizing anti-tumor MAbs, while others appeared to be less efficacious. These studies demonstrate a rapid, biologically relevant "yes/no" in vivo screening method for the evaluation of anti-tumor antigen MAbs.
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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 | 0.001 | 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) | 0.000 | 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 it