A Rapid and Efficient <i>In Vivo</i> Method for Determining the Biologic Efficacy of Monoclonal Antibodies in Animal Models of Cancer
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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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".