Preclinical Evaluation of ON203, A Novel Bioengineered mAb Targeting Oxidized Macrophage Migration Inhibitory Factor as an Anticancer Therapeutic
Bibliographic record
Abstract
High levels of macrophage migration inhibitory factor (MIF) in patients with cancer are associated with poor prognosis. Its redox-dependent conformational isoform, termed oxidized MIF (oxMIF), is a promising tumor target due to its selective occurrence in tumor lesions and at inflammatory sites. A first-generation anti-oxMIF mAb, imalumab, was investigated in clinical trials in patients with advanced solid tumors, where it was well tolerated and showed signs of efficacy. However, imalumab has a short half-life in humans, increased aggregation propensity, and an unfavorable pharmacokinetic profile. Here, we aimed to optimize imalumab by improving its physicochemical characteristics and boosting its effector functions. Point mutations introduced into the variable regions reduced hydrophobicity and the antibodies' aggregation potential, and increased plasma half-life and tumor accumulation in vivo, while retaining affinity and specificity to oxMIF. The introduction of mutations into the Fc region known to increase antibody-dependent cellular cytotoxicity resulted in enhanced effector functions of the novel antibodies in vitro, whereas reduced cytokine release from human peripheral blood mononuclear cells in the absence of target antigen by the engineered anti-oxMIF mAb ON203 versus imalumab reveals a favorable in vitro safety profile. In vivo, ON203 mAb demonstrated superior efficacy over imalumab in both prophylactic and established prostate cancer (PC3) mouse xenograft models. In summary, our data highlight the potential of the second-generation anti-oxMIF mAb ON203 as a promising immunotherapy for patients with solid tumors, warranting clinical evaluation.
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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) | 0.001 | 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 | 0.001 |
| 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".