A Novel Glycoengineered Bispecific Antibody Format for Targeted Inhibition of Epidermal Growth Factor Receptor (EGFR) and Insulin-like Growth Factor Receptor Type I (IGF-1R) Demonstrating Unique Molecular Properties
Bibliographic record
Abstract
In the present study, we have developed a novel one-arm single chain Fab heterodimeric bispecific IgG (OAscFab-IgG) antibody format targeting the insulin-like growth factor receptor type I (IGF-1R) and the epidermal growth factor receptor (EGFR) with one binding site for each target antigen. The bispecific antibody XGFR is based on the "knob-into-hole" technology for heavy chain heterodimerization with one heavy chain consisting of a single chain Fab to prevent wrong pairing of light chains. XGFR was produced with high expression yields and showed simultaneous binding to IGF-1R and EGFR with high affinity. Due to monovalent binding of XGFR to IGF-1R, IGF-1R internalization was strongly reduced compared with the bivalent parental antibody, leading to enhanced Fc-mediated cellular cytotoxicity. To further increase immune effector functions triggered by XGFR, the Fc portion of the bispecific antibody was glycoengineered, which resulted in strong antibody-dependent cell-mediated cytotoxicity activity. XGFR-mediated inhibition of IGF-1R and EGFR phosphorylation as well as A549 tumor cell proliferation was highly effective and was comparable with a combined treatment with EGFR (GA201) and IGF-1R (R1507) antibodies. XGFR also demonstrated potent anti-tumor efficacy in multiple mouse xenograft tumor models with a complete growth inhibition of AsPC1 human pancreatic tumors and improved survival of SCID beige mice carrying A549 human lung tumors compared with treatment with antibodies targeting either IGF-1R or EGFR. In summary, we have applied rational antibody engineering technology to develop a heterodimeric OAscFab-IgG bispecific antibody, which combines potent signaling inhibition with antibody-dependent cell-mediated cytotoxicity induction and results in superior molecular properties over two established tetravalent bispecific formats.
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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 | 0.000 |
| 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 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".