A Novel Antibody Engineering Strategy for Making Monovalent Bispecific Heterodimeric IgG Antibodies by Electrostatic Steering Mechanism
Bibliographic record
Abstract
Producing pure and well behaved bispecific antibodies (bsAbs) on a large scale for preclinical and clinical testing is a challenging task. Here, we describe a new strategy for making monovalent bispecific heterodimeric IgG antibodies in mammalian cells. We applied an electrostatic steering mechanism to engineer antibody light chain-heavy chain (LC-HC) interface residues in such a way that each LC strongly favors its cognate HC when two different HCs and two different LCs are co-expressed in the same cell to assemble a functional bispecific antibody. We produced heterodimeric IgGs from transiently and stably transfected mammalian cells. The engineered heterodimeric IgG molecules maintain the overall IgG structure with correct LC-HC pairings, bind to two different antigens with comparable affinity when compared with their parental antibodies, and retain the functionality of parental antibodies in biological assays. In addition, the bispecific heterodimeric IgG derived from anti-HER2 and anti-EGF receptor (EGFR) antibody was shown to induce a higher level of receptor internalization than the combination of two parental antibodies. Mouse xenograft BxPC-3, Panc-1, and Calu-3 human tumor models showed that the heterodimeric IgGs strongly inhibited tumor growth. The described approach can be used to generate tools from two pre-existent antibodies and explore the potential of bispecific antibodies. The asymmetrically engineered Fc variants for antibody-dependent cellular cytotoxicity enhancement could be embedded in monovalent bispecific heterodimeric IgG to make best-in-class therapeutic antibodies.
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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) | 0.000 | 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 | 0.001 |
| 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".