Rearranging the domain order of a diabody-based IgG-like bispecific antibody enhances its antitumor activity and improves its degradation resistance and pharmacokinetics
Bibliographic record
Abstract
One approach to creating more beneficial therapeutic antibodies is to develop bispecific antibodies (bsAbs), particularly IgG-like formats with tetravalency, which may provide several advantages such as multivalent binding to each target antigen. Although the effects of configuration and antibody-fragment type on the function of IgG-like bsAbs have been studied, there have been only a few detailed studies of the influence of the variable fragment domain order. Here, we prepared four types of hEx3-scDb-Fc, IgG-like bsAbs, built from a single-chain hEx3-Db (humanized bispecific diabody [bsDb] that targets epidermal growth factor receptor and CD3), to investigate the influence of domain order and fusion manner on the function of a bsDb with an Fc fusion format. Higher cytotoxicities were observed with hEx3-scDb-Fcs with a variable light domain (VL)-variable heavy domain (VH) order (hEx3-scDb-Fc-LHs) compared with a VH-VL order, indicating that differences in the Fc fusion manner do not affect bsDb activity. In addition, flow cytometry suggested that the higher cytotoxicities of hEx3-scDb-Fc-LH may be attributable to structural superiority in cross-linking. Interestingly, enhanced degradation resistance and prolonged in vivo half-life were also observed with hEx3-scDb-Fc-LH. hEx3-scDb-Fc-LH and its IgG2 variant exhibited intense in vivo antitumor effects, suggesting that Fc-mediated effector functions are dispensable for effective anti-tumor activities, which may cause fewer side effects. Our results show that merely rearranging the domain order of IgG-like bsAbs can enhance not only their antitumor activity, but also their degradation resistance and in vivo half-life, and that hEx3-scDb-Fc-LHs are potent candidates for next-generation 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 | 0.000 |
| 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".