A biparatopic agonistic antibody that mimics fibroblast growth factor 21 ligand activity
Why this work is in the frame
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Bibliographic record
Abstract
Bispecific antibodies have become important formats for therapeutic discovery. They allow for potential synergy by simultaneously engaging two separate targets and enable new functions that are not possible to achieve by using a combination of two monospecific antibodies. Antagonistic antibodies dominate drug discovery today, but only a limited number of agonistic antibodies (i.e. those that activate receptor signaling) have been described. For receptors formed by two components, engaging both of these components simultaneously may be required for agonistic signaling. As such, bispecific antibodies may be particularly useful in activating multicomponent receptor complexes. Here, we describe a biparatopic (i.e. targeting two different epitopes on the same target) format that can activate the endocrine fibroblast growth factor (FGF) 21 receptor (FGFR) complex containing -Klotho and FGFR1c. This format was constructed by grafting two different antigen-specific VH domains onto the VH and VL positions of an IgG, yielding a tetravalent binder with two potential geometries, a close and a distant, between the two paratopes. Our results revealed that the biparatopic molecule provides activities that are not observed with each paratope alone. Our approach could help address the challenges with heterogeneity inherent in other bispecific formats and could provide the means to adjust intramolecular distances of the antibody domains to drive optimal activity in a bispecific format. In conclusion, this format is versatile, is easy to construct and produce, and opens a new avenue for agonistic antibody discovery and development.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it