Functional NK engagers from OmniClic, a common light-chain platform producing fully human-sequence antibodies in a chicken host species
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Next-generation antibodies include a growing number of bispecific and multispecific antibodies that are commonly used to redirect the immune system to fight cancer. Herein, we assessed the depth and breadth of epitope coverage as a proxy for functional diversity in human immune repertoires produced by two complementary in vivo platforms utilizing a common light chain, in a chicken (OmniClicTM) or rat (OmniFlic®) host species. We adopted NKp46 as a model to target antigen due to its use in emerging natural killer (NK) immune engagers that are being explored clinically as potentially safer alternatives to traditional CD3-based T cell engagers. To probe the epitope diversity of our antibody repertoires, we performed a detailed high throughput epitope binning study using surface plasmon resonance and corroborated our binning assignments with epitope mapping data deduced from hydrogen deuterium exchange mass spectrometry. Our results revealed broad epitope coverage and nuanced diversity both within and across repertoires, with few epitopes shared, suggesting that the complementary use of OmniClicTM and OmniFlic® produces more comprehensive coverage than either alone. Furthermore, our epitope binning assignments aligned with our complementarity-determining region-based sequence lineage assignments, enabling a direct comparison of sequence diversity across Clic and Flic repertoires despite their use of different scaffolds, a single functionally rearranged V(D)J scaffold versus multiple combinatorially assembled V(D)J scaffolds, respectively. The rich epitope diversity of both OmniClicTM and OmniFlic® yielded multiple candidates for functional NK activators, as determined in an antibody-dependent cellular cytotoxicity assay, demonstrating their value as building blocks in constructing optimized immune engagers.
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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 | 0.000 |
| 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 it