A Simplified Function-First Method for the Discovery and Optimization of Bispecific Immune Engaging Antibodies
Bibliographic record
Abstract
Abstract Bi-specific T-cell engager antibodies (BITEs) are synthetic soluble molecules derived from antibodies that induce active contact between T-cells and other target cells in the body. BITE therapeutics have shown great promise for the treatment of various forms of cancer; however, the current development process for BITEs is time consuming and costly. BITE development requires empirical testing and characterization of the individual antigen binding domains, followed by extensive engineering and optimization in bi-specific molecular format to generate a molecule with strong biological activity and appropriate characteristics for clinical development. Here, we sought to create a cost efficient high-throughput method for creating and evaluating BITEs using a simplified function first approach to identify bioactive molecules without purification. Using a plasmid with a modular structure to allow high efficiency exchange of either binder arm, we established a simple method to combine many novel tumour-targeting single chain variable (scFv) domains with the well-characterized OKT3 scFv CD3-targeting domain. After generating these novel plasmids, we demonstrate two systems for high throughput functional screening of BITE molecules based on Jurkat T cells (referred to as BITE-J). Using BITE-J we evaluate four EGFRvIII BITEs, identifying two constructs with superior activity. We then confirmed this activity in primary T cells, where novel EGFRvIII-BITEs induced T cell activation and antigen selective tumor killing. We also demonstrate that we can similarly exchange the CD3-interacting element of our bi-modular plasmid. By testing several novel CD3-targeting scFv elements for activity in EGFRvIII-targeted BITEs, we were able to identify highly active BITE molecules with desirable properties for downstream development. In summary, BITE-J presents a low cost, high-throughput method for the rapid assessment of novel BITE molecules without the need for purification and quantification.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".