A simplified function-first method for the discovery and optimization of bispecific immune engaging antibodies
Bibliographic record
Abstract
Bi-specific T-cell engager antibodies (BiTEs) are synthetic fusion molecules that combine multiple antibody-binding domains to induce active contact between T-cells and antigen expressing cells in the body. Blinatumomab, a CD19-CD3 BiTE is now a widely used therapy for relapsed B-cell malignancies, and similar BiTE therapeutics have shown promise for treating various other forms of cancer. The current process for new BiTE development is time consuming and costly, requiring characterization of the individual antigen binding domains, followed by bi-specific design, protein production, purification, and eventually functional screening. Here, we sought to establish a more cost-efficient approach for generating novel BiTE sequences and assessing bioactivity through a function first approach without purification. We generate a plasmid with a bi-modular structure to allow high-throughput exchange of either binding arm, enabling rapid screening of novel tumour-targeting single chain variable (scFv) domains in combination with the well-characterized OKT3 scFv CD3-targeting domain. We also demonstrate two systems for high throughput functional screening of BiTE proteins based on Jurkat T cells (referred to as BiTE-J). Using BiTE-J we evaluate four EGFRvIII-scFv sequenced in BiTE format, identifying two constructs with superior activity for redirecting T-cells against the EGFRvIII-tumour specific antigen. We also confirm activity in primary T cells, where novel EGFRvIII-BiTEs induced T cell activation and antigen selective tumor killing. We finally demonstrate similar 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 functional activity 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 | 0.000 |
| 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) | 0.003 | 0.003 |
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".