Abstract 1868: Target-dependent considerations for the design of bispecific T-cell engagers
Bibliographic record
Abstract
Abstract In this study, we present data from four T-cell engager (TCE) programs in which we leveraged our highly diverse CD3-binding antibodies to generate functional bispecifics for each tumor target. By aggregating tumor-cell killing and cytokine data across four tumor targets with different properties and target expression levels, we have gained novel insights into target-specific considerations for the development of CD3 TCEs. TCEs recapitulate a synapse between T cells and target cells. T cell function downstream of this synapse is determined by the complex interplay between multiple independent factors. These include properties of the CD3- and tumor-binding arms, such as binding kinetics and epitope, as well as target-dependent factors such as cell type and target density. We used our diverse CD3 antibodies to engineer bispecific TCEs against four solid tumor targets: PSMA, B7-H4, 5T4, and the peptide-MHC target MAGE-A4. For each target, we engineered and characterized hundreds of bispecific molecules using high-throughput assays, including T-cell dependent tumor-cell killing, cytokine release, developability assessments, and structural studies. Here, we compare data from across these programs to elucidate the impact of target-dependent parameters on TCE function. These data provide important insights for the selection of appropriate TCE targets and efficient design of bispecific antibodies with high therapeutic potential. Citation Format: Matt Mai, Raffi Tonikian, Peter Bergqvist, Alaa Amash, Nathalie Blamey, Gabrielle Conaghan, Valentine de Puyraimond, Patrick Farber, Allison Goodman, Ahn Lee, Jessica Fernandes Scortecci, Cindy-Lee Crichlow, Akram Khodabandehloo, Tova Pinsky, Kate Caldwell, Jessica Patterson, Philippe Pouliot, Davide Tortora, Oscar Urtatiz, Ping Xiang, Irene Yu, Kirstin Brown, Kelly Bullock, Andrea Chee, Stephanie K. Masterman, Neil Aubuchon, Lindsay Devorkin, Bryan C. Barnhart, Tim Jacobs. Target-dependent considerations for the design of bispecific T-cell engagers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1868.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".