Abstract 312: Redirecting T cells to tumor targets with functionally diverse CD3-binding antibodies
Bibliographic record
Abstract
Abstract Bispecific antibodies that redirect cancer-killing T cells towards tumors are promising next-generation cancer therapies. While there are hundreds of T cell engagers (TCEs) in development, there is only one approved and marketed CD3-binding TCE. The high rate of attrition is largely attributable to dose-limiting toxicities, including cytokine release syndrome, due in part to the small pool of high affinity CD3-binding antibodies that are commonly used. The discovery of safe and effective TCEs is limited because diverse panels of parental CD3 antibodies are hard to produce, the pairing of parentals is hard to perfect, and the sheer complexity and volume of data is hard to action. In this study, we will present a panel of functionally diverse, fully human CD3-binding parental antibodies. We will present data characterizing the diversity of our panel across multiple parameters, including sequence diversity, CD3 affinity, epitope binding, T cell activation, cytokine release, and tumor cell killing. Using OrthoMab࣪, our clinically-validated bispecific engineering platform, allows this panel to be tested with a series of tumor-associated antigens (TAAs) in a matrix format. Results from high-throughput production and characterization of bispecific antibodies will be presented. These multidimensional datasets of TCE composition and function allow for the identification of pairs that are optimal candidates for clinical development. This work will show how the diversity of our CD3-binding panel, combined with a robust bispecific protein engineering technology, can be used to quickly assess large and diverse TAA-binding panels discovered through our technology stack. An integrated workflow that doubles the data with diverse panels of parentals, assembles stable, safe, and manufacturable TCEs, and visualizes multidimensional datasets are critical to successfully identifying lead therapeutic candidates to bring the next generation of cancer therapies to patients sooner. Citation Format: Lindsay DeVorkin, Tim M. Jacobs, Raffi Tonikian, Karine Hervé, Kate Caldwell, Yuri Hwang, Cristina Faralla, Wei Wei, Katherine J. Lam, Harveer Dhupar, Tran NT Tran, Melissa Cid, Lena M. Bolten, Tova Pinsky, Kush Dalal, Kevin A. Heyries, Bryan C. Barnhart. Redirecting T cells to tumor targets with functionally diverse CD3-binding antibodies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 312.
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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 | 0.000 |
| 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) | 0.003 | 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".