Abstract 1886: Identifying T-cell engagers with optimal potency and cytokine-release profiles with a diverse panel of CD3-binding antibodies
Bibliographic record
Abstract
Abstract In this study, we describe the characterization and validation of a diverse panel of fully human CD3-binding antibodies, including hundreds of human and cyno cross-reactive binders. We used two proof-of-concept TCE targets to demonstrate that this panel streamlines CD3 T-cell engager (TCE) development, enabling identification of optimal tumor cell-killing and cytokine-release profiles. CD3 TCEs have potential to be powerful cancer treatments, but the small number of available CD3-binding antibodies and limited multispecific engineering technologies have been barriers to development. Identifying TCEs that balance anti-tumor potency with potential toxicities, such as cytokine release syndrome, requires simultaneous tuning of both the CD3- and tumor-binding arms. Pairs of antibodies that achieve this balance are rare, creating a need for diverse panels of developable antibodies that can be combined and tested to identify optimal clinical candidates. To streamline TCE development, we discovered a diverse panel of CD3-binding antibodies. We screened over 5 million single cells from humanized mice and identified 585 unique CD3-specific antibody sequences. Of these, over 170 were identified as cross-reactive to human and cyno CD3 in primary screening. We then used high-throughput characterization to curate a panel of diverse and developable antibodies. We found a wide range of CD3εδ and CD3εγ binding specificities, affinities, and kinetics. Epitope binning analysis revealed multiple bins containing human and cyno cross-reactive binders, some of which are distinct from previously described cross-reactive antibodies, such as SP34-2. We assessed their biophysical properties and identified antibodies with good developability properties, including high thermal stability and low hydrophobicity, self-association, polyspecificity, and aggregation. To validate these antibodies, we used OrthoMab™ to generate proof-of-concept TCE panels with fixed tumor-binding arms. We identified CD3 x EGFR TCEs with high potency, low cytokine release, functional cross-reactivity in a cyno T cell-mediated tumor killing assay, and good pharmacokinetic properties in Tg32 mice. A second proof-of-concept CD3 x PSMA panel further validated our antibodies in bispecific formats. Together, these studies demonstrate that starting with diverse CD3-binding antibodies streamlines identification of developable TCEs with optimal potency and cytokine release. We leveraged data from our extensive characterization of CD3-binding antibodies in mono- and bispecific formats to develop a strategy for down-selection and pairing of CD3- and tumor-binding antibodies, and a high-throughput method for analysis of resulting TCEs. By categorizing antibodies based on functional properties, we are able to rapidly pinpoint optimal potential clinical candidates for specific tumor targets. Citation Format: Juntao (Matt) Mai, Kate Caldwell, Lindsay DeVorkin, Grace P. Leung, Karine Herve, Yuri Hwang, Cristina Faralla, Wei Wei, Emma Lathouwers, Valentine de Puyraimond, Lauren Clifford, Rhys S. Chappell, Stefan Hannie, Katherine J. Lam, Harveer Dhupar, Tran N. Tran, Melissa Cid, Lena M. Bolten, Tova Pinsky, Ping Xiang, Courteney Lai, Ahn Lee, Vivian Z. Li, Patrick Chan, Jasmine Chin, Steve Booth, Amy C. Lee, Stephanie Masterman, Sherie Duncan, Aaron Yamniuk, Kush Dalal, Tim M. Jacobs, Raffi Tonikian, Bryan C. Barnhart. Identifying T-cell engagers with optimal potency and cytokine-release profiles with a diverse panel of CD3-binding antibodies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1886.
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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) | 0.000 | 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 | 0.001 |
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".