Abstract 6359: Development of PSMA x CD3 T-cell engagers using an integrated, functional approach
Bibliographic record
Abstract
Abstract In this study, we used a functional approach to generate CD3 T-cell engager (TCE) molecules targeting prostate-specific membrane antigen (PSMA). We present data on the robust in vitro characterization of T-cell activity and selection of molecules for further assessment. TCE function is dictated by the interplay between multiple factors that form the immune synapse, including binding kinetics, binding geometry, and epitopes of the CD3- and tumor-binding arms. To diversify immune synapse parameters, we engineer hundreds of bispecific molecules from highly diverse parental antibodies, and employ a high-throughput process to select molecules with desired functional properties. We applied this approach to engineer CD3 TCEs targeting PSMA. PSMA is an attractive target for the treatment of castration-resistant prostate cancer due to its high expression in prostate cancer cells and low relative expression in other tissues. We discovered hundreds of diverse, fully human PSMA-binding antibodies using high-throughput single B-cell screening and selected human/cynomolgus cross-reactive binders with a broad range of affinities and epitopes. We paired these with human/cynomolgus cross-reactive CD3-binding antibodies from our TCE platform to generate 180 bispecific PSMA x CD3 TCEs. CD3-binding antibodies covered several orders of magnitude of affinity (nanomolar to micromolar) and included binders specific for different CD3 subunits. We used high-throughput T-cell dependent cellular cytotoxicity (TDCC) and cytokine release assays to identify molecules with desired functional profiles. Bispecifics selected for further assessment had functional profiles spanning a range of properties that have been observed in clinical molecules, including potent killing and low cytokine release. To profile T-cell properties that are associated with anti-tumor immune responses, we subjected selected molecules to a battery of additional in vitro functional assessments, including proliferation, cytokine and chemokine production, and TDCC using cynomolgus T cells. Results demonstrate that a functional approach that begins with highly diverse CD3- and tumor-binding antibodies can generate promising TCEs with minimal need for protein engineering and optimization. Citation Format: Valentine de Puyraimond, Matt Mai, Alaa Amash, Nathalie Blamey, Gabrielle Conaghan, Jessica Fernandes Scortecci, Allison Goodman, Ahn Lee, Irene Yu, Franziska von Bank, Kate Caldwell, Lauren Clifford, Ingrid Knarston, Kelly Bullock, Melissa Cid, Cindy-Lee Crichlow, Lindsay Devorkin, Fiona Dickson, Patrick Farber, Stefan Hannie, Courteney Lai, Vivian Li, Stephanie K. Masterman, Iwona Niemietz, Philippe Pouliot, Ping Xiang, Bryan C. Barnhart, Raffi Tonikian. Development of PSMA x CD3 T-cell engagers using an integrated, functional approach [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 6359.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".