1291 Profiling bispecific T-cell engagers: strategies for enhancing potency while minimizing cytokine release
Bibliographic record
Abstract
Background CD3 T-cell engagers (TCEs) represent a promising approach in cancer immunotherapy, yet challenges in efficacy and safety have hindered clinical development. To address these barriers, we developed a TCE platform comprised of novel CD3-binding antibodies, bispecific engineering technology, and a high-throughput process for identifying pairs of CD3- and tumor-binding antibodies with desired properties. By assessing hundreds of CD3-binding antibodies in bispecific formats, we identified rare CD3-binders that can be used to create TCEs that show decoupling of cell killing and cytokine release in vitro. In addition, we have generated antibodies targeting costimulatory receptors CD28 and 4-1BB. Here, we present strategies to combine optimized TCEs with costimulation, which has the potential to increase efficacy while mitigating cytokine release-associated toxicities. Methods We generated 180 PSMA x CD3 TCEs and used high-throughput T-cell-dependent cellular cytotoxicity (TDCC) and cytokine release assays to identify molecules with desired functional profiles. To explore drivers of high potency/low cytokine release TCE phenotypes, we performed TDCC and cytokine release assays. Functional results were used to assess the impact of TCE design features, including CD3 binding affinity and kinetics, epitope recognition, and tumor-binding antibody properties. Using single B-cell screening, we identified additional TCE building blocks, including γδ-, CD28-, and 4-1BB-binding antibodies, with costimulatory potential assessed using T-cell activation assays. Results In our studies, TCEs with high potency and low cytokine release are derived from three clonally-related groups of novel CD3-binding antibodies that do not compete with the commonly used CD3-binder, SP34-2, in epitope binning experiments. In contrast, TCEs derived from a large number of CD3-binders with a range of affinities and binding kinetics do not exhibit this property. These results suggest that CD3 binding epitope is a key contributor to high tumor-cell killing and low cytokine release, and that this property cannot be achieved by affinity-tuning alone. We further explore the potential to enhance TCE potency with costimulation strategies using CD28- and 4-1BB-binding antibodies. Conclusions Building on previous work, we define parameters that influence TCE function and provide mechanistic insights into the high potency/low cytokine release profile. We also demonstrate potential strategies to enhance TCE activity for challenging targets and indications. These data provide important insights for the design of TCEs that enhance the potency, durability, and specificity of T-cell responses.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".