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1291 Profiling bispecific T-cell engagers: strategies for enhancing potency while minimizing cytokine release

2024· article· en· W4404064451 on OpenAlexaff
Ryan Blackler, Patrick Farber, Gesa Volkers, Nathalie Blamey, Creagh Briercliffe, Kate Caldwell, Stefania Carrara, Lauren Clifford, Melissa Cid, Cindy-Lee Crichlow, Harveer Dhupar, Jared Dutra, Cristina Faralla, Jéssica Fernandes Scortecci, Marian Haustein, Lucas Kraft, Katherine Lam, Esther Odekunle, Patrick J. Rowe, Britany Rufenach, Elena Viganò, Wei Wei, Shirley Zhi, S E Cullen, Sherie Duncan, Ester Falconer, Kevin A. Heyries, Michael Kennedy, Ingrid Knarston, Nicole Lee, Grace Leung, Kathleen Lisaingo, Stephanie K Masterman, Marta Szabat, Katherine A. Vousden, Christopher Williamson, Bryan C. Barnhart, Allison Goodman, Lindsay DeVorkin

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2024
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsAbCellera (Canada)
Fundersnot available
KeywordsProfiling (computer programming)PotencyComputer scienceBispecific antibodyCytokineHuman–computer interactionChemistryMedicineImmunologyAntibodyOperating system

Abstract

fetched live from OpenAlex

<h3>Background</h3> 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 <i>in vitro</i>. 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. <h3>Methods</h3> 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. <h3>Results</h3> 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. <h3>Conclusions</h3> 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.290
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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