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1196 Streamlining T cell engager development with a diverse panel of fully human CD3-binding antibodies, bispecific engineering technology, and an integrated discovery engine

2022· article· en· W4308395446 on OpenAlexaff
Lindsay DeVorkin, Juntao Mai, Kate Caldwell, Timothy M. Jacobs, Raffi Tonikian, Karine Hervé, Yuri Hwang, Cristina Faralla, Wei Wei, Emma Lathouwers, Rhys Chappell, Stefan Hannie, Katherine Lam, Harveer Dhupar, Tran Tran, Melissa Cid, Lena M. Bolten, Tova Pinsky, Ping Xiang, Courtenay Lai, Ahn Lee, Patrick Chan, Jasmine Chin, Aaron P. Yamniuk, Kush Dalal, Bryan C. Barnhart

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2022
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsAbCellera (Canada)
Fundersnot available
KeywordsCD3Bispecific antibodyAntibodyFlow cytometryT cellChemistryMolecular biologyCancer researchBiologyComputational biologyMonoclonal antibodyAntigenImmunologyImmune system

Abstract

fetched live from OpenAlex

<h3>Background</h3> CD3 T cell engagers have the potential to be a cornerstone of immuno-oncology. However, a limited pool of CD3-binding antibodies and technological challenges in engineering bispecifics have hindered development. Discovering effective T cell engagers requires two target-binding arms — a CD3 arm that fine-tunes T cell activation and a tumor arm with high specificity for cancer cells — optimized as a whole to work in concert with each other. Beginning with diverse panels of antibodies increases the probability of finding appropriately potent and developable T cell engagers and reduces the need for downstream engineering. <h3>Methods</h3> We used microfluidic technology to screen more than 3.5 million single cells from humanized mice and identified &gt;200 CD3-specific antibodies. Using high-throughput assays, we determined affinity for CD3εδ and CD3εγ, cross-reactivity to human and cyno primary T cells, CD3 binding kinetics, and epitope bins. We assessed T cell activation by measuring CD25 and CD69 expression by flow cytometry. We then used our bispecific engineering platform, OrthoMab<sup>TM</sup>, to generate a proof-of-concept panel of CD3 x EGFR bispecific antibodies. Developability properties were assessed, including hydrophobicity (aHIC), self-association (AC-SINS), polyspecificity (BVP-ELISA), stability (nanoDSF), and aggregation (aSEC). CD3 T cell engager potencies were measured using an NFAT reporter T cell activation assay and an xCELLigence tumor cell killing assay, and cytokine release was assessed by FLEXMAP CD. <h3>Results</h3> We identified hundreds of fully human CD3-specific antibodies that are diverse, developable, and validated. The antibodies displayed a wide range of CD3 binding affinities (K<sub>D</sub> ~1 nM to 1 μM), binding kinetics, and T cell activation potencies (EC<sub>50</sub> ~6 to 190 nM). Data on this novel panel includes epitope binning, which revealed human and cyno CD3-binders that are distinct from previously described cross-reactive antibodies. The antibodies were assessed using a range of biophysical assays and have favorable developability properties. In an expanded proof-of-concept study, we used OrthoMab<sup>TM</sup> to generate a panel of CD3 x EGFR bispecific antibodies. The resulting bispecifics had favorable developability properties, and displayed a wide range of antigen-dependent T cell activation (EC<sub>50</sub> ~2 pM to 2 nM) and tumor cell killing potencies (EC<sub>50</sub> ~0.01 to 1 nM). From this panel, we identified potent T cell engagers that achieved &gt;90% tumor cell killing with low levels of cytokine release. <h3>Conclusions</h3> By integrating our panel of CD3-binding antibodies with our bispecific engineering and high-throughput antibody assessment capabilities, we identified developable CD3 T cell engagers with potent tumor cell-killing activity and minimal cytokine release.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.173
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.030
GPT teacher head0.258
Teacher spread0.228 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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