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1372 TriTCE Co-Stim: a novel trispecific T cell engager platform, with integrated CD28 costimulation, engineered to widen the therapeutic window for treatment of poorly infiltrated tumors

2023· article· en· W4388048531 on OpenAlexaff
Lisa Newhook, Purva Bhojane, Peter Repenning, Desmond W. M. Lau, Nichole Escalante, Diego Perez Escanda, Polly Shao, Maya C. Poffenberger, Alec Robinson, Kesha Patel, Alexandra Livernois, Chayne L. Piscitelli, Nicole Afacan, Thomas Spreter von Kreudenstein, Nina E. Weisser

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2023
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsZymeworks (Canada)
Fundersnot available
KeywordsCD28Cancer researchT cellCD3CytokineIn vivoAntibodyImmunotherapyImmunologyChemistryImmune systemMedicineBiologyCD8

Abstract

fetched live from OpenAlex

<h3>Background</h3> Bispecific T cell engagers (TCEs) have shown clinical benefit in treating hematological cancers, but limited success in solid tumors. Overcoming the immunosuppressive environment and low T cell infiltration remain some of the key challenges limiting the activity of traditional CD3-engaging bispecific TCEs. Conventional T cell activation requires signaling via CD3 (signal 1) and costimulatory molecules (signal 2), such as CD28. Superagonist anti-CD28 antibodies activate T cells but resulted in clinical toxicities with severe cytokine release. Therefore, the balance between signals 1 and 2 is critical for optimal T cell activation and proliferation. Using our Azymetric<sup>TM</sup> and EFECT<sup>TM</sup> technologies, we generated heterodimeric costimulatory trispecific TCE (TriTCE Co-stim) antibodies with silenced Fc gamma function to optimally engage CD3, CD28, and CLDN18.2. We previously identified a lead trispecific format with improved in vitro cytotoxicity and in vivo anti-tumor activity compared to traditional bispecific TCEs. Here, we further characterize the safety profile, anti-tumor properties, and the mechanism of action of our lead TriTCE Co-stim. <h3>Methods</h3> To understand safety, we investigated cytokine production by monocultures of T cells or PBMCs as well as using predictive in vitro and in vivo models of cytokine release syndrome (CRS). We further assessed the ability of our lead format to induce cytotoxicity of T cells. Human PBMC-engrafted CLDN18.2-expressing xenograft models were used to assess in vivo anti-tumor activity and T cell infiltration following treatment with TriTCE Co-stim. Upregulation of effector and central memory and exhaustion markers were interrogated in vitro. To understand the impact of co-engagement of CD3 and CD28 with a trispecific molecule, we evaluated cytokine production and tumor cytotoxicity in vitro compared to a combination of CD3 and CD28-engaging bispecific TCEs. <h3>Results</h3> We observed minimal induction of cytokine by TriTCE Co-stim in monocultures of PBMCs and T cells and similarly with in vitro and in vivo models of CRS. Furthermore, our lead TriTCE Co-stim induced no cytotoxicity of T cells. TriTCE Co-stim exhibited enhanced antitumor activity and T cell infiltration in vivo compared to bispecific TCE with increased effector and memory subsets in vitro. Finally, our lead TriTCE Co-stim exhibited enhanced cytotoxicity with reduced cytokine production by T cells compared to a combination of CD3- and CD28-engaging bispecific TCEs. <h3>Conclusions</h3> These data suggest TriTCE Co-stim may provide tolerable and more durable anti-tumor responses and re-invigorate poorly infiltrated tumors. Taken together, our lead TriTCE Co-stim demonstrates favorable characteristics that may contribute to improved clinical outcomes. <h3>Ethics Approval</h3> The protocol and procedures involving the care and use of animals in these studies was conducted in accordance with the regulations of the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) and were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC; CrownBio, Jackson ImmunoResearch).

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 score0.215
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.057
GPT teacher head0.303
Teacher spread0.246 · 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
Published2023
Admission routes1
Has abstractyes

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