Abstract 1886: Identifying T-cell engagers with optimal potency and cytokine-release profiles with a diverse panel of CD3-binding antibodies
Notice bibliographique
Résumé
Abstract In this study, we describe the characterization and validation of a diverse panel of fully human CD3-binding antibodies, including hundreds of human and cyno cross-reactive binders. We used two proof-of-concept TCE targets to demonstrate that this panel streamlines CD3 T-cell engager (TCE) development, enabling identification of optimal tumor cell-killing and cytokine-release profiles. CD3 TCEs have potential to be powerful cancer treatments, but the small number of available CD3-binding antibodies and limited multispecific engineering technologies have been barriers to development. Identifying TCEs that balance anti-tumor potency with potential toxicities, such as cytokine release syndrome, requires simultaneous tuning of both the CD3- and tumor-binding arms. Pairs of antibodies that achieve this balance are rare, creating a need for diverse panels of developable antibodies that can be combined and tested to identify optimal clinical candidates. To streamline TCE development, we discovered a diverse panel of CD3-binding antibodies. We screened over 5 million single cells from humanized mice and identified 585 unique CD3-specific antibody sequences. Of these, over 170 were identified as cross-reactive to human and cyno CD3 in primary screening. We then used high-throughput characterization to curate a panel of diverse and developable antibodies. We found a wide range of CD3εδ and CD3εγ binding specificities, affinities, and kinetics. Epitope binning analysis revealed multiple bins containing human and cyno cross-reactive binders, some of which are distinct from previously described cross-reactive antibodies, such as SP34-2. We assessed their biophysical properties and identified antibodies with good developability properties, including high thermal stability and low hydrophobicity, self-association, polyspecificity, and aggregation. To validate these antibodies, we used OrthoMab™ to generate proof-of-concept TCE panels with fixed tumor-binding arms. We identified CD3 x EGFR TCEs with high potency, low cytokine release, functional cross-reactivity in a cyno T cell-mediated tumor killing assay, and good pharmacokinetic properties in Tg32 mice. A second proof-of-concept CD3 x PSMA panel further validated our antibodies in bispecific formats. Together, these studies demonstrate that starting with diverse CD3-binding antibodies streamlines identification of developable TCEs with optimal potency and cytokine release. We leveraged data from our extensive characterization of CD3-binding antibodies in mono- and bispecific formats to develop a strategy for down-selection and pairing of CD3- and tumor-binding antibodies, and a high-throughput method for analysis of resulting TCEs. By categorizing antibodies based on functional properties, we are able to rapidly pinpoint optimal potential clinical candidates for specific tumor targets. Citation Format: Juntao (Matt) Mai, Kate Caldwell, Lindsay DeVorkin, Grace P. Leung, Karine Herve, Yuri Hwang, Cristina Faralla, Wei Wei, Emma Lathouwers, Valentine de Puyraimond, Lauren Clifford, Rhys S. Chappell, Stefan Hannie, Katherine J. Lam, Harveer Dhupar, Tran N. Tran, Melissa Cid, Lena M. Bolten, Tova Pinsky, Ping Xiang, Courteney Lai, Ahn Lee, Vivian Z. Li, Patrick Chan, Jasmine Chin, Steve Booth, Amy C. Lee, Stephanie Masterman, Sherie Duncan, Aaron Yamniuk, Kush Dalal, Tim M. Jacobs, Raffi Tonikian, Bryan C. Barnhart. Identifying T-cell engagers with optimal potency and cytokine-release profiles with a diverse panel of CD3-binding antibodies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1886.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».