Abstract 2942: ZW171, a T cell-engaging, bispecific antibody for the treatment of mesothelin-expressing solid tumors
Bibliographic record
Abstract
Abstract Mesothelin (MSLN) is a GPI-linked membrane glycoprotein that is overexpressed in many cancer indications for which there is a high unmet medical need including pancreatic, mesothelioma, and ovarian. While MSLN-targeting agents have shown early signs of clinical activity, therapies with improved safety and efficacy are still needed. T cell engager (TCE) therapies have exhibited clinical utility against hematological malignancies, but limited success against solid tumors due to dose-limiting toxicities associated with cytokine release syndrome (CRS) and on-target off-tumor effects. To improve the therapeutic intervention of MSLN-expressing tumors, we engineered a bispecific TCE, designed to have an improved therapeutic window by enhancing both safety and anti-tumor activity profiles. To avoid dose-limiting toxicities related to CRS observed with high-affinity anti-CD3 paratopes (e.g. based on OKT3 or SP34), we engineered an anti-CD3 paratope (ZW_CD3_1) with low affinity CD3 binding and, in the context of a bispecific TCE, potent redirected T cell cytotoxicity. Compared to a bispecific antibody containing the high CD3 affinity SP34 paratope, the ZW_CD3_1 anti-CD3 paratope showed reduced and less potent T cell binding, stimulated less cytokine release yet elicited equivalent T cell mediated killing of MSLN-expressing tumor cells. Using the bispecific AzymetricTM platform, the ZW_CD3_1 paratope was incorporated into a panel of bispecific antibodies with different geometries, formats, and anti-MSLN paratope affinities then screened for T cell mediated lysis of target cells and production of cytokines. Based on reiterative screening and engineering, we determined the MSLN paratope affinity and the 2+1 format, consisting of two anti-MSLN single chain variable fragments (scFvs) and one anti-CD3 fragment antigen binding (Fab) domain, as the lead candidate format. In vivo, the lead format also showed significantly greater anti-tumor activity than other 2+1 bispecific formats. Following additional engineering and screening a lead MSLN x CD3 2 +1 bispecific antibody, ZW171, was selected for development. We further characterized the mechanistic and anti-tumor activity of ZW171 and showed that incubation of human T cells and tumor cells with ZW171 led to potent preferential killing of MSLN-mid and -high target cells while sparing MSLN-low expressing target cells. ZW171 only stimulated T cells to proliferate and produce cytokines in the presence of MSLN-expressing tumor cells, mitigating the risk of peripheral T cell activation and CRS. Importantly, ZW171 exhibited potent tumor growth inhibition in multiple established tumor models. Collectively, these data suggest that ZW171 has the potential to be an efficacious and safe therapeutic for the treatment of MSLN-expressing cancers. Citation Format: Nicole J. Afacan, Chayne Piscitelli, Patricia Zwierzchowski, Siran Cao, Janessa Li, Wingkie Wong, Kara White-Moyes, Thomas Spreter von Kreudenstein, Nina E. Weisser. ZW171, a T cell-engaging, bispecific antibody for the treatment of mesothelin-expressing solid tumors [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 2942.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".