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Record W4409628417 · doi:10.1158/1538-7445.am2025-1565

Abstract 1565: Design and development of biparatopic antibody-drug conjugates against protein tyrosine kinase 7

2025· article· en· W4409628417 on OpenAlexaff
Luying Yang, Vincent Fung, Alexander T.H. Wu, Dunja Urosev, Saki Konomura, Tik Nga Tong, Katina Mak, D.A. Alonzo, Catrina Kim, Lei Fu, Lemlem Degefie, Kaylee J. Wu, Matthew Bonderud, Jamie R. Rich, Stuart D. Barnscher

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsZymeworks (Canada)
Fundersnot available
KeywordsConjugateAntibodyTyrosine kinaseDrugPharmacologyTyrosineMedicineBiologyImmunologyBiochemistrySignal transduction

Abstract

fetched live from OpenAlex

Abstract Background: Biparatopic antibodies and antibody-drug conjugates (ADCs) can provide many functional benefits over their traditional monoparatopic counterparts including increased binding, greater cell surface decoration, superior receptor blockade, and enhanced internalization and payload delivery; however, not all antigens are amenable to biparatopic targeting. We sought to leverage our prior knowledge of biparatopic antibody development and our AzymetricTM technology to develop a novel biparatopic ADC against protein tyrosine kinase 7 (PTK7) that could provide a functional enhancement over monoparatopic ADCs. PTK7 is an attractive ADC target due to preliminary clinical activity demonstrated with cofetuzumab peledotin in non-small cell lung cancer, ovarian cancer, and metastatic breast cancer and evidence that PTK7 is overexpressed in other solid tumor indications including esophageal cancer, colorectal cancer, head and neck cancer, and cervical cancer. Materials and Methods: Twenty-nine PTK7-targeting paratopes were isolated from a mouse antibody discovery campaign and paratope pairs were combined into a panel of biparatopic antibodies using AzymetricTM Het_Fc mutations. A high-throughput screen was used to assess the functional properties of these antibodies and to identify the top paratope combinations. ADCs were produced by conjugating the top biparatopic and monoparatopic antibodies to ZD06519, a topoisomerase 1 inhibitor payload used in ZW191, an ADC targeting FRα under evaluation in a phase 1 clinical trial (NCT06555744). ADC activity was investigated in a series of in vitro and in vivo experiments using ovarian, lung, and breast cancer models. Results: Across multiple cancer cell lines with varying PTK7 expression we compared a lead monoparatopic antibody and cofetuzumab with the top biparatopic antibody which demonstrated greater cell surface decoration and better internalization. At clinically relevant doses, the biparatopic ZD06519 ADC was more efficacious than cofetuzumab pelidotin in triple negative breast cancer and lung cancer cell line derived xenograft models. Additional studies of the PTK7 biparatopic ADC in patient-derived xenograft models and a non-human primate tolerability study are planned. Citation Format: Luying Yang, Vincent Fung, Alex Wu, Dunja Urosev, Saki Konomura, Tik Nga Tong, Katina Mak, Elizabeth M. Porter, Diego A. Alonzo, Catrina M. Kim, Janice P. Tsui, Linglan Fu, Lemlem Degefie, Kaylee Wu, Matthew Bonderud, Jamie R. Rich, Stuart D. Barnscher. Design and development of biparatopic antibody-drug conjugates against protein tyrosine kinase 7 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1565.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.127
GPT teacher head0.468
Teacher spread0.341 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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