Abstract 6926: An AI-guided biparatopic DLL3-targeting ADC demonstrates enhanced preclinical efficacy

Abstract Purpose: This study aimed to develop a novel biparatopic antibody-drug conjugate (ADC) targeting delta-like ligand 3 (DLL3) for small cell lung cancer (SCLC) and other neuroendocrine neoplasms, addressing limitations of previous DLL3-targeted therapies. Methods: Leveraging our AI-guided antibody development platform, we engineered a biparatopic anti-DLL3 antibody with optimized binding and internalization properties. The antibody was conjugated with various linker-payload combinations. In vitro cytotoxicity of the ADCs were evaluated using DLL3-expressing cell lines and in vivo efficacy were assessed using CDX mice models. Safety was assessed in transgenic mice and non-human primates. Results: The biparatopic antibody demonstrated higher internalization efficiency compared to the monoclonal antibody counterparts. The ADC showed potent cytotoxicity across multiple DLL3-expressing cell lines and achieved significant tumor suppression in CDX models. Toxicological studies revealed a favorable safety profile in both transgenic mice and non-human primates. Conclusion: This biparatopic ADC represents a promising therapeutic candidate for DLL3-expressing tumors, demonstrating enhanced efficacy and favorable preclinical safety compared to previous approaches. Citation Format: Chuan Chen, Yue Wu, Chenpeng Su, Zhaohui Chen, Dandan Liu, Jiyuan Tian, Xiaoqian Chen, Yang He, Yongxin Shang, Rongmei Yan, Liang Tian, Jian Peng, Zhenping Zhu, . An AI-guided biparatopic DLL3-targeting ADC demonstrates enhanced preclinical efficacy abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6926.