Abstract Tumor tissues exhibit a mildly acidic extracellular microenvironment (pH 6. 0-6. 8), in contrast to normal tissues at physiological pH (∼7. 4), and exploiting this difference could enable tumor-selective antibody targeting and reduce systemic toxicity; however, rationally engineering pH-dependent binding into antibodies remains challenging. We developed an deep learning model that predicts how mutations in antibody complementarity-determining regions (CDRs) modulate antigen binding as a function of pH and applied it to redesign a B7-H3-targeting antibody to preferentially bind under acidic conditions. The resulting antibodies showed strong pH-dependent binding, achieving affinity ratios (pH 6. 0 / pH 7. 4) exceeding 100-fold, with high affinity maintained in acidic conditions representative of the tumor microenvironment and markedly reduced binding at physiological pH. When reformatted as antibody-drug conjugates (ADCs), these pH-responsive antibodies exhibited improved selectivity and a threefold expansion of the drug administration window compared with the parent antibody. These results demonstrate that AI-driven CDR engineering enables systematic design of microenvironment-responsive antibodies and offers a generalizable strategy to enhance the therapeutic index of antibody-based cancer therapeutics. Citation Format: Qilin Yu, Mingchen Chen, Ying Lu, Yuxi Wang. AI guided engineering of pH responsive antibodies enables tumor selective targeting and improves the therapeutic index abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts) ; 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86 (8Suppl): Abstract nr LB027.
Yu et al. (2026) studied this question.