Antibody therapeutics · 2026
Generative AI-driven de novo design of high-affinity, epitope-specific antibodies targeting the CD93-IGFBP7 axis for cancer immunotherapy.
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Qian Yixin, Feng Youxin, Zou Ming, Cai Huiyu, Ye Jintong, Song Teng, Li Ping, Tang Jian, Deng Mi
Аннотация
BACKGROUND: CD93 is an emerging immune checkpoint. Blocking its interaction with insulin-like growth factor-binding protein-7 (IGFBP7) restores antitumor immunity, yet discovering high-affinity antibodies against this specific functional epitope remains a formidable challenge for traditional methods.
METHODS: We employed a proprietary generative artificial intelligence (AI) model, GeoFlow, to de novo design antibodies targeting the predicted binding epitope of a reference antibody 7F3. A library constructed from 105 sequences was screened via phage display, and the lead candidate, 7-4_com10, was validated through biolayer interferometry, flow cytometry, and in vivo efficacy studies.
RESULTS: To dissect the contribution of endothelial CD93 to immunotherapy resistance, we utilized an endothelial-specific conditional knockout model in MB49 bladder cancer, where the loss of endothelial CD93 significantly sensitized tumors to PD-1 inhibition. The genetic findings identified endothelial CD93 as a fundamental driver of immunotherapy resistance and provided the rationale for our GeoFlow generative AI platform, which was employed to de novo design ~105 candidates targeting the IGFBP7-binding interface on CD93. Our lead candidate, 7-4_com10, demonstrated superior affinity and more potent functional blockade of the CD93-IGFBP7 axis compared to the reference 7F3. Crucially, 7-4_com10 sensitized H22 solid tumors to anti-PD-1 therapy in vivo, which confirmed that 7-4_com10 successfully targeted the identified immunosuppressive axis and pharmacologically phenocopied the benefits of genetic endothelial ablation.
CONCLUSIONS: This study validated generative AI for designing epitope-specific antibodies with superior potency, offering a robust candidate for next-generation immunotherapy.
METHODS: We employed a proprietary generative artificial intelligence (AI) model, GeoFlow, to de novo design antibodies targeting the predicted binding epitope of a reference antibody 7F3. A library constructed from 105 sequences was screened via phage display, and the lead candidate, 7-4_com10, was validated through biolayer interferometry, flow cytometry, and in vivo efficacy studies.
RESULTS: To dissect the contribution of endothelial CD93 to immunotherapy resistance, we utilized an endothelial-specific conditional knockout model in MB49 bladder cancer, where the loss of endothelial CD93 significantly sensitized tumors to PD-1 inhibition. The genetic findings identified endothelial CD93 as a fundamental driver of immunotherapy resistance and provided the rationale for our GeoFlow generative AI platform, which was employed to de novo design ~105 candidates targeting the IGFBP7-binding interface on CD93. Our lead candidate, 7-4_com10, demonstrated superior affinity and more potent functional blockade of the CD93-IGFBP7 axis compared to the reference 7F3. Crucially, 7-4_com10 sensitized H22 solid tumors to anti-PD-1 therapy in vivo, which confirmed that 7-4_com10 successfully targeted the identified immunosuppressive axis and pharmacologically phenocopied the benefits of genetic endothelial ablation.
CONCLUSIONS: This study validated generative AI for designing epitope-specific antibodies with superior potency, offering a robust candidate for next-generation immunotherapy.