Development of a selectively AURKB targeting peptide degradation drug with artificial intelligence-assisted design for the treatment of acute lymphoblastic leukemia.

INTRODUCTION: Acute lymphoblastic leukemia (ALL) is a highly heterogeneous hematologic malignancy with poor prognosis in refractory and relapsed cases. Aurora kinase B (AURKB), a member of the Aurora kinase family, is markedly overexpressed in ALL patients with cytogenetic abnormalities and is associated with unfavorable clinical outcomes. Targeting AURKB represents a promising therapeutic approach to address this unmet clinical need. OBJECTIVES: This study aimed to develop and validate a novel, selective degradation strategy against AURKB in ALL, utilizing artificial intelligence-assisted drug design to create a peptide-based degrader. The goal was to demonstrate the efficacy of this degrader in reducing AURKB expression and suppressing leukemic cell growth. METHODS: We performed database analyses and confirmed AURKB overexpression in patient-derived ALL samples. Using AI-assisted design, we developed ATPD (AURKB Targeting Peptide Degrader), a selective peptide-based degrader of AURKB. The activity of ATPD was evaluated through in vitro cell proliferation assays, in vivo leukemia models, and ex vivo cytotoxicity tests using primary ALL cells. RESULTS: ATPD effectively induced the degradation of AURKB and significantly inhibited the proliferation of ALL cells both in vitro and in vivo. Furthermore, ATPD exhibited potent cytotoxic activity against primary leukemic cells derived from ALL patients, B-All PDX, and mini-PDX models. CONCLUSION: Our findings demonstrate that ATPD is the first AI-designed, selective AURKB degrader with potent anti-leukemic activity. This study highlights ATPD's potential as a novel, precise therapeutic strategy for the treatment of ALL, addressing a critical gap in managing refractory and relapsed disease.