Oral ENPP1 inhibitor designed using generative AI as next generation STING modulator for solid tumors

利用人工智能生成技术设计的口服ENPP1抑制剂,作为下一代用于治疗实体瘤的STING调节剂

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作者:Congying Pu #,Hui Cui #,Huaxing Yu #,Xin Cheng,Man Zhang,Luoheng Qin,Zhilin Ning,Wen Zhang,Shan Chen,Yuhang Qian,Feng Wang,Ling Wang,Xiaoxia Lin,David Gennert,Frank W Pun,Feng Ren ,Alex Zhavoronkov

Abstract

Despite the STING-type-I interferon pathway playing a key role in effective anti-tumor immunity, the therapeutic benefit of direct STING agonists appears limited. In this study, we use several artificial intelligence techniques and patient-based multi-omics data to show that Ectonucleotide Pyrophosphatase/Phosphodiesterase 1 (ENPP1), which hydrolyzes STING-activating cyclic GMP-AMP (cGAMP), is a safer and more effective STING-modulating target than direct STING agonism in multiple solid tumors. We then leverage our generative chemistry artificial intelligence-based drug design platform to facilitate the design of ISM5939, an orally bioavailable ENPP1-selective inhibitor capable of stabilizing extracellular cGAMP and activating bystander antigen-presenting cells without inducing either toxic inflammatory cytokine release or tumor-infiltrating T-cell death. In murine syngeneic models across cancer types, ISM5939 synergizes with targeting the PD-1/PD-L1 axis and chemotherapy in suppressing tumor growth with good tolerance. Our findings provide evidence supporting ENPP1 as an innate immune checkpoint across solid tumors and reports an AI design-aided ENPP1 inhibitor, ISM5939, as a cutting-edge STING modulator for cancer therapy, paving a path for immunotherapy advancements.

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