化学
生物信息学
变构调节
药物发现
立体化学
计算生物学
药理学
生物化学
酶
生物
基因
作者
Zhe Nie,Michael Trzoss,Andrew T. Placzek,Lynnie Trzoss,Goran Krilov,Shulu Feng,Morgan Lawrenz,Min Ye,Netonia Marshall,Karen H. Dingley,Robert Pelletier,Weidong G. Lai,Jeffrey A. Bell,Haifeng Tang,Paul Devine,Zhijian Liu,Peter J. Skrdla,Roman Shimanovich,Matt Liu,Renchao Wang
标识
DOI:10.1021/acs.jmedchem.5c01494
摘要
MALT1 is a key component of the CARD11-BCL10-MALT1 (CBM) complex downstream from BTK on the B-cell receptor signaling pathway. It is a key mediator of NF-κB signaling and considered a potential therapeutic target for several subtypes of non-Hodgkin's B-cell lymphomas. By applying advanced physics-based modeling techniques, including combining free energy calculations with machine learning methods and a chemistry-aware compound enumeration workflow, extensive sets of de novo design ideas were explored to quickly identify a novel hit series. Multiparameter optimization allowed efficient prioritization of molecules with good potency and drug-like properties during lead optimization, which led to the discovery of a highly potent MALT1 inhibitor, SGR-1505, with a well-balanced property profile. It demonstrated strong antitumor activity alone and in combination with BTK inhibitor in multiple in vivo B-cell lymphoma xenograft models and progressed to a phase 1 clinical trial in patients with mature B-cell neoplasms.
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