Discovery and optimization of selective liver X receptor β (LXRβ) agonists are challenging due to the high homology of LXRα and LXRβ in the ligand binding domain (LBD). There is only one different residue (Val versus Ile) at the LBD of LXRs. With machine learning methods, we identified pan LXR agonists with a novel scaffold (spiro[pyrrolidine-3,3′-oxindole]). Then, we figured out the mechanism of LXR isoform selectivity from co-crystal structures. Based on the mechanism and the new scaffold, LXRβ selective agonists were designed and synthesized. This led to the discovery of LXRβ agonists 4-7rr, 4–13 and 4-13rr with IC₅₀ values ranging from 1.78 to 6.36 μM against glioblastoma in vitro. Treatment with 50 mg/kg/day of 4-13 for 15 days significantly reduced tumor growth using an in vivo xenograft glioblastoma model.

由于LXRα和LXRβ在配体结合域（LBD）中的高度同源性，选择性肝X受体β（LXRβ）激动剂的发现和优化具有挑战性。LXR的LBD处只有一个不同的残基（Val与Ile）。通过机器学习方法，我们确定了具有新型支架（螺环[吡咯烷-3,3'-羟吲哚]）的泛LXR激动剂。然后，我们从共晶结构中找出了LXR异构体选择性的机理。基于机理和新型支架，设计合成了LXRβ选择性激动剂。这导致了在体外针对胶质母细胞瘤的LXRβ激动剂4-7rr，4-13和4-13rr的IC ₅₀值为1.78至6.36μM 。。使用体内异种移植胶质母细胞瘤模型，以50 mg / kg /天的4-13剂量治疗15天可显着降低肿瘤的生长。