Phenotypic Screening of Chemical Libraries Enriched by Molecular Docking to Multiple Targets Selected from Glioblastoma Genomic Data

Like most solid tumors,
glioblastoma multiforme (GBM) harbors multiple
overexpressed and mutated genes that affect several signaling pathways.
Suppressing tumor growth of solid tumors like GBM without toxicity
may be achieved by small molecules that selectively modulate a collection
of targets across different signaling pathways, also known as selective
polypharmacology. Phenotypic screening can be an effective method
to uncover such compounds, but the lack of approaches to create focused
libraries tailored to tumor targets has limited its impact. Here,
we create rational libraries for phenotypic screening by structure-based
molecular docking chemical libraries to GBM-specific targets identified
using the tumor’s RNA sequence and mutation data along with
cellular protein–protein interaction data. Screening this enriched
library of 47 candidates led to several active compounds, including 1 (IPR-2025), which (i) inhibited cell viability of low-passage
patient-derived GBM spheroids with single-digit micromolar IC50 values that are substantially better than standard-of-care
temozolomide, (ii) blocked tube-formation of endothelial cells in
Matrigel with submicromolar IC50 values, and (iii) had
no effect on primary hematopoietic CD34+ progenitor spheroids
or astrocyte cell viability. RNA sequencing provided the potential
mechanism of action for 1, and mass spectrometry-based
thermal proteome profiling confirmed that the compound engages multiple
targets. The ability of 1 to inhibit GBM phenotypes without
affecting normal cell viability suggests that our screening approach
may hold promise for generating lead compounds with selective polypharmacology
for the development of treatments of incurable diseases like GBM.

与多数实体瘤类似，多形性胶质母细胞瘤（glioblastoma multiforme, GBM）携带多种过度表达及突变的基因，这些基因可调控多条信号通路。针对GBM等实体瘤，在避免毒性的前提下抑制其肿瘤生长，可通过选择性调控跨多条信号通路的一系列靶点的小分子化合物实现，该策略亦被称为选择性多靶点药理学。表型筛选（phenotypic screening）是发现此类化合物的有效手段，但目前缺乏针对肿瘤靶点定制聚焦化合物库的方法，这限制了该技术的应用效果。本研究通过基于结构的分子对接（structure-based molecular docking），将化学化合物库对接至通过肿瘤RNA测序、突变数据以及细胞蛋白质-蛋白质相互作用数据鉴定得到的GBM特异性靶点，以此构建用于表型筛选的理性化化合物库。对该包含47个候选化合物的富集库进行筛选后，我们得到了多种活性化合物，其中包括化合物1（IPR-2025）：(i) 其对低传代患者来源的GBM球体的细胞活力具有抑制作用，半最大抑制浓度（IC50）处于单微摩尔级别，效果显著优于临床一线用药替莫唑胺；(ii) 其可在基质胶（Matrigel）中以亚微摩尔级别的IC50阻断内皮细胞的管腔形成；(iii) 其对原代造血CD34+祖细胞球体以及星形胶质细胞的细胞活力无显著影响。通过RNA测序我们明确了化合物1潜在的作用机制，而基于质谱的热蛋白质组分析（mass spectrometry-based thermal proteome profiling）进一步证实，该化合物可结合多个靶点。化合物1能够抑制GBM相关表型且不影响正常细胞活力，这表明我们的筛选策略有望为开发针对GBM这类不治之症的治疗药物生成具备选择性多靶点药理学特性的先导化合物。