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Gambogenic acid inhibits fibroblast growth factor receptor signaling pathway in erlotinib-resistant non-small-cell lung cancer and suppresses patient-derived xenograft growth
Feb 15,2018
Gambogenic acid inhibits fibroblast growth factor receptor signaling pathway in erlotinib-resistant non-small-cell lung cancer and suppresses patient-derived xenograft growth

Erlotinib resistance causes a high degree of lethality in non-small-cell lung cancer (NSCLC) patients. The high expression and activation of several receptor tyrosine kinases, such as JAK/STAT3, c-Met, and EGFR, play important roles in drug resistance. The development of tyrosine kinase inhibitors is urgently required in the clinic. Our previous study found that Gambogenic acid (GNA), a small molecule derived from the traditional Chinese medicine herb gamboge, induced cell death in several NSCLC cell lines through JAK/STAT3 inhibition. In this study, we investigated the mechanism of action of GNA in erlotinib-resistant NSCLC and patient-derived cells. The inhibition of GNA on FGFR signaling pathway was examined using biochemical kinase assays. NSCLC cell lines (HCC827, HCC827-Erlotinib-resistant, and H1650) and primary cells from patients with NSCLC with clinical resistance to erlotinib were treated with GNA, erlotinib, or their combination. Both kinase assays and cell- based assays showed that GNA inhibits the phosphorylation of multiple kinases in FGFR signaling pathway in NSCLC. The combination of GNA and erlotinib significantly attenuates the tumor growth of HCC827 and erlotinib-resistant HCC827 xenografts with low toxicity. Importantly, GNA significantly suppresses tumor growth in a lung patient-derived xenograft (PDX) model with FGFR fusion and low EGFR expression. Our findings provide preclinical evidence for using GNA as an FGFR signaling pathway inhibitor to overcome erlotinib resistance in NSCLC treatment or to enhance erlotinib efficacy when used as a combined administration.

Xu L, et al. Cell Death Dis. 2018 Feb 15;9(3):262. doi: 10.1038/s41419-018-0314-6.

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A microfluidics-based mobility shift assay to identify new inhibitors of β-secretase for Alzheimer’s disease
Nov 1,2017
A microfluidics-based mobility shift assay to identify new inhibitors of β-secretase for Alzheimer’s disease

The β-secretase (BACE1) initiates the generation of toxic amyloid-β peptide (Aβ) from amyloid-β precursor protein (APP), which was widely considered to play a key role in the pathogenesis of Alzheimer’s disease (AD). Here, a novel microfluidics-based mobility shift assay (MMSA) was developed, validated, and applied for the screening of BACE1 inhibitors for AD. First, the BACE1 activity assay was established with a new fluorescent peptide substrate (FAM-EVNLDAEF) derived from the Swedish mutant APP, and high-quality ratiometric data were generated in both endpoint and kinetic modes by electrophoretic separation of peptide substrate from peptide cleaved product (FAM-EVNL) before fluorescence quantification. To validate the assay, the inhibition and kinetic parameter values of two known inhibitors (AZD3839 and AZD3293) were evaluated, and the results were in good agreement with those reported by other methods. Finally, the assay was applied to screen for new inhibitors from a 900-compound library in a 384-well format, and one novel hit (IC50 = 26.5 ± 1.5 μM) was identified. Compared with the common fluorescence-based assays, the primary advantage of the direct MMSA was to discover novel BACE1 inhibitors with lower auto-fluorescence interference, and its superb capability for kinetic study. Graphical abstract Microfluidics-based mobility shift assay for BACE1.

Liu R, et al. A microfluidics-based mobility shift assay to identify new inhibitors of β-secretase for Alzheimer’s disease. Anal Bioanal Chem. 2017 Nov;409(28):6635-6642. doi: 10.1007/s00216-017-0617-y. Epub 2017 Sep 9.

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Potent, Selective, and Cell Active Protein Arginine Methyltransferase 5 (PRMT5) Inhibitor Developed by Structure-Based Virtual Screening and Hit Optimization
Jul 27,2017
Potent, Selective, and Cell Active Protein Arginine Methyltransferase 5 (PRMT5) Inhibitor Developed by Structure-Based Virtual Screening and Hit Optimization

PRMT5 plays important roles in diverse cellular processes and is upregulated in several human malignancies. Besides, PRMT5 has been validated as an anticancer target in mantle cell lymphoma. In this study, we found a potent and selective PRMT5 inhibitor by performing structure-based virtual screening and hit optimization. The identified compound 17 (IC50 = 0.33 μM) exhibited a broad selectivity against a panel of other methyltransferases. The direct binding of 17 to PRMT5 was validated by surface plasmon resonance experiments, with a K d of 0.987 μM. Kinetic experiments indicated that 17 was a SAM competitive inhibitor other than the substrate. In addition, 17 showed selective antiproliferative effects against MV4-11 cells, and further studies indicated that the mechanism of cellular antitumor activity was due to the inhibition of PRMT5 mediated SmD3 methylation. 17 may represent a promising lead compound to understand more about PRMT5 and potentially assist the development of treatments for leukemia indications.

Mao R, Zhu K, Tao H, Song JL, Jin L, Zhang Y, Liu J, Chen Z, Jiang CS, Luo C, Zhang H. Potent, Selective, and Cell Active Protein Arginine Methyltransferase 5 (PRMT5) Inhibitor Developed by Structure-Based Virtual Screening and Hit Optimization. J Med Chem. 2017 Jul 27;60(14):6289-6304. doi: 10.1021/acs.jmedchem.7b00587. Epub 2017 Jul 12.

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Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4
Jul 24,2017
Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4

Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with different selectivity profiles and potential antiresistance properties. Structure-based drug design (SBDD) and virtual screening (VS) are the most frequently used approaches. Here, we demonstrate a novel, structure-based VS approach that uses machine-learning algorithms trained on the priori structure and activity knowledge to predict the likelihood that a compound is a BRD4i based on its binding pattern with BRD4. In addition to positive experimental data, such as X-ray structures of BRD4-ligand complexes and BRD4 inhibitory potencies, negative data such as false positives (FPs) identified from our earlier ligand screening results were incorporated into our knowledge base. We used the resulting data to train a machine-learning model named BRD4LGR to predict the BRD4i-likeness of a compound. BRD4LGR achieved a 20-30% higher AUC-ROC than that of Glide using the same test set. When conducting in vitro experiments against a library of previously untested, commercially available organic compounds, the second round of VS using BRD4LGR generated 15 new BRD4is. Moreover, inverting the machine-learning model provided easy access to structure-activity relationship (SAR) interpretation for hit-to-lead optimization.

Jing Xing et al. J Chem Inf Model. 2017 Jul 24;57(7):1677-1690.

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