Inhibition of Mast Cell Degranulation by Novel Small Molecule MRGPRX2 Antagonists.

BACKGROUND: Mas-related G-protein coupled receptor X2 (MRGPRX2) is a promiscuous receptor on mast cells that mediates IgE-independent degranulation and has been implicated in multiple mast cell-mediated disorders, including chronic urticaria, atopic dermatitis, and pain disorders. Although it is a promising therapeutic target, few potent, selective, small molecule antagonists have been identified, and functional effects of human MRGPRX2 inhibition have not been evaluated in vivo. OBJECTIVE: We identified and characterized novel, potent, and selective orally active small molecule MRGPRX2 antagonists for potential treatment of mast cell-mediated disease. METHODS: Antagonists were identified using multiple functional assays in cell lines overexpressing human MRGPRX2, LAD2 mast cells, human peripheral stem cell-derived mast cells, and isolated skin mast cells. Skin mast cell degranulation was evaluated in Mrgprb2em(-/-) knockout (KO) and Mrgprb2em(MRGPRX2) transgenic human MRGPRX2 knock-in (KI) mice by assessment of agonist-induced skin vascular permeability. Ex vivo skin mast cell degranulation and associated histamine release was evaluated by microdialysis of human skin tissue samples. RESULTS: MRGPRX2 antagonists potently inhibited agonist-induced MRGPRX2 activation and mast cell degranulation in all mast cell types tested, in an IgE-independent manner. Orally administered MRGPRX2 antagonists also inhibited agonist-induced degranulation and resulting vascular permeability in MRGPRX2 KI mice. In addition, antagonist treatment dose dependently inhibited agonist-induced degranulation in ex vivo human skin. CONCLUSION: MRGPRX2 small molecule antagonists potently inhibited agonist-induced mast cell degranulation in vitro and in vivo as well as ex vivo in human skin, supporting potential therapeutic utility as a novel treatment for multiple human diseases involving clinically relevant mast cell activation.

Quantitative cell imaging approaches to metastatic state profiling.

Genetic heterogeneity of metastatic dissemination has proven challenging to identify exploitable markers of metastasis; this bottom-up approach has caused a stalemate between advances in metastasis and the late stage of the disease. Advancements in quantitative cellular imaging have allowed the detection of morphological phenotype changes specific to metastasis, the morphological changes connected to the underlying complex signaling pathways, and a robust readout of metastatic cell state. This review focuses on the recent machine and deep learning developments to gain detailed information about the metastatic cell state using light microscopy. We describe the latest studies using quantitative cell imaging approaches to identify cell appearance-based metastatic patterns. We discuss how quantitative cancer biologists can use these frameworks to work backward toward exploitable hidden drivers in the metastatic cascade and pioneering new Frontier drug discoveries specific for metastasis.

1-Aminomethyl SAR in a novel series of flavagline-inspired eIF4A inhibitors: Effects of amine substitution on cell potency and in vitro PK properties.

Flavaglines such as silvestrol (1) and rocaglamide (2) constitute an interesting class of natural products with promising anticancer activities. Their mode of action is based on inhibition of eukaryotic initiation factor 4A (eIF4A) dependent translation through formation of a stable ternary complex with eIF4A and mRNA, thus blocking ribosome scanning. Herein we describe initial SAR studies in a novel series of 1-aminomethyl substituted flavagline-inspired eIF4A inhibitors. We discovered that a variety of N-substitutions at the 1-aminomethyl group are tolerated, making this position pertinent for property and ADME profile tuning. The findings presented herein are relevant to future drug design efforts towards novel eIF4A inhibitors with drug-like properties.

Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma.

Deep learning has emerged as the technique of choice for identifying hidden patterns in cell imaging data but is often criticized as "black box." Here, we employ a generative neural network in combination with supervised machine learning to classify patient-derived melanoma xenografts as "efficient" or "inefficient" metastatic, validate predictions regarding melanoma cell lines with unknown metastatic efficiency in mouse xenografts, and use the network to generate in silico cell images that amplify the critical predictive cell properties. These exaggerated images unveiled pseudopodial extensions and increased light scattering as hallmark properties of metastatic cells. We validated this interpretation using live cells spontaneously transitioning between states indicative of low and high metastatic efficiency. This study illustrates how the application of artificial intelligence can support the identification of cellular properties that are predictive of complex phenotypes and integrated cell functions but are too subtle to be identified in the raw imagery by a human expert. A record of this paper's transparent peer review process is included in the supplemental information. VIDEO ABSTRACT.

Targeting Oncogene mRNA Translation in B-Cell Malignancies with eFT226, a Potent and Selective Inhibitor of eIF4A.

The PI3K/AKT/mTOR pathway is often activated in lymphoma through alterations in PI3K, PTEN, and B-cell receptor signaling, leading to dysregulation of eIF4A (through its regulators, eIF4B, eIF4G, and PDCD4) and the eIF4F complex. Activation of eIF4F has a direct role in tumorigenesis due to increased synthesis of oncogenes that are dependent on enhanced eIF4A RNA helicase activity for translation. eFT226, which inhibits translation of specific mRNAs by promoting eIF4A1 binding to 5'-untranslated regions (UTR) containing polypurine and/or G-quadruplex recognition motifs, shows potent antiproliferative activity and significant in vivo efficacy against a panel of diffuse large B-cell lymphoma (DLBCL), and Burkitt lymphoma models with ≤1 mg/kg/week intravenous administration. Evaluation of predictive markers of sensitivity or resistance has shown that activation of eIF4A, mediated by mTOR signaling, correlated with eFT226 sensitivity in in vivo xenograft models. Mutation of PTEN is associated with reduced apoptosis in vitro and diminished efficacy in vivo in response to eFT226. In models evaluated with PTEN loss, AKT was stimulated without a corresponding increase in mTOR activation. AKT activation leads to the degradation of PDCD4, which can alter eIF4F complex formation. The association of eFT226 activity with PTEN/PI3K/mTOR pathway regulation of mRNA translation provides a means to identify patient subsets during clinical development.

Design of Development Candidate eFT226, a First in Class Inhibitor of Eukaryotic Initiation Factor 4A RNA Helicase.

Dysregulation of protein translation is a key driver for the pathogenesis of many cancers. Eukaryotic initiation factor 4A (eIF4A), an ATP-dependent DEAD-box RNA helicase, is a critical component of the eIF4F complex, which regulates cap-dependent protein synthesis. The flavagline class of natural products (i.e., rocaglamide A) has been shown to inhibit protein synthesis by stabilizing a translation-incompetent complex for select messenger RNAs (mRNAs) with eIF4A. Despite showing promising anticancer phenotypes, the development of flavagline derivatives as therapeutic agents has been hampered because of poor drug-like properties as well as synthetic complexity. A focused effort was undertaken utilizing a ligand-based design strategy to identify a chemotype with optimized physicochemical properties. Also, detailed mechanistic studies were undertaken to further elucidate mRNA sequence selectivity, key regulated target genes, and the associated antitumor phenotype. This work led to the design of eFT226 (Zotatifin), a compound with excellent physicochemical properties and significant antitumor activity that supports clinical development.