Discovery and Characterization of PROTACs Targeting Tissue Transglutaminase (TG2)

Tissue transglutaminase (TG2) is a multifunctional enzyme involved in the cross-linking of extracellular matrix proteins, formation of complexes with fibronectin (FN) and integrins, and GTP hydrolysis. TG2 is activated in several pathological conditions, including cancer. We recently described a novel series of ligands that bind to TG2 and inhibit its interaction with FN. Because TG2 acts via multiple mechanisms, we set out to pursue a targeted protein degradation strategy to abolish TG2's myriad functions. Here, we report the synthesis and characterization of a series of VHL-based degraders that reduce TG2 in ovarian cancer cells in a proteasome-dependent manner. Degradation of TG2 resulted in significantly reduced cancer cell adhesion and migration in vitro in scratch-wound and migration assays. These results strongly indicate that further development of more potent and in vivo efficient TG2 degraders could be a new strategy for reducing the dissemination of ovarian and other cancers.

Abstract 3281: DNMT targeting enhances vulnerability of glioblastoma cells to MNK inhibition

Many factors complicate therapeutic strategies for glioblastoma (GBM), including the existence of the blood brain barrier and a heterogenous population of difficult to treat glioma stem cells. Innovative strategies targeting novel pathways alone or in combination are needed for sustainable therapeutic improvements. The MAPK pathway has been implicated in many cancers. MAPK interacting kinases (MNK1 and MNK2) are downstream of MAPKs and phosphorylate the eukaryotic translation initiation factor 4E (eIF4E), a protein involved in translation of oncogenic mRNAs. We have previously established pharmacological MNK inhibition as a promising strategy for GBM. However, most currently available MNK inhibitors lack specificity and exhibit off-target effects. We developed novel selective MNK inhibitors that show MNK inhibition specificity in GBM established cell lines as well as patient-derived cell lines propagated under stem cell permissive conditions as 3-D neurospheres. MNK inhibitors reduced cell viability and neurosphere growth. Our previous work with MNK inhibitors showed involvement in negative feedback loops activated with treatment of other pharmacological agents, so we conducted a high-throughput screening to identify potential targets for combination treatment. One of the top hits was a DNA methyltransferase (DNMT) inhibitor that enhanced MNK inhibitor antineoplastic effects in GBM cells. Dual MNK and DNMT inhibition synergistically reduced neurosphere growth in 3-D glioma stem-like cells. The combination promoted apoptosis in the mesenchymal glioma stem-like cells as shown through flow cytometry and increased expression of cleaved PARP, cleaved caspase 3, and Bax. Also, DNMT targeting enhanced the viability reduction effects of siRNA mediated MNK1 knockdown in GBM cells. This combination of our novel MNK inhibitor with DNMT inhibition elicited antineoplastic benefits in both 2-D cultures and 3-D glioma stem cell-like populations, demonstrating a potential novel therapeutic strategy in GBM.

Citation Format: Candice Mazewski, Frank Eckerdt, Aneta Baran, Mariafausta Fischietti, Purav P. Vagadia, Ricardo E. Perez, Charles D. James, Gary E. Schiltz, Leonidas C. Platanias. DNMT targeting enhances vulnerability of glioblastoma cells to MNK inhibition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3281.

Modeling MEK4 Kinase Inhibitors through Perturbed Electrostatic Potential Charges

MEK4, mitogen-activated protein kinase kinase 4, is overexpressed and induces metastasis in advanced prostate cancer lesions. However, the value of MEK4 as an oncology target has not been pharmacologically validated because selective chemical probes targeting MEK4 have not been developed. With advances in both computer and biological high-throughput screening, selective chemical entities can be discovered. Structure-based quantitative structure-activity relationship (QSAR) modeling often fails to generate accurate models due to poor alignment of training sets containing highly diverse compounds. Here we describe a highly predictive, nonalignment based robust QSAR model based on a data set of strikingly diverse MEK4 inhibitors. We computed the electrostatic potential (ESP) charges using a density functional theory (DFT) formalism of the donor and acceptor atoms of the ligands and hinge residues. Novel descriptors were then generated from the perturbation of the charge densities of the donor and acceptor atoms and were used to model a diverse set of 84 compounds, from which we built a robust predictive model.

Synthesis and Biological Evaluation of 3-Arylindazoles as Selective MEK4 Inhibitors

Herein we report the discovery of a novel series of highly potent and selective mitogen-activated protein kinase kinase 4 (MEK4) inhibitors. MEK4 is an upstream kinase in MAPK signaling pathways that phosphorylates p38 MAPK and JNK in response to mitogenic and cellular stress queues. MEK4 is overexpressed and induces metastasis in advanced prostate cancer lesions. However, the value of MEK4 as an oncology target has not been pharmacologically validated because selective chemical probes targeting MEK4 have not been developed. Optimization of this series via structure-activity relationships and molecular modeling led to the identification of compound 6 ff (4-(6-fluoro-2H-indazol-3-yl)benzoic acid), a highly potent and selective MEK4 inhibitor. This series of inhibitors is the first of its kind in both activity and selectivity and will be useful in further defining the role of MEK4 in prostate and other cancers.