Dataset Information

Design and pharmacology of a highly specific dual FMS and KIT kinase inhibitor.

ABSTRACT: Inflammation and cancer, two therapeutic areas historically addressed by separate drug discovery efforts, are now coupled in treatment approaches by a growing understanding of the dynamic molecular dialogues between immune and cancer cells. Agents that target specific compartments of the immune system, therefore, not only bring new disease modifying modalities to inflammatory diseases, but also offer a new avenue to cancer therapy by disrupting immune components of the microenvironment that foster tumor growth, progression, immune evasion, and treatment resistance. McDonough feline sarcoma viral (v-fms) oncogene homolog (FMS) and v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (KIT) are two hematopoietic cell surface receptors that regulate the development and function of macrophages and mast cells, respectively. We disclose a highly specific dual FMS and KIT kinase inhibitor developed from a multifaceted chemical scaffold. As expected, this inhibitor blocks the activation of macrophages, osteoclasts, and mast cells controlled by these two receptors. More importantly, the dual FMS and KIT inhibition profile has translated into a combination of benefits in preclinical disease models of inflammation and cancer.

SUBMITTER: Zhang C

PROVIDER: S-EPMC3619323 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Design and pharmacology of a highly specific dual FMS and KIT kinase inhibitor.

Zhang Chao C Ibrahim Prabha N PN Zhang Jiazhong J Burton Elizabeth A EA Habets Gaston G Zhang Ying Y Powell Ben B West Brian L BL Matusow Bernice B Tsang Garson G Shellooe Rafe R Carias Heidi H Nguyen Hoa H Marimuthu Adhirai A Zhang Kam Y J KY Oh Angela A Bremer Ryan R Hurt Clarence R CR Artis Dean R DR Wu Guoxian G Nespi Marika M Spevak Wayne W Lin Paul P Nolop Keith K Hirth Peter P Tesch Greg H GH Bollag Gideon G

Proceedings of the National Academy of Sciences of the United States of America 20130314 14

Inflammation and cancer, two therapeutic areas historically addressed by separate drug discovery efforts, are now coupled in treatment approaches by a growing understanding of the dynamic molecular dialogues between immune and cancer cells. Agents that target specific compartments of the immune system, therefore, not only bring new disease modifying modalities to inflammatory diseases, but also offer a new avenue to cancer therapy by disrupting immune components of the microenvironment that fost ...[more]

PMID: 23493555

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Project description:Many complex diseases such as cancer are associated with multiple pathological manifestations. Moreover, the therapeutics for their treatments often lead to serious side effects. Thus, it is needed to develop multi-indication therapeutics that can simultaneously target multiple clinical indications of interest and mitigate the side effects. However, conventional one-drug-one-gene drug discovery paradigm and emerging polypharmacology approach rarely tackle the challenge of multi-indication drug design. For the first time, we propose a one-drug-multi-target-multi-indication strategy. We develop a novel structural systems pharmacology platform 3D-REMAP that uses ligand binding site comparison and protein-ligand docking to augment sparse chemical genomics data for the machine learning model of genome-scale chemical-protein interaction prediction. Experimentally validated predictions systematically show that 3D-REMAP outperforms state-of-the-art ligand-based, receptor-based, and machine learning methods alone. As a proof-of-concept, we utilize the concept of drug repurposing that is enabled by 3D-REMAP to design dual-indication anti-cancer therapy. The repurposed drug can demonstrate anti-cancer activity for cancers that do not have effective treatment as well as reduce the risk of heart failure that is associated with all types of existing anti-cancer therapies. We predict that levosimendan, a PDE inhibitor for heart failure, inhibits serine/threonine-protein kinase RIOK1 and other kinases. Subsequent experiments and systems biology analyses confirm this prediction, and suggest that levosimendan is active against multiple cancers, notably lymphoma, through the direct inhibition of RIOK1 and RNA processing pathway. We further develop machine learning models to predict cancer cell-line's and a patient's response to levosimendan. Our findings suggest that levosimendan can be a promising novel lead compound for the development of safe, effective, and precision multi-indication anti-cancer therapy. This study demonstrates the potential of structural systems pharmacology in designing polypharmacology for precision medicine. It may facilitate transforming the conventional one-drug-one-gene-one-disease drug discovery process and single-indication polypharmacology approach into a new one-drug-multi-target-multi-indication paradigm for complex diseases.

Dataset Information

Design and pharmacology of a highly specific dual FMS and KIT kinase inhibitor.

Publications

Design and pharmacology of a highly specific dual FMS and KIT kinase inhibitor.

Similar Datasets

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