Mishra, Madhulika and Jayal, Priyanka and Karande, Anjali A and Chandra, Nagasuma (2018) Identification of a co-target for enhancing efficacy of sorafenib in HCC through a quantitative modeling approach. In: FEBS JOURNAL, 285 (21). pp. 3977-3992.
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Abstract
Sorafenib (SFB), a multi-kinase inhibitor, is the only approved drug for treating hepatocellular carcinoma (HCC). However, SFB shows low efficacy in many cases. HCC related mortality therefore remains to be high worldwide. SFB, a multi-kinase inhibitor is also known to modulate the redox homeostasis in cancer cells. To understand the effect of SFB on the redox status, a quantitative understanding of the system is necessary. Kinetic modeling of the relevant pathways is a useful approach for obtaining a quantitative understanding of the pathway dynamics and to rank the individual factors based on the extent of influence they wield on the pathway. Here, we report a comprehensive model of the glutathione reaction network (GSH(net)), consisting of four modules and includes SFB-induced redox stress. We compared GSH(net) simulations for HCC of six different etiologies with healthy liver, and correctly identified the expected variations in cancer. Next, we studied alterations induced in the system upon SFB treatment and observed differential H2O2 dynamics in all the conditions. Using metabolic control analysis, we identified glutathione S-transferase (GST) as the enzyme with the highest selective control coefficient, making it an attractive co-target for potentiating the action of SFB across all six etiologies. As a proof-of-concept, we selected ethacrynic acid (EA), a known inhibitor of GST, and verified ex vivo that EA synergistically potentiates the cytotoxic effect of SFB. Being an FDA approved drug, EA is a promising candidate for repurposing as a combination therapy with SFB for HCC treatment.
Item Type: | Journal Article |
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Publication: | FEBS JOURNAL |
Publisher: | WILEY |
Additional Information: | Copy right for this article belong to WILEY |
Keywords: | co-target; glutathione metabolism; liver cancer; mathematical modeling; oxidative stress; redox regulation |
Department/Centre: | Division of Biological Sciences > Biochemistry Division of Interdisciplinary Sciences > Centre for Biosystems Science and Engineering |
Date Deposited: | 28 Nov 2018 15:13 |
Last Modified: | 28 Nov 2018 15:13 |
URI: | http://eprints.iisc.ac.in/id/eprint/61178 |
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