Oncotarget

Research Papers:

New HSP27 inhibitors efficiently suppress drug resistance development in cancer cells

Jörg C. Heinrich, Sainitin Donakonda, V. Joachim Haupt, Petra Lennig, Yixin Zhang, Michael Schroeder _

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Oncotarget. 2016; 7:68156-68169. https://doi.org/10.18632/oncotarget.11905

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Abstract

Jörg C. Heinrich1,*, Sainitin Donakonda1,*, V. Joachim Haupt1,*, Petra Lennig2, Yixin Zhang2, Michael Schroeder1

1Biotechnology Center, Technische Universität Dresden, 01307 Dresden, Germany

2B CUBE – Center for Molecular Bioengineering, Technische Universität Dresden, 01307 Dresden, Germany

*These authors contributed equally to this work

Correspondence to:

Michael Schroeder, email: ms@biotec.tu-dresden.de

Keywords: drug repositioning, drug repurposing, HSPB1, BVDU, binding site

Received: June 29, 2016     Accepted: August 26, 2016     Published: September 08, 2016

ABSTRACT

Drug resistance is an important open problem in cancer treatment. In recent years, the heat shock protein HSP27 (HSPB1) was identified as a key player driving resistance development. HSP27 is overexpressed in many cancer types and influences cellular processes such as apoptosis, DNA repair, recombination, and formation of metastases. As a result cancer cells are able to suppress apoptosis and develop resistance to cytostatic drugs. To identify HSP27 inhibitors we follow a novel computational drug repositioning approach. We exploit a similarity between a predicted HSP27 binding site to a viral thymidine kinase to generate lead inhibitors for HSP27. Six of these leads were verified experimentally. They bind HSP27 and down-regulate its chaperone activity. Most importantly, all six compounds inhibit development of drug resistance in cellular assays. One of the leads – chlorpromazine – is an antipsychotic, which has a positive effect on survival time in human breast cancer. In summary, we make two important contributions: First, we put forward six novel leads, which inhibit HSP27 and tackle drug resistance. Second, we demonstrate the power of computational drug repositioning.


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