bioRxivCancer BiologyPreprint
Identification of non-covalent inhibitors for the atypical peroxiredoxin PRDX5 as a therapeutic strategy in malignant pleural mesothelioma
Josep MonserratiD, Floriane MontanariiD, Victor LaurentiD, Pierre‐Benoit AnceyiD, Nicolas JeaniD, Cécilia Jeannu, Guimin WangiD, Genshu You, Qiancheng SheniD, Alice Mac KainiD, Yacine BarècheiD, Loïc HerpiniD, Nadia JeremiahiD, Roberta Codato, Alberto RomagnoniiD, Alex J. CornishiD, Ekaterina RozhavskayaiD, Lucia PattariniiD, Constance Petit, Pierre-Joachim Zindy, Jinal ShuklaiD, Sylvie Gomez, Markus EcksteiniD, Almoatazbellah YoussefiD, Ulrich KeilholziD, , Elodie Pronier
Abstract
Malignant pleural mesothelioma (MPM) is an aggressive asbestos-linked cancer with limited therapeutic options and a dismal 5-year survival rate of [~]5%. While aberrant production of reactive oxygen and nitrogen species (ROS/RNS) is a hallmark of MPM, targeted approaches to exploit these redox vulnerabilities remain scarce. Here, using the MOSAIC multimodal cancer patient atlas, we identify Peroxiredoxin 5 (PRDX5) as being significantly upregulated in the epithelioid subtype of MPM. We show that MPM cells exhibit enhanced resistance to nitrosative and oxidative stress compared to healthy mesothelial cells, a phenotype correlated with basal PRDX5 expression. Next, utilising a machine learning guided discovery pipeline, we identified three putative allosteric pockets in PRDX5 and conducted a virtual screen of 3.6 million compounds. High-throughput biochemical validation of 452 candidates yielded 36 non-covalent hits, including sub-micromolar inhibitors. These findings establish PRDX5 as a novel, subtype specific therapeutic target in MPM and provide a chemical framework for the development of next-generation redox-modulating oncology treatments.
- organism: human
- assay: proteomics
- research focus: drug response / drug discovery
- approach: machine learning (classical)
- Citations
- 0
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