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Repurposing zileuton as a depression drug using an AI and in vitro approach

AuthorsKubick, Norwin; Pajares, Marta; Enache, Ioana; Manda, Gina; Mickael, Michel-Edwar
Text mining
Deep neural network
Issue Date2020
PublisherMultidisciplinary Digital Publishing Institute
CitationMolecules 25(9): 2155 (2020)
AbstractRepurposing drugs to target M1 macrophages inflammatory response in depression constitutes a bright alternative for commonly used antidepressants. Depression is a significant type of mood disorder, where patients suffer from pathological disturbances associated with a proinflammatory M1 macrophage phenotype. Presently, the most commonly used antidepressants such as Zoloft and Citalopram can reduce inflammation, but suffer from dangerous side effects without offering specificity toward macrophages. We employed a new strategy for drug repurposing based on the integration of RNA-seq analysis and text mining using deep neural networks. Our system employs a Google semantic AI universal encoder to compute sentences embedding. Sentences similarity is calculated using a sorting function to identify drug compounds. Then sentence relevance is computed using a custom-built convolution differential network. Our system highlighted the NRF2 pathway as a critical drug target to reprogram M1 macrophage response toward an anti-inflammatory profile (M2). Using our approach, we were also able to predict that lipoxygenase inhibitor drug zileuton could modulate NRF2 pathway in vitro. Taken together, our results indicate that reorienting zileuton usage to modulate M1 macrophages could be a novel and safer therapeutic option for treating depression.
DescriptionThis article belongs to the Special Issue AI in Drug Design.
Publisher version (URL)https://doi.org/10.3390/molecules25092155
Appears in Collections:(IIBM) Artículos
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