A novel drug repurposing approach for non-small cell lung cancer using deep learning

PLoS One. 2020 Jun 11;15(6):e0233112. doi: 10.1371/journal.pone.0233112. eCollection 2020.

Abstract

Drug repurposing is an attractive and pragmatic way offering reduced risks and development time in the complicated process of drug discovery. In the past, drug repurposing has been largely accidental and serendipitous. The most successful examples so far have not involved a systematic approach. Nowadays, remarkable advances in drugs, diseases and bioinformatic knowledge are offering great opportunities for designing novel drug repurposing approach through comprehensive understanding of drug information. In this study, we introduced a novel drug repurposing approach based on transcriptomic data and chemical structures using deep learning. One strong candidate for repurposing has been identified. Pimozide is an anti-dyskinesia agent that is used for the suppression of motor and phonic tics in patients with Tourette's Disorder. However, our pipeline proposed it as a strong candidate for treating non-small cell lung cancer. The cytotoxicity of pimozide against A549 cell lines has been validated.

MeSH terms

  • A549 Cells
  • Carcinoma, Non-Small-Cell Lung / drug therapy*
  • Computational Biology / methods*
  • Deep Learning
  • Drug Discovery
  • Drug Repositioning / methods*
  • Gene Expression Profiling / methods
  • Humans
  • Pimozide / metabolism
  • Pimozide / pharmacology
  • Transcriptome / genetics

Substances

  • Pimozide

Grants and funding

Beijing Deep Intelligent Pharma Technologies Co., Ltd. provided support for this study in the form of salaries for authors: BRL, CD, LJW, HLD, ZG, YYL, and HNN. The specific roles of these authors are articulated in the ‘author contributions’ section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding was received for this study.