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Lee SY, Song MY, Kim D, et al. A Proteotranscriptomic-Based Computational Drug-Repositioning Method for Alzheimer's Disease. Front Pharmacol. 2020;10:1653doi: 10.3389/fphar.2019.01653.
Lee, S. Y., Song, M. Y., Kim, D., Park, C., Park, D. K., Kim, D. G., Yoo, J. S., & Kim, Y. H. (2019). A Proteotranscriptomic-Based Computational Drug-Repositioning Method for Alzheimer's Disease. Frontiers in pharmacology, 101653. https://doi.org/10.3389/fphar.2019.01653
Lee, Soo Youn, et al. "A Proteotranscriptomic-Based Computational Drug-Repositioning Method for Alzheimer's Disease." Frontiers in pharmacology vol. 10 (2019): 1653. doi: https://doi.org/10.3389/fphar.2019.01653
Lee SY, Song MY, Kim D, Park C, Park DK, Kim DG, Yoo JS, Kim YH. A Proteotranscriptomic-Based Computational Drug-Repositioning Method for Alzheimer's Disease. Front Pharmacol. 2020 Jan 29;10:1653. doi: 10.3389/fphar.2019.01653. eCollection 2019. PMID: 32063857; PMCID: PMC7000455.
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Front Pharmacol. 2020 Jan 29;10:1653. doi: 10.3389/fphar.2019.01653. eCollection 2019.

A Proteotranscriptomic-Based Computational Drug-Repositioning Method for Alzheimer's Disease.

Frontiers in pharmacology

Soo Youn Lee, Min-Young Song, Dain Kim, Chaewon Park, Da Kyeong Park, Dong Geun Kim, Jong Shin Yoo, Young Hye Kim

Affiliations

  1. Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, South Korea.
  2. Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, South Korea.

PMID: 32063857 PMCID: PMC7000455 DOI: 10.3389/fphar.2019.01653

Abstract

Numerous clinical trials of drug candidates for Alzheimer's disease (AD) have failed, and computational drug repositioning approaches using omics data have been proposed as effective alternative approaches to the discovery of drug candidates. However, little multi-omics data is available for AD, due to limited availability of brain tissues. Even if omics data exist, systematic drug repurposing study for AD has suffered from lack of big data, insufficient clinical information, and difficulty in data integration on account of sample heterogeneity derived from poor diagnosis or shortage of qualified post-mortem tissue. In this study, we developed a proteotranscriptomic-based computational drug repositioning method named Drug Repositioning Perturbation Score/Class (DRPS/C) based on inverse associations between disease- and drug-induced gene and protein perturbation patterns, incorporating pharmacogenomic knowledge. We constructed a Drug-induced Gene Perturbation Signature Database (DGPSD) comprised of 61,019 gene signatures perturbed by 1,520 drugs from the Connectivity Map (CMap) and the L1000 CMap. Drugs were classified into three DRPCs (High, Intermediate, and Low) according to DRPSs that were calculated using drug- and disease-induced gene perturbation signatures from DGPSD and The Cancer Genome Atlas (TCGA), respectively. The DRPS/C method was evaluated using the area under the ROC curve, with a prescribed drug list from TCGA as the gold standard. Glioblastoma had the highest AUC. To predict anti-AD drugs, DRPS were calculated using DGPSD and AD-induced gene/protein perturbation signatures generated from RNA-seq, microarray and proteomic datasets in the Synapse database, and the drugs were classified into DRPCs. We predicted 31 potential anti-AD drug candidates commonly belonged to high DRPCs of transcriptomic and proteomic signatures. Of these, four drugs classified into the nervous system group of Anatomical Therapeutic Chemical (ATC) system are voltage-gated sodium channel blockers (bupivacaine, topiramate) and monamine oxidase inhibitors (selegiline, iproniazid), and their mechanism of action was inferred from a potential anti-AD drug perspective. Our approach suggests a shortcut to discover new efficacy of drugs for AD.

Copyright © 2020 Lee, Song, Kim, Park, Park, Kim, Yoo and Kim.

Keywords: Alzheimer disease; computational drug repositioning; drug discovery; drug repositioning; proteomics; proteotranscriptomics; system based approach; transcriptomics

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