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Related Concept Videos

Alzheimer's Disease: Treatment01:22

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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Related Experiment Video

Updated: Dec 28, 2025

Assessment of Spontaneous Alternation, Novel Object Recognition and Limb Clasping in Transgenic Mouse Models of Amyloid-β and Tau Neuropathology
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A Proteotranscriptomic-Based Computational Drug-Repositioning Method for Alzheimer's Disease.

Soo Youn Lee1, Min-Young Song1, Dain Kim1

  • 1Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, South Korea.

Frontiers in Pharmacology
|February 18, 2020
PubMed
Summary

Computational drug repositioning for Alzheimer's disease (AD) is challenging due to limited omics data. This study introduces a proteotranscriptomic method (DRPS/C) to identify potential anti-AD drugs by analyzing gene and protein perturbations, predicting 31 candidates.

Keywords:
Alzheimer diseasecomputational drug repositioningdrug discoverydrug repositioningproteomicsproteotranscriptomicssystem based approachtranscriptomics

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Area of Science:

  • Computational biology
  • Pharmacogenomics
  • Neuroscience

Background:

  • High failure rates in Alzheimer's disease (AD) drug trials necessitate novel drug discovery approaches.
  • Computational drug repositioning using omics data offers an alternative, but AD research is hindered by limited multi-omics data and sample heterogeneity.
  • Existing drug repurposing studies for AD face challenges with big data, insufficient clinical information, and data integration difficulties.

Purpose of the Study:

  • To develop a proteotranscriptomic-based computational drug repositioning method (DRPS/C) for identifying potential anti-AD drug candidates.
  • To leverage inverse associations between disease- and drug-induced gene/protein perturbation patterns and pharmacogenomic knowledge.
  • To address the limitations of data availability and integration in previous AD drug repurposing efforts.

Main Methods:

  • Developed the Drug Repositioning Perturbation Score/Class (DRPS/C) method integrating gene and protein perturbation patterns.
  • Constructed a Drug-induced Gene Perturbation Signature Database (DGPSD) from Connectivity Map (CMap) and L1000 CMap data.
  • Calculated DRPS using DGPSD and disease-induced signatures from The Cancer Genome Atlas (TCGA) and AD-specific omics datasets (RNA-seq, microarray, proteomics).

Main Results:

  • The DRPS/C method was validated using TCGA data, achieving high accuracy (AUC) for glioblastoma.
  • Predicted 31 potential anti-AD drug candidates exhibiting high DRPS in both transcriptomic and proteomic signatures.
  • Identified four specific drug candidates (bupivacaine, topiramate, selegiline, iproniazid) with potential anti-AD mechanisms, including voltage-gated sodium channel blockers and monoamine oxidase inhibitors.

Conclusions:

  • The proteotranscriptomic DRPS/C approach provides an effective strategy for discovering new therapeutic applications of existing drugs for Alzheimer's disease.
  • This method offers a streamlined pathway for identifying novel anti-AD drug candidates, overcoming data limitations in traditional approaches.
  • The predicted drug candidates warrant further investigation for their efficacy in treating Alzheimer's disease.