Drug Repositioning for Alzheimer's Disease Based on Systematic 'omics' Data Mining

Area of Science:

• Neuroscience
• Pharmacology
• Genomics

Background:

• Traditional Alzheimer's disease (AD) drug development is inefficient, costly, and has a low success rate.
• Drug repositioning, repurposing existing medications, presents a viable alternative strategy.
• Advancements in omics data necessitate comprehensive analysis for AD pathogenesis and drug discovery.

Purpose of the Study:

• To identify novel anti-Alzheimer's disease (AD) drug candidates through drug repositioning.
• To analyze multi-omics data for novel AD protein targets.
• To prioritize potential drug targets and identify existing drugs for AD intervention.

Main Methods:

• Integrated analysis of genomics, epigenomics, proteomics, and metabolomics data for AD-related proteins.
• Extracted AD pathogenesis information from OMIM and PubMed databases.
• Collected drug-target interaction data from DrugBank and Therapeutic Target Database.

Main Results:

• Identified 524 AD-related proteins, with 18 serving as targets for 75 existing drugs.
• Prioritized CD33 and MIF as top targets, each associated with seven existing drugs.
• Highlighted 7 drugs targeting acetylcholinesterase for cognitive symptom treatment and identified other promising protein targets like CAD and APOE.

Conclusions:

• Systematic omics data mining identified promising drug candidates for AD intervention, particularly those modulating the immune system or neuroinflammation.
• The identified 524 AD-related proteins can serve as potential therapeutic targets and aid in AD biomarker development.
• Drug repositioning, guided by omics data, offers a strategic approach to accelerate the development of effective Alzheimer's disease treatments.