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Overview of Alzheimer's Disease (AD)

Alzheimer’s Disease (AD) is the most common cause of dementia and a significant unmet need in medicine.
Drug development for AD has faced challenges with unsuccessful clinical trials in the last decade.
Drug repositioning is an appealing strategy for developing new therapeutics for AD by finding new uses for existing drugs.

Reduced time and cost compared to traditional drug development.
Safety and pharmacokinetic properties of most repositioning candidates are already determined.
Strategies for repositioning often emerge from clinical observations or animal model research.
Systematic multidisciplinary approaches are encouraged to enhance drug repositioning efforts, including:
- Unbiased phenotypic screening in relevant model systems (e.g., stem cell-derived neurons).
- Computational prediction using RNA expression profiles.
- Genome-wide association studies (GWAS).

Decrease in drug development productivity due to:
- Increasing development costs.
- Generic competition after patent expirations.
- Conservative regulatory policies.
- Low success rates in drug development for AD (≥0.4% success during 2001–2012).
High attrition rates arise from the inability to predict safety/effectiveness prior to human testing.

Screening of existing drug libraries using relevant assays (e.g., pharmacological effects on neurodegenerative models).
The Johns Hopkins University Clinical Compound Library includes over 2000 drugs for phenotypic screening.
Commercial sources of drug collections include:
- NIH Clinical Collections.
- Sigma and other suppliers.

Traditional drug identification was based on phenotypic changes.
Phenotypic Drug Discovery (PDD) focuses on discovering bioactivities in model cells without targeting a specific cellular target.
PDD has been reported to yield more successful novel drugs compared to target-based approaches.
Need for physiological models in AD research (e.g., primary brain cells, glial cells).

Identify new activities of existing drugs based on advanced understanding of disease biology.
Major challenges with focus on high-affinity compounds to inhibit known AD targets.
Phenotypic screening paradigms can reveal new drug pathway relationships leading to target-based strategies.

Systematic computational analyses can predict novel drug-disease connections.
Transcriptomic approaches involve profiling gene expression related to drugs/diseases.
GWAS helps correlate genetic variations with AD, potentially identifying drug targets.
Integration of genomic data with drug information can enhance repositioning efforts.

Example: Galanthamine, initially for poliomyelitis, later repurposed for AD as an acetylcholinesterase inhibitor.
Selective Serotonin Reuptake Inhibitors (SSRIs) have shown cognitive benefits in AD patients, with potential disease-modifying effects.
Antiepileptic drugs like levetiracetam are under clinical trials for possible benefits on AD-related cognitive functions.
Antihypertensive medications, e.g., ACE inhibitors and CCBs, show promise in reducing AD plaque and improving cognitive functions in trials.
Emerging drugs like GLP-1 analogues (e.g., liraglutide) show neuroprotective effects in animal models and are undergoing human trials.

Need for clinical trials that remain risky, especially in AD.
Issues surrounding commercialization and patent protection with repositioned drugs.
Strategies for obtaining intellectual property protection include forming drug combinations or applying for orphan drug status for exclusivity.

Drug repositioning holds the potential to enrich the pipeline for AD therapeutics given existing clinical data and advanced analytical tools.
Increased collaboration between academia, industry, and government is essential for successful drug repositioning strategies.