Abstract
Alzheimer's disease (AD) is a complex and multifactorial neurodegenerative disorder, recognized as the most prevalent form of dementia. It is characterized by multiple pathological processes, including amyloid-beta accumulation, neurofibrillary tangles, and neuroinflammation. The therapeutic efficacy of traditional single-target drugs has been limited, failing to cure, halt, or reverse disease progression. Therefore, this complex disease warrants comprehensive therapeutic strategies like multi-target drug design (MTDD). MTDD represents a promising strategy to target multiple pathological pathways concurrently. The integration of advanced technologies, including artificial intelligence, machine learning, and nanomedicine, can further enhance the precision and effectiveness of MTDD. This review explores various MTDD approaches, including multi-target-directed ligands, multi-target compound combinations, and polypharmacology. These strategies aim to address the multifaceted nature of AD pathology more effectively than single-target approaches. MTDD offers key advantages, including pathway-level synergy, broader therapeutic scope, and potential for improved efficacy. However, MTDD faces various challenges and limitations, such as the complexity of drug design, difficulty of crossing the blood-brain barrier, and regulatory hurdles. Despite these challenges, recent advancements in computational methods and drug delivery systems show promise in overcoming these barriers. Future research should focus on optimizing delivery systems, improving in silico modeling, and translating multi-target strategies into clinically viable therapies for AD. This review addresses these needs by critically analyzing recent technologies, advantages, challenges, limitations, and future directions of MTDD, underscoring its potential to transform AD treatment.