Multi-target Agents in Complex Diseases: From Design Principles to Therapeutic Applications

Multifactorial complex diseases such as cancer, neurodegeneration, and infections are poorly treated with traditional single-target therapies because biological networks are redundant and adaptively resistant.

A comprehensive literature review was conducted to investigate the theoretical basis, design approaches (pharmacophore linking, fusing, and merging), and clinical uses of multi-target agents using network pharmacology and systems biology.

Multi-kinase inhibitors (imatinib, sunitinib, cabozantinib) approved by the Food and Drug Administration have shown superior efficacy to traditional monotherapies due to multiple driver inhibition; dual acetylcholinesterase and Beta-site amyloid precursor protein cleaving enzyme 1 inhibitors show enhanced neuroprotective effects against Alzheimer's disease; and β-lactam/βlactamase inhibitor combinations address drug resistance. Artificial intelligence can accelerate target identification, and novel design technologies, such as fragment-based screening, can generate balanced polypharmacology.

Multi-target strategies are ideal for overcoming redundancy in biological networks and minimizing drug resistance. However, several issues remain, including the complexity of target selection, the need to achieve balanced efficacy across multiple targets, ADMET optimization, and regulatory hurdles. Emerging technologies, such as quantum computing, precision polypharmacology based on multiomics profiling, and digital health integration, could improve target selection and optimization.

Multi-target agents are no longer constrained by single-target effects; however, issues of balanced potency, ADMET, and control still exist. The combination of AI, quantum computing, and precision polypharmacology may enable more effective multi-target interventions to address unmet demands in complex diseases.