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Multi-target Agents in Complex Diseases: From Design Principles to Therapeutic Applications
Swastika Maity1, Mahendra Gowdru Srinivas2, Geetha Nayak3
1Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Current Drug Targets
|June 17, 2026
Summary
Multi-target therapies overcome drug resistance in complex diseases by inhibiting multiple biological targets. Emerging technologies like AI and quantum computing promise more effective treatments for conditions such as cancer and neurodegeneration.
Area of Science:
- Pharmacology
- Systems Biology
- Computational Biology
Background:
- Complex diseases like cancer and neurodegeneration are poorly treated by single-target therapies due to biological network redundancy and adaptive resistance.
- Traditional monotherapies often fail against multifactorial diseases, necessitating novel therapeutic strategies.
Purpose of the Study:
- To review the theoretical basis, design approaches, and clinical applications of multi-target agents.
- To explore the role of network pharmacology and systems biology in developing multi-target therapies.
Main Methods:
- Comprehensive literature review.
- Analysis of multi-target agent design strategies (pharmacophore linking, fusing, merging).
- Investigation of network pharmacology and systems biology applications.
Main Results:
- FDA-approved multi-kinase inhibitors demonstrate superior efficacy in cancer by inhibiting multiple drivers.
- Dual inhibitors show promise for Alzheimer's disease neuroprotection.
- Beta-lactam/beta-lactamase combinations combat drug resistance.
- Artificial intelligence (AI) and fragment-based screening accelerate target identification and polypharmacology.
Conclusions:
- Multi-target strategies are crucial for overcoming biological redundancy and drug resistance.
- Challenges include target selection, balanced efficacy, ADMET optimization, and regulatory hurdles.
- Emerging technologies (AI, quantum computing, multiomics) offer potential solutions for advanced multi-target interventions.
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