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Diagonal Method to Measure Synergy Among Any Number of Drugs
Published on: June 21, 2018
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Synergies between data science methods and innovative drug delivery technologies
1La Trobe Institute for Molecular Science, La Trobe University, Bundoora, Victoria 3086, Australia; Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia; School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom.
Advanced Drug Delivery Reviews
|January 29, 2026
Summary
Computational methods like AI are revolutionizing drug delivery systems. This review highlights their potential to accelerate the development of more effective therapies by optimizing drug delivery system design and simulation.
Area of Science:
- Advancements in automation, robotics, computational power, and AI/machine learning are transforming science and technology.
- Drug delivery systems are crucial for optimizing therapeutic efficacy, patient compliance, and reducing side effects.
Background:
- While many scientific fields rapidly adopt computational tools, drug delivery systems lag in applying informatics, physics-based design, and AI/machine learning.
- This gap represents a missed opportunity for innovation in therapeutic development.
Purpose of the Study:
- To review the application of computational methods in drug delivery system design, optimization, and simulation.
- To identify areas where computational approaches are underutilized or suboptimal in drug delivery.
- To provide a perspective on future synergistic partnerships between computational/AI methods and drug delivery.
Main Methods:
- Review of existing studies employing computational methods for drug delivery systems.
- Focus on less-addressed and emerging drug delivery modalities.
- Analysis of the potential and limitations of computational approaches in this field.
Main Results:
- Selected computational methods have shown promise in accelerating the development of drug delivery systems.
- Significant opportunities exist for improved application of AI and machine learning in drug delivery design.
- Certain drug delivery systems have not yet benefited optimally from computational advancements.
Conclusions:
- Computational methods, including AI, hold immense potential to revolutionize drug delivery system design and optimization.
- Further integration of informatics and simulation is needed to fully realize the benefits for therapeutic development.
- A synergistic approach combining computational power with drug delivery innovation is key for future advancements.
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