The Use of Computational Approaches to Design Nanodelivery Systems

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Computational Science

Background:

• Nano-based drug delivery systems offer improved therapeutic efficacy and safety through targeted transport and controlled release.
• Computational approaches, including Molecular Dynamics (MD) simulations and Artificial Intelligence (AI), are revolutionizing nanocarrier design and optimization.

Purpose of the Study:

• To review advances in MD simulations and AI for gold and lipid nanoparticle platforms.
• To highlight the role of computational methods in enhancing therapeutic performance.
• To evaluate how in silico models guide experimental validation and streamline drug development.

Main Methods:

• Utilizing MD simulations to gain atomic-to-mesoscale insights into nanoparticle-biological membrane interactions.
• Employing AI-driven models to analyze chemical datasets and predict optimal nanocarrier formulations.
• Synthesizing key advances in computational approaches for nanoparticle design.

Main Results:

• MD simulations elucidate the impact of nanoparticle characteristics (e.g., surface charge, size) on cellular uptake and stability.
• AI models accelerate the discovery of effective lipid-based nanoparticles for gene delivery and vaccines.
• Computational approaches enable refinement of nanoparticle composition for improved biocompatibility and reduced toxicity.

Conclusions:

• MD simulations and AI are powerful tools for rational design and optimization of nanodelivery systems.
• In silico models facilitate the transition of nanotherapeutics from research to clinical application.
• Addressing challenges like data scarcity and in vivo complexity is crucial for future advancements in nanodelivery systems.