Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery.

Area of Science:

• Drug Delivery and Nanotechnology
• Computational Chemistry and Materials Science

Background:

• Lipid nanocarriers (LNCs) enable the delivery of challenging active pharmaceutical ingredients (APIs), including those with poor solubility, high toxicity, or instability.
• A comprehensive understanding of the composition-structure-function relationships in LNCs is essential for rational design but is currently lacking.

Purpose of the Study:

• To review and present available computational methods for investigating, screening, and designing LNCs.
• To highlight the strengths and weaknesses of physics-based approaches, particularly molecular dynamics simulations.
• To introduce machine learning as a data-driven approach for optimizing LNC design.

Main Methods:

• Detailed description of physics-based computational methods, including all-atom and coarse-grained molecular dynamics simulations.
• Discussion of the necessary considerations for obtaining reliable simulation results.
• Introduction to machine learning approaches for analyzing experimental and theoretical data to guide LNC design.

Main Results:

• Molecular dynamics simulations offer insights into LNC behavior at different resolutions.
• Machine learning can process diverse datasets to identify optimal LNC formulations.
• Integration of computational and experimental data is key for advancing LNC design.

Conclusions:

• Computational tools, including molecular dynamics and machine learning, are vital for the rational design of LNCs.
• Addressing the current gaps in understanding composition-structure-function relationships will enhance LNC performance.
• Future research should focus on synergizing experimental and computational approaches for optimized lipid-mediated drug delivery systems.