Simulation and Optimization: A New Direction in Supercritical Technology Based Nanomedicine.

Area of Science:

• Pharmaceutical Technology
• Chemical Engineering
• Computational Science

Background:

• Nanomedicines prepared using supercritical technology offer enhanced structural stability, bioavailability, and safety.
• Drug solubility and mixing efficiency in supercritical fluids (SCFs) are critical for nanomedicine preparation but challenging to measure directly due to extreme conditions.

Purpose of the Study:

• To review the application of mathematical models, artificial intelligence (AI), and computational fluid dynamics (CFD) in supercritical nanomedicine preparation.
• To discuss methodologies for calculating drug solubility and understanding the influence of operational parameters and apparatus on nanomedicine outcomes.

Main Methods:

• Literature review of mathematical modeling, AI, and CFD techniques applied to supercritical fluid technology in medicine.
• Synthesis and discourse on methodologies for drug solubility calculation and mixing efficiency simulation in SCFs.
• Comparative analysis of the merits and demerits of various computational models.

Main Results:

• Models can prognosticate drug solubility in SCFs and visualize mixing efficiency, aiding experimental design.
• Computational tools provide valuable guidance for selecting operational conditions and optimizing nanomedicine preparation.
• The review elucidates implementation procedures and commonly employed models across different methodologies.

Conclusions:

• Mathematical and simulation models are dependable tools for computing drug solubility and simulating experimental processes in supercritical nanomedicine preparation.
• These models facilitate optimization, aid in experimental design, and guide the selection of appropriate operational conditions.
• The application of these computational approaches fosters innovation in the development of supercritical pharmaceuticals.