AI and machine learning assisted development of greener formulations and pharmaceutical processes based on amorphous solid dispersions

Pharmaceutical manufacturers are swapping energy-hungry solvents for a cleaner method called hot melt extrusion (HME), guided by artificial intelligence to design better drug formulations. This matters because making medicines today often relies on large volumes of organic solvents that must be evaporated or recovered—a process that consumes significant energy and generates chemical waste. HME works by melting a drug and a polymer together, then cooling the mixture into a solid dispersion that improves how poorly soluble drugs dissolve in the body. The challenge is that HME formulations are complex to design, requiring precise control over temperature, polymer choice, and drug loading. This project brings together researchers from different organisations to build AI and machine learning tools that predict the right HME conditions, removing much of the trial-and-error. If successful, the framework could shift pharmaceutical manufacturing toward a continuous, solvent-free process that is both cheaper and greener. The environmental assessment component ensures that sustainability is measured, not assumed. For patients, this could mean faster access to medicines that currently struggle with solubility—without the hidden environmental cost of solvent-based production.