A new study led by the University of Strathclyde will investigate how artificial intelligence (AI) and machine learning could expedite the development and production of mRNA-based vaccines and medicines. The initiative has received funding of over £440,000 from Innovate UK to explore faster processes in genomic medicine—analysing DNA to understand genetic makeup for treating cancer, rare diseases, or producing new vaccines.
The study will focus on testing lipid-based nanoparticles that encapsulate ribonucleic acid (RNA) for delivery into cells. RNA, present in all living cells, enables the body to construct cells or respond to immune challenges, while messenger RNA (mRNA) carries genetic instructions to create cells.
The consortium is led by Micropore Technologies, a Redcar-based drug formulation and manufacturing company, and includes researchers from the Universities of Northumbria, Teesside, and Strathclyde.
Professor Yvonne Perrie, Head of the Strathclyde Institute of Pharmacy and Biomedical Sciences, stated, “This research has the potential to transform the development and manufacturing of genomic medicines. By applying machine learning to these intricate processes, we aim to make significant strides in how quickly and effectively treatments can be brought to patients. We’re delighted to contribute our expertise in nanoparticle testing to this collaboration.”
Professor Wai Lok Woo of Northumbria University, Chair in Machine Learning, explained the challenges and goals of the study, “The development of genomic medicines is complex, with the encapsulation of nucleic acids within protective nanoparticles being perhaps the most critical stage in the manufacturing process. Process equipment design, operating approach, formulation and active product all impact on how these intracellular drugs behave, and current iterative approaches to understand these behaviours is highly time consuming. This creates a barrier to the timely and successful development and manufacture of nano-delivered intracellular drugs.
“Our intention is to use machine learning to identify and learn this complex series of relationships and build models which can accelerate formulation development. This will make a step-change improvement to the speed and efficiency with which new genomic medicines can progress from discovery to real application in disease prevention and treatment.”
Micropore Technologies, known for its Advanced Crossflow technology, will implement machine learning models alongside university researchers to enhance the efficiency of drug formulation.
Dave Palmer, Technical Manager at Micropore Technologies, said, “We are looking forward to working with our partners to exploit machine learning to speed up the initial lab scale development and production pathway to improve the manufacturing process and ultimately bring new genomic medicines into use much more quickly than had previously been possible.”
This project highlights the University of Strathclyde’s role in fostering health innovation and collaborating with academic and industry leaders to address critical challenges in medicine.