School of Engineering Department of Chemical and Biological Engineering 80 Engineering of Next Generation mRNA Vaccine Construct Supervisor: KUANG Becki Yi / CBE Student: SUBRAMANIAN Vasantha / BIEN Course: UROP 1000, Summer Messenger RNA (mRNA) vaccines have become a groundbreaking technology, especially following the worldwide impact of the COVID-19 pandemic. Their production mostly depends on in vitro transcription (IVT) from DNA templates that have been specifically engineered. However, these traditional techniques face major challenges, such as limited scalability, higher costs, and dependence on single-use DNA templates. This project presents a new chemical modification strategy targeting the ends of DNA to improve template stability and enable reuse. By combining this with IVT using solid beads, the process achieves efficient transcription while maintaining DNA integrity and allowing multiple uses. The proposed approach offers a promising route toward affordable, scalable, and sustainable mRNA vaccine manufacturing. Engineering of Next Generation mRNA Vaccine Construct Supervisor: KUANG Becki Yi / CBE Student: YAU Tak Marx / BIOT Course: UROP 1100, Fall UROP 2100, Spring Current vaccines using weakened/inactivated viruses risk side effects. To enhance safety and efficacy, this study explores virus-like particles (VLPs) as mRNA carriers, leveraging the RNA-packaging protein shell of bacteriophage MS2. However, natural MS2 lacks efficiency for medical use. Using AI tools like AlphaFold 3, we redesigned the MS2 protein, engineering a stable “tandem dimer” (two linked proteins) that binds mRNA more tightly than natural monomers. Lab tests demonstrated the dimer’s superior mRNA packaging, requiring lower doses for equivalent effects. This innovation minimizes side risks, as VLPs lack viral genetic material, avoiding unintended immune responses. Combining AI and lab experiments accelerates breakthroughs; future work focuses on optimizing the dimer’s linker and testing functionality. This approach could revolutionize vaccine development, offering safer, potent, and stable mRNA delivery systems. Predicting the Drug Release of Supersaturating Drug Delivery Systems Supervisor: Richard LAKERVELD / CBE Student: CHANG Yubeen / CENG Course: UROP 1100, Spring Supersaturating drug delivery systems (SDDS) enhance bioavailability of poorly soluble drugs, but nucleation rate variability’s impact on release kinetics remains unclear. This study uses computational modelling to analyse how nucleation heterogeneity and dosage affect drug release. Results show that higher dosages increase the coefficient of variation (CV) in the area-under-the-curve (AUC), indicating greater absorption unpredictability. This trend persists across different nucleation rate CVs, confirming nucleation kinetics as a key factor in release performance. The findings highlight the need to optimize both dosage and nucleation control to minimize bioavailability variability. Future work will explore formulation strategies, such as excipient selection and nucleation suppression, to improve SDDS consistency.
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