Sophie Oerlemans

My research is devoted to advancing the physical and chemical stability of lipid nanoparticles (LNPs) utilised in the delivery of RNA therapeutics, encompassing vaccines and personalised therapeutics. With a focus on elucidating the factors that affect the stability of LNPs and their encapsulated cargo including various RNA types such as mRNA and siRNA, I aim to translate these findings into practical strategies for the optimisation of LNP-RNA formulations. By enhancing the stability of LNPs for diverse applications, I strive to contribute to the creation of more dependable and efficacious mRNA delivery systems, scalable for manufacturing processes across a broad spectrum of therapeutic uses. This research will facilitate the precise tuning of RNA-LNP formulations, ultimately propelling the field of nucleic acid medicine forward, particularly in the areas of vaccines and personalised medicine. Additionally, I am fascinated by the broader applications of LNPs, including their potential in agritech and biocosmetics.

An integral aspect of my research involves the implementation of Quality by Design (QbD) principles in the manufacturing processes of mRNA and LNP formulations. QbD is a systematic approach that ensures the quality of biopharmaceutical products by understanding and controlling manufacturing processes. In my project, I employ statistical tools such as fractional and full factorial designs, Analysis of Variance (ANOVA), and multiple linear regressions to identify and optimise critical quality attributes (CQAs) and critical process parameters (CPPs) that influence the stability and efficacy of LNP-RNA systems. The use of design space within QbD allows for a defined range of process parameters and material attributes that result in products meeting pre-defined quality criteria, thereby providing a basis for inbuilt process controls to maintain consistent quality during manufacturing. These statistical methods enable a comprehensive understanding of the interactions between different variables, leading to the development of robust and reproducible formulations. This approach not only enhances the reliability and reproducibility of mRNA therapeutics but also streamlines the development process, ensuring robust scalability for industrial applications. The application of QbD extends beyond my specific project, offering significant advantages in the broader field of bioprocessing and biopharmaceutical engineering. It provides a framework for developing high-quality, safe, and effective therapeutic products, ultimately benefiting the entire healthcare ecosystem.

In the computational realm, my role as an AI trainer enhances my expertise in leveraging advanced technologies. My interests lie with the utilisation of bioinformatics techniques alongside advanced simulation and modelling software to gain insights into molecular interactions and behaviours. These tools have the capacity to provide a base understanding of these behaviours, guiding the rational design of effective delivery systems. Utilising computational methods such as simulation, modelling, and LLMs in biopharmaceutical and bioprocessing applications has the potential to aid in streamlining the optimisation process for a variety of therapeutics.