Combining biophysical analysis and computational methods to understand critical molecular attributes

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Context & impact: New antibodies and novel formats such as bispecifics continue to pose challenges in downstream processing and formulation that are highly dependent on molecular sequence and structure. Key challenge behaviours include aggregation, particle formation, gelation and increased viscosity, as a result of stresses. Such stresses can include elevated temperature, increased protein concentration, the presence of silicone oil or other packing components, or the dilution of formulations into infusion bags. It is desirable to gain increased understanding of the critical molecular attributes that influence their behaviours in DSP, formulation and final drug administration steps. This would ultimately enable such properties to be engineered out an early stage of development.Aims and objectives: The aim of this project will be to explore the combination of biophysical analysis approaches with the use of machine-learning / statistical analyses, as well as all-atom molecular dynamics simulations, and molecular docking approaches to gain insights into the molecular attributes and underlying mechanisms of protein aggregation, viscosity and gelation, particularly at high protein concentrations, elevated temperature, and in the presence of tungsten and silicone oil.Research methodology: including new knowledge or techniques in engineering and physical sciences that will be investigated The project will collaborate with Kymab/Sanofi to define a platform of biophysical analytical approaches to characterise molecular formulations for a range of molecular variants. This will then be used in a DoE-driven formulation screen, assessed for Tm, aggregation, viscosity and gelation effects, under stress conditions. State of the art MD simulations and molecular docking will also be performed for selected biologic-buffer-excipient conditions, under stress conditions.Statistical and ML analyses will then be used to link molecular features and properties obtained from MD simulations, docking, and biophysical measurements, with the performance under each stress condition, including manufacturing conditions. Thus, the project will generate new knowledge in the characteristics of protein solutions that govern stability, and enable the rapid selection of optimal formulations and manufacturable molecular variants. It will also train the student in digital skills. The project is aligned directly to the EPSRC Manufacturing the Future theme.