Computational methods to predict stable biologics formulations

Engineered therapeutic proteins (biologics) are the fastest growing class of medicines and vaccines. However, it remains challenging to obtain protein designs that meet all requirements for their manufacturability, stability and pharmacological efficacy. A particularly acute challenge is their marginal stability and high sensitivity to subtle changes in pH, temperature, ionic strength, and mechanical agitation. Molecular lead identification selects for candidates that bind best to the disease target, but does not ensure manufacturability and stability in drug dosage forms. Subsequent optimisation by protein engineering or formulation uses highly labour- and time-intensive automated high-throughput experiments, yet with no guarantee of success.

This secondment will undertake fundamental research to transform and embed techniques developed at UCL into an industrial setting, with immediate impact on biologic development pipelines. It will establish a computational framework to efficiently predict promising biologics formulations, based on experimental performance in carefully selected formulations, thus saving considerable time and resource. The small set of formulations (varying excipients, pH and ionic strength), best represent features of a much wider range of potential formulations. Computational molecular dynamics simulations across the pH and ionic strength range, identify the major protein conformations accessed. Simulated excipient binding (docking) to these conformations, evaluates the extent and strength of their interactions within each solution condition. At UCL, based on antibody-derived therapeutics, these tools have already measured how pH, temperature, ionic strength and excipients impact surface polarity, charge, protein dynamics, protein-excipient interactions, and preferential exclusion of excipients, and ultimately protein stability. Neural network (NN) methods at UCL have shown significant potential for predicting protein stability from spectroscopic measurements of native protein formulations. Such NN methods are transforming our daily life, enabling us to extract predictions from complex data. This secondment will apply these tools to a new class of therapeutic proteins developed by IPSEN, based on engineered botulinum neurotoxins (BoNTs). It will use NN approaches on the combined experimental, simulation and docking data, to explore the relative balance of the factors above and build a powerful predictive framework for improved formulations. These will be validated at IPSEN in their manufacturing processes. This will have immediate value and impact for BoNT-derived protein therapies, and address issues specific to this exciting class of molecules. The predictive framework generated will also have wider utility across different classes of protein therapeutics for their rapid and more successful formulation. The methods will be described in publications and transferred into industry through training.