Lui, Lok Hin; (2019) In-silico discovery and experimental verification of excipients for biologics. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Protein-based pharmaceuticals such as monoclonal antibodies are the fastest growing class of therapeutic agent. As with all protein therapeutics, antibody aggregation must be avoided during production, storage and use. With recent advances in computing power, it is becoming feasible to simulate protein-protein interactions in-silico. Combining computational and experimental studies may offer a platform solution to design specific in-process stabilisers and excipients to accelerate the development of aggregation-resistant formulations. An antibody Fv fragment was first evaluated to understand the early stages of aggregate formation by identifying aggregation-prone regions. Three-dimensional structural information and protein-protein docking were used to identify exposed hydrophobic patches. Virtual screening was used to identify compounds that bind to the exposed hydrophobic patches as a means to prevent Fv-Fv interactions that could result in aggregation. An excipient with the highest calculated binding affinity was found to prevent Fv-Fv interactions as determined with the diffusion interaction parameter (kD) using DLS. Excipient performance was then evaluated using coarse-grained molecular dynamics (MD) simulations with MARTINI force field to provide a more in-depth view on Fv fragment dimer complex formation. Simulation results were further evaluated with free-energy calculations but these free-energy calculations were found to produce highly variable and therefore unreliable results. This coarse-grained MD approach was also used to virtually screen a library of dipeptides to identify peptide excipients. The results revealed a positive correlation between the calculated mean interaction energies and the diffusion interaction parameter measured with DLS. Use of the MD approach was further extended to accommodate challenging an antibody without published structural data through homology modelling and to suggest possible excipients to prevent high-affinity antibody-antibody interactions. Therefore this MD approach could potentially be used as a first step for the selection of excipients for antibodies.

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