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arXiv · 2407.00885

Impact of Co-Excipient Selection on Hydrophobic Polymer Folding: Insights for Optimal Formulation Design

Abstract

The stabilization of liquid biological products is a complex task that depends on the chemical composition of both the active ingredient and any excipients in solution. Frequently, a large number of unique excipients are required to stabilize biologics, though it is not well-known how these excipients interact with one another. To probe these excipient-excipient interactions, we performed molecular dynamics simulations of arginine -- a widely used excipient with unique properties -- in solution either alone or with equimolar lysine or glutamate. We studied the effects of these mixtures on a hydrophobic polymer model to isolate excipient mechanisms on hydrophobic interactions, relevant to both protein folding and biomolecular self-assembly. We observed that arginine is the most effective single excipient in stabilizing hydrophobic polymer collapse, and its effectiveness can be augmented by lysine or glutamate addition. We utilized a decomposition of the potential of mean force to identify that the key source of arginine-lysine and arginine-glutamate synergy on polymer collapse is a reduction in attractive polymer-excipient direct interactions. Further, we applied principles from network theory to characterize the local solvent network that embeds the hydrophobic polymer. Through this approach, we found that arginine enables a more highly connected and stable network than in pure water, lysine, or glutamate solutions. Importantly, these network properties are preserved when lysine or glutamate are added to arginine solutions. Overall, we highlight the importance of identifying key molecular consequences of co-excipient selection, aiding in the establishment of rational formulation design rules.

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BibTeXRIS

Jonathan W. P. Zajac, Praveen Muralikrishnan, Caryn L. Heldt, Sarah L. Perry, Sapna Sarupria. 2024-07-01. Impact of Co-Excipient Selection on Hydrophobic Polymer Folding: Insights for Optimal Formulation Design. https://arxiv.org/abs/2407.00885

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