(439i) Computational Design of Excipient Formulations for Monoclonal Antibody Solutions

The high degree of selectivity and affinity for target antigens makes monoclonal antibodies (mAbs) highly effective at targeting particular mechanisms, making them useful as therapeutic treatments for many diseases. The design and administration of mAbs at high concentrations, requires more rational design of formulations, involving excipients as stabilizers. A computational approach to screening common excipient molecules’ ability to reduce mAb self-interactions has the potential to meet this need. Here we discuss an approach which involves characterizing the self-interactions of mAbs, and interactions of solvent and cosolvent molecules with the mAbs. A major challenge with this problem is that mAbs are relatively large proteins, making them computationally expensive to work with at atomic resolution. To overcome this problem, we apply rigid protein-protein docking[1], and fast solvation models[2] for protein-protein interactions, and protein-(co)solvent interactions respectively. The end goal of this work is a tool that can be used to rapidly predict the effects of different excipients on mAb self-association and solution viscosity, thus reducing the time and capital costs of therapeutic mAb formulation development.

1. Kozakov, D., Hall, D.R., Xia, B., Porter, K.A., Padhorny, D., Yueh, C., Beglov, D. and Vajda, S., 2017. The ClusPro web server for protein–protein docking. Nature protocols, 12(2), pp.255-278.
2. Li, L., Fennell, C.J. and Dill, K.A., 2014. Field-SEA: a model for computing the solvation free energies of nonpolar, polar, and charged solutes in water. The Journal of Physical Chemistry B, 118(24), pp.6431-6437.