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Armoring the Interface with Surfactants to Prevent the Adsorption of Monoclonal Antibodies.

Ankit D KantheMary E KrauseSongyan ZhengAndrew IlottJinjiang LiWei BuMrinal K BeraBinhua LinCharles MaldarelliRaymond S Tu
Published in: ACS applied materials & interfaces (2020)
The pharmaceutical industry uses surface-active agents (excipients) in protein drug formulations to prevent the aggregation, denaturation, and unwanted immunological response of therapeutic drugs in solution as well as at the air/water interface. However, the mechanism of adsorption, desorption, and aggregation of proteins at the interface in the presence of excipients remains poorly understood. The objective of this work is to explore the molecular-scale competitive adsorption process between surfactant-based excipients and two monoclonal antibody (mAb) proteins, mAb-1 and mAb-2. We use pendant bubble tensiometry to measure the ensemble average adsorption dynamics of mAbs with and without the excipient. The surface tension measurements allow us to quantify the rate at which the molecules "race" to the interface in single-component and mixed systems. These results define the phase space, where coadsorption of both mAbs and excipients occurs onto the air/water interface. In parallel, we use X-ray reflectivity (XR) measurements to understand the molecular-scale dynamics of competitive adsorption, revealing the surface-adsorbed amounts of the antibody and excipient. XR has revealed that at a sufficiently high surface concentration of the excipient, mAb adsorption to the surface and subsurface domains was inhibited. In addition, despite the fact that both mAbs adsorb via a similar mechanistic pathway and with similar dynamics, a key finding is that the competition for the interface directly correlates with the surface activity of the two mAbs, resulting in a fivefold difference in the concentration of the excipient needed to displace the antibody.
Keyphrases
  • monoclonal antibody
  • aqueous solution
  • high resolution
  • emergency department
  • computed tomography
  • magnetic resonance
  • mass spectrometry
  • machine learning
  • small molecule