In Silico Process Development via Computational Modeling: Insights into
Molecular Biophysics to Advance and Improve Biologics Purification
Abstract
The goal of this research is to leverage computational molecular
biophysics to guide process development, reduce experimental burden and
focus purification activities on feasible targets. Here, we distill a
complex separation problem (e.g. chromatographic retention of monoclonal
antibodies) into a tangible model (ligand/protein complex), which is
computationally feasible while preserving enough detail (atomistic level
for interaction site) to support industrially relevant separation
challenges. Computational docking, coupled with molecular dynamics
simulation, produces results that are directionally consistent with
chromatography for proteins (mAb). This approach is generalizable and
can be applied to a range of ligands (AEX, CEX, and Mixed Mode). A
detailed model of the chromatography base matrix (agarose) was
constructed to obtain a biophysical understanding of potential
protein/base matrix interactions. The base matrix was then modified in
silico with ligands over a range of ligand densities representative of
commercial chromatography resins to generate an agarose/ligand complex.
A generic approach was developed to model the impact of avidity and
ligand density on mAb/ligand interaction. The results revealed that
increasing ligand density mask contributions of base matrix binding.
Increasing the number of ligands that can interact with mAb results in
more favorable free energy of binding or ΔG (more negative) with a
limited incremental increase in ΔG by increasing N (number of ligands
per agarose cluster) above three. Additionally, for protein/ligand
interactions at each binding site, not all ligands contribute equally to
the binding affinities or interaction energies and a redistribution of
binding interactions/energies occur as N increases. These observations
yield insights into the impact of avidity on retention (macroscopic
affinity measurement via k’). The generic approach described in this
manuscript can be leveraged to inform resin selection and design as well
as targeted ligand selection/purification development in a rational
manner.