Introduction
Technological advancement in cell culture has led to an increase in therapeutic protein titers over the last decade. This advancement has shifted the attention towards downstream processes as the bottleneck in the manufacture and production of biopharmaceuticals (Guiochon & Beaver; Hanke & Ottens, 2014). For biologics, antibodies are extremely complex and provide an almost unlimited design space to engineer binding with target molecules (antigens) (Maier & Labute). This results in a diverse array of biophysical properties and a challenge for separation. The relative ease with which new antibody candidates are generated and low toxicity of their degradation products make this class of molecules an excellent therapeutic agent (Tiller et al., 2008). Yet, the structural complexity of these molecules and diverse biophysical properties (including conformational flexibility) pose a significant challenge to the selection of candidates and development of appropriate purification modalities. An approach is proposed that can be applied to determine feasibility of separation of specific degraded products (e.g. oxidation, deamidation sites) via in silico screening of ligands to a targeted location. This in silico screening approach is analogous to the experimental developability (Lorenz et al., 2014; Yang et al., 2013) approach and hence can be viewed as in silicodevelopability.
The process of developing therapeutic antibodies require the generation of many variants. The number of molecules generated make empirical biophysical characterization of each molecule difficult, resource intensive, and time consuming (Jarasch et al., 2015; Shah et al., 2015). In-depth biophysical characterization of antibodies requires a significant amount of protein mass to determine stability profiles, purification, and formulation conditions. In addition, this process can be very time consuming even with the advent of high-throughput screening (HTS) approaches (Petroff et al., 2016). Thus, the ability to usein silico computational modeling will not only save time, but also optimize candidate selection for the best therapeutic candidate and accelerate the path from discovery to first-in-human clinical trials. Computational approaches may also be able to assess mechanisms and pathways that are not readily accessible experimentally and hence provide additional insights into molecular development.
Recently published work has demonstrated the utility of computational algorithms to isolate antibody complementary determining region (CDR) loops and associated structural features that infer biological and biophysical properties when bound to receptors and antigens (Morea, Lesk, & Tramontano, 2000). Further, computational modeling algorithms to generate 3D structure of any novel antibody based on currently available protein databank (PDB) structures are becoming increasingly routine (Morea, Leplae, & Tramontano, 1998; Morea, Tramontano, Rustici, Chothia, & Lesk, 1997). In silico docking studies have yielded results that are consistent with relative chromatographic retention (k’), and surface properties for a range of biologics (Insaidoo et al., 2015). The next step is to combine these advances in antibody homology modeling and in silico computational docking to understand chromatographic separation and select appropriate ligands for bioprocess development for a specific separation.
In this paper, we investigate the biophysical principles governing protein-chromatographic ligand interactions. We show in silicodocking studies in combination with molecular dynamics simulations can inform bio-process development and biologics purification. Specifically, we connect antibody biophysical properties to ligand selection and impact of ligand density on retention and selectivity.
In silico models of agarose fibers functionalized with chromatographic ligands showed increased binding affinity as a function of increasing number of interacting ligands (N). The observed increase is consistent with experimental correlation between resin ligand density and k’. These results have implications not only for bioprocess development but also for resin design and our understanding of the principal criteria for biologics design for therapeutic efficacy and successful downstream development.
Currently, the selection of lead candidates from a range of highly potent efficacious antibodies relies on limited downstream process development data (Jarasch et al., 2015; Lorenz et al., 2014). Even with high through-put screening, it would still require significant resources to fully screen and develop the operational design space for the process. This paper maps the interaction between chromatographic ligands and monoclonal antibodies at an atomic level using a general, extendable computational approach. This in silico approach to process development allows us to discern the impact of subtle differences on candidate selection and process development.