RESUMEN
Protein A chromatography is an enabling technology in current manufacturing processes of monoclonal antibodies (mAbs) and mAb derivatives, largely due to its ability to reduce the levels of process-related impurities by several orders of magnitude. Despite its widespread application, the use of mathematical modeling capable of accurately predicting the full protein A chromatographic process, including loading, post-loading wash and elution stages, has been limited. This work describes a mechanistic modeling approach utilizing the general rate model (GRM), the capabilities of which are explored and optimized using two isotherm models. Isotherm parameters were estimated by inverse-fitting simulated breakthrough curves to experimental data at various pH values. The parameter values so obtained were interpolated across the relevant pH range using a best-fit curve, thus enabling their use in predictive modeling, including of elution over a range of pH. The model provides accurate predictions (< 3% mean error in 10% dynamic binding capacity predictions and â¼ 5% mean error in elution mass and pool volume predictions, both on scale-up) for various residence times, buffer conditions and elution schemes and its effectiveness for use in scale-up and process development is shown by applying the same parameters to larger columns and a wider range of residence times.
Asunto(s)
Cromatografía , Proteína Estafilocócica A , Proteína Estafilocócica A/química , Modelos Teóricos , Anticuerpos Monoclonales/química , Cromatografía por Intercambio Iónico/métodosRESUMEN
Protein A chromatography is a workhorse in monoclonal antibody (mAb) manufacture since it provides effective separation of mAbs from impurities such as host-cell proteins (HCPs) in a single capture step. HCP clearance can be aided by the inclusion of a wash step prior to low-pH elution. Although high-pH washes can be effective in removing additional HCPs from the loaded column, they may also contribute to a reduced mAb yield. In this work we show that this yield loss is reflected in a pH-dependent variation of the equilibrium binding capacity of the protein A resin, which is also observed for the capacity of the Fc fragments alone and therefore not a result of steric interactions involving the Fab fragments in the intact mAbs. We therefore hypothesized that the high-pH wash loss was due to protonation or deprotonation of ionizable residues on the protein A ligand. To evaluate this, we applied a rational protein engineering approach to the Z domain (the Fc-binding component of most commercial protein A ligands) and expressed engineered mutants in E. coli. Biolayer interferometry and affinity chromatography experiments showed that some of the Z domain mutants were able to mitigate wash loss at high pH while maintaining similar binding characteristics at neutral pH. These experiments enabled elucidation of the roles of specific interactions in the Z domain - Fc complex, but more importantly offer a route to ameliorating the disadvantages of high-pH washes in protein A chromatography.
Asunto(s)
Escherichia coli , Proteína Estafilocócica A , Cricetinae , Animales , Proteína Estafilocócica A/química , Ligandos , Escherichia coli/metabolismo , Cricetulus , Células CHO , Anticuerpos Monoclonales/química , Cromatografía de Afinidad/métodos , Concentración de Iones de HidrógenoRESUMEN
Hypoxia is a critical factor in solid tumors that has been associated with cancer progression and aggressiveness. We recently developed a hypoxia fate mapping system to trace post-hypoxic cells within a tumor for the first time. This approach uses an oxygen-dependent fluorescent switch and allowed us to measure key biological features such as oxygen distribution, cell proliferation, and migration. We developed a computational model to investigate the motility and phenotypic persistence of hypoxic and post-hypoxic cells during tumor progression. The cellular behavior was defined by phenotypic persistence time, cell movement bias, and the fraction of cells that respond to an enhanced migratory stimulus. This work combined advanced cell tracking and imaging techniques with mathematical modeling, to reveal that a persistent invasive migratory phenotype that develops under hypoxia is required for cellular escape into the surrounding tissue, promoting the formation of invasive structures ("plumes") that expand toward the oxygenated tumor regions.
RESUMEN
The majority of cancer-related deaths are due to metastasis, hence improved methods to biologically and computationally model metastasis are required. Computational models rely on robust data that is machine-readable. The current methods used to model metastasis in mice involve generating primary tumors by injecting human cells into immune-compromised mice, or by examining genetically engineered mice that are pre-disposed to tumor development and that eventually metastasize. The degree of metastasis can be measured using flow cytometry, bioluminescence imaging, quantitative PCR, and/or by manually counting individual lesions from metastatic tissue sections. The aforementioned methods are time-consuming and do not provide information on size distribution or spatial localization of individual metastatic lesions. In this work, we describe and provide a MATLAB script for an image-processing based method designed to obtain quantitative data from tissue sections comprised of multiple subpopulations of disseminated cells localized at metastatic sites in vivo. We further show that this method can be easily adapted for high throughput imaging of live or fixed cells in vitro under a multitude of conditions in order to assess clonal fitness and evolution. The inherent variation in mouse studies, increasing complexity in experimental design which incorporate fate-mapping of individual cells, result in the need for a large cohort of mice to generate a robust dataset. High-throughput imaging techniques such as the one that we describe will enhance the data that can be used as input for the development of computational models aimed at modeling the metastatic process.