RESUMEN
Multimodal chromatography resins are becoming a key tool in the purification of biomolecules. The main objective of this research was the establishment of an iterative framework for the rapid development of new multimodal resins to provide novel selectivity for the future purification challenges. A large chemically diverse virtual library of 100 multimodal Capto™ MMC ligand analogues was created, and a broad array of chemical descriptors were calculated for each ligand in silico. Principal component analysis (PCA) was used to map the chemical diversity and guide selection of ligands for synthesis and coupling to the Capto ImpRes agarose base matrix. Twelve new ligands were prepared in two groups: 'group one' consist of L00-L07 and 'group two' consist of L08-L12. These ligands are diverse in the influence of varied secondary interactions such as hydrophobic interactions, H-bonding, etc. Additional resin prototypes were also prepared to look at the chromatographic impact of ligand density variation. High-throughput plate-based studies were performed for parallel resin screening for batch-binding of six model proteins at different chromatographic binding pH and sodium chloride concentration conditions. Principal component analysis of the binding data provided a chromatographic diversity map leading to the identification of ligands with improved binding. Further, the new ligands have improved separation resolution between a monoclonal antibody (mAb1) and product related impurities, a Fab fragment and high molecular weight (HMW) aggregates, using linear salt gradient elutions. To quantify the importance of secondary interactions, analysis of the retention factor of mAb1 on the ligands at various isocratic conditions lead to estimations of (a) the total number of water molecules and counter salt ions released during adsorption, and (b) hydrophobic contact area (HCA). The iterative mapping approach of chemical and chromatography diversity maps described in the paper proves to be a promising method for identifying new chromatography ligands for biopharmaceutical purification challenges.
Asunto(s)
Cromatografía , Ensayos Analíticos de Alto Rendimiento , Ligandos , Anticuerpos Monoclonales/química , Interacciones Hidrofóbicas e HidrofílicasRESUMEN
With growing demands for therapeutic monoclonal antibodies, in silico downstream process development based on mechanistic modeling of chromatography separation process is being increasingly used for process optimization and process characterization. Application of mechanistic modeling in biopharmaceutical industry has been sparse due to the significant investment of time and resources that are required for performing model calibration. Mechanistic modeling of the chromatography process involves a large number of mass transport and binding parameters and their initial input values are required for simulations. These input values of column parameters can be easily obtained either from experiments or from empirical correlations available in literature. On the other hand, obtaining the model input valves for binding kinetic parameters is usually a cumbersome process as it involves performing batch experiments which are not only tedious but also require significant quantities of purely isolated main product and its related impurities, which is challenging as the product related impurities are typically present in smaller quantities and hence are difficult to obtain as pure species. In the present work, a mechanistic model that is based on the general rate model coupled with extended Langmuir binding model has been used for prediction of linear gradient elution peaks of monoclonal antibody on cation exchanger chromatography. The present work describes an accelerated approach for obtaining the input values for binding kinetic parameters in the extended Langmuir binding model from the two Yamamoto coefficient A and B values obtained by Yamamoto method directly from the model calibration linear gradient elution runs of different gradient slopes and at low to moderate protein loadings. The equations that can relate the two coefficients to the extended Langmuir model equation binding kinetic parameters were derived. Therefore, once A and B are determined, the binding kinetic parameter values were determined straightforward, thereby avoiding the problem of multiple solutions for the model parameters. The estimated binding parameters were successfully validated from isocratic elution experiments performed at low loading. What we demonstrate is that the proposed approach allows us to estimate binding kinetic parameters in a significantly more efficient and accelerated manner than presently used approaches, thereby accelerating development and implementation of mechanistic modeling for process chromatography.
Asunto(s)
Anticuerpos Monoclonales , Cationes , Cromatografía Líquida de Alta Presión , Cromatografía por Intercambio Iónico/métodos , CinéticaRESUMEN
A major challenge in chromatography purification of therapeutic proteins is batch-to-batch variability with respect to impurity levels and product concentration in the feed. Mechanistic model can enable process analytical technology (PAT) implementation by predicting impact of such variations and thereby improving the robustness of the resulting process and controls. This article presents one such application of mechanistic model of hydrophobic interaction chromatography (HIC) as a PAT tool for making robust pooling decisions to enable clearance of aggregates for a monoclonal antibody (mAb) therapeutic. Model predictions were performed before the actual chromatography experiments to facilitate feedforward control. The approach has been successfully demonstrated for four different feeds with varying aggregate levels (3.84%-5.54%) and feed concentration (0.6 mg/mL-1 mg/mL). The resulting pool consistently yielded a product with 1.32 ± 0.03% aggregate vs. a target of 1.5%. A comparison of the traditional approach involving column fractionation with the proposed approach indicates that the proposed approach results in achievement of satisfactory product purity (98.68 ± 0.03% for mechanistic model based PAT controlled pooling vs. 98.64 ± 0.16% for offline column fractionation based pooling). © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2758, 2019.
Asunto(s)
Anticuerpos Monoclonales/análisis , Biotecnología , Modelos Químicos , Cromatografía Líquida de Alta Presión , Interacciones Hidrofóbicas e HidrofílicasRESUMEN
Protein A chromatography is quite commonly used for capture of monoclonal antibodies from the clarified cell culture broths. Protein A resins are expensive and economic feasibility demands that the resin be reused for 50-300 cycles. Resin reuse is, however, accompanied by resin fouling, impacting both the binding and mass transfer characteristics of the resin. In the present study, we attempt to model the variations in binding and mass transfer characteristics of a commercially available Protein A resin, mAbSelect SuRe™, as a function of resin's reuse. Simplified linear driving force modeling and kinetic modeling of Protein A chromatography step elution cycling data has been successfully used to predict resin performance up to 100 cycles based on fouling data up to 50 cycles. Fouling factor for Protein A resin has been empirically modeled as a function of binding and mass transfer characteristics of resin and the resin's reuse using a combination of Buckingham's π theorem and statistical analysis. The proposed empirical model enables reliable prediction of performance of Protein A resin as well as offers an improved understanding of the underlying mechanism behind the decline in resin performance during fouling.
Asunto(s)
Anticuerpos Monoclonales/aislamiento & purificación , Cromatografía de Afinidad/instrumentación , Cromatografía por Intercambio Iónico/instrumentación , Resinas Sintéticas/química , Proteína Estafilocócica A/química , Cromatografía de Afinidad/métodos , Cromatografía por Intercambio Iónico/métodosRESUMEN
Centrifugation continues to be one of the most commonly used unit operations for achieving efficient harvest of the product from the mammalian cell culture broth during production of therapeutic monoclonal antibodies (mAbs). Since the mammalian cells are known to be shear sensitive, optimal performance of the centrifuge requires a balance between productivity and shear. In this study, Computational Fluid Dynamics (CFD) has been successfully used as a tool to facilitate efficient optimization. Multiphase Eulerian-Eulerian model coupled with Gidaspow drag model along with Eulerian-Eulerian k-ε mixture turbulence model have been used to quantify the complex hydrodynamics of the centrifuge and thus evaluate the turbulent stresses generated by the centrifugal forces. An empirical model has been developed by statistical analysis of experimentally observed cell lysis data as a function of turbulent stresses. An operating window that offers the optimal balance between high productivity, high separation efficiency, and low cell damage has been identified by use of CFD modeling.
Asunto(s)
Anticuerpos Monoclonales/aislamiento & purificación , Separación Celular/métodos , Centrifugación/métodos , Hidrodinámica , Animales , Anticuerpos Monoclonales/uso terapéutico , Biofarmacia/métodos , Técnicas de Cultivo de Célula/métodos , Humanos , Mamíferos , ReologíaRESUMEN
Mixing in bioreactors is known to be crucial for achieving efficient mass and heat transfer, both of which thereby impact not only growth of cells but also product quality. In a typical bioreactor, the rate of transport of oxygen from air is the limiting factor. While higher impeller speeds can enhance mixing, they can also cause severe cell damage. Hence, it is crucial to understand the hydrodynamics in a bioreactor to achieve optimal performance. This article presents a novel approach involving use of computational fluid dynamics (CFD) to model the hydrodynamics of an aerated stirred bioreactor for production of a monoclonal antibody therapeutic via mammalian cell culture. This is achieved by estimating the volume averaged mass transfer coefficient (kL a) under varying conditions of the process parameters. The process parameters that have been examined include the impeller rotational speed and the flow rate of the incoming gas through the sparger inlet. To undermine the two-phase flow and turbulence, an Eulerian-Eulerian multiphase model and k-ε turbulence model have been used, respectively. These have further been coupled with population balance model to incorporate the various interphase interactions that lead to coalescence and breakage of bubbles. We have successfully demonstrated the utility of CFD as a tool to predict size distribution of bubbles as a function of process parameters and an efficient approach for obtaining optimized mixing conditions in the reactor. The proposed approach is significantly time and resource efficient when compared to the hit and trial, all experimental approach that is presently used. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:613-628, 2016.