Automated small-scale protein purification and analysis for accelerated development of protein therapeutics.

Small-scale protein purification presents opportunities for accelerated process development of biotherapeutic molecules. Miniaturization of purification conditions reduces time and allows for parallel processing of samples, thus offering increased statistical significance and greater breadth of variables. The ability of the miniaturized platform to be predictive of larger scale purification schemes is of critical importance. The PerkinElmer JANUS BioTx Pro and Pro-Plus workstations were developed as intuitive, flexible, and automated devices capable of performing parallel small-scale analytical protein purification. Preprogrammed methods automate a variety of commercially available ion exchange and affinity chromatography solutions, including miniaturized chromatography columns, resin-packed pipette tips, and resin-filled microtiter vacuum filtration plates. Here, we present a comparison of microscale chromatography versus standard fast protein LC (FPLC) methods for process optimization. In this study, we evaluated the capabilities of the JANUS BioTx Pro-Plus robotic platform for miniaturized chromatographic purification of proteins with the GE ӒKTA Express system. We were able to demonstrate predictive analysis similar to that of larger scale purification platforms, while offering advantages in speed and number of samples processed. This approach is predictive of scale-up conditions, resulting in shorter biotherapeutic development cycles and less consumed material than traditional FPLC methods, thus reducing time-to-market from discovery to manufacturing.

Model-based high-throughput design of ion exchange protein chromatography.

This work describes the development of a model-based high-throughput design (MHD) tool for the operating space determination of a chromatographic cation-exchange protein purification process. Based on a previously developed thermodynamic mechanistic model, the MHD tool generates a large amount of system knowledge and thereby permits minimizing the required experimental workload. In particular, each new experiment is designed to generate information needed to help refine and improve the model. Unnecessary experiments that do not increase system knowledge are avoided. Instead of aspiring to a perfectly parameterized model, the goal of this design tool is to use early model parameter estimates to find interesting experimental spaces, and to refine the model parameter estimates with each new experiment until a satisfactory set of process parameters is found. The MHD tool is split into four sections: (1) prediction, high throughput experimentation using experiments in (2) diluted conditions and (3) robotic automated liquid handling workstations (robotic workstation), and (4) operating space determination and validation. (1) Protein and resin information, in conjunction with the thermodynamic model, is used to predict protein resin capacity. (2) The predicted model parameters are refined based on gradient experiments in diluted conditions. (3) Experiments on the robotic workstation are used to further refine the model parameters. (4) The refined model is used to determine operating parameter space that allows for satisfactory purification of the protein of interest on the HPLC scale. Each section of the MHD tool is used to define the adequate experimental procedures for the next section, thus avoiding any unnecessary experimental work. We used the MHD tool to design a polishing step for two proteins, a monoclonal antibody and a fusion protein, on two chromatographic resins, in order to demonstrate it has the ability to strongly accelerate the early phases of process development.