Predicting diafiltration solution compositions for final ultrafiltration/diafiltration steps of monoclonal antibodies.

Many liquid formulations for monoclonal antibodies (MAbs) require the final ultrafiltration/diafiltration step to operate at high protein concentrations, often at or above 100 g/L. When operating under these conditions, the excipient concentrations and pH of the final diafiltered retentate are frequently not equal to the corresponding excipient concentrations and pH of the diafiltration buffer. A model based on the Poisson-Boltzmann equation combined with volume exclusion was extended to predict both pH and excipient concentrations in the retentate for a given diafiltration buffer. This model was successfully applied to identify the diafiltration buffer composition required to achieve the desired pre-formulated bulk drug substance (retentate) conditions. Predictions were in good agreement with the experimental results, and reduced the number of experimental iterations needed to define the diafiltration buffer composition. Additionally, the predictive model was applied in a sensitivity analysis across ranges of protein charge, protein concentration, and diafiltration buffer pH and excipient concentration. This sensitivity analysis can facilitate the design of experiments for robustness testing, and allow for generalized predictions across classes of molecules such as MAbs.

Theoretical analysis of excipient concentrations during the final ultrafiltration/diafiltration step of therapeutic antibody.

Diafiltration of a protein solution into a new buffer is a common final step in biopharmaceutical manufacturing. However, the excipient concentrations in the retentate are not always equal to their corresponding concentrations in the new buffer (diafiltration buffer). This phenomenon was observed repeatedly during diafiltration of different therapeutic monoclonal antibodies in which the concentrations of histidine and either sorbitol or sucrose (depending on which was chosen for the diafiltration buffer) in the retentate were lower than in the diafiltration buffer. Experimental studies and theoretical analyses of the ultrafiltration/diafiltration (UF/DF) step were carried out to determine the primary causes of the phenomenon and to develop a mathematical model capable of predicting retentate excipient concentrations. The analyses showed that retentate histidine concentration was low primarily because of repulsive charge interactions between positively-charged histidine molecules and positively-charged protein molecules, and that volume exclusion effects were secondary for like-charged molecules. The positively-charged protein molecules generate an electrical potential that cause an uneven distribution of charged histidine molecules. This interaction was used to construct a mathematical model based on the Poisson-Boltzmann equation. The model successfully predicted the final histidine concentration in the diafiltered product (retentate) from the UF/DF development and production runs, with good agreement across a wide range of protein and histidine concentrations for four therapeutic monoclonal antibodies. The concentrations of uncharged excipients (sorbitol or sucrose) were also successfully predicted using previously established models, with volume exclusion identified as the primary cause of differences in uncharged excipient concentrations in the retentate and diafiltration buffer.

Predictive chromatographic simulations for the optimization of recovery and aggregate clearance during the capture of monoclonal antibodies.

Predictive chromatographic simulations were used to assess whether significant aggregate clearance, in addition to high step recovery and limited eluate pool volumes, can be achieved during protein A affinity chromatography capture steps. Such aggregates of the antibody monomer are commonly found in manufacturing processes. A lumped desorption-kinetic limiting model was used to describe the elution from the chromatography column, as batch isotherm measurements indicated no adsorption under elution conditions. In order to quantify the trade-off between step recovery and aggregate clearance, independent experiments were first performed to obtain the key kinetic parameters. These parameters were used in simulations to predict the behavior of bench-scale protein A column runs and identify robust operating windows within which good yields and significant aggregate clearance can be achieved. Two examples are described. For antibody A, a robust window of operation was identified. In this case, the optimal conditions were transferred to pilot-plant scale, and the resulting experimental data were shown to be in good agreement with model predictions. For antibody B, it was found that conditions resulting in high recovery and good aggregate clearance were not robust: at the optimal elution conditions, changes of +/-0.1 units in pH or +/-1mS/cm in conductivity affected the results substantially.

Development and application of an automated, low-volume chromatography system for resin and condition screening.

A small-volume chromatography system was developed for rapid resin and parameter screening and applied to the purification of a therapeutic monoclonal antibody from a key product-related impurity. Accounting for constraints in peripheral volume, gradient formation, column integrity, and fraction collection in microtiter plates, the resulting system employed 2-mL columns and was successfully integrated with plate-based methods for rapid sample analysis (e. g., use of automated liquid handlers, plate readers, and HPLC). Several cation-exchange chromatography resins were screened using automated programs and tailored gradients for the combination of a particular resin and a given antibody feedstock produced during Phase 1 development. Results from the tailored gradient runs were used to select a resin, and to arrive at efficient stepwise elution schedules for the chosen resin. By maintaining a constant residence time, final operating parameters were successfully scaled to representative bed heights and column diameters up to 2.6 cm (106 mL). This approach significantly improved throughput while reducing development time and material consumption.