No abstract

The emergence of monoclonal antibody (mAb) therapies has created a need for faster and more efficient bioprocess development strategies in order to meet timeline and material demands. In this work, a high-throughput process development (HTPD) strategy implementing several high-throughput chromatography purification techniques is described. Namely, batch incubations are used to scout feasible operating conditions, miniature columns are then used to determine separation of impurities, and, finally, a limited number of lab scale columns are tested to confirm the conditions identified using high-throughput techniques and to provide a path toward large scale processing. This multistep approach builds upon previous HTPD work by combining, in a unique sequential fashion, the flexibility and throughput of batch incubations with the increased separation characteristics for the packed bed format of miniature columns. Additionally, in order to assess the applicability of using miniature columns in this workflow, transport considerations were compared with traditional lab scale columns, and performances were mapped for the two techniques. The high-throughput strategy was utilized to determine optimal operating conditions with two different types of resins for a difficult separation of a mAb monomer from aggregates. Other more detailed prediction models are cited, but the intent of this work was to use high-throughput strategies as a general guide for scaling and assessing operating space rather than as a precise model to exactly predict performance.

No abstract

For rapid development of initial solvent extraction processes, knowledge of the solubility and partition behavior of surfactants and solubility enhancers is required. Unfortunately, experimental solubility data for many common surfactants and solubility enhancers in aqueous and organic solvents have not been reported. There are also few references to the partitioning behavior of these additives between water and common extraction solvents. In this paper, the solubility and partition coefficients were measured at 293 K for a range of additives in solvent systems of varying polarities and classes:  ethyl acetate, isobutyl alcohol, toluene, methyl ethyl ketone, methyl tert-butyl ether, and 0.2 mol·L-1 potassium phosphate buffer (pH 7). The additives chosen were based on common usage and represent a cross-section of the surfactant classes:  UCON LB-625, P2000, Triton X-100, sodium dodecyl sulfate (SDS), Tween 20, Tween 80, hexadecyltrimethylammonium bromide (CTAB), ammonium sulfate, and methyl-β-cyclodextrin. The partition behavior of these additives (except Tween 20) was also investigated. The effect of ionic strength, pH, and cosolvents on the partition coefficient was also determined to provide a database for surfactant and solubility enhancer behavior in order to allow for rapid optimization of initial extraction processes. The solubility results showed that the antifoams were extremely soluble in the organic solvents but had limited solubility in water. The nonionic surfactants were soluble in all solvents tested. The anionic surfactant was soluble in all solvents tested, with the exception of toluene. The cationic surfactant and ammonium sulfate had limited solubility in most solvents. The methyl-β-cyclodextrin had varying degrees of solubility depending on polarity. The partition results can largely be predicted from the solubility data, with the exception of the nonionic surfactants. For all of the compounds that partitioned, the behavior could also be predicted based on solvent polarity, with larger partition coefficients for the more polar solvents. These data can be used to design initial extraction processes containing these additives and, by analogy, for other related additives as well.

Recent advances in high‐throughput (HTP) automated mini‐bioreactor systems have significantly improved development timelines for early‐stage biologic programs. Automated platforms such as the ambr® 250 have demonstrated the ability, using appropriate scale‐down approaches, to provide reliable estimates of process performance and product quality from bench to pilot scale, but data sets comparing to large‐scale commercial processes (>10,000 L) are limited. As development moves toward late stages, specifically process characterization (PC), a qualified scale‐down model (SDM) of the commercial process is a regulatory requirement as part of Biologics License Application (BLA)‐enabling activities. This work demonstrates the qualification of the ambr® 250 as a representative SDM for two monoclonal antibody (mAb) commercial processes at scales >10,000 L. Representative process performance and product quality associated with each mAb were achieved using appropriate scale‐down approaches, and special attention was paid to pCO2 to ensure consistent performance and product quality. Principal component analysis (PCA) and univariate equivalence testing were utilized in the qualification of the SDM, along with a statistical evaluation of process performance and product‐quality attributes for comparability. The ambr® 250 can predict these two commercial‐scale processes (at center‐point condition) for cell‐culture performance and product quality. The time savings and resource advantages to performing PC studies in a small‐scale HTP system improves the potential for the biopharmaceutical industry to get products to patients more quickly.