@article{altern_kocot_lebarre_boi_phillips_roush_menegatti_cramer_2024, title={Mechanistic model-based characterization of size-exclusion-mixed-mode resins for removal of monoclonal antibody fragments}, volume={1718}, ISSN={["1873-3778"]}, DOI={10.1016/j.chroma.2024.464717}, abstractNote={Although antibody fragments are a critical impurity to remove from process streams, few platformable purification techniques have been developed to this end. In this work, a novel size-exclusion-mixed-mode (SEMM) resin was characterized with respect to its efficacy in mAb fragment removal. Inverse size-exclusion chromatography showed that the silica-based resin had a narrow pore size distribution and a median pore radius of roughly 6.2 nm. Model-based characterization was carried out with Chromatography Analysis and Design Toolkit (CADET), using the general rate model and the multicomponent Langmuir isotherm. Model parameters were obtained from fitting breakthrough curves, performed at multiple residence times, for a mixture of mAb, aggregates, and an array of fragments (varying in size). Accurate fits were obtained to the frontal chromatographic data across a range of residence times. Model validation was then performed with a scaled-up column, altering residence time and feed composition from the calibration run. Accurate predictions were obtained, thereby illustrating the model's interpolative and extrapolative capabilities. Additonally, the SEMM resin achieved 90% mAb yield, 37% aggregate removal, 29% F(ab′)2 removal, 54% Fab/Fc removal, 100% Fc fragments removal, and a productivity of 72.3 g mAbL×h. Model predictions for these statistics were all within 5%. Simulated batch uptake experiments showed that resin penetration depth was directly related to protein size, with the exception of the aggregate species, and that separation was governed by differential pore diffusion rates. Additional simulations were performed to characterize the dependence of fragment removal on column dimension, load density, and feed composition. Fragment removal was found to be highly dependent on column load density, where optimal purification was achieved below 100 mg protein/mL column. Furthermore, fragment removal was dependent on column volume (constant load mass), but agnostic to whether column length or diameter was changed. Lastly, the dependence on feed composition was shown to be complex. While fragment removal was inversely related to fragment mass fraction in the feed, the extent depended on fragment size. Overall, the results from this study illustrated the efficacy of the SEMM resin in fragment and aggregate removal and elucidated relationships with key operational parameters through model-based characterization.}, journal={JOURNAL OF CHROMATOGRAPHY A}, author={Altern, Scott H. and Kocot, Andrew J. and Lebarre, Jacob P. and Boi, Cristiana and Phillips, Michael W. and Roush, David J. and Menegatti, Stefano and Cramer, Steven M.}, year={2024}, month={Mar} }
 @article{lebarre_chu_altern_kocot_bhandari_barbieri_sly_crapanzano_cramer_phillips_et al._2024, title={Mixed-mode size-exclusion silica resin for polishing human antibodies in flow-through mode}, volume={1720}, ISSN={["1873-3778"]}, DOI={10.1016/j.chroma.2024.464772}, abstractNote={The polishing step in the downstream processing of therapeutic antibodies removes residual impurities from Protein A eluates. Among the various classes of impurities, antibody fragments are especially challenging to remove due to the broad biomolecular diversity generated by a multitude of fragmentation patterns. The current approach to fragment removal relies on ion exchange or mixed-mode adsorbents operated in bind-and-gradient-elution mode. However, fragments that bear strong similarity to the intact product or whose biophysical features deviate from the ensemble average can elude these adsorbents, and the lack of a chromatographic technology enabling robust antibody polishing is recognized as a major gap in downstream bioprocessing. Responding to this challenge, this study introduces size-exclusion mixed-mode silica (SEMM-silica) resins as a novel chromatographic adsorbent for the capture of antibody fragments irrespective of their biomolecular features. The pore diameter of the silica beads features a narrow distribution and is selected to exclude monomeric antibodies, while allowing their fragments to access the pores where they are captured by the mixed-mode ligands. The static and dynamic binding capacity of the adsorbent ranged respectively between 30-45 and 25-33 grams of antibody fragments per liter of resin. Selected SEMM-silica resins also demonstrated the ability to capture antibody aggregates, which adsorb on the outer layer of the beads. Optimization of the SEMM-silica design and operation conditions – namely, pore size (10 nm) and ligand composition (quaternary amine and alkyl chain) as well as the linear velocity (100 cm/h), ionic strength (5.7 mS/cm), and pH (7) of the mobile phase – afforded a significant reduction of both fragments and aggregates, resulting into a final antibody yield up to 80% and monomeric purity above 97%.}, journal={JOURNAL OF CHROMATOGRAPHY A}, author={Lebarre, Jacob and Chu, Wenning and Altern, Scott and Kocot, Andrew and Bhandari, Dipendra and Barbieri, Eduardo and Sly, Jae and Crapanzano, Michael and Cramer, Steven and Phillips, Michael and et al.}, year={2024}, month={Apr} }
 @article{zhang_barbieri_lebarre_rameez_mostafa_menegatti_2023, title={Peptonics: A new family of cell-protecting surfactants for the recombinant expression of therapeutic proteins in mammalian cell cultures}, volume={10}, ISSN={["1860-7314"]}, DOI={10.1002/biot.202300261}, abstractNote={Abstract}, journal={BIOTECHNOLOGY JOURNAL}, author={Zhang, Ka and Barbieri, Eduardo and Lebarre, Jacob and Rameez, Shahid and Mostafa, Sigma and Menegatti, Stefano}, year={2023}, month={Oct} }
 @article{kilgore_minzoni_shastry_smith_barbieri_wu_lebarre_chu_o'brien_menegatti_2023, title={The downstream bioprocess toolbox for therapeutic viral vectors}, volume={1709}, ISSN={["1873-3778"]}, DOI={10.1016/j.chroma.2023.464337}, abstractNote={Viral vectors are poised to acquire a prominent position in modern medicine and biotechnology owing to their role as delivery agents for gene therapies, oncolytic agents, vaccine platforms, and a gateway to engineer cell therapies as well as plants and animals for sustainable agriculture. The success of viral vectors will critically depend on the availability of flexible and affordable biomanufacturing strategies that can meet the growing demand by clinics and biotech companies worldwide. In this context, a key role will be played by downstream process technology: while initially adapted from protein purification media, the purification toolbox for viral vectors is currently undergoing a rapid expansion to fit the unique biomolecular characteristics of these products. Innovation efforts are articulated on two fronts, namely (i) the discovery of affinity ligands that target adeno-associated virus, lentivirus, adenovirus, etc.; (ii) the development of adsorbents with innovative morphologies, such as membranes and 3D printed monoliths, that fit the size of viral vectors. Complementing these efforts are the design of novel process layouts that capitalize on novel ligands and adsorbents to ensure high yield and purity of the product while safeguarding its therapeutic efficacy and safety; and a growing panel of analytical methods that monitor the complex array of critical quality attributes of viral vectors and correlate them to the purification strategies. To help explore this complex and evolving environment, this study presents a comprehensive overview of the downstream bioprocess toolbox for viral vectors established in the last decade, and discusses present efforts and future directions contributing to the success of this promising class of biological medicines.}, journal={JOURNAL OF CHROMATOGRAPHY A}, author={Kilgore, Ryan and Minzoni, Arianna and Shastry, Shriarjun and Smith, Will and Barbieri, Eduardo and Wu, Yuxuan and Lebarre, Jacob P. and Chu, Wenning and O'Brien, Juliana and Menegatti, Stefano}, year={2023}, month={Oct} }