General solutions to decompose heterogeneous compositions using antibody afucosylation as a model system.

Methods involving the use of mathematical models of competitive ligand-receptor binding to characterize mixtures of ligands in terms of compositions and properties of the component ligands have been developed. The associated mathematical equations explicitly relate component ligand physical-chemical properties and mole fractions to measurable properties of the mixture including steady state binding activity, 1/Kd,apparent or equivalently 1/EC50, and kinetic rate constants kon,apparent and koff,apparent allowing: (1) component ligand physical property determination and (2) mixture property predictions. Additionally, mathematical equations accounting for combinatorial considerations associated with ligand assembly are used to compute ligand mole fractions. The utility of the methods developed is demonstrated using published experimental ligand-receptor binding data obtained from mixtures of afucosylated antibodies that bind FcγRIIIa (CD16a) to: (1) extract component ligand physical property information that has hitherto evaded researchers, (2) predict experimental observations, and (3) provide explanations for unresolved experimental observations. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:500-510, 2017.

Challenges with afucosylation content in antibody-based drugs: Guidance provided by mathematical modeling.

The theory of competitive ligand-receptor binding has been used to analyze the effect of afucosylation-based antibody heterogeneity on Fc-FcγRIIIa ligand-receptor binding activity. In vitro activity is found to represent a linear combination of the component antibody activities, weighted by the relative concentrations of the different afucosylated antibody forms. An analysis of ELISA binding activity data has allowed for the dissection of the activity contributions of the different afucosylated antibodies, revealing that the heterogeneous afucosylated antibody exhibits greater activity, on a per mole basis, when compared to the homogeneous afucosylated antibody. The ratio of the afucosylated antibody equilibrium dissociation constants is computed to be KAF /KA ≈ 0.6-0.9, where KAF and KA denote the dissociation equilibrium constant of the heterogeneous and the homogeneous afucosylated antibodies, respectively. Our analysis also reveals that, in general, activity scales quadratically with the afucosylated glycan content of a sample. Linear activity-afucosylated glycan fraction correlations reported in the literature are shown to represent specific cases of this general scaling and result from oversimplifying the underlying antibody concentration distributions. The implications of our findings for drug development are also discussed.

High cell density induces spontaneous bifurcations of dissolved oxygen controllers during CHO cell fermentations.

High cell density cultures of CHO cells growing in a bioreactor under dissolved oxygen control were found to undergo spontaneous bifurcations and a subsequent loss of stability some time into the fermentation. This loss of stability was manifested by sustained and amplified oscillations in the bioreactor dissolved oxygen concentration and in the oxygen gas flow rate to the reactor. To identify potential biological and operational causes for the phenomenon, linear stability analysis was applied in a neighborhood of the experimentally observed bifurcation point. The analysis revealed that two steady state process gains, K(P1) and K(P2), regulated k(l)a and gas phase oxygen concentration inputs, respectively, and the magnitude of K(P1) was found to determine system stability about the bifurcation point. The magnitude of K(P1), and hence the corresponding open-loop steady state gain K(OL1), scaled linearly with the bioreactor cell density, increasing with increasing cell density. These results allowed the generation of a fermentation stability diagram, which partitioned K(C)-N operating space into stable and unstable regions separated by the loci of predicted critically stable controller constants, K(C,critical), as a function of bioreactor cell density. This consistency of this operating diagram with experimentally observed changes in system stability was demonstrated. We conclude that time-dependent increases in cell density are the cause of the observed instabilities and that cell density is the critical bifurcation parameter. The results of this study should be readily applicable to the design of a more robust controller.

Application of factorial design to accelerate identification of CHO growth factor requirements.

To accelerate recombinant CHO media and process development, we describe a simple approach to integrating multiple tasks associated with these processes including initial media design, serum-free adaptation, stability analysis and first generation scale-up. Factorial design techniques and normal probability chart representation of the results were first applied to identify potent parental CHO cell growth factors in a lean basal medium. These results were then applied to identify a suitable manufacturing medium from a panel of commercial and proprietary media formulations. When this approach was applied to recombinant CHO cell line, rapid adaptation of the cell line to an appropriate production medium occurred during culture expansion in the presence of the identified growth factor(s). This approach allows media component screening to be naturally integrated into the adaptation and scale-up processes since components that have little or no relative effect on cell proliferation are selected against as the "best" cultures are moved forward. The rapidity of the adaptation process allowed cell line stability studies to be initiated relatively early in the development process, thus providing preliminary stability information by the time the "outgrowing" culture could be scaled to 100-L reactors some 30 days after adaptation commenced. The application of full factorial design techniques allowed us to calculate the maximum number of interaction effects, the interpretation of which we believe can provide insights into growth factor biology.