(675b) Ultrafiltration of Monoclonal Antibodies: Combined Optimization of Formulation Parameters and Process Operating Conditions Under Uncertainty

Monoclonal antibodies (mAbs) are an important type of therapeutic agent that are effective treatments in several therapeutic areas, such as immunology, oncology and infectious disease. These immunoglobins owe their success to their higher efficacy, specificity and safety compared to conventional drugs, which is a direct consequence of their mechanism of action. Unfortunately, all these benefits come at a price, namely, mAbs are expensive to produce (Rudge and Ladisch, 2020; Maruthamuthu et al, 2020).

One of the reasons why monoclonal antibodies are extremely expensive is their complex and low-yield production process, which includes several challenging manufacturing steps, namely, an initial fed-batch fermentation followed by centrifugation, depth filtration, several chromatographic purifications, virus filtration and, finally, concentration by ultrafiltration. Therefore, one of the most direct ways to reduce the average cost of monoclonal antibodies is to optimize their production process, in terms of both processing sequence and individual manufacturing step yields.

Several authors have proposed optimal design configurations for mAb production processes (Liu et al., 2016) and computed the optimal operating conditions at which the fermentation and chromatographic separations should be carried out (Alhuthali et al., 2019; Papathanasiou et al., 2017). Some of these contributions have also analyzed the impact of model uncertainty on the results of their design/dynamic optimization calculations (Liu and Gunawan, 2017). Surprisingly, the ultrafiltration step has received very little attention so far, even though the typical yield of this operation may be as low as 85 %, in the absence of a recovery backflush (as the monoclonal antibody is being concentrated, a significant amount of it forms a hydrogel that sticks onto the ultrafiltration membrane). One of the very few studies, which deals with the systematic optimization of ultrafiltration systems for the concentration of monoclonal antibody solutions, has been conducted by Rossi et al. (2021). This contribution expands the scope of the aforementioned study by investigating the extent to which the combined optimization of formulation parameters (e.g., the buffer pH) and standard operational variables (e.g., the feed flowrates to the retentate and permeate channels of the ultrafiltration membrane cassette and the pressure levels at the retentate and permeate outlets) can help further enhance the overall performance of mAb ultrafiltration, measured in terms of final mAb concentration/recovery and operation time.

More specifically, we first update the hybrid mechanistic-statistical model of the ultrafiltration device, developed by Rossi et al. (2021), which consists of a first-principles-inspired model of the physical system, augmented with a Bayesian neural network, by re-training both model components with a more comprehensive set of experimental data (this training dataset includes physical property measurements for the mAb of interest, collected over an appropriate range of pHs, combined with ultrafiltration experiments, conducted at several different pHs). We then validate the updated hybrid model with an appropriate set of validation experiments, using both Bayesian and frequentist uncertainty propagation techniques. Finally, we utilize the validated hybrid model and multi-objective stochastic optimization techniques to identify the most appropriate mAb formulation, which we should use in the ultrafiltration step, and compute the optimal process conditions, at which we should operate, with the aim of maximizing the final mAb concentration/recovery and minimizing the operation time (under uncertainty).

The results of our calculations are very promising, in that we can further improve the final mAb concentration/recovery and reduce the ultrafiltration time, compared to Rossi et al. (2021), without violating any operational constraints, even in the presence of significant model uncertainty. This additional performance boost can be attributed to the very idea of optimizing formulation parameters and operational variables simultaneously.

References

Alhuthali, S., Fadda, S., & Kontoravdi, C. (2019). Constrained optimisation of cell culture feeding strategy and temperature shift duration to enhance monoclonal antibody titre and purity. 2019 AIChE Annual Meeting, Orlando (FL).

Liu, Y., & Gunawan, R. (2017). Bioprocess optimization under uncertainty using ensemble modeling. Journal of biotechnology, 244, 34-44.

Liu, S., Farid, S. S., & Papageorgiou, L. G. (2016). Integrated optimization of upstream and downstream processing in biopharmaceutical manufacturing under uncertainty: a chance constrained programming approach. Industrial & Engineering Chemistry Research, 55(16), 4599-4612.

Maruthamuthu, M. K., Rudge S. R., Ardekani, A. M., Ladisch, M. R., Verma, M. S. (2020). Process Analytical Technologies and Data Analytics for the manufacture of Monoclonal Antibodies, Trends in Biotechnology, 38(10), 1169-1186.

Papathanasiou, M. M., Quiroga‐Campano, A. L., Steinebach, F., Elviro, M., Mantalaris, A., & Pistikopoulos, E. N. (2017). Advanced model‐based control strategies for the intensification of upstream and downstream processing in mAb production. Biotechnology progress, 33(4), 966-988.

Rossi, F., Zuponcic, J., Ximenes, E., Geng, S., Tao, Y., Corvari, V., Ladisch, M., Reklaitis, G. (2021). Dynamic optimization of an ultrafiltration system for the concentration of monoclonal antibody solutions under uncertainty. 2021 AIChE Annual Meeting, Boston (MA).

Rudge, S. R., and Ladisch, M. R. (2020). Industrial Challenges of Recombinant Proteins, Current Applications of Pharmaceutical Biotechnology, A. C. Silva, J. N. Moreira, J. M. S. Lobo, H. Almaida, editors, in Adv Biochem Eng Biotechnol, 171, 1 to 22.