(144d) Dynamic Optimization Strategies Towards Addressing the Challenges in Protein Therapeutics

Protein therapeutics is an important field of the pharmaceutical industry. Current challenges in this field include meeting the increasing market demands, handling the entrance of biosimilar products and complying with quality by design (QbD) principles [1-3]. To tackle these challenges effectively, efficient use of existing tools, methods and solution approaches together with the development of novel ones is necessitated. This work provides an overview of our recent efforts to address some of the aforementioned challenges using model-based dynamic optimization strategies.

Model-based approaches are powerful tools towards facilitating process development and advancing process performance. They offer a promising alternative to costly and time-consuming experimentation [1], and can have a profound effect on process sustainability and profitability. Recent advances in cell culture modeling (with cell cultures representing a typical production platform for many of the products of this class) have enabled significant insight to the system’s dynamics and the underlying production mechanisms, and have paved the way towards developing dynamic optimization strategies for process intensification (cf., [4-6]).

Existing numerical methods and optimization software for solving the resulting dynamic optimization problems rely almost exclusively on local techniques. Due to the noncovexity of the described complex biological systems, the vast majority of these problems exhibits the presence of multiple local minima. Nevertheless, deriving suboptimal local solutions can have direct quality and cost impacts. Despite the significant progress in deterministic global optimization [7], it is not yet tractable for the considered systems. Hence, alternative methods to overcome the hazards of suboptimal local optimality are required. Stochastic and heuristic approaches can be advantageous to this end. In our previous work [8], we have shown how incorporating biological process knowledge into the optimization problem formulation can facilitate the derivation of superior local solutions. In a similar direction, we illustrate how proper decomposition of the optimization problem can outweigh certain advantages [9].

Beyond increasing product yield, monitoring and controlling product quality is essential. Regulatory authorities including FDA are increasingly recommending consideration of QbD to develop processes that consistently meet quality specifications [3, 10]. This requires further attempts in modeling, as well as reconsiderations in the formulation of the dynamic optimization problem to effectively account for direct quality attributes. Yet, current methods in this direction are still in an early stage. The recent work in [11] demonstrates a successful example of model-based optimization to maximize galactosylation (a glycosylation related metric, with glycosylation presenting a critical quality attribute for many therapeutic proteins [12, 13]) content for an antibody case study. In our recent study [14], we have combined model simulations and design of experiments to investigate the effects of nutrients and nucleotide sugars feeding in protein glycosylation. Building on this basis, we have developed an optimization framework to maximize process performance while meeting quality specifications, and thus provided a successful example for utilizing dynamic optimization to apply the QbD paradigm in protein therapeutics [15].

Acknowledgements:
This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no.675251.

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[15] Kappatou, C. D., Ehsani, A., Niedenführ, S., Mhamdi, A., Schuppert, A., & Mitsos, A. Quality-targeting dynamic optimization of monoclonal antibody production. (Submitted)