Cost of Goods Modeling and Quality by Design for Developing Cost-Effective Processes - Combine cost analyses with QbD to improve operations and lower costs. - BioPharm International

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Cost of Goods Modeling and Quality by Design for Developing Cost-Effective Processes
Combine cost analyses with QbD to improve operations and lower costs.


BioPharm International
Volume 23, Issue 6

ABSTRACT

Increases in recombinant protein titers expressed in mammalian cells and cell culture process volumes have shifted the economic paradigm from upstream processing to downstream processing for the manufacturing of recombinant proteins, especially monoclonal antibodies (MAbs). Moreover, the pressure exerted by authorities on the healthcare industry to decrease the costs associated with treatments, and the recent entry of biosimilars into the market, have forced the biopharmaceutical community to find new solutions and strategies to reduce production costs. MAb process development currently is focused on the optimization of yield and throughput to deliver cost-effective processes. In this article, we describe the use of a cost of goods model in combination with a Design of Experiment approach for optimizing the capture step by affinity chromatography. The most economical operating conditions identified through this approach imply sacrificing some protein without affecting the quality of the final product.


Merck Serono
The market for therapeutic proteins, particularly monoclonal antibodies (MAbs), is rapidly expanding. Indeed, it represents the industry segment with the highest growth rate over the last decade.1,2,3 To meet the increasing market demand, the biopharmaceutical industry is looking for new strategies to increase bioprocessing productivity while minimizing production costs. Bioprocesses have to be carefully fine-tuned to ensure the consistent quality of the material produced, and initiatives such as Quality by Design (QbD) should result in the implementation of robust and flexible processes from the start. Additionally, biotechnology companies must develop processes that are cost effective. The pressure from competition—molecules being approved for the same indications, patent expiry, and the arrival of biosimilars—will put increased pressure on sale prices and as a result, the minimization of production costs will be essential for maintaining economically viable products.1,4,5,6

Understanding manufacturing process costs is not trivial; many important factors are not directly related to the process itself—plant capacity, equipment depreciation, allocation, and other fixed costs—can make cost estimation extremely difficult.7 For an accurate evaluation of expenses, a detailed process description with facility and equipment depreciation is needed. An exhaustive process simulation tool such as SuperPro Designer (Intelligen, Scotch Plains, NJ) can be used to model bioprocesses. This modeling includes a detailed description of the process steps, from vial thawing to final drug substance, with all related costs (direct and indirect). It is then possible to accurately estimate the final unit cost of the product generated at manufacturing scale.


Figure 1
This article describes the successful combination of a cost of goods (COGs) model (using SuperPro Designer) with a Design of Experiments (DOE) approach to reduce the cost associated with the affinity chromatography step for the capture of a MAb.

COST OF GOODS CALCULATION


Table 1. Cost of goods estimation
SuperPro Designer is a process simulation tool developed for modeling industrial recombinant protein production processes. SuperPro Designer takes into account all the process and subprocess steps and their associated costs. Additionally, fixed costs related to the infrastructure, such as building and equipment, also can be incorporated. The resulting COG models generated allow the user to predict the production costs of processes—performed at manufacturing scale—from cell thawing to final drug substance bulk.


Figure 2
The MAb process modeled in this case study (Figure 1) was a typical three-step platform process adapted to fit into a facility with a maximum cell culture capacity of 120,000 L, a protein titer of 2 g/L, an overall yield of 80% on the downstream process (DSP), and an annual MAb throughput of 4,500 kg. The facility described by the model was assumed to be an entirely new plant used at full capacity. For simplicity, the upstream process (USP)—from inoculum to clarified harvest—was lumped into a single unit operation. To accurately calculate the upstream costs, the entire USP was modeled separately, and a cost per liter of clarified harvest was determined. This harvest cost was then used as an input for the DSP process calculations. The different cost categories used for calculations are listed in Table 1.


Figure 3
A more complex model also was developed to account for the progressive degradation of the capacity of the resin when increasing the number of cycles performed on the column, a well-documented phenomenon for Protein A affinity chromatography steps. This additional level of complexity, however, does not provide a better illustration of the concepts proposed in this case study.


Figure 4
The output of the MAb platform COG analysis is shown in Figure 2. Facility-associated costs account for almost 35% of the total costs, while raw materials and consumables represent 31% of the entire production costs. A further breakdown of the process-related costs for the different unit operations (Figure 3) shows that DSP accounts for more than 65% of the total production costs. Among the three DSP steps, the capture alone accounts for 25% of the total costs. By further breaking down the costs of this unit operation (Figure 4), you can see that the capture step contribution is driven by consumables, specifically the Protein A resin. Therefore, to reduce a MAb process cost, decreasing the consumption of the Protein A resin, by maximizing loading or increasing resin lifetime, for example, will have the greatest effect.


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