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A Statistical Approach to Expanding Production Capacity
Annette Kaya, Andrea Uebele, Anke Seeger, Hans ter Maat, David Estapé
BioProcess International, Vol. 11, No. 3, March 2013, pp. 54–59
 

Contract manufacturer DSM Biologics — at its current good manufacturing practices (CGMP) facility in Groningen, The Netherlands — provides services for clinical development and commercial production based on mammalian cell culture technology (Photo 1). During the 2011–2012 year, the facility went through a major expansion project to enlarge its capacity and fulfill a growing customer demand. From a business point of view, the project had a well-defined target for future production capacity as well as investment volume.

Photo 1:



A major part of the project focused on restructuring downstream operations to match upstream capacities. The objectives required full understanding of the annual output that is possible if the facility is extended by one or two downstream processing suites. The optimal solution would be to achieve target capacity with minimum investment. M+W Group supported DSM Biologics to find the right approach and identify the best solution.

PRODUCT FOCUS: ALL BIOLOGICALS

PROCESS FOCUS: MANUFACTURING

WHO SHOULD READ: PROCESS DEVELOPMENT AND MANUFACTURING

KEYWORDS: SIMULATION, PROCESS MODELING, FACILITY DESIGN, PRODUCTION CAPA#CITY, CELL CULTURE

LEVEL: INTERMEDIATE

Production Capacity

The Groningen facility offers multiple production capabilities and high flexibility (Table 1), including fed-batch, perfusion, and proprietary XD technology processes (1). For each of those, the company offers production volumes from small to commercial scale. In addition, process development data have shown product titer ranges from milligrams to 20 g/L or more.

Table 1: Production technologies, bioreactor volumes, and titers at DSM Biologics; perfusion output is the sum of continuous processing over time.



The combination of different technologies, volumes, and product titers has led to multiple production processes with different production times and resource needs (e.g., equipment use and/or production suites). For example, fed-batch cell culture may take two to three weeks, whereas perfusion takes four to six weeks, and XD cell culture normally takes between 10 and 14 days. The total amount of product will affect the use of downstream equipment and production suites. DSM Biologics can run XD cell culture at 50-, 250-, and 500-L scales, with expected product titer ranges between 5 and 20 g/L. So the amount of protein to be purified will be from 250 g to 10 kg per batch. With such a large range of protein amounts, the facility can process a batch only if it is handled in cycles (once the largest piece of equipment is in use). As more protein is produced, more cycles are needed, and downstream equipment and suites are occupied for longer periods. In the worst case, downstream operation becomes a bottleneck that limits total production capacity.

Because customer demand defines the type of batches or processes, it is difficult to estimate the output number of batches per year in advance. If we assume full occupancy of the facility, the number of batches may be higher when DSM Biologics runs short, small-volume, and low-titer processes. Longer, large-volume, and high-titer processes may decrease the total number of batches processed in one year.

If the number of batches depends on the processes and customers’ demands, how can we define a production capacity to provide a clear answer to business needs? A typical approach is to look into the worst- and best-case scenarios by analyzing the different situations one by one. However, with such a large number of process options, that approach can be cumbersome. More important, it does not provide a clear satisfactory answer to the true capacity and the number of additional downstream suites that may be required.

Statistical Analysis for Process Simulation

If the number of batches differs each year, it should be logical to determine the probability of achieving target capacity. To provide a satisfactory answer, we applied statistical analysis to process simulation (2). That allowed us to build a process model and predict a production schedule by taking into account different resources. Our model included all production processes, equipment, and production areas.

The method is very simple and comprises the following steps:

  • Assume a possible production sequence for a period longer than one year (e.g., fed batch– perfusion–perfusion–XD–XD and so on).

  • Simulate the production sequence over a period of one year.

  • Then count the number of batches that have been fully processed.

  • Repeat the above steps multiple times (e.g., 500). Note that each simulation cycle represents one production year (500 years of production).

  • The number of batches achieved each time is the basis for a statistical analysis to determine the probability of achieving a certain number of batches.

Figure 1 shows an example of the statistical analysis in which we can see the probability of achieving a certain number of batches in one year. For example, assume that the production target is n batches per year. We can see that the most likely is to achieve n – 2 or n – 3 batches each year. However, it can be as low as n – 6 but not less. We should also not expect more than n + 3 batches in one year as a best case.



On the other hand, we can also clearly define that we have a cumulative probability of 95% to reach n – 4 or more batches. The accumulative probability is reduced to just 12% for the target of a minimum of n batches per year.

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