Using Information Technology to Enhance Upstream Productivity - - BioPharm International

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Using Information Technology to Enhance Upstream Productivity


BioPharm International Supplements


University of Delaware

Integrating Analytical Data

The critical product quality attributes must be defined and the effect of process variables on these specific attributes must be identified to implement PAT in bioprocess development. To obtain this type of information, the use of inline measurements and the integration of analytical data of the cultivation process is critical. However, a conventional bioreactor is limited to the control of dissolved oxygen (DO), pH, temperature, and agitation. A bioreactor generally does not have the capability to control all relevant analytes that can affect culture conditions and by extension, product quality. Hence, a bioreactor system integrating glucose and glutamine, lactate, NH4+, and biomass media concentration measurements is extremely helpful to increase product quality.


Table 2. Effects of culture conditions on glycosylation
At the University of Delaware, Babatunde A. Ogunnaike and his students have established the foundation for effective online, real-time control of glycosylation patterns on monoclonal antibodies produced with Chinese hamster ovary (CHO) cells. The control strategy, based on the premise that external culture conditions are known to affect glycosylation patterns (Table 2), involves an inner loop for base regulatory control of the key process variables and an outer loop to maintain the glycosylation pattern at a desired set-point. Unfortunately, with current technology, glycosylation measurements are not available frequently enough for traditional feedback loops. Therefore, the outer glycosylation control loop involves model-based control through estimation.


Figure 3
For establishing base regulatory control of the key process variables known to affect glycosylation, they designed a bioreactor system with nutrient control and cellular metabolite monitoring in addition to typical bioreactor measurements. With this system, eight analytes (pH, Glutamate, Glutamine, Glucose, Lactate, N+, K+, NH4+) are measured with a Bioprofile 100+ bioanalyzer with autosampler (Nova Biomedical, Waltham, MA) that is equipped with object linking and embedding for process control (OPC) software. With the OPC connection, the Nova Bioprofile 100+ was integrated with the DASGIP Bioreactor Control Software, which allows for closed loop control to be implemented for glucose (Figure 3) and glutamine concentrations in the media. A multi-scale model using process variables to predict glycosylation patterns was developed to establish the outer glycosylation control loop. The controlled inputs of the model include glucose media concentration, glutamine media concentration, DO, pH, temperature, and agitation rate. Specific uptake or excretion rates of glucose, glutamine, lactate, and ammonia were computed by linear regression with biomass. This model allows for model predictive control and targeted modification of process variables.

"This research has shown how a bioanalyzer equipped with an autosampler was integrated with the bioreactor system through OPC. This integration is advantageous because it allows for the implementation of feedback control and at-line monitoring of the nutrients and metabolites," said Ogunnaike. "The validated DASGIP/Nova reactor system demonstrated the successful control of glucose and glutamine media concentrations. With this system in place, we have established base regulatory control of the key process variables known to affect glycosylation, which is the first step in our strategy to achieve online glycosylation control," he added

In the future, Ogunnaike and his students propose to characterize glycosylation macro-heterogeneity at-line by adding Groton Biosystem's automated reactor sampling system with Agilent's 2D HPLC to the current bioreactor system. In addition, liquid chromatography coupled to mass spectroscopy will be used to characterize glycan micro-heterogeneity offline.

Conclusion

The case studies presented here are only two examples showing how information technology can help to increase efficiency in process and product development by the integration of Quality by Design and process analytical technology into standard laboratory procedures. Today's research and development laboratories face more challenges in an increasingly competitive worldwide market.

The generation of larger and larger amounts of data often results in huge data graveyards. By using the capabilities of extended data mining software, one can simply link the process data with user-defined attributes such as the strain or cell line with the media composition, the controller set-points, and feeding profiles as well as with achieved product yields or viable cell densities. Thus, raw process data turns into usable research information.

The next challenge is to keep this knowledge available in the long run. Open communication standards not only allow the integration of analyzers into bioreactor systems but also the integration of bioreactor systems into so-called historians. These long-term archives save all process relevant information beyond the single laboratory and can easily be accessed by supervisory control systems globally. Gaining more flexibility in online monitoring and control of the bioprocess is a matter of efficiency. Supervisory control systems and remote control through iPhone, iPad, or any other server provide bench-top scientists as well as laboratory and plant managers with multiple ways to control the running processes online and from any location.

In conclusion, to meet today's challenges, process development and information technology must work in tandem to create the most beneficial and efficient outcome for their laboratory.


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