How BioPAT® Supports Capacity Adaptation

Cultivating a cell line in an ambr® or a BIOSTAT® has different physical properties to a large volume (e.g. 1kL) bioreactor. The energies, pressure and mixing time of a process are exaggerated and will have an influence on the process performance if not understood, transferred and controlled correctly. It is best said by H. Baekeland; “Commit your blunders on a small scale and make your profits on a large scale.” and that ethos is at the core of BioPAT® toolbox of sensor and software control solutions.

BioPAT® gives the analytical tools to accurately record multiple univariate and multivariate process parameters (parameters and settings). It supports the linking of cause and effect to critical quality attributes (product) in a scale-down model. Thus, inline and online key process parameters can be used to establish a control strategy for commercial process which factors in volume scale, processing time and product performance (space-time-yield/quality). This enables smoother adaptions in scale allowing flexible capacity adaptation and simplified process transfers between sites. As a result, the experienced process "know how" is transferred as automated control strategy and takes responsibility for real-time quality assurance by monitoring batch trajectory. Further, data from production batches can serve to validate the process and reflect the system design, essentially supporting validation with each manufacturing batch.

  Smooth Scale up of Bioprocesses

Taking a process from the research lab to commercial manufacture is a journey that all must take on the road to success. The chosen path and the obstacles encountered will determine whether this journey is accomplished or not. The risks and costs involved need to be balanced and considered and an optimal compromise found with the technology design, product/process understanding, ultimate system control and regulatory compliance.

The aim to create a homogenous or uniform solution throughout the whole vessel is considerably easier when that vessel is small. Therefore, understanding the supply needs of your microscopic production factories (cells) can partly be attributed to the metabolic oxygen requirements. Therefore, knowing the volumetric oxygen uptake rate (OUR) and having feedback control to bioreactors actuators to fine tune the supply, ensures no oxygen limitation. The placement, calibration, sample frequency, accuracy and communication of the analytical devices will determine the success of that control. As small scale experiments lowers the impact or sensor placement and long processing times can reduce the sample frequency it leaves paramount importance with calibration, accuracy and communication.

All aerobic bioreactor gassing strategies primarily use air with levels or enrichment from oxygen, carbon dioxide and nitrogen depending on the process complexity or cellular requirements. As all living things use oxygen and produce carbon dioxide the total activity of your cells can be understood from how they are respiring. As the composition variability of the inlet gases is generally minimal and we can accurately use a BIOSTAT® mass flow controllers to measure and control the flow rates. Thus, an analytical device to measure the exhaust gas composition would allow a mass balance calculation giving the oxygen uptake and carbon dioxide production. If this data and calculation is performed in real-time, a metabolic control loop can be established which tailors the BIOSTAT® volumetric oxygen and carbon dioxide transfer rates (at all scales) to the cellular oxygen requirements.

  • Oxygen uptake rate
  • Carbon dioxide emmision rate
  • inlet oxygen percentage
  • inlet carbon dioxide percentage
  • Exhaust oxygen percentage
  • Exhaust carbon dioxide percentage
  • Gas flowrate
  • Easier scalable process transfer factors 
  • Increased growth rates
  • Minimal shear and interfacial damage
  • Improved batch tracking and electronic monitoring
  • Increased flexibility to deal with variable growth rates
  • Lower cost of process qualification
  • Reduced cost of enrichment gasses
  • Minimal paper based batch recording
  • Eased influence from process operator knowledge
  • Diminished risk of batch failure
  • QbD approach for easy filing and subsequent changes
  • Increase process safety margin
  • Increased data-driven decisions for product release

Reduced Upstream Processing Time

Time is money and in the world of commercial manufacturing every minute can count, especially with biological products. Therefore, process tracking and having a defined point in order to move to the next stage of the process will eliminate under/overshooting, thus decreasing batch to batch variability and allowing tight scheduling of an upstream manufacturing process line.

In commercial biomanufacturing the method to cascade cell growth by reaching a desired cell density and inoculating the next sized vessel has a strong influence on the overall upstream processing time. If an unknown influences the cellular growth rate, this time will fluctuate and generate problems in planning and scheduling a process. Therefore, online tracking of cell density allows the transfer of inoculants precisely at the desired point and give earlier indication of schedule changes allowing improve process planning. This will minimize the downtime related to scale-up transfers and material availability, opening the possibility of just in time manufacturing and increased upstream capacity.

  • Cell density
  • Cell viability
  • Processing time
  • Improved process timing
  • Process trajectory and consistency mapping
  • Reduced off-line sampling
  • Minimal contamination risk
  • Increased process flexibility
  • Lower analytical sampling costs
  • Reduced upstream processing time
  • Increased system flexibility and capacity
  • Improved electronic batch records
  • Decrease process deviation
  • Improve batch safety margin and control

Increased Yield and Product Quality

One of the main goals of process development is to increase the yield, recovery and improve the quality of the final product in commercial manufacturing. A rigorously planned and documented investigation of the established method is needed to improve the process. Thus, screening the factors which can be changed, mapping their interactions while keeping to the commercial processes physical limits will yield a surface response of operating conditions.

The many physical and chemical factors that influence the production and purification steps of a bioprocess cannot all be considered. However, with use of screening experiments the key factors which have the strongest effect on critical quality attributes can be mapped out and better understood. From this experimental design, further evaluation of the strength of each factor's influence and possible interaction narrows the window of operation into a more defined surface plot. At this point, clear knowledge is developed about how deviations in factors will impact quality. Therefore, safety margins and regions of failure can be clearly defined. Finally, robustness testing can exploited to validate the model with real scale up data and implement alarm functions and process specifications based on operating bands rather than finite fixed points.

  • Temperature
  • Glucose concentration
  • Manufacturing time
  • Operator resources
  • Efficient manufacturing planning
  • Reduced operator stress
  • Consitant material transfer
  • Reduced operator costs
  • Lower risk of batch deviation
  • Increased production capacity

Improved PAT process control and understanding

Fine Tune Process Schedule and End point

Issues, delays and fitting into manufacturing time tables frequently needs to happen for smooth bioprocessing to occur. Therefore, being able to predictably slow or delay the end point of a cultivation step without impeding critical quality attributes gives increased flexibility to commercial manufacturing. This allows better operator management planning and reduced costs of manufacturing.

  • Glucose
  • Lactate
  • Consistent Feed,  
  • Reduction of operator influence
  • Allows greater focus on other tasks
  • Reduced waste and error
  • Improved operator efficiency leads to process efficiency
  • Reducing production cycle times by using on-, in-, and/or at-line measurements and controls
  • Preventing rejects, scrap, and re-processing

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