The Reproducibility Challenge in Bioprocessing

Achieving bioprocess reproducibility in cell culture-based production environments that are inherently variable can be frustrating for biomanufacturers. Not only that, it also contributes to increased batch costs, overall process inefficiencies and extended timelines. As an industry, this reproducibility challenge is well-known—fortunately, there are solutions that can be implemented during process development and scale-up to address it.

An e-Book released by Eppendorf in partnership with BioPharm International takes an in-depth look at Increasing the Reproducibility of Cell Culture Bioprocesses. You can expect to find articles focused on the state of the industry with respect to reproducibility, strategies and tools to increase reproducibility in bioreactor processes during development and scale-up when you download your copy.

Enhancing Bioprocess Efficiencies Through Run Reproducibility

The first article in the e-Book looks at Enhancing Bioprocess Efficiencies Through Run Reproducibility. A review of run reproducibility in preclinical research and in biomanufacturing processes along with commentary from industry experts is presented. In studies executed by the Global Biological Standards Institute (GBSI), numerous factors contribute to irreproducibility in the manufacturing process including study design, laboratory protocols, data analysis, reporting where reagent quality/performance was cited as one of the key factors that is easiest to address.

When reagents are not validated, researchers may draw conclusions from their experiments that are simply inaccurate. Although reproducibility exercises can be expensive, overlooking this evaluation process can be more expensive in the long run. If a reagent’s purity and homogeneity is not characterized early on, it can have negative consequences on the manufacturing process.

In efforts to overcome the reproducibility issues, the NIH has developed standards and verification strategies to help overcome the problem of reagent identification and variable quality. For instance, antibodies should be “validated by Western blot, ELISA, immunoprecipitation, immunofluorescence, or flow cytometry using knockdown cells and positive and negative controls” to minimize the variability of starting reagents across experiments. This also extends to the cell lines used for bioproduction.

Run reproducibility is undoubtedly crucial to the advancement of the field of bioprocessing and verification studies, while arduous, could uncover significant economic inefficiencies important for the optimization of end-to-end continuous processing for biologics.

Of Cells and Bioreactors: Strategies to Increase Reproducibility of Cell Culture Bioprocesses

This second article is an interview with bioprocess experts Amanda Suttle and Robert Glaser from Eppendorf who discuss contributing factors that result in inconsistent results and provide readers with valuable tips on how to increase the reproducibility of upstream cell culture bioprocesses during process development and scaling procedures.

The cells, culture medium, and bioprocess control systems are key components of a typical upstream bioprocess but each is a source of variability that can affect cell growth, viability, product formation, and product quality. Below are some key takeaways from the interview to increase the reproducibility between runs and allow easier scale-up:

  • Establishing a standardized cell culture protocol, including an optimal passage schedule and careful monitoring of the cell culture to keep within tight parameters to achieve consistent cell growth curves and yields
  • Rather than using a range of different equipment with variable characteristics, implementing a family of equipment with constant characteristics across scales will simplify process transfer
  • Parallel bioprocess systems such as the Eppendorf DASbox® Mini Bioreactor System controlled by the DASware® software can increase run reproducibility by enabling parallel processing of up to 24 vessels
  • Implementing process analytical technologies (for example exhaust gas analyzers, optical density sensors, and capacitance sensors) can provide valuable process insight, but to get reliable readings, the sensors need to be well-maintained and precisely calibrated
  • Bioprocess control systems and software tools can deliver reliable and reproducible results every time and the historical data collected by these systems is valuable for troubleshooting to determine the root cause of process variability

pH Sensor Recalibration Based on Exhaust CO2 Concentration for Bioprocess Transfer and Scaling

The pH of the culture medium is a critical process parameter in mammalian and microbial bioprocesses and to successfully transfer processes between systems and sites during process development and scale up, pH readings must be comparable. This article focuses on a method for accurate in-line pH sensor recalibration based on measuring the CO2 concentration in the bioreactor exhaust, using a DASGIP® GA4 exhaust analyzer. This non-invasive method allows users to match the starting pH of carbonate-buffered systems in process transfers, scaling procedures across manufacturing plants and sites.

In cell culture bioprocessing, pH measurement requires the calibration of a pH sensor outside of the bioreactor using buffers of known pH to set the offset and slope. Bioprocess engineers routinely recalibrate the sensor after it is connected to the bioreactor for sterilization to correct for possible changes caused by the autoclaving. This step is typically done using offline pH measurements and is susceptible to many sources of error that makes direct comparability of sensor readings between systems and sites difficult.

To simplify process transfer, an alternative recalibration procedure and experimental data using the Eppendorf DASGIP GA4 exhaust analyzer is described in detail. The device indirectly determines pH by using dual-beam infrared CO2 sensor technology (BlueSens gas sensor GmbH, Germany) to measure the CO2 concentration in the bioreactor exhaust. This data is used for the pH sensor recalibration step avoiding the possible errors of offline pH measurements.

The exhaust-related pH measurement method is a simple, cost effective, reproducible, and robust way to indirectly monitor and control pH in cell free systems. It is possible to establish pH comparability, matching start-pH in process transfers across plants, sites, and scales.

pH Sensor Best Practices

In the final article, BioPharm International sits down with Dave Ferragamo, Director of Sales at Mettler-Toledo Process Analytics USA, to talk about glass pH sensors and best practices to avoid sensor errors during bioprocessing.

pH is one of the most monitored bioprocess parameters but to get reliable readings, which are the prerequisite for precise pH control, pH sensors must be properly handled, calibrated, and maintained. David cites the pH calibration buffers and the temperature as two key variables that can impact the quality of calibration.

Numerous problems can arise if the pH sensor is not in good working condition—it can even result in batch loss. David discusses several indicators that can be used to judge whether a pH sensor is in optimal condition:

  • The slope is an important factor or more precisely the actual slope divided by the theoretical slope, which generates a percent value or mV value
  • The time of the response is a good indicator and a pH reading should reach its true value in under 10 seconds
  • A good sensor at 25° C should have a E0 value (the zero potential or millivolt reading of pH 7) very close to 0 mV +/- 15 mV

With an analog pH sensor, it is on the user to judge the sensor’s health, which means either they change their sensors too early or too late—both choices are not optimal. The use of digital pH sensors makes it easier, providing an overview of the sensor health through a user interface. As David explains, the digital technology removes the need for extensive user expertise to know how values correlate to the state of the sensor because the user interface/transmitter automatically carries out this function and outputs this on the digital display. Other benefits of the digital sensors include the ability to execute remote calibration, which enables users to since calibrate the sensor in a lab before carrying to the point of measurements, for example, a manufacturing suite. Replacing an analog sensor with a digital version is an easy way to increase the accuracy and reproducibility of pH measurements that can improve process efficiencies for a wide range of bioprocesses.

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