BioProcess Control: What the Next 15 Years Will Bring

Mammalian cell culture bioreactor processes are difficult to characterize and they demonstrate variability between batches. Without sufficient online detection and control, variations can lead to a high number of failed batches.

To eliminate batch variations, many biopharmaceutical manufacturers are moving beyond today’s “quality-by-inspection” methodology and adopting Quality by Design (QbD) methods. These methods center on measuring more variables online and increasing the sophistication of automation.

In-line process variables that could not be monitored in the past, can now be measured, analyzed, and used for advanced control schemes. Producers can use this additional process data to better understand how to reproduce consistent batch quality and yield, and to advance toward the goal of consistent batch-to-batch profiles.

Measuring cell health and metabolites can reduce risk, increase operators’ confidence level and provide more consistent results. Online detection of abnormal consumption and apoptosis would allow operators to terminate bad batches at an early stage. For instance, feedback control of glucose concentration enables a fixed, low concentration of glucose in a fed batch bioreactor that cannot be replicated with periodic, manual or open loop feeding.

Fixing the glucose concentration at a low value holds the potential to increase the quality of a glycosolated product by reducing uncontrolled glycation [1] and improve efficiencies of nutrient utilization [2]. The pre-requisite to monitoring is improved sensors. Analyzers coming onto the market can take key measurements online continuously or at-line periodically through automated samplers to optimize product quality and yield.

Measurements that, until now, were intermittent and off-line, can now be made periodically or continuously, online. In some cases, users can choose among several different techniques to analyze a component. Some online and at-line sensors that can fulfill bioreactor processing needs are already on the market and need only to be adapted to bioreactors. Other sensors specifically for use in microbial fermentors and cell culture bioreactors are in late stages of development. This article will review the next generation of sensors that will enable closer control of biopharmaceutical cell culturing. Most sensors discussed here are expected to be in common use within the next few years. Part 2 of this article, to be published next month, focuses on bioprocess modeling.

Dielectric Spectroscopy

The dielectric or passive electrical properties of cells can be exploited to measure viable cell density online. In an induced electrical field, an intact cell membrane is a physical barrier to ion migration. The induced electrical field turns each cell into a small capacitor.

If the direction of the electrical field is alternated over a range of frequencies, the capacitance of the cell varies from a low frequency capacitance plateau to a background capacitance due to water at high frequencies. Capacitance measured in picoFarads plotted against the frequency of change of the electrical field, measured in MHz, is called a beta-dispersion spectrum. This curve is affected by electrode geometry but can be normalized.

The end result of the normalization is a similar curve based on a dimensionless relative permittivity verses frequency. However, the electrode is subject to a time-dependent electrode polarization. The polarization effect is an increase in permittivity at low frequencies. The effective measurement frequency range is limited at low frequencies by the polarization effect and at high frequencies by background capacitance.

Older analyzers measured a single low frequency as close as practical to the electrode polarization effect. Newer analyzers take random measurements at frequencies between 0.1 and 15 MHz and use a non-linear least squared fit to generate the entire frequency spectrum. Cell suspension bio-volume, cell radius, membrane capacitance, internal conductivity of cells, homogeneity of cells, and medium conductivity parameters are computed from the spectra [3].

Although a computation-induced measurement lag exists, it is not significant compared to cell culture growth rate. Cell culture viable cell counts and bio-volume measurements from dielectric spectroscopy sensors have been found to match off-line measurements.

Encoded Photometric Infrared Spectroscopy

In the past, infrared analysis has not applied in the pharmaceutical industry as frequently as in other industries. New technology like Encoded Photometric Infrared analysis and FDA’s PAT and QbD incentives increase the importance of this technology to the pharmaceutical industry.

Any infrared senor used online in a cell culture bioreactor requires the analysis to be rapid and sensitive. The instrument must be stable and not require frequent re-standardization or calibration updates. Dispersive IR analyzers split the light source into individual frequencies before the light passes through the sample. Wavelengths over the entire spectrum are measured sequentially. The analysis is slow and only portions of the spectrum are needed for a given analysis.

Non-dispersive IR analyzers send the entire light source through the sample and use fixed frequency pass-band filters to select desired frequencies. This technique is most often used in field instruments for a specific analytical function. Although non-dispersive IR analyzers may meet online requirements of speed and stability, the analytical function is limited by the reduction in signal-to-noise ratio as the number of filters is increased [4].

Recently developed encoded photometric infrared analyzers can detect the constituents of multiple frequencies simultaneously. Unfortunately, array detectors that would allow rapid detection of the complete spectrum and are so common in visible light applications are too complex for mass production [5]. Instead, EP IR analyzers modulate the amplitudes of the components of each wavelength of interest in a process termed encoding.