Tracing NIR’s Trajectory for Bioprocess Monitoring

By better combining and interpreting readily available data, biomanufacturing experts are gaining better control of fermentation and other processes, says Sartorius’ Roland Bienert.

By Paul Thomas, Senior Editor

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One of the many good talks at the recent MKS Umetrics user meeting in Chicago was that of Roland Bienert, Senior Scientist for PAT and Sensors & Chemometrics at Sartorius Stedim Biotech. Bienert talked about various new applications of NIR for bioprocessing. Recently, I followed up with Bienert via email to find out more about how he and colleagues are using NIR and multivariate data analysis (MVDA) to enable improved bioprocess monitoring.

PhM: What's new and cutting edge in terms of using NIR for bioprocess monitoring? What's being done now that wasn't done a few years ago?

R.B.: In the early stage of the PAT initiative, people strived to measure single analytes (online) as accurately as offline lab methods. That was, of course, hard to achieve, especially for low-concentration analytes such as glutamate or ammonia.

The new perspective is that NIR spectroscopy can do so much more for process understanding. It is capable of measuring a metabolism sum-parameter, which does not rely on highly accurate single parameters but on the overall changes in spectra due to metabolite accumulation. Isn’t that the actual parameter we are interested in, which has not been accessible by offline sampling?

In order to learn about our processes, and that is exactly the QbD approach, we do not necessarily need a number of isolated analyte concentrations, but to combine the data we acquired. This allows us to establish new tools like batch trajectories and endpoint determination or real-time release which provide the most important trends in an easy-to-interpret manner.

PhM: How about fermentation specifically—what improvements have been made in the application of NIR?

R.B.: From a technical point of view, it is a big advantage to be now able to use a free beam spectrometer with a standard Ingold port adaptor instead of fiber optics. The large free aperture in combination with a very high light yield and no moving parts within the spectrometer results in excellent spectra quality throughout the whole fermentation. Even in extremely rough environments like large-scale bacterial fermentations, a free beam spectrometer can automatically filter large fluctuations arising from air bubbles, for instance. This is possible since free beam spectrometers use diode array detector instead of scanning techniques, which results in very fast measurements.

PhM: Do you see free beam NIR as a substitute for fiber optic bioprocess monitoring, or do you see them each having strengths and weaknesses?

R.B.: With our new Ingold port adaptor, free beam spectrometers are surely a substitute for fiber optics NIR systems. Visiting the ACHEMA 2012 fair recently, we observed a lot of progress in this field and a clear commitment to this kind of spectrometer. Comparing our system to fiber optic technologies such as MIR—as we have done recently in a project in cooperation with TCI Hannover—they are adding both their benefits to a complete package. The MIR system can be used to monitor small molecules, which can be used for feed control, whereas NIR in our solution is much more powerful in monitoring cell parameters and process trends.

PhM: Combined with MVDA, you can now use NIR to gain a much better sense of, for example, the activity of nutrients and metabolites during fermentation. What specific process parameters are more easily monitored today than even a few years ago?

R.B.: First of all, NIR spectra show a large number of variations which can be used to predict almost all the important parameters. The major challenge for vendors and manufacturers interested in NIR spectroscopy is to figure out which parameters can be directly measured by this technique and which are just based on correlations to some uninterpretable spectral changes. Therefore, Sartorius classifies between control parameters, which show directly changes within the spectra and are able to be discriminated from others.

Monitoring parameters, on the other hand, are in this context based on correlations and should be interpreted carefully. For parameter classification, Sartorius uses spiking experiments that break correlations between the analyte of interest and all the others. 

With our approach, we are very strong regarding prediction of cell parameters, especially the important parameters total cell count and viability. Those are both important during cell growth and production phase.

Together with a reliable titer prediction, we have all important variables to map the process during product formation. 

PhM: What are some best practices for MVDA for fermentation processes? Or, conversely, what are some missteps or missed opportunities that you often see from manufacturers?

R.B.: Besides predicting the concentration of single analytes, MVDA can provide so much more information from online data. Concerning NIR, for instance, we offer several tools for qualitative analysis of the process.

These tools are directly able to answer the most important questions, like: Are the starting conditions consistent with earlier campaigns? Is the process alright? When is the perfect time for harvesting? These are exactly the same questions that offline methods were supposed to answer several years ago, but they have not been suitable.

These questions can be perfectly addressed by qualitative process control tools. This starts with media classification just before inoculation in a statistically evaluated good-bad approach. The whole batch can be monitored as so-called trajectories, which describe the process evolution road and help to identify significant deviations in real time and thereby allow a guided sampling. Finally, an endpoint determination is possible in real time, which results in a perfect product tailored for optimal downstream performance. Of course, the information from NIR-data can be supported by other sensors, which results in more robust models.

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