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Tracing NIR’s Trajectory for Bioprocess Monitoring

    Tracing NIR’s Trajectory for Bioprocess Monitoring

    By Paul Thomas, Senior Editor

    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 spoke about various new applications of NIR for bioprocessing, and I followed up with him via email to find out more about the work that he and colleagues in Sartorius’ PAT Unit are doing. This unit, Bienert says, is involved in numerous projects that are part of an internal global PAT program initiated several years ago.

    PharmaQbD: 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.

    PharmaQbD: 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.

    PharmaQbD: 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.

    PharmaQbD: 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 which 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.

    PharmaQbD: 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 which 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.

    From my perspective, a misstep we are still facing is the sole focus on accurate analyte concentration as a universal remedy for process monitoring. As far as closed loop process control is involved, that is crucial, no doubt, but in terms of process optimization a qualitative overview of the process is much more beneficial than an ammonia profile. The strong demand of monitoring analyte concentrations inveigled some vendors and users to over-interpret correlations between analyte concentrations and spectral information, which led to instable calibration models.

    Model robustness is another issue which is often concealed. A model based on one or two cultivation runs can not be used in production environment but shows nice error bars if an internal prediction set or cross validation is used. But to take batch-to-batch variations into account, a validation test set necessarily needs to be “unseen”. That means the prediction error must be calculated from one (or more) cultivation runs which are not part of the calibration set. By doing so all errors increase to a certain extend but reflect real life. However, this is the recommended way of method validation for any NIR application.

    PharmaQbD: In your view, are manufacturers getting more sophisticated in, say, titer prediction or end-point determination of fermentation processes? What’s allowing them to do so?

    R.B.: Both are highly requested and a lot of efforts have been made to set up robust models especially for titer. NIR spectroscopy is capable of high titer prediction as well as highly suitable for end-point determination. For low titer concentration and other hard-to-get parameters, the important step is to overcome the univariate sensor by sensor evaluation but use a multivariate model with several sensors contributing valuable information regarding the desired parameter. The sensor information is usually available in real time. The crucial step here is data handling and automated combination for multivariate evaluation. Sartorius, in close cooperation with Umetrics, offers sophisticated solutions for next level models using multiple sensors. To think that a little further, it is not satisfactory to either offer sensors or software. It is important to have the expertise from spectra acquisition to generation and interpretation of trajectory in order to offer a one-stop solution. This is the road Sartorius will take.

    PharmaQbD: Finally, can you share a bit more about your group’s work with TCI Hannover?

    R.B.: We understand that not all players in the pharmaceutical industry want to evaluate a new sensor technology from scratch. Therefore, we are very happy to have a strong partner in academia like the group of Prof. Thomas Scheper. This cooperation allows us to provide our customers an overview of the new NIR adaptor, based on sound data and knowledge. The results of the Ingold port evaluation will be published in peer-reviewed journals. With this first step in sensor evaluation we can now focus on process specific topics at the customer site.

     

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    One Response to “Tracing NIR’s Trajectory for Bioprocess Monitoring”

    1. Right on target.
      Since my first IFPAC I made public this that NIR is a de facto PAT tool because it intrinsically combines different quality attributes and it allows us – thought whole sample analysis – to assess process state as opposed to a single parameter.
      The author is right and unfortunately it is not enough to repeat it. The stories of tracing back variability to prior processing steps or monitoring process trajectories (drawn from whole sample NIR spectra) is really familiar to me. We did it in the past 10 years for the industrial manufacturing of small molecules (API fermentations) and large molecules (Mab cultivations) it works nicely. And if GMP conditions slow you down just measure culture supernatants at-line. It works !

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