Tracing NIR's Trajectory for Bioprocess Monitoring
By better combining and interpreting readily available data, biomanufacturing experts are improving control of fermentation and other processes, says Sartoriusí Roland Bienert.
By Paul Thomas, Contributing Editor
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 concerned 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 unstable calibration models.
Model robustness is another issue that is often concealed. A model based on one or two cultivation runs can not be used in a 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 extent but reflect real life. However, this is the recommended way of method validation for any NIR application.
PhM: 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?
RB: 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 solutions for next level models using multiple sensors. It is not enough to offer either 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.
PhM: What is the focus of Sartorius Stedim’s PAT offerings?
RB: For years, Sartorius has offered a large variety of online sensors for both stainless steel and single-use bioreactors, including NIR but also microwave resonance, off-gas sensors, glucose-lactate analysers, refractometry and other technologies.
Despite this broad portfolio, for Sartorius, PAT does not just mean implementing a sensor. Our PAT unit is involved in numerous in-house projects that are part of an internal global PAT program initiated many years ago. We use PAT to optimize our own manufacturing processes and also help our customers implement suitable PAT solutions.
As a total solution provider, Sartorius offers the whole range of PAT tools. This includes PAT consulting starting with risk analysis and identifying critical process parameters. Thus, we implemented Umetric DoE tool MODDE in our own SCADA software, the multi-fermentation control software (MFCS), in order to run designed experiments in several bioreactors in parallel. We also support our customers if requested in terms of MVDA and process integration. Here we accompany or perform the generation of robust calibration models based on one or more sensors. In terms of MVDA, we cooperate closely with Umetrics leading in multivariate software tools.
PhM: Can you share a bit about your group’s work with TCI Hannover?
RB: We understand that not all players in the pharmaceutical industry want to evaluate a new sensor technology from scratch. Therefore, we are 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. 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.