In the past decade, near-infrared (NIR) spectroscopic analysis has become an important analytical tool for real-time, in-situ monitoring of bioprocesses—in the spirit of the FDA’s process analytical technology (PAT) initiative. In order to use NIR successfully for bioprocess applications, robust prediction models must be developed, which requires an understanding of key features of the technology and how it is best employed. This article discusses those features and describes a unified approach to robust model development and the successful implementation of NIR analysis of bioprocesses.
Slow to Take
A fundamental element of PAT is the use of in-line analysis to increase process understanding and control and to verify product quality prior to release. For bioprocesses, in-line monitoring using NIR has been applied for years , yet its rate of implementation has been slower than expected. This is due in part to the relative lack of professional NIR practitioners compared with those for HPLC, mid IR and other technologies. Implementation has also been hindered by the biopharmaceutical industry’s proprietary environment, which often forces NIR practitioners to work in isolation. Few biopharma companies have published anything but general discussions of NIR applications. An exception has been the Strathclyde Fermentation Center at the University of Strathclyde in Glasgow, U.K. .
Analyzing bioprocesses in-line is always a challenge. The complexity of the bioreactor media creates a matrix of absorbance bands. Often, commercial media may derive from a proprietary recipe, and a media matrix may contain literally hundreds of nutrients and buffers. Nutrients are depleted throughout the run, but some are fed back into the bioreactor when nutrient levels become critically low. In addition, metabolites like lactate and ammonia increase with cell growth throughout the run and must be neutralized to reduce their concentration below levels that would be toxic to the cells. The cells do increase through the run, but not geometrically like microbial cells and therefore transmission spectroscopy can be used. The titer (hopefully) increases throughout the run.
With NIR in-situ monitoring, these process attributes can be monitored and controlled with feedback (or feed forward) through digital or analog I/O connectivity to the bioreactor PLC.
Some background as to why: the fundamental absorption bands of chemical functional groups occur in the mid-infrared region of the electromagnetic spectrum. These absorptions are very strong and dilutions, very short pathlengths or ATR (attenuated total reflectance) methods are required to bring the absorbances within the linear range of the detector. The overtone absorptions of these fundamental bands occur in the NIR spectral region, which is between the mid-IR and the visible. This region allows direct measurement without sample preparation due to the relative weakness of absorption.
Since NIR sources are very powerful and detectors for this region of the spectrum
are very sensitive, measurements with high signal-to-noise are possible. Economic fiber-optic cables can be used to measure processes at locations remote from the analyzer through the use of probes inserted into bioprocessing equipment. With fused silica fibers that do not absorb strongly in the NIR region, one instrument can be multiplexed to monitor up to nine bioreactors.
Challenges to Model Development
Common analytes that have been measured with NIR in situ in bioreactors include: glucose, lactate, glutamine, glutamate and ammonia. Amino acid levels and product titer have also been measured, as have bioprocess parameters including pH and cell density .
However, NIR is very sensitive to analyte levels in the complex matrix of the growth media. Thus, there can be problems with calibration model development:
- The absorption bands overlap in NIR, so variance of one analyte can change the prediction of others. Therefore, all variance from expected analytes (within range of real operation) must be included for PLS modeling.
- Also, “non-analyte” variance (that is, variance from sources not commonly thought of as chemical species) must be considered in order to develop a robust model that will predict well on all subsequent runs. These non-analyte variables may include: temperature; pH; probe-to-probe variance; channel-to-channel variance; instrument-to-instrument optical variance; type and manufacture of stock growth media; cell type or line; and titer molecule produced.
This variance is not completely contained in a single two-week bioreactor run, and thus multiple runs are necessary. The initial goal is to make a simple model for a single media type with a single cell type and a single titer. The models should later be developed for multiple probes that may initially have bias and slope changes. The next goal would be to develop more global models that can predict on various media types and cell types and titer.
Reference data for a given bioreactor are usually collected only once per day. Some analyte values are available rapidly, such as those for glucose, lactate, ammonia, glutamine and glutamate from at-line blood analyzers (such as the NOVA BioProfile Analyzer). Data for the other amino acids (and more accurate glutamine and glutamate values) are sought via HPLC and are typically not available for many days or weeks.
The environment in the reactor due to pH and temperature is kept very stable, but there may be a step change at initial inoculation or infection. The pre-inoculation period usually has very little reference data for modeling, so in order to model it well, data from this period, accounting for several runs, must be used.