Based on his ongoing work as a chemistry professor at the University of Iowa, Mark A. Arnold, Ph.D., vp at ASL Analytical is working on NIR monitors for Pichia pastoris fermentation. Dr. Arnold believes that Pichia fermentations are a more logical way to introduce NIR industry-wide.
From a monitoring perspective, Pichia fermentations are more straightforward than cell culture, and more similar to one another as well. “This allows us to build calibration models that are closer to platform monitoring than we could with CHO cells,” Dr. Arnold says.
The monitor measures glycerol, methanol, and biomass in real time. Pichia use glycerol as their primary carbon source, and methanol to induce protein production.
As with other spectroscopic methods, Dr. Arnold employs multivariate calibration methods to fish out analytes from a complex milieu. Eventually, he would like to incorporate feedback control for methanol levels.
Cell density, by contrast, is a general method that relates to light transmitted through the cell suspension and scattered by increasing cell mass.
NIR spectral data correspond to overtones and combinations of vibrations associated with O-H, N-H, and C-H bonds within the molecules of interest but not water. Species monitored during Pichia fermentations are present in significantly higher concentrations than protein products.
According to Dr. Arnold, many bioprocessing companies have attempted to develop NIR, Raman, mid-IR, and fluorescence techniques in-house for PAT-style analysis for online bioreactor monitoring. “They’ve all failed for the most part,” he says, which explains the slow uptake of spectroscopy in bioprocess monitoring.
Raman: The Anti-NIR?
Raman is another bioprocess monitoring modality that is long on promise, short on implementation—but not for lack of trying.
A group at Biogen Idec headed by John Paul Smelko has reported the use of in situ Raman for determining growth and metabolic profiles of CHO cell cultures in real time. Smelko's objective was development of an inline PAT-enabling method.
Biogen Idec reported “high-quality quantitative results” for glucose, lactate, glutamine, glutamate, ammonium, osmolality, and viable cell density based on models developed at 3 L and 200 L scales.
Raman and NIR are complementary techniques. IR is based on absorption, Raman on scattering. The spectra in fact appear almost as mirror images. For both methods the location of the signal indicates what is being measured, and the height tells how much. And both techniques require chemometrics—a math-intensive technique for relating spectral bands from compounds of interest to the test system.
One downside of Raman, says Lee Smith, Ph.D., president of Process Instruments, is when highly colored or fluorescent materials are present in the sample. “They will swamp the weak Raman signal,” he says. A corollary is that Raman is not particularly sensitive below part-per-million concentrations.
Yet the technique has improved over the years. Instruments are smaller and much less expensive; advances in electronic and optical components have greatly improved Raman signals and expanded the technique's suitability for monitoring bio- and other process industries.
Water interferes seriously in mid-IR, and to a much lesser degree in NIR, but in Raman the water interference peak is insignificant. “Without interference from water there is no need for spectral subtraction, and you get high chemical specificity for most of the components of interest in a bioprocess,” notes Maryann Cuellar, an applications scientist at Kaiser Optical Systems.
Functional groups absorb in similar regions in Raman and IR. Thus, users can apply traditional IR vibrational frequency charts to predict Raman signals.
Raman uses a laser light source, so the optical fiber delivery system can extend hundreds of meters from the bioreactor and probes can be quite robust. NIR has a much shorter useful range and requires “exotic materials,” according to Cuellar. Raman's robustness has made it very popular in nonpharmaceutical process industries like materials, polymers, and chemicals, where the technique has been fully deployed as a process analytic for decades.
Small molecule drug makers are slowly warming up to Raman, particularly for solid dosage forms, but bioprocessors are apparently still sorting out details.
“We will need a paradigm shift for bioprocessors to adopt Raman fully,” says Cuellar. “There's a serious lack of analytically trained scientists in bioprocessing, and most engineers and biologists are unfamiliar with spectroscopy. There is also a shortage of trained chemometricians capable of extracting information from Raman data. The technique is about more than just acquiring spectra.”