In continuation with our three post series on Integrating Analzyers with Automation Systems, Greg McMillan shared his Top Ten Limitations to Analyzers last summer through a Q&A series with recognized leaders in the field analyzers. As more life sciences take their offline lab analysis online, they are realizing bigger benefits.
Greg notes about the Life Sciences/biopharmaceutical industry:
The biopharmaceutical industry is expanding their ability to analyze batch profiles online. In mammalian cell bioreactors a dielectric spectroscopy probe is used as an online analyzer to measure viable cell concentration and glutamine besides glucose addition is scheduled. Flexible at-line analyzers using electrochemistry, digital imaging, freezing point depression, and photometry (e.g. Nova BioProfile FLEX) can measure in 2-8 minutes cell density, cell diameter, cell viability, osmolality, substrates (glucose and glutamine), and many byproducts such as carbon dioxide, lactate, and ammonia that inhibit growth and product formation. Near Infrared (NIR) spectroscopy can be used to provide online measurements of substrate, byproduct, and product concentrations. Mass spectrometers can measure the amount of oxygen and carbon dioxide in the bioreactor off-gas for oxygen uptake rate and carbon dioxide production rate calculations.
In another article where Greg interviewed a couple of folks to discuss "The New Paradigms for Lab Control Systems", it was noted the demand for data in the labs is increasing:
Q: Why is the demand for data so great?
A: Besides the design of experiments (DOE) to determine optimums and operating condition limits in the definition of the process for the Food and Drug Administration (FDA), statistical analysis requires a large number of vessel runs. Each vessel run takes 2 weeks and with project time always being critical, many more bioreactor runs are required to run in parallel for the same experiment. In our most recent installation we had a total of 64 one liter bioreactors at two different sites for a particularly visionary and astute biopharmaceutical PD lab. The data was networked revealing essentially the same results independent of site and operator. Furthermore, the automation of the labs at both facilities enabled twice as many runs to be completed with half as many operators. The data obtained had minimal variance, was reproducible, and was explainable within the design space. The data variance was actually cut in half. With all biopharmaceutical budgets being squeezed this 4x improvement in productivity is drawing a lot of attention.
As scientists realize their increased productivity and access to data to compare experiments across labs, more life science plants are looking for real-time integration of analyzers in their processes.
Also See Part 1: Integrating Analyzers with Automation Systems