Showing posts with label Design of Experiments. Show all posts
Showing posts with label Design of Experiments. Show all posts

Tuesday, May 31, 2011

The Art of Bioreactor/Fermenter Scale-Up (or Scale-Down)

by Dr. Deb Quick

Effective bioreactor or fermenter scale-up/down is essential for successful bioprocessing. During development, small scale systems are employed to quickly evaluate and optimize the process, but larger scale systems are necessary for producing commercial quantities at a reasonable cost. But how does one effectively transfer the process between scales so that the process performs the same?



In an ideal world, the physiological microenvironment within the cells/microorganisms will be conserved at the different scales, but with no direct measure of that microenvironment the scientist identifies relevant macroproperties to measure and control to ensure comparability. There are many macroproperties and operating parameters that define the process at each scale, and while the goal is to keep as many of those parameters constant between the scales, it simply isn’t possible to keep them all the same.

When using the same operating parameters at small and large scale is impractical, there are several correlations that are commonly used: mass transfer coefficient (kLa [the volumetric transfer coefficient, 1/hr] or OTR [oxygen transfer rate, mmol/hr]; volumetric power consumption (P/V, agitation power per unit volume); agitator tip speed; and mixing time.

Matching the kLa at different scales is generally considered the most important factor in scaling cell culture and microbial processes. The second most common approach is to match the power consumption. For both of these correlations, there are often multiple combinations of operating parameters that provide the same kLa or the same power consumption at the different scale. And herein lies the art of bioreactor and fermenter scale-up/down. Selecting the best combination of parameters to match process performance at different scales is an art. There is no magic combination that works best for all cell types and products.

To establish comparability at different scales, you’ll make your life significantly easier if you start with the same vessel design at the different scales, but this luxury is rarely reality. More often, the development lab has significantly different equipment than the manufacturing facility. But even with different reactor designs, comparable performance can be obtained at different scales through appropriate experimentation.
  • First, you’ll need to understand your equipment at all scales: measure the kLa and P/V of the different scales over a wide range of air flows, agitation rates, working volumes, and backpressures. It’s best to perform the testing in your process media, if possible. If you can find the time, it’s useful to evaluate different mixing schemes at small scale - different impeller styles and positions, baffles, and sparger styles and positions (particularly valuable if you already know the differences in these features between small and large scale systems available to you).
  • Second, you’ll need to understand how your product responds to the different operating parameters. Those dreaded statistically designed experiments (DoE) are particularly useful for understanding the effects and interactions of the many parameters that can be changed. Performing DoE experiments at small scale with your product to evaluate the effects of aeration, agitation, and volume will not only help you with scale-up, but will also provide useful information for setting acceptable ranges for the operating parameters at large scale. As with the kLa studies, it’s useful to study different mixing schemes at small scale if time allows. One set of experiments that is highly useful but rarely performed is the evaluation of the process performance at the same kLa (or P/V) obtained using different operating parameters.
Understanding your equipment and how your product responds to various operating conditions is the key to effective process scale-up and scale-down. Despite the historical and ongoing need for scaling bioprocesses up and down, there is no strategy that works in all situations. The art of successful scale-up lies in thoughtful experimental design and thorough data analysis in order to obtain the information that allows equivalent performance at all scales.

Tuesday, June 23, 2009

Why is Quality by Design so heavy with statistics?

Why is the literature on Quality by Design so laden with statistics and experimental design space jargon? After all, the definition of the term “design” doesn't seem to include the analysis of messy data leading to rough correlations with results that are valid only over a limited range. So what gives?

The idea behind QbD was to use mathematical, predictive models to predict process outcomes. This concept can be applied directly to simple unit operations, such as drying, distilling, heating and cooling. However, unlike in the petrochemical business, the thermodynamic properties of most active pharmaceutical ingredients are not known and are difficult to measure. The unit operations used to manufacture common biotechnology products, such as cell culture, chromatography and fermentation have been modeled, but the models are very sensitive to unknown or unmeasureable adjustable parameters. The batch nature of these operations also makes their control difficult, as classical control theory relies on the measurement of an output to make an adjustment to an input to correct the output back towards the design specification.

Since there does not appear to be a clear path to using models, an approach has been chosen that emphasizes getting as much phenomenological information from as few experiments as possible. This is the Design of Experiments approach, where input conditions or operating parameters are systematically varied over a range and the process outputs measured, with statistics used to deconvolute the results. The combined ranges tested become the “design space”, and the process performance outputs with the variations closest to the process failure limit become the critical performance parameters. The results are useful, but only within the design space, and only with the certainty that the statistics report. Also, since the results are phenomenological, the effect of scale is often unknown.

The statistical approach is acceptable, and for the immediate future it's probably the best that we can expect. But the focus on this approach seems to drown out the more pressing need for good process models and physical properties data. These are the elements that allowed the petrochemical and commodity chemicals industries to scale up processes with assurance that quality specifications would be met. There are countless models available for bioprocessing's more complicated unit operations, but they have parameters that we don't know and can't calculate from first principles. There is no question that we need to find ways to collect this data, and a commitment to publish or share it. There are also simpler unit operations that we can model, scale up and scale down with complete assurance. These include operations such as mixing and storing solutions, filtration, diafiltration, centrifugation and some reactions. We shouldn't let the more complicated operations that still require statistical DOE approaches prevent us from applying the true principles of QbD to our simpler unit operations.