Is Membrane Chromatography the Answer?

by Dr. Scott Rudge

Membrane chromatography gets a fair amount of hype.  It’s supposed to be faster, cheaper, it can be made disposable.  But is it the real answer to the “bottleneck” in downstream processing?  Was Allen Iverson the answer to the Nugget’s basketball dilemma?  I’m still skeptical.

The idea to add ligand functionality to membranes was not new at the time, but the idea really got some traction when it was endorsed by Ed Lightfoot in 1986.  Lightfoot’s paper pointed out that the hydrodynamic price paid for averaging of flow paths in a packed bed might not be worth it.  If thousands of parallel hollow fibers of identical length and diameter could be placed in a bundle, and the diameter of these fibers could be small enough to make the diffusion path length comparable to that in a bed of packed spheres, or smaller, then performance would be equivalent or superior at a fraction of the pressure drop.  This is undoubtedly true; there is no reason to have a random packing if flowpaths can be guaranteed to be exactly equivalent.  However, every single defect in this kind of system works against its success.  For example, hollow fibers that are slightly more hollow will have lower pressure drop, lower surface to volume ratio, lower binding capacity and higher proportional flow.  Slightly longer fibers will have slightly higher pressure drop, slightly higher binding capacity, carry proportionally less of the flow.  Length acts linearly on pressure drop and flow rate, but internal diameter acts to the fourth power, so minor variations in internal diameter would dominate performance of such systems. 

Indeed, according to Mark Etzel, these systems were abandoned as impractical for membrane chromatography based on conventional membrane formats that have been derivatized to add binding functionality.  As this technology has been developed, its application and scale up has begun to look very much like packed bed chromatography.  Here are some particulars:

1.       Development and scale up is based on membrane volume.   However, breakthrough curves are measured in 10’s, or even 70’s of equivalent volumes (see Etzel, 2007) instead of 2’s or 3’s as found in packed beds

2.       Binding capacities are less in membrane chromatography.  In a recent publication by Sartorious, the ligand density in Sartobind Q is listed as 50 mM, while for Sepharose Q-HP it is 140 mM.  In theory, the membrane format has a higher relative dynamic binding capacity, but this has yet to be demonstrated (see above)

3.       The void volume in membranes is surprisingly high, at 70%, compared to packed beds at 30%.  This is a reason for the low relative binding capacity.

4.       Disposable is all the rage, but there’s no evidence that, on a volume basis, derivatized membranes are cheaper than chromatography resins.  In fact, economic comparisons published by Gottshalk always have to make the assumption that the packed bed will de facto be loaded 100 times less efficiently than membranes, just to make the numbers work.  The cost per volume per binding event goes down dramatically during the first 10 reuses of chromatography resins.

It turns out that membrane chromatography has a niche, and that is for flow-through operations in which some trace contaminant, like the residual endotoxin or DNA in a product is removed.  This too can be done efficiently with column chromatography when operated in a high capacity (for the contaminant) mode.  But there is a mental block among chromatographers who want to operate adsorption steps in chromatographic, resolution preserving modes. This block has not yet affected membraners.  A small, high-capacity column operated at an equivalent flowrate to a membrane (volumes per bed or membrane volume) will work as well, and in my opinion more cheaply if regenerated.

These factors should be considered when choosing between membrane and packed bed chromatography.

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.

TFF Under Pressure

By Dr. Scott Rudge

Are there scale up issues for cross flow filtration?  In general, this step is overlooked as a scale up concern, and usually, given the primarily clean feed streams encountered in simple buffer exchange, this is warranted.  However, forewarned is forearmed when scale up is concerned.

Primarily, there is just one scale up issue with cross flow filtration, and that is the path length on the retentate side of the filter.  The flow on the retentate side of the filter is meant to continuously clean the filter surface, and prevent fouling, or at least limit it to a thin boundary layer.  The shear rate created by the fluid at the filter surface increases as the square of the linear velocity of the fluid.  The pressure drop through the filter module, from inlet to outlet, depends linearly on the length of the module, and also on the square of the linear velocity.  In many cases, a manufacturing scale module is about a meter in length.  However, on the lab scale, a module is likely to be closer to 10 cm.  Therefore, the pressure drop from the inlet to outlet on the retentate side will be 10 times higher at constant linear velocity on scale up from lab to manufacturing.  Since decreasing the flow rate will dramatically decrease the shear rate, the increased pressure will drive higher flux towards the membrane surface, increasing the thickness of the boundary layer and resulting in more surface polarization (fouling or gel formation, potentially). 

One approach taken to this predicament is to keep the pathlength constant on scale up.  This is analogous to maintaining constant bed height on chromatography scale up, an approach I disfavor.  The result of this approach is a “horizontal” scale up, where more and more units of lab proportion are lined up side by side.  This approach works, but is cumbersome and requires more and more manifolding for flow distribution, and other inconveniences.  It also assumes that the length of filter the manufacturer provides is the best and only length for every application, which is absurd.  However, this is an approach commonly pursued, and recommended by the filter manufacturers for its speed and certainty.

Another approach that is taken to this phenomenon is to increase the back pressure on the permeate.  This slows down the permeate independent of changes on the retentate side of the filter.  However, if the back pressure on the retentate side is greater than the pressure at any point along the filter on the retentate side, permeate will flow back to the retentate side.  This is clearly inefficient, it means a particular fluid element will be filtered at least three times, crossing from retentate to permeate, then back to retentate, and then eventually back to permeate on a subsequent pass.  This also means that the effective filtration area is decreased, as some portion of the filter is working in reverse, and another portion is working to correct the back flow.  The negative flow counts against filter area that is filtering in the positive direction.

Finally, employing a constant pressure gradient along the retentate side is worth trying.  Presuming the membrane geometry is essentially maintained on scale up (including spacers in the flow channel) maintaining constant pressure gradient along the retentate channel length means shear will be constant on scale up.  Pressure drop from retentate to permeate will be higher at the retentate inlet, but if the shear is appropriate and the boundary layer controlled, this will only lead to higher flux, which may be preferred.  This can be tested on the small scale by applying back pressure on the retentate and looking for leveling off of the flux vs. pressure curve.  As long as flux vs. back pressure is increasing linearly, you can get improved performance at higher pressure.  Then upon scale up, the pressure at the retentate inlet is held constant.  It is certainly worth exploring longer path lengths on scale up, performance may improve!

In the end, either horizontal scale up will be used, or some reduction in retentate flow rate will probably be required.  The result of the latter will be less shear at the membrane surface, but the payback will be in increased filtration efficiency.  Some back pressure should be applied to the retentate side on the lab scale, as more pressure due to path length will almost surely need to be applied in manufacturing.  Maintaining pressure drop on the retentate side with increased module length, along with back pressure on the permeate side usually results in successful scale up of a lab cross flow filtration procedure.

Sizing Up Filters

By Dr. Scott Rudge

Of all the unit operations used in pharmaceutical manufacture, filtration is used the most frequently, by far. Filters are used on the air and the water that makes its way into the production suite. They are used on the buffers and chemical solutions that are used to feed the process. They are used to vent the tanks and reactors that the products are held and synthesized in. But the sizing of the filters is largely an afterthought in process design.

Liquid filters that will be used to remove an appreciable amount of solid must be sized with the aid of experimental data. Typically, a depth filter is used, or a filter that contains a filtration aid, such as diatomaceous earth. A depth filter is a filter in which there are no defined pores, rather, they are usually some kind of spun fiber, like polyethylene, that serves as a matt for capturing particulate. You probably did a depth filtration experiment in high school with glass wool. Or you’ve used a depth filter in your home aquarium with the gravel (under gravel filter) or an external filter pump (where the fibrous cartridge you install is a depth filter, such as the "blue bonded filter pads" shown below).

A depth filter uses both its fiber mesh to trap particles, but also then uses the bed of particles to capture more particles. It is actually the nature of the particles that controls most of the filtration properties of the process.

Because of the solids being deposited onto the filter, the resistance of the filter to flow increases as the volume that has been filtered increases. Therefore, knowing the exact size of filter that will be required for your application can be complicated. The complication is overcome by developing a specific solids resistance that is normalized to the volume that has been filtered, and the solids load in the slurry. Once this is done, these depth filters can be sized by measuring the volume filtered at constant pressure in a laboratory setting. The linearized equation for filtration volume is:

By measuring the volume filtered with time at constant pressure, the two filtration resistances can be found as the slope and intercept of a plot of t/(V/A) vs. (V/A). The area of a depth filter is the cross section of the flow path. On scale up, the depth of a depth filter is held constant, and this cross section is increased. An example of the laboratory data that should be taken, and the resulting plots, is shown below:

As expected, the filter starts to clog as more filtrate is filtered. The linearized plot gives a positive y-axis intercept and a positive slope, which can be used to calculate the resistance of the filter and the resistance of the solids cake on the filter.

The resistance of the filter should be a constant and independent of any changes in the feed stream. However, the specific cake resistance, α, will vary with the solids load. It is important to know the solids load in the representative sample(s) tested, and the variability in the solids load in manufacturing. The filter then should be sized for the highest load anticipated. This will result in the under-utilization of the filter area for most of the batches manufactured, but will reduce or eliminate the possibility that the filter will have to be changed mid-batch.

Of course, reducing variability in the feed stream will increase the efficiency of the filter utilization, and reduce waste in other ways, such as reducing variability in manufacturing time, reducing manufacturing investigations and defining labor costs.

Bending the Curve

By Dr. Scott Rudge

To understand the best ways to develop preparative and industrial scale adsorption separations in biotechnology, it’s critical to understand the thermodynamics of solute binding. In this blog, I’ll review some basics of the Langmuir binding isotherm. This isotherm is a fairly simplistic view of adsorption and desorption, however, it applies fairly well to typical protein separations, such as ion exchange and affinity chromatography.


A chemical solution that is brought into contact with a resin that has binding sites for that chemical will partition between the solution phase and the resin phase. The partitioning will be driven by some form of affinity or equilibrium, that can be considered fairly constant at constant solution phase conditions. By “solution phase conditions”, I mean temperature, pH, conductivity, salt and other modifier concentrations. Changing these conditions changes the equilibrium partitioning. If we represent the molecule in solution by “c” and the same molecule adsorbed to the resin by “q”, then the simple mathematical relationship is:

If the capacity of the resin for the chemical is unlimited, then this is the end of the story, the equilibrium is “linear” and the behavior of the adsorption is easy to understand as the dispersion is completely mass transfer controlled. A example of this is size exclusion chromatography, where the resin has no affinity for the chemical, it simply excludes solutes larger than the pore or polymer mesh length. For resins where there are discrete “sites” to which the chemical might bind, or a finite “surface” of some kind with which the chemical has some interaction, then the equilibrium is described by:

and the maximum capacity of the resin has to be accounted for with a “site” balance, such as shown below:

Where S_tot represents the total number of binding sites, and S₀ represents the number of binding sites not occupied by the chemical of interest. The math becomes a little more complicated when you worry about what might be occupying that site, or if you want to know what happens when the molecule of interest occupies more than one site at a time. We’ll leave these important considerations for another day. Typically, the total sites can be measured. Resin vendors use terms such as “binding capacity” or “dynamic binding capacity” to advertise the capacity of their resins. The capacity is often dependent on the chemical of interest. The resulting relationship between c and q is no longer linear, it is represented by this equation:

When c is small, the denominator of this equation becomes 1, and the equilibrium equation looks like the linear equilibrium equation. When c is large, the denominator becomes K_eqc, and the resin concentration of the chemical is equal to the resin capacity, S_tot. When c is in between small and large, the isotherm bends over in a convex shape. This is shown in the graph below.

There are three basic conditions in preparative and industrial chromatographic operations. In the first, K_eq is very low, and there is little or no binding of the chemical to the resin. This is represented by the red squares in the graph above. This is the case with “flow through” fractions in chromatography, and would generally be the case when the chemical has the same charge as the resin. In the third, K_eq is very high, and the chemical is bound quantitatively to the resin, even at low concentrations. This is represented by the green triangles in the graph above. This is the case with chemicals that are typically only released when the column is “stripped” or “regenerated”. In these cases, the solution phase conditions are changed to turn K_eq from a large number to a small number during the regeneration by using a high salt concentration or an extreme pH. The second case is the most interesting, and is the condition for most “product” fractions, where a separation is being made. That is, when the solution phase conditions are tuned so that the desired product is differentially adsorbing and desorbing, allowing other chemicals with slightly higher or lower affinities to elute either before or after the desired product, it is almost always the case that the equilibrium constant is not such that binding is quantitative or non-existent. In these cases, the non-linearity of the isotherm has consequences for the shape of the elution peak. We will discuss these consequences in a future blog.

In a “Quality-by-Design” world, these non-linearities would be understood and accounted for the in the design of the chromatography operation. An excellent example of the resulting non-linearity of the results was shown by Oliver Kaltenbrunner in 2008. 

Relying on linear statistics to uncover this basic thermodynamic behavior is a fool’s errand. However, using basic lab techniques (a balance and a spectrophotometer) the isotherm for your product of interest can be determined directly, and the chromatographic behavior understood. This is the path to process understanding!

What's Your Velocity?

By Dr. Scott Rudge

With the development of very high titer cell culture and fermentation processes, downstream processing has been identified as a new bottleneck in biotechnology. The productivity of chromatography in particular, has become a bottleneck. There are two schools of thought for scaling up chromatography: in one, linear velocity (flow rate divided by column cross sectional area) is held constant; in the other, the total (volumetric) flow rate divided by the volume of the column is held constant. In the former method, the length of the column has to be held constant. In the latter method, the geometry of the column is not important, as long as the column can be packed efficiently and flow is evenly distributed. This makes the latter method more flexible, and accommodating of commercially available off the shelf column hardware packages. But does it work?

In my experience, holding flow rate divided by column volume constant between scales works very well. There is plenty of theoretical basis for the methodology as well. Yamamoto has published extensively on the reasons that this technique works. This method is also the basis for scale up described in my textbook. Here, briefly, and using plate height theory, is the theoretical basis:

The basic goal in chromatography scale up is to maintain resolution. “Resolution” is a way to describe the power of a chromatography column to separate two components. It depends on the relative retention of the components, which is fixed by the thermodynamics of the column and remains constant as long as the chemistry (the resin type, the buffer composition) remains constant. It also depends on the peak dispersion in the column, which is a function of the transport phenomena, and is only related to the chemistry by the inherent diffusivity of the molecules involved. Otherwise, it is dependent on mass transfer, flow rate, temperature, flow distribution. Treating the thermodynamics as constant, we can say:

where Rs is Resolution, and N is the number of theoretical plates. N is the ratio of the column length L to the plate height, H, so

 

In liquids, H is approximately a linear function of linear velocity v, according to van Deemter, as discussed in a previous post. So we can say that H = Cv (where C= the van Deemter constant). Now, the linear velocity is the flow rate divided by the cross sectional area of the column, A, and the column volume, V, is the cross sectional area times the column length. The total flow rate (F) divided by the column volume is held constant between scales, we’ll call this constant “f”.

 

This bit of mathematical gymnastics says that Resolution only depends on two fundamental properties of the scale up, van Deemter’s term “C”, which considers the dispersion caused by convection relative to mass transfer to and from a resin particle, and “f”, the flow rate relative to the column volume that is chosen. There is no need to hold column length or linear velocity constant as long as the flow rate relative to column volume is held constant. You might also notice from the math that doubling the column length has the same effect on resolution as dropping the flowrate by half. However, doubling the length costs more in terms of resin, and consumes more solvent than decreasing the flow rate (that’s for you, Mike Klein!) and increases the pressure.

The plate count analysis is very phenomenological, but it does hold up under practice (otherwise it would be abandoned). And the more delicate mathematical models predict the same performance, so confidence in this scale up model is high.

One common mistake made by those using the constant linear velocity model is in adding extra column capacity. Since most people are unwilling to pay for a custom diameter column, but base their loadings on the total volume of resin, they add bed volume by adding length. But since they are unwilling to change the linear velocity, they end up decreasing the productivity of the column (because, for example, the resolution they achieved at a smaller scale in a 12 cm long column at 60 cm/hr is now being performed in a 15 cm long column, at 60 cm/hr, therefore taking 25% more time).

If the less well known constant F/V model is used for a process involving mammalian cells, it would be imperative to explain and demonstrate this model in the scale down validation that is a critical part of the viral clearance package.

But how can you get even more performance out of your chromatography? Treating the unit operation as an adsorption step, and scaling up using Mass Transfer Zone (MTZ) concepts will be treated in a future posting.

Mixed Up?

By Dr. Scott Rudge

Determining and defending mixing time is a common nuisance in process validation. There are rarely data existing from process development, and there is rarely time or enthusiasm for actually studying the tank dynamics to set mixing times appropriately. Although there typically are design criteria for tank and impeller dimensions, motor size and power input into the tank, these design criteria are rarely translated to process development and process validation functionaries.

http://www.chemineer.com/mixing_technology.php

There are resources for mixing time determination if the basic initial work has been done. A very elegant study is included in the recent PQLI A-Mab Case Study produced by the ISPE Biotech Working Group. This study shows how to scale mixing from a lab scale 50 L vessel where a correlation between power and Reynolds number has been developed, to mixing vessels of 500 and 1500 L scales. The study requires that two critical dimensional ratios remain nearly constant on scale up, the diameter of the impeller divided by the diameter of the tank, and the height of the fluid level to the diameter of the tank. The study shows very close agreement between predicted mixing time and actual mixing time. The basis for the scale up is that the power input per unit mixing volume should be constant from scale to scale.

When the dimensional ratios cannot be kept constant, there are still rules for scale up. For example, as shown in the chart below (from Perry and Chilton’s Chemical Engineers’ Handbook, 5th edition, 1973), as the impeller diameter increases relative to the tank diameter, the relative power requirement declines, but the torque required to turn the impeller increases. This correlation can be used to adjust the power requirement to the scale up condition.

Additionally, there is “general agreement that the effect of mixer power level on mass-transfer coefficient is greater before than after off-bottom motion of all particles in a solute-solvent suspension is achieved (op.cit.)”.

In other words, once particles have been fluidized off the bottom of the vessel, whether they are carried all the way to the top of the vessel or not is not so important when it comes to predicting complete dissolution of the solids. At that point, the mass transfer coefficient is related only weakly to the power input, as shown below. Mass transfer coefficients for the dissolution of solids can be easily determined in the lab, and do not have to be determined again and again for new processes.

Knowing the minimum power requirements for particle suspension and the mass transfer coefficients for the solids being dissolved allows estimation of mixing times required for preparing a buffer. Knowing the mixing time allows the manufacturer to schedule buffer or medium preparation more precisely, eliminating over-processing or incorrect processing (a principle of lean manufacturing) and helps to guarantee a quality reagent/intermediate is produced each time, on time, and ready to implement in the next manufacturing step.