Manufacturing Biologics with CHO Cells? What’s the Risk for Viral Contamination?

by Dr. Ray Nims

Chinese hamster ovary (CHO) cells are frequently used in the biopharmaceutical industry for the manufacture of biologics such as recombinant proteins, antibodies, peptibodies, and receptor ligands. One of the reasons that CHO cells are often used is that these cells have an extensive safety track record for biologics production. This is considered to be a well-characterized cell line, and as a result the safety testing required may be less rigorous in some respects (e.g., retroviral safety) than that required for other cell types. But how susceptible is the cell line to viral contamination?

There are a couple of ways of answering this question. One way is to examine, in an empirical fashion, the susceptibility of the cell type to productive infection by model exogenous viruses. This type of study has been conducted at least three times over the past decades by different investigators. Wiebe and coworkers (In: Advances in Animal Cell Biology and Technology for Bioprocesses. Great Britain, 1989; 68-71) examined over 45 viruses from 9 virus families for ability to infect CHO-K1 cells, using immunostaining and cytopathic effect to detect infection. Only 7 of the viruses (Table 1) were capable of infecting the cells. Poiley and coworkers (In Vitro Toxicol. 4: 1-12, 1991) followed with a similar study in which 9 viruses from 6 families were evaluated for ability to infect CHO-K1 cells as detected by cytopathic effect, hemadsorption, and hemagglutination. This study did not add any new viruses to the short list (Table 1). The most recent study was conducted by Berting et al. This study involved 14 viruses from 12 families. The viruses included a few known to have contaminated CHO cell-derived biologics in the past two decades, and therefore did add some new entities to the list in Table 1. Still, the list of viruses that are known to replicate in CHO cells is relatively short.

Chinese hamster cells possess an endogenous retrovirus which expresses its presence in the form of retroviral particles, however these particles have been consistently found to be non-infectious for cells from other animals, including human cells. This endogenous retrovirus therefore does not present a safety threat (Dinowitz et al. Dev. Biol. Stand. 76:210–207, 1992).

A second way of looking at the question of viral susceptibility of CHO cells is to examine the incidence and types of reported viral contaminations of manufacturing processes employing CHO cell substrates. This subject has been reviewed a number of times, most recently by Berting et al. The types of viral contaminants fill a fairly short list (Table 2). In most cases, the contaminations have been attributed to the use of a contaminated animal-derived raw material, such as bovine serum.

Sources: Rabenau et al.1993; Garnick 1996; Oehmig et al., 2003; Nims Dev. Biol. 123:153-164, 2006; Nims et al., 2008; Genzyme 2009..

Considering the frequency with which CHO cell substrates have been used in biologics production, this history of viral contamination is remarkably sparse. This is further testament to the overall safety of this particular cell substrate.

FDA to viral vaccine makers: it's time to update viral testing methods

By Dr. Ray Nims

If you have been following the recent (2010) unfolding of the discovery of porcine circovirus DNA contamination in rotavirus vaccines from GSK and Merck, you may not be surprised to hear that the FDA has asked viral vaccine manufacturers to outline, by October, their plans to update their testing methodologies to prevent future revelations of this type.
 
I had predicted earlier that biologics manufacturers would be asked to provide evidence, going forward, that their porcine raw materials (trypsin being the most common) are free of porcine circovirus. This testing has not been manditory in the past, but adding this to the porcine raw material virus screening battery moving forward is a prudent action in light of the recent rotavirus vaccine experience.

The FDA has appropriately gone a little farther in it's request to the viral vaccine manufacturers. The regulators would like to assure that the future will not bring additional discoveries of viral contaminants in licensed vaccines, and the best way to accomplish this at the moment appears to be to request implementation of updated viral screening methodologies. Does this mean that viral vaccine makers will need to employ deep sequencing on a lot-by-lot basis? Most likely not. It appears that reliance on the in vivo and in vitro virus screening methods which have been the gold standards since the 1980s will, however, no longer be sufficient. So what does this leave us with? What FDA appears to be asking for is a relatively sensitive universal viral screening method.

The in vivo and in vitro methods were, until now, the best option for this purpose. These methods detect infectious virus only and depend upon the ability of the virus to cause an endpoint response in the system (cytopathic effect, hemagglutination, hemadsorption, or pathology in the laboratory animal species used). So viral genomic material would not be detected, and the methods have had to be supplemented with specific nucleic acid-based tests for viruses which could not otherwise be detected (e.g., HIV, hepatitis B, human parvovirus B9, porcine circovirus).

Some options for sensitive and universal viral screening methods which might fit the requirements include DNA microarrays and universal sequencing methods performed on cell and viral stocks. The latter technology may be preferable, as microarrays are constructed to detect known viruses, while the desire is that the technology be universal in the sense that it detect both known and unknown viruses. Such a test will provide additional assurance that the virus and cell banks used to manufacture viral vaccines do not harbor a viral contaminant.

Other universal viral screening methods which are less labor intensive than the sequencing technologies may be developed in the near future and addition of one of these to the release testing battery for viral vaccine lots may need to be considered in satisfying the FDA's goals.

Is Clarence calculating clearance correctly?

by Dr. Ray Nims

As pointed out by Dr. Rudge in a recent posting “Do we have clearance, Clarence?”, spiking studies conducted for the purpose of validating impurity clearance are often done at only one spiking level (indeed often at the highest possible impurity load attainable). This is especially true for validation of adventitious agent (virus and mycoplasma) clearance in downstream processes. The studies are done in this way in order to determine the upper limit of agent clearance (in terms of log10 reduction) by the process. Such log10 reduction factors from individual process steps are then summed in order to determine the overall capability of the downstream processes to clear adventitious agents. The regulatory agencies have fairly clear expectations around such clearance capabilities which must generally be met by biologics manufacturers.

The limiting factor in such clearance studies is typically the amount or titer of the agent that is able to be spiked into the process solution, which is determined by: 1) the titer of the stock used for spiking, and 2) the maximum dilution of the process solution allowed during spiking (typically 10%). Under these circumstances, as Scott points out, there is a possibility that the determined clearance efficiency (i.e., the percentage of the load which is cleared during the step) is an underestimate of the actual clearance that might be obtained at lower impurity loading levels.

Adventitious agent clearance is comprised of two possible modalities, removal and inactivation. Removal refers to physical processes designed to eliminate the agent from the process solution, usually through filtration or chromatography. Removal efficiency through filtration would not be expected to display variability based on impurity loading. On the other hand, chromatographic separation of agents (by, for example, ion-exchange columns) may display saturation at the highest loadings, and therefore use of the highest possible loading levels may result in underestimates of removal efficiency at lower (i.e., more typical) impurity levels.

Inactivation refers to physical or chemical means of rendering the agent non-infectious. Agent inactivation is not always a simple, first-order reaction. It may be more complex, with a fast phase 1 stage of inactivation followed by a slow phase 2 stage of inactivation. An inactivation study is planned in such a way that samples are taken at different times so that an inactivation time curve can be constructed. As with removal studies, the highest possible impurity levels are typically used to determine inactivation kinetics.

Source: Omar et al. Transfusion 36:866-872, 1996

While the information obtained through clearance studies of this type may be incomplete from the point of view of understanding the relationships between impurity loading levels and clearance efficiency, the results obtained are consistent with the regulatory expectation that the clearance modalities be evaluated under worst-case conditions. Therefore, at least in the case of adventitious agent clearance validation, I would say that Clarence is calculating clearance correctly!

Do We Have Clearance, Clarence?

By Dr. Scott Rudge

As in take offs and landings in civil aviation, the ability of a pharmaceutical manufacturing process to give clearance of impurities is vital to customer safety. It’s also important that clearance mechanism be clear, and not confused, as the conversation in the classic movie “Airplane!” surely was (and don’t call me Shirley).

There are two ways to demonstrate clearance of impurities.

The first is to track the actual impurity loads. That is, if an impurity comes into a purification step at 10%, and is reduced through that step to 1%, then the clearance would typically be called 1 log, or 10 fold.

The second is to spike impurities. This is typically done when an impurity is not detectable in the feed to the purification step, or when, even though detectable, it is thought desirable to demonstrate that even more of the impurity could be eliminated if need be.

The first method is very usable, but suffers from uneven loads. That is, batch to batch, the quantity and concentration of an impurity can vary considerably. And the capacity of most purification steps to remove impurities is based on quantity and concentration. Results from batch to batch can vary correspondingly. Typically, these results are averaged, but it would be better to plot them in a thermodynamic sense, with unit operation impurity load on the x-axis and efflux on the y-axis. The next figures give three of many possible outcomes of such a graph.

In the first case, there is proportionality between the load and the efflux. This would be the case if the capacity of the purification step was linearly related to the load. This is typically the case for absorbents, and adsorbents at low levels of impurity. In this case (and only this case, actually) does calculating log clearance apply across the range of possible loads. The example figure shows a constant clearance of 4.5 logs.

In the second case, the impurity saturates the purification medium. In this case, a maximum amount of impurity can be cleared, and no more. The closer to loading at just this capacity, the better the log removal looks. This would be the point where no impurity is found in the purification step effluent. All concentrations higher than this show increasing inefficiency in clearance.

In the third case, the impurity has a thermodynamic or kinetic limit in the step effluent. For example, it may have some limited solubility, and reaches that solubility in nearly all cases. The more impurity that is loaded, the more proportionally is cleared. There is always a constant amount of impurity recovered.

For these reasons, simply measuring the ratio of impurity in the load and effluent to a purification step is inadequate. This reasoning applies even more so to spiking studies, where the concentration of the impurity is made artificially high. In these cases, it is even more important to vary the concentration or mass of the impurity in the load, and to determine what the mechanism of clearance is (proportional, saturation or solubility).

Understanding the mechanism of clearance would be beneficial, in that it would allow the practitioner to make more accurate predictions of the effect of an unusual load of impurity. For example, in the unlikely event that a virus contaminates an upstream step in the manufacture of a biopharmaceutical, but the titer is lower than spiking studies had anticipated, if the virus is cleared by binding to a resin, and is below the saturation limit, it’s possible to make the argument that the clearance is much larger, perhaps complete. On the other hand, claims of log removal in a solubility limit situation can be misleading. The deck can be stacked by spiking extraordinary amounts of impurity. The reality may be that the impurity is always present at a level where it is fully soluble in the effluent, and is never actually cleared from the process.

Clearance studies are good and valuable, and help us to protect our customers, but as long as they are done as single points on the load/concentration curve, their results may be misleading. When the question comes, “Do we have clearance, Clarence?” we want to be ready to answer the call with clear and accurate information. Surely varying the concentration of the impurity to understand the nature of the clearance is a proper step beyond the single point testing that is common today.

And stop calling me Shirley.

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.