Ridding serum of viruses with gamma irradiation: part 2

by Dr. Ray Nims

In a previous posting, we described the susceptibility of viruses from various families to inactivation in frozen serum treated with gamma irradiation (data from the literature). Gamma irradiation is a commonly employed risk mitigation strategy for biopharmaceutical manufacture, and indeed the European Agency for the Evaluation of Medicinal Products in its Note for guidance on the use of bovine serum states that some form of inactivation (such as gamma irradiation) is expected. The use of non-treated serum in the production of biologics for human use must be justified, due to the potential for introducing a viral contaminant through use of this animal-derived material.

But how effective is this particular risk mitigation strategy? To answer this, we expressed the susceptibility to inactivation by gamma radiation for a series of 16 viruses in terms of log10 reduction in titer per kiloGray (kGy) of gamma radiation. With this value in hand, one may easily calculate the log10 reduction in titer of a given virus which might be expected following irradiation of frozen serum at any given kGy dose (serum is typically irradiated at a dose of 25-40 kGy).

The next step is to try to understand the results obtain with this relatively limited series of viruses, so that we may extrapolate the results to other viruses. If we take a look at the viral characteristics that might confer susceptibility to inactivation by gamma irradiation, will we find what we are looking for?

Viral characteristics that have been postulated to contribute to susceptibility or resistance to inactivation by gamma irradiation include: 1) radiation target size (genome size, particle size, genome shape, segmentation of the genome); 2) strandedness (in double stranded genomes the genomic information is recorded in duplicate, so loss of information on one strand may not be as damaging as it would be in the case of a single stranded genome); 3) presence or absence of a lipid envelope (non-enveloped viruses are resistant to a variety of chemical and physical inactivation approaches); and 4) genome type (RNA vs. DNA). The characteristics for our series of 16 viruses are displayed in the following table (click on table to enlarge).

For evaluating the contribution of radiation target size, we are able to make use of quantitative values available for each virus for genome size (in nucleotides) and particle size (in nm). Plotting genome size vs. log10 reduction in titer per kGy yields the results shown below. The coefficient of determination obtained is just 0.32, suggesting that factors other than (or in addition to!) genome size in nucleotides must be important..

A somewhat better concordance is obtained between particle size and log10 reduction in titer per kGy as shown below. The fit line in this case is non-linear, with a coefficient of determination of 0.60.

The contributions of genome type (RNA vs. DNA), genome strandedness, and lipid envelope (present or absent) to susceptibility to inactivation by gamma irradiation appear to be minimal, within this limited series of viruses. As a result, we are left with the conclusion that the most clear, albeit incomplete, determinant of susceptibility to inactivation by gamma irradiation appears to be particle size. This probably explains the resistance to inactivation displayed by the extremely small viruses such as circoviruses, parvoviruses, and caliciviruses. It is less clear why the polyomaviruses (e.g., SV-40 at 40-50 nm) are so resistant to gamma irradiation while certain of the picornaviruses (25-30 nm) are less resistant to inactivation. More work in this area is needed to better understand all of the factors that contribute to susceptibility to gamma radiation inactivation in viruses and bacteriophage.

< This information was excerpted in part from Nims, et al. Biologicals (2011) >

Is Your Chromatography in Control, or in Transition?

By Dr. Scott Rudge

While chromatography continues to be an essential tool in pharmaceutical manufacturing, it remains frustratingly opaque and resistant to feedback control of any kind.  Once you load your valuable molecule, and insufferable companion impurities, onto the column, there is little that you can do to affect the purification outcome that waits you some minutes to hours later.

Many practitioners of preparative and manufacturing scale chromatography perform “Height Equivalent to a Theoretical Plate” testing prior to putting a column into service, and periodically throughout the column’s lifetime.  Others also test for peak shape, using a measurement of peak skewness or Asymmetry.  However, these measurements can’t be made continuously or even frequently, and definitely cannot be made with the column in normal operation.  More over, the standard methods for performing these tests leave a lot of information “on the table” so to speak, by making measurements at half peak height, for example.

To address this shortcoming, many have started to use transition analysis to get more frequent snapshots of column suitability during column operation.  This option has been made possible by advances in computer technology and data acquisition.

Transition analysis is based on fairly old technology called moment theory.  It was originally developed to describe differences in population distributions, and applied to chromatography after the groundbreaking work of Martin and Synge (Biochem. J. 35, 1358 (1941)).  Eugene Kucera (J. Chromatog. 19, 237, (1965) derived the zeroth through fifth moments based on a linear model for chromatography that included pore diffusion in resins, which is fine reading for the mathematically enlightened.  Larson et al. (Biotech. Prog. 19, 485, (2003)) applied the theory to in-process chromatography data.  These authors examined over 300 production scale transitions resulting from columns ranging from 44 to 140 cm in diameter.  They found that the methods of transition/moment analysis were more informative than measurements of HETP and Asymmetry traditionally applied to process chromatography.

What is transition analysis, and how is it applied?  Any time there is a step change in conditions at the column inlet, there will occur, some time later, a transition in that condition at the column outlet.  For example, when the column is taken out of storage and equilibrated, there is commonly a change in conductivity and pH.  Ultimately, a wave of changing conductivity, or pH, or likely both, exits the column.  The shape of this wave gives important information on the health of the column, as described below.  Any and all transitions will do.  When the column is loaded, there is likely a transition in conductivity, UV, refractive index and/or pH.  When the column is washed or eluted, similar transitions occur.  As with HETPs, the purest transitions are those that don’t also have thermodynamic implications, such as those in which chemicals are binding to or exchanging with the resin.  However, the measurements associated with a particular transition should be compared “intra-cycle” to the same transition in subsequent chromatography cycles, not “inter-cycle” to different transitions of different natures within the same chromatography cycle.

Since transition analysis uses all the information in a measured wave, it can be very sensitive to effects that are observed any where along the wave, not just at, for example, half height.  For example, consider the two contrived transitions shown below:

In Case 1, a transition in conductivity is shown that is perfectly normally distributed.  In Case 2, an anomaly has been added to the baseline, representing a defect in the chromatography packing, for example.  Transition analysis consists of finding the zeroth, first and second moments of the conductivity wave as it exits the column.  These moments are defined as:

These are very easy calculations to make numerically, with appropriate filtering of noise in the data, and appropriate time steps between measurements.  The zeroth moment describes the center of the transition relative to the inlet step change.  It does not matter whether or not the peak is normally distributed.  The zeroth moments are nearly identical for Case 1 and Case 2, to several decimal places.  The first moment describes the variance in the transition, while the second moment describes the asymmetry of the peak.  These are markedly different between the two cases, due to the anomaly in the Case 2 transition.  Values for the zeroth, first and second moments are in the following table:

	Case 1	Case 2
Zeroth moment	50.0	50.0
First moment	1002	979.6
Second moment	20,300	19,623

It would be sufficient to track the moments for transitions from cycle to cycle.  However, there is a transformation of the moments into a “non-Gaussian” HETP, suggested by McCoy and Goto (Chem. Eng. Sci., 49, 2351 (1994)):

Where

Using these relationships the variance and non-Gaussian HETP are shown in the table below for Case 1 and Case 2:

Using this method, a comparative measure of column performance can be calculated several times per chromatography cycle without making any chemical additions, breaking the column fluid circuit, or adding steps.  The use of transition analysis is still just gaining foothold in the industry, are you ahead of the curve, or behind?

A much improved Ph. Eur. Chapter 5.3.2

By Dr. Ray Nims

Vaccine manufacturers intending to market in the EU should be aware of a recent change in the European Pharmacopoeia (Ph. Eur.) chapter 5.2.3 Cell substrates for production of vaccines for human use. This chapter addresses the characterization of vaccine cell substrates. The section on Test Methods for Cell Cultures within the chapter includes an instruction to perform a co-cultivation study. The language previously was as follows: “Co-cultivation. Co-cultivate intact and disrupted cells separately with other cell systems including human cells and simian cells. Carry out examinations to detect possible morphological changes. Carry out tests on the cell culture fluids to detect haemagglutinating viruses. The cells comply with the test if no evidence of any extraneous agent is found.”

This section has been changed, as of Ph. Eur. version 7.2 effective in January of 2011, to the following: “Co-cultivation. For mammalian and avian cell lines, co-cultivate intact and/or disrupted cells separately with other cell systems including human cells and simian cells. For insect cell lines, extracts of disrupted cells are incubated with other cell systems, including human, simian, and at least 1 cell line that is different from that used in production, is permissible to insect viruses and allows detection of human arboviruses (for example BHK-21). Carry out examinations to detect possible morphological changes. Carry out tests on the cell culture fluids to detect haemagglutinating viruses, or on cells to detect haemadsorbing viruses. The test for haemagglutinating viruses does not apply for arboviruses to be detected in insect cells. The cells comply with the test if no evidence of any extraneous agent is found.”

So what is the big deal? Co-cultivation is a commonly employed technique for detecting infectious retrovirus in a cell bank. It is effective for this purpose because the chances for spread of infectious virus from test cell to indicator (host) cell are optimized by the cultivation of live cells of each kind in close proximity. The endpoint of the retrovirus assay, be it reverse transcriptase enzyme induction or rescue of an S+L- virus, is not interfered with by the presence of two cell types in one culture. The same is not always true for a co-cultivation of a test cell with an indicator (host) cell for detection of infectious virus when morphological changes (viral cytopathic effects) are one of the assay endpoints. The reason is that the diploid human cells (e.g., MRC-5 or WI-38) used as one of the indicator cells in such assays are rapidly displaced during co-cultivation with intact continuous cell lines used to produce vaccines, such as the simian cell Vero. The result of this is that within a short period of time in co-cultivation, the test culture is no longer predominated by the diploid cell but rather by the test cells and observation of the culture for cytopathic effects becomes problematic. Changing the language of this section to read “…co-cultivate intact and/or disrupted cells separately with other cell systems…” allows the user to eliminate the inoculation of intact test cells onto a diploid indicator cell.

The other useful modification to the language of this section is the following addition: “For insect cell lines, extracts of disrupted cells are incubated with other cell systems, including human, simian, and at least 1 cell line that is different from that used in production, is permissible to insect viruses and allows detection of human arboviruses (for example BHK-21).” Testing of insect cells for extraneous virus is only marginally effective when it is conducted per the usual method of inoculating another insect cell. Why? The insect cells that are available are most commonly suspension cultures, making observation for cytopathic effect problematic. The extraneous viruses that are of most concern for an insect production cell are the arboviruses (viruses transmitted via insect vectors). It has been known for some time that the Syrian hamster cell line BHK-1 is an excellent host cell for detecting arboviruses. The new language in this section of Ph. Eur. chapter 5.2.3 now clears the way for the use of the monolayer BHK-1 cell line to be used for the testing of insect cells for extraneous virus. In this regard the Ph. Eur. chapter is now more closely aligned with the World Health Organization’s 2009 Evaluation of cell substrates for the production of biologicals: revision of WHO recommendations. The latter has the following passage: "For instance, in the case of insect cell substrates, certain insect cell lines may be used for detection of insect viruses, and BHK cells may serve for the detection of arboviruses."

   

Taken together, the recent changes to Ph. Eur. Chapter 5.2.3 greatly improve the chapter and the viral safety testing of vaccine production cell banks specifically proscribed within it.

Ridding serum of viruses with gamma irradiation: part 1

by Dr. Ray Nims

Blood serum, while at times required as a medium component for cell growth in vitro, is an animal-derived material that can introduce contaminating viruses such as Cache Valley virus, REO virus, vesivirus, and epizootic hemorrhagic disease virusinto a biological product. If animal serum must be used in upstream manufacturing processes, the risk of introducing a virus may be mitigated by gamma irradiation of the frozen serum prior to use. How effective is this treatment, and against which viruses?

To answer this question, I have surveyed the literature from the past two decades. A number of investigations have been conducted and the results are in the public domain. The most useful of these studies have examined the dose-response relationships for viral inactivation (rendering of the virus as non-infectious) by gamma irradiation.

In the table below, I have assembled the results obtained for 7 viruses, including four that might be expected to be found in bovine serum: (bovine viral diarrhea virus [BVDV], infectious bovine rhinotracheitis virus [IBR], respiratory-enteric orphan virus [REO virus], and parainfluenza type 3 virus [PI3]). The other three (canine adenovirus, porcine parvovirus [PPV], and mouse minute virus [MMV]), while perhaps not expected to be found in bovine serum, have been studied as model viruses for the adenovirus and parvovirus families (click on table to enlarge).

The efficacy of gamma irradiation for viral inactivation is reported as log10 reduction in titer per kGy, rather than the more commonly employed D10 (Mrad dose required to reduce the titer by 1 log10), as I find the former value to be more useful. To estimate the effectiveness of a given dose of gamma radiation for inactivation of a virus, just multiply the dose in kGy by the log10 reduction in titer per kGy value from the table. The result is the number of logs of inactivation estimated to be achieved for that virus at that radiation dose.

These data tell us that the mid- to large-sized viruses BVDV, IBR, PI3, REO, and CAV should be readily inactivated at the gamma radiation doses normally applied to frozen serum for risk mitigation (25-45 kGy). On the other hand, the two parvoviruses, PPV and MMV, are more difficult to inactivate, presumably due to their small size. Parvoviruses are often used to challenge viral removal and inactivation processes due to their size and lack of an envelope. Higher kGy dose levels may increase the effectiveness of inactivation for these viruses, although at such levels the performance of the animal serum being irradiated may be adversely impacted.

Gamma-irradiation can effectively mitigate the risk of introducing other potential contaminants of bovine serum, including Cache Valley virus, blue tongue virus, and epizootic hemorrhagic disease virus. Like the parvoviruses, however, other relatively small non-enveloped viruses of the calicivirus, picornavirus, polyomavirus, and circovirus families may represent cases where gamma irradiation is less effective at the doses normally applied. Other means of mitigating the risk associated with these viruses may need to be considered.

< This information was excerpted in part from Nims, et al. Biologicals (2011) >


Information sources: Daley et al., FOCUS 20(3):86-88, 1998; Wyatt et al. BioPharm 1993: 6(4):34-40; Purtel et al., 2006; Hanson and Foster, Art to Science 16:1-7, 1997; Hyclone Labs Art to Science 12(2): 1-6, 1993; Gauvin and Nims 2010; Plavsic et al. BioPharm 2001: 14(4):32-36.

Our take on process-specific vs. generic host cell protein assays

By Drs. Ray Nims and Lori Nixon 

The residual host cell protein (HCP assay) is used to determine the concentration, in process streams, of protein originating from the production cell (Chinese hamster, NS0, E. coli, etc.) used in the manufacture of a biologic. Host cell protein is considered to be a process-related impurity/contaminant. The most typical approach to quantitation of residual host cell protein in a test sample is the enzyme-linked immunosorbant assay (ELISA) platform.

Generic vs. process-specific assays.  A question that often arises is whether a process-specific HCP assay is required, or whether a generic assay can be used. Although there is a spectrum of “process specificity” that can be described, in this discussion, we are considering a generic assay to be one that is developed from a broad set of antigens from the host strain or closely-related strains, but not necessarily under specific process conditions mimicking the actual production/purification process of the protein of interest. That is, the reagents for a generic assay could be developed in advance of a defined production process. For example, it is often possible to purchase a “generic” HCP ELISA kit from a commercial provider, such as Cygnus Technologies, selecting a kit that matches the production cell line. Practically speaking, most firms start with generic HCP methods in early development for an obvious reason: you can’t have a process-specific assay until you have a defined process. Even after the process is defined, the lead time for developing custom (process-specific) HCP reagents and an assay using these can be as long as 1-2 years.

It should be appreciated from the outset that there is no perfect HCP assay. Production cells can express thousands of proteins, with expression patterns differing under different growth conditions. At each successive stage of the recovery and purification process, the population of HCPs is altered. At the final drug substance stage, there may be only a few HCPs present that have co-purified with the product. If you attempt to design an assay that only selects those few remaining HCPs, then some will argue that you might miss HCPs that might make it through under other circumstances (such as a process deviation).  If you attempt to design antibody reagents using the entire unpurified mixture of proteins from the production cell, the proteins of most interest may not elicit a good antibody response and you may have inadequate sensitivity to quantify HCP in your final drug substance. In fact, it has been reported that process-specific antibodies lead to HCP assays that are more sensitive for certain HCP species (and sometimes more broadly reactive) than the antibodies provided in a generic HCP assay. 

In recent years, there has been some regulatory pressure setting an expectation that a process-specific HCP assay must be developed for product registration. In our opinion, this is unjustified as a blanket prescription.  There are a multitude of approaches for creating antigen and antibody reagents, but the resulting assay should be judged primarily on its own merits.

Suitability of any HCP assay (whether generic or process-specific) must be evaluated on a case-by-case basis, and the assay validated for the specific product in question. One of the first characteristics to aim for is an assay that is able to readily quantitate HCP in the final drug substance. Without this sensitivity, it is difficult to get results that are meaningful to guide process optimization or ensure control of your product. If the available generic method(s) have limited sensitivity for the sample of interest (results are below the required range of quantitation), then it is a good idea to start planning the development of more process-specific reagents—which are likely to afford better sensitivity. Since the numerical values reported from these assays are semi-quantitative at best, sensitivity should be judged on the performance in actual samples rather than on the product specification. Of course there are other assay characteristics (non-reactivity to product, precision, dilutional linearity, spike recovery, etc) that are required to meet validation acceptance criteria. The antigen coverage should be determined, typically by 2D electrophoresis or 2D HPLC-ELISA. It is unrealistic to expect 100% coverage of all of the proteins present in the production cell; on the other hand, it is relatively more important to ensure response against bands that persist in the purification process.  All other things being equal, of course greater coverage is more desirable.

Example of 2D electrophoresis of E. coli proteins, from Kendrick Labs

Whether a generic or process-specific method is implemented, it is vital to ensure a consistent reagent supply that will last throughout the commercial life of the product. If using vendor-supplied antigen and antibody, be aware that by nature these are “single-source” reagents with associated supply risks. If creating a custom reagent set, make enough supplies to last for years to decades.

As mentioned above, most firms start with a generic assay and may move to a process-specific assay in later stages of development. A close evaluation of the generic assay during its characterization and validation may lead to the conclusion that the generic assay is suitable for your product.  In this case, be prepared to make a strong case in defense of your generic assay—based on empirical data with your product, rather than theoretical speculation.