comparison of different options for harvest of a therapeutic protein product from high cell density...

Comparison of Different Options for Harvestof a Therapeutic Protein Product From HighCell Density Yeast Fermentation Broth

Alice Wang,1 Rachael Lewus,2 Anurag S. Rathore1

1Process Development, Amgen, Inc., M/S 30-2-A, One Amgen Center Dr.,Thousand Oaks, California 91320; telephone: 805-447-4491; fax: 805-499-5008;e-mail: [email protected] of Chemical Engineering, University of Virginia, Charlottesville, Virginia

Received 1 June 2005; accepted 22 November 2005

Published online 26 January 2006 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/bit.20816

Abstract: Recovery of therapeutic protein from high celldensity yeast fermentations at commercial scale is achallenging task. In this study, we investigate andcompare three different harvest approaches, namelycentrifugation followed by depth filtration, centrifugationfollowed by filter-aid enhanced depth filtration, andmicrofiltration. This is achieved by presenting a casestudy involving recovery of a therapeutic protein fromPichia pastoris fermentation broth. The focus of this studyis on performance of the depth filtration and the micro-filtration steps. The experimental data has been fitted tothe conventional models for cake filtration to evaluatespecific cake resistance and cake compressibility. In thecase of microfiltration, the experimental data agrees wellwith flux predicted by shear induced diffusionmodel. It isshown that, under optimal conditions, all three optionscan deliver the desired product recovery (>80%), harvesttime (<15 h including sequential concentration/diafiltra-tion step), and clarification (<6 NTU). However, thethree options differ in terms of process developmenttime required, capital cost, consumable cost, ease ofscale-ability and process robustness. It is recommendedthat these be kept under consideration when making afinal decision on a harvesting approach.� 2006 Wiley Periodicals, Inc.

Keywords: depth filtration; filter aid; microfiltration;yeast fermentation; harvest

INTRODUCTION

Harvest of biotechnology products from cell culture or

fermentation process streams is often performed by a

combination of several unit operations. The drivers for the

process design include maximizing product recovery, scale-

ability, robustness, and clarification of process stream while

operating in a physical and chemical environment where the

product is stable. Centrifugation, depth filtration, and

microfiltration are some of the commonly used unit

operations for performing harvest.

Continuous centrifugation has been widely used for

harvest of large-scale cell culture (Berthold and Kempken,

1994; Winter, 2004) and microbial fermentation (Clarkson

et al., 1996; Varga et al., 2001) processes in the biopharma-

ceutical industry. For extracelluar protein product, secondary

clarification using depth filter after centrifugation is normally

required prior to further downstream processing. Recently,

Yavorsky and Mcgee (2002) presented an approach toward

selection and sizing of a depth filtration step for clarification

of cell culture and fermentation broths. A discussion of the

various strategies that can be adoptedwhile designing a depth

filtration step to achieve process compression, improved

yield, lower operating costs, and reduced process footprint

has also been published (Yavorsky et al., 2003).

Use of filter-aids to enhance the capacity of a filtration step

has been proposed in the literature. Heertjes and Zuideveld

(1978a, 1978b) performed depth filtration experiments with

polystyrene particles as model impurity to characterize filter

aids using effective particle diameter and pore diameter in the

filter aid cake. They found that the type of precoat and the

way in which it was formed were very important. They also

observed that electrostatic repulsion played an important role

and that interception and straining were the key mechanisms

of capture in depth filtration. Other applications using filter

aid assisted filtration for recovery of plasmid DNA

(Theodossiou et al., 1997) and of yeast cells (da Matta and

Medronho, 2000) from fermentation broth have also been

published. Reynolds et al. (2003) presented a design for

predicting changes in cake compressibility over time to allow

for accurate estimation of flux profiles that are obtained in

large-scale filtration steps that use filter aids. Literature from

the vendors also provides useful information about the

underlying principles that govern the performance of a filter

aid enhanced depth filtration step (Hurst, 2004).

Perhaps the most common approach that is used for

harvesting product from fermentation broths, in particular

high-density fermentations such as with yeast, is microfiltra-

tion (Tanaka et al., 1992; Zeman and Zydney, 1996). Both,

�2006 Wiley Periodicals, Inc.

Correspondence to: Dr. A.S. Rathore

plate and frame and hollow fiber formats have been shown to

be useful in these applications. Recently published chapters

have reviewed the progress made in this area (Russotti and

Goklen, 2001; Schlegel andMeagher, 2001). Bell andDavies

(1987) presented the several advantages that cross-flow

filtration (CFF) offers for harvesting yeast fermentations over

centrifugation and found that the performance of CFF

depends on several factors including viscosity, concentra-

tion, membrane fouling due to media components, and

influence of osmotic pressure. Patel et al. (1987) have

compared the different filter formats: pleated-sheet micro-

filter, tubularmicrofilter, and hollowfiber ultrafiltration (UF),

in terms of flux and cell yields obtained with CFF of yeast

suspensions. They found that the UFmodule hadmuch lower

fouling rate than the microfilters with the pleated-sheet

microfilter experiencing rapid plugging and significant

cleaning issues. Bailey and Meagher (1997) performed a

similar comparison between the hollow fiber and plate and

frame formats for microfiltration of recombinant E. coli

lysates and found both options to be comparable in

performance under optimized conditions. Sheehan et al.

(1988) compared centrifugation with membrane based

separations of extracellular bacterial protease and found the

membrane process to be twice as cost effective as

centrifugation and equivalent to a precoat filter, on basis of

unit cost of enzyme product recovered. Industrial studies

demonstrating robust operation of tangential flow filtration

(TFF) based harvest of mammalian cell culture (van Reis

et al., 1991) and CFF based harvest of recombinant yeast

product (Russotti et al., 1995) have also been reported. More

fundamental studies investigating the various aspects of

filtration processes such as, mathematical modeling, mem-

brane fouling, and critical flux determination have also been

published in the literature (Belfort et al., 1994; Jacob et al.,

1998; Kwon et al., 2000; Redkar and Davis, 1993).

More recently, operating at constant flux rather than

constant TMP has been proposed for microfiltration applica-

tions. It has been suggested that it is very important to operate

below the critical flux, which is the maximum permeate flux

that the system can sustain before concentration polarization

becomes significant. Once critical flux is reached, the

crossflow can no longer sweep solids from the surface as

quickly as the permeate flow brings the solids to the

membrane surface (Kwon et al., 2000). It has been observed

that severe and often permanent fouling that occurs with

operation under constant TMP due to very high initial

permeate fluxes can be avoided by operating under constant

flux (van Reis and Zydney, 2001). Sheehan et al. (1988)

observed an average flux increase of 2.5 times and protein

transmission of 90% upon using permeate flow control for

recovery of an extracellular protease. Harvest of mammalian

cell culture using constant permeate flux at industrial scale

has been shown to provide an average yield of 99% with the

total cell number and viability maintained throughout the

process (van Reis et al., 1991).

Several efforts had been devoted to overcoming mem-

brane-fouling limitations via improved fluid mechanics

across the membrane. These include use of rotating disk

dynamic filtration to harvest yeast cells (Lee et al., 1995),

vibrating membrane filtration for high cell density yeast

harvest (Postlethwaite et al., 2004), Dean vortex microfiltra-

tion of E. coli inclusion bodies (Schutyser et al., 2002) and

coiled hollow-fiber module for microfiltration of microbial

suspensions (Luque et al., 1999). Periodic backpulsing of the

permeate fluid to the feed for fouling reduction has been

reviewed by Davis (2001)). Although these cutting edge

technologies may provide significant benefits in terms of

sustaining flux and reducing fouling, they have not gained

wide industrial acceptance so far and the large-scale

equipment that would be required to perform these in a

manufacturing environment is not readily available.

In this study, we compare the three different harvest

approaches, as illustrated in Figure 1, namely two-pass

centrifugation followed by depth filtration, single-pass

centrifugation followed by filter-aid enhanced depth filtra-

tion, and microfiltration. This is achieved by presenting a

case study that involves recovery of a therapeutic protein

from Pichia pastoris fermentation broth.

THEORETICAL ASPECTS

Two models have been commonly used for describing

filtration performance in harvest process. These are the

resistance model and shear induced diffusion model. In the

following, we briefly summarize these models as they are

later used for data analysis.

Resistance Model

This model is based on cake filtration theory and can be used

to describe both filter aid assisted depth filtration and

microfiltration (Cheryan, 1998). Filtrate flux (J) can be

Figure 1. Different options for harvest of a therapeutic protein from high

cell density Pichia fermentation.

92 Biotechnology and Bioengineering, Vol. 94, No. 1, May 5, 2006

DOI 10.1002/bit

expressed by Darcy’s law

J ¼ DPmðRm þ Rf þ RcÞ ð1Þ

In Equation 1, DP is the driving force, Rm is the intrinsic

membrane resistance that can be determined by clear water

flux. Rf accounts for internal fouling of membrane pores and

Rc is the resistance from concentration polarization and cake

formation due to deposition of particulates on the membrane

surface. Internal fouling is generally assumed related to

physicochemical interactions and relatively unaffected by

operating parameters and is frequently lumped with Rm as

R0m, where R

0m ¼ Rm þ Rf (Cheryan, 1998). Cake resistance

Rc is a function of the permeability, thickness, and

compressibility of cake layer. Assuming homogeneous cake

layer, Rc can be expressed as

Rc ¼ rc � r� dc ð2Þwhere dc and r are the cake thickness and density. The

specific cake resistance, rc, is related to pressure drop as

follows (Perry and Green, 1984)

rc ¼ r0c � DPk ð3Þwhere, r0c is a constant that depends on the cake porosity anddiameter of the particles that form the cake, k is the cake

compressibility that varies from 0 for rigid, incompressible

cakes to 1 for highly compressible cakes.

In the case of constant flow cake filtration, cake thickness,

dc, is related to filtrationvolume (V), filter area (A), and solids

content (Cp) in the feed by the following mass balance

expression

dc ¼ Cp

rV

Að4Þ

For constant flow cake filtration, Equations 1–4 can be

combined as

J ¼ DPm R0

m þ r0cCpVADPk

� � ð5Þ

Typically, R0m is negligible compared to the cake resistance

and so Equation 5 can be simplified as

V

A¼ 1

mr0cCpJDP1�k ð6Þ

Equation 6 indicates that filtration throughput (V/A) is

proportional to (1�k)th power of differential pressure and

inversely proportional to fluid viscosity (m), solids

contents (Cp), cake resistance constant (r0c), and filtrate

flux (J). Increase in filtration throughput can be achieved

by reducing solids contents, cake resistance, cake compres-

sibility, flow rate, or by increasing maximum allowed

differential pressure.

For filter aid assisted filtration, Rm became the sum of

membrane resistance and precoat resistance, rc is the

combined specific resistance from the mixture of filter aid

and original solids in the feed, Cp is the total mass of

filter aid and original solids in the suspension, and k is

the combined compressibility of filter aid and original

solids.

In the case of microfiltration, DP is the transmembrane

pressure and is defined as:

TMP ¼ Pfeed þ Pretentate

2� Ppermeate ð7Þ

Equation 1 can then be expressed as follows for micro-

filtration

J ¼ TMP

mðRm þ Rf þ RcÞ ð8Þ

Shear Induced Diffusion Model

This model applies to the microfiltration system. It correlates

permeate flux to shear rate, concentration and particle size of

the feed material, filtration channel length, and viscosity.

Belfort et al. (1994) have reviewed Brownian diffusion,

inertial lift model and shear-induced diffusion model,

specifically as they apply to microfiltration. They concluded

that for open channel module operated at laminar flow

(Reynolds number, Re <2,000), Brownian diffusion is the

dominant mechanism for particles with diameter <0.1 mm,

shear induced diffusion is dominant for particles with

diameter between 1 and 10 mm and inertial lift model

becomes dominant for particles larger than 100 mm.

Reynolds number is defined as

Re ¼ rvdhms

ð9Þ

where dh, the hydraulic diameter, is equal to tube

diameter (d), or channel height (b) and ms is the suspensionviscosity.

For our case, shear induced diffusion model is expected to

be the dominant mechanism because Pichia cells are in

micron range. Zydney and Colton (1986) have proposed that

the filtrate flux during crossflow microfiltration in an open

channel can be described as

J ¼ 0:078r4sL

� �1=3

gw lnfw

fb

� �ð10Þ

where, L is the channel length, gw is the wall shear rate, fw,

and fb are particle volume fraction at membrane surface and

bulk solution, respectively. Further, fw is assumed to be

close-packed particle concentration and ranges from 0.6 for

rigid particles and 0.8–0.9 for deformable particles. Shear

rate at membrane surface is related to bulk average linear

velocity (v) and channel dimensions as expressed in the

following equation (Cheryan, 1998).

Wang et al.: Harvest of a Therapeutic Protein Product 93

Biotechnology and Bioengineering. DOI 10.1002/bit

For tube of diameter d,

gw ¼ 8v

dð11Þ

For a slit of height b,

gw ¼ 6v

bð12Þ

Estimation of Yield in Diafiltration

Product recovery of microfiltration process is dependent on

number of diafiltration volumes (DV) and protein sieving

coefficient (S), and can be expressed as follows:

Y ¼ 1� e�DV�S ð13Þwhere, the protein sieving coefficient is defined as

S ¼ Cpermeate

Cretentate

ð14Þ

where, Cpermeate and Cretentate are the product concentrations

in the permeate and retentate side.

MATERIALS AND METHODS

Preparation of Feed Material

The yeast fermentation process consisted of seed vial thaw,

inoculum development and production fermentation stages.

The seed vial was thawed and used to inoculate a 2 L baffled

shake flask. After 30 h growth in the shake flask stage, cells

were then transferred into a 500 L fermentor containing

specially formulated growth medium. The fermentation

process went through batch growth, fed-batch growth,

adaptation, and production stages, and reached final target

protein titer, and �50% (v/v) packed wet cells. Operating

conditions were similar to what have been widely reported in

literature forPichia (Byrne et al., 2000 andLi et al., 2003). At

the end of fermentation, aliquots were transferred into

Carboys and stored at 48C with agitation for microfiltration

experiment. The remaining of broth was chilled and agitation

was lowered to 200 rpm. The cell broth was then diluted and

centrifuged using a Westfalia CSA-8 continuous disk stack

centrifuge. As shown in Figure 1, double pass centrate

containing 0%–1% solids was used as feed material in

depth filtration experiments and single pass centrate with

1%–7% solids was used for filter aid assisted depth filtration

runs. Several fermentation lots were used to perform

experiments presented in this study. However, based on

our evaluation of the robustness of the fermentation process

(data not presented here), the lot-to-lot variation is not

expected to cause significant impact on the performance of

harvest steps.

Depth Filter, Filter Aid, and MicrofiltrationMembranes

Depth filter K150 (Cat#SC060X150), EKSP (Cat#SC060-

PEKS), SUPRAEK1P (Cat#SC060PEK1), and SUPRA80 P

(Cat#SC060P080) were purchased from Pall Corporation

(East Hills, New York). Depth filter CUNO 10 SP

(Cat#BC0030A10SP), 90 SP (Cat#BC0030A90SP), 30M03

(Cat#BC0030A30M03), 90M08 (Cat#BC0030A90M08),

and 120M08 (Cat#BC0030A120M08) were purchased from

CUNO Incorporated (Meriden, CT). MillstakþA1HC

(Cat#MA1HC23HH3) and B1HC (Cat#MB1HC23HH3)

were purchased fromMillipore (Bedford, MA). Descriptions

of pore size and filter media of these depth filters have been

summarized in Table IA.

Four grades of filter aid, Celpure P65 (Cat#52523-5),

P100 (Cat#52626-6), P300 (Cat#52524-3), and P1000

(Cat#52522-7) were purchased from Sigma-Aldrich (St.

Louis, MO). Celpure 65 has the smallest particles size while

Celpure 1000 is the largest. The permeability and particle

retention properties are listed in Table IB.

Table IA. Properties of depth filters evaluated*.

Vendor Filter Pore size Description

Millipore MillistakþA1HC 0.1–0.4 mm (DE65)/<0.1mm (DE75) Two layers of inorganic filter aid (DE) and 0.1 mm nominal

cellulosic membrane (RW01)

Millipore MillistakþB1HC 0.2–0.7 mm (DE50)/<0.1 mm (DE75) Single layer of pharmaceutical grade media

CUNO 10SP 0.8–4 mmCUNO 90SP 0.2–0.65 mmCUNO 30M03 0.8–4 mm(10SP)/0.6–2 mm (30SP) Dual-zone construction, high contaminant holding capacity

CUNO 90M08 0.45–0.8 mm (60SP)/0.2–0.65 mm (90SP)

CUNO 120M08 0.45–0.8 mm (60SP)/0.1–0.45 mm (120SP)

Pall

Pall

Pall

Pall

SupraEK1P

EKSP

Supra 80P

K150

0.2–4 mm0.1–0.3 mm1–3 mm2.5–4 mm

P series depth filter, combination of cellulose fibers, DE and

perlite, pyrogen removal capability.

K series depth filter, combination of cellulose fibers, DE and

perlite.

*Information available from vendor literature.


DOI 10.1002/bit

Hydrosart microfiltration membrane (Cat#305-186-07W-

SG) was obtained from Sartorius (Goettingen, Germany),

0.2 mm Supor with suspended screen (Cat#PSM20C11)

from Pall (East Hills, New York), Pellicon II with v screen

(Cat#P2GVPPV01) and Prostak (Cat#PSGVAG021) from

Millipore and hollow fiber cartridge (Cat#CFP-1-E-5A)

from GE healthcare (Piscataway, NJ). Properties of these

microfiltration membranes are listed in Table IC.

Depth Filtration Method

Constant flow filtration (also called Pmax study) was utilized.

Filtration volume, differential pressure, and filtrate turbidity

were recorded at different time intervals until differential

pressure of 30 psiwas reached. In the case offilter aid assisted

filtration, the filter pad was first precoated with 3 mm same

grade of Celpure media (0.1 g/cm2 surface area). The desired

amount of filter aid (also called body feed) was added and

suspended in feedstock. The body-fed feedstock was then

filtered at a constant flow rate of 250–350 LMH until either

the pressure was �30 psig, or the filter assembly no longer

had any headspace available to accommodate further

increase in cake thickness. The cake height was measured

using a graduated scale.

Microfiltration Method

The protein of interest is known to be stable in post-

production broth at room temperature. Therefore, during

microfiltration process, the retentate tank was maintained at

room temperature (22–258C) to reduce fluid viscosity and

maximize flux. The permeate pool was chilled to 2–88C to

minimize product degradation. Depending on the required

circulation flow rate, a peristaltic pump (Masterflex HV-

77963-10) or a diaphragm pump (Quattroflow 4000S) was

used to circulate fermentation broth. Operating under

constant permeate flux instead of constant TMP has been

recommended for microfiltration application involving

excessive membrane fouling (van Reis and Zydney, 2001).

A bench scale peristaltic pump was used to control permeate

flux. The feed was first circulated in the system for a few

minutes with the permeate line closed. Then backpressure

was applied by closing the retentate valve, and the permeate

pump was started at the desired flow rate. Permeate was

pooled for every 0.5 diafiltration volume (DV) up to 2 DV.

Permeate and retentate samples at the same processing time

were also taken to evaluate protein transmission.

To measure critical flux at a given crossflow rate, the

systemwas set up in total recyclemode, and the permeate flux

was increased in increments. With each incremental step in

flux, the systemwas allowed to equilibrate. Pressure and flow

readings were noted at the beginning and at the end of the

stabilization period. Typically, as permeate flux increased,

TMP also increased and then stabilized. TMP continued to

increase in a stepwise fashion until critical flux was reached

and beyond this the TMP continues to rise without

stabilizing. After each microfiltration run, membranes were

regenerated and sanitized by sequential recirculation of a

cleaning solution per vendor suggested methods.

Analytical Assays

Solids content (v/v %) in fermentation broth as well as

filtration feed material was determined by processing the

samplewith aBeckman J2-HS centrifuge. If no pellet resulted

upon centrifugation, the samplewas designated as containing

0% solids. Particle size of Pichia cells was measured by

Malvern laser light diffraction system. Filtrate/permeate

turbidity was measured using Hach1 portable turbidity

meter. Protein concentration in the feed and filtrate were

analyzed by SDS–PAGE using 4%–20% Tris-Glycine gel

from Invitrogen (Cat# EC6025). The gels were stained using

Coomassie blue and imaged with a scanning laser densit-

ometer (BioRad Model GS800). The band intensity was

quantified byQuantity One imaging software (Version 4.2.1).

RESULTS AND DISCUSSION

Depth Filtration (Option 1A)

Evaluation of the filtration performanceswas performedwith

respect to filter capacity, filtrate quality as determined by

Table IB. Properties of filter aids evaluated*.

Celpure

grade

Permeability

(mDarcy)

Surface area

(m2/g)

99% Retention

(mm)

65 40–80 6–7 >0.2

100 70–140 5–6 >0.3

300 150–300 3–4 >0.45

1000 750–1250 1–2 >1.0

*Information available from vendor literature (Hurst, 2004).

Table IC. Properties of microfiltration membranes evaluated*.

Vendor Format Pore size (mm) Material Surface area (m2)

Sartorius Cassette/open channel 0.45 Reg. cellulose 0.1

Pall Cassette/suspended screen 0.2 PES 0.1

Millipore Pellicon II/V screen 0.2 PVDF 0.1

Millipore Prostak/open channel 0.2 PVDF 0.17

GE Hollow fiber, 1 mm lumen, 30 cm 0.1 PS 0.12

*Information available from vendor literature.



filtrate turbidity, targeted protein recovery, and robustness

regarding variation in feed characteristics. Ten different

filters from Pall, Millipore and CUNO were examined for

their performance in this application (Rathore et al., 2004). In

order to have a robust process with respect to feed

characteristics % solids and feed turbidity, feeds with 0%

solids and 0.7% solids were used for filter screening. The

differential pressure versus normalized throughput at differ-

ent time intervals for each depth filter was plotted and

compared. As listed in Table II, most filters exhibit good

capacity with feed containing 0% solids. However, filtration

capacity is significantly lower with feed containing 0.7%

solids except forMilliporeMillistakA1HC andB1HCfilters,

which consist of an open diatomaceous earth (DE) layer,

tighter DE layer and a 0.1 mm cellulosic membrane. The

recovery for all the experiments is found to be between 80%

and 95%. With feed containing 0.7% solids, the filtrate pool

turbidity is�3 nephelometric turbidity units (NTU). For feed

with 0% solids and at differential pressure of 30 psi, turbidity

breakthrough (a sudden and sharp increase in filtrate

turbidity) is observed and pool turbidity is measured to be

8NTU forMillistak A1HC, 11NTU forMillistak B1HC, and

10–15 NTU for all other cases. Filter screening results

indicate that the A1HC and B1HC filters yield high capacity

and lowfiltrate turbiditywith respect to variation in feedstock

characteristics.

As shown inTable II, A1HCfilter has a higher capacity than

the B1HC filter with 0% solids feed, but a lower capacity with

0.7%solids feed.We believe this is due to the different particle

size distribution of the two feeds; with the 0% solids feed

containing smaller particles that lead to a faster pluggingof the

B1HC filter, which is structurally more open in comparison to

the A1HC. Also, while no turbidity breakthrough is observed

with 1% solids feed, breakthrough occurred earlier with the

0% solids feed for the B1HC filter thanwith the A1HC. Based

on these results, Millistak A1HC was chosen due to its

superior filter capacity and lower filtrate turbidity.

To ensure robust operation at large scale, the effects of key

operating parameters on the performance of this unit

operation were evaluated. These parameters include% solids

in the feed, lot-to-lot variation in feed, batch-to-batch

variation in filters, scale of operation (bench, pilot, and

production scales), filtration flow, temperature, and feed hold

time. In Figure 2, three lots ofA1HC depth are compared side

by side using same feed material. Lot A outperformed the

control run (lot B) with respect to throughput, indicating that

A1HC filter capacity may vary from lot-to-lot and an

appropriate safety factor should be applied for large scale

production. At pilot scale, the step was performed with an 8

cell 1600 A1HC with 1.8 m2 membrane area to process 300 L

fermentation broth. Comparable performancewith respect to

product recovery, filter capacity and filtrate turbidity is

observed between two scales, as illustrated in Figure 3.

Breakthrough in pressure and turbidity is seen at bench scale.

However, for pilot scale, this was outside the operating

conditions evaluated.

The harvest process using Millistak A1HC provides high

product recovery (95%) and low turbidity filtrate (<3 NTU).

However, depth filters such as Millistak A1HC are designed

for secondary clarification purpose. They remove fine

particles by capturing them within the depth of the filter.

When the % solids in the feedstock is too high, most of

particles are retained on the surface of depth filter to form an

impermeable cake and result in filter clogging. Data plotted

in Figure 4 shows that the capacity of MillistakþA1HC

decreases dramatically with increasing solids content in the

feed. This observation is consistent with Equation 6 that

states that the filtration capacity is inversely proportional to

feed particle concentration. Thus, it can be postulated that if

the centrifugation step underperforms and yields centrate

Table II. Comparison of throughput for different depth filters (L/m2 at

15 psi). Filtration experiments were performed at flow rate of 250 LMH and

48C.

Filter train Feed w/0% solids Feed w/0.7% solids

CUNO

90SP 268

120M08 133 62.5

10SPþ 90SP 42.5

30M08þ 120M08 71.4

30M08þ 90M08 89.3

Millipore

A1HC 260 137

B1HC 174 160

Pall

SupraEK1P 161.5 69.2

Supra80PþEK1P 54.8

K150þEK1P 115

Figure 2. Effect of lot to lot variation inMillistak A1HC filters. Three lots

of A1HC depth filter in the format of Minicap (23 cm2) were compared side

by side using same lot of feed material at flow of 250 LMH, 48C. Feedcontained 0.55% solids. [Color figure can be seen in the online version of this

article, available at www.interscience.wiley.com.]


DOI 10.1002/bit

with higher % solids (>1%); the filter area required for the

depth filtration step will increase significantly.

Filter Aid Enhanced Depth Filtration (Option 1B)

In order to gain manufacturing flexibility with respect to

variation in% solids in feed, filter aid assisted depth filtration

was explored as the second process option. Filter aid is made

of rigid and porous diatomaceous earth. With addition of

filter aid, the diatomite deposits alongside with the

compressible solids throughout filtration, resulting in a

permeable and incompressible cake layer (Hurst, 2004). In

the following, we describe optimization of operating

conditions for this option.

Celpure Grade Selection

Four grades of Celpure materials with different particle sizes

were compared side-by-side in order to select the most

suitable filter aid. Small sized filter aids generate small pores

when accumulated as a cake and are able to retain small sized

particulate. Therefore, high cake resistance (Rc) is expected.

On the other hand, coarse filter aids are not able to retain

small sized particulate, resulting in low cake resistance (Rc)

and high filtrate turbidity. The goal of filter aid screening is to

identify a filter aid that generates the lowest cake resistance

(Rc), as well as, low filtrate turbidity (<10 NTU) for our

application. As shown in Figure 5, all the filter aids

performed in a comparable fashion with respect to filtration

capacity. Celpure 65 is the filter aid with the smallest particle

size and generates the highest resistance during the entire

filtration. Celpure 300 and Celpure 1000 have lower

Figure 4. Effect of % solids in the feed on filtration capacity of Millipore

A1HC filter. [Color figure can be seen in the online version of this article,

available at www.interscience.wiley.com.]

Figure 5. Differential pressure versus filtrate volume with four different

filter aids at lab scale. The filter pad was first precoated with 3 mm Celpure

Media (0.1 g/cm2 surface area), followed by addition of 30 g/L body feed to

feedstock containing 7% solids. The suspensions were filtered at flow rate of

350 LMH and 48C. [Color figure can be seen in the online version of this

article, available at www.interscience.wiley.com.]

Figure 3. Scale-up ofA1HCdepth filtration step to pilot plant.Mini disk (23 cm2)was used at lab scale and 8 cells, 1600 diameter cartridge (1.8m2)was used in

pilot plant. Filtrationwas performed at flow rate of 250LMHand feed contains 0% solids. [Color figure can be seen in the onlineversion of this article, available

at www.interscience.wiley.com.]



resistance in the early stage of filtration due to their large

particle sizes. However, the cake layers are too open to

capture the fine particles in the feedstock, which results in

filter pad clogging and higher resistance in the later stage of

filtration. Filtrate turbidity was 5.8, 6.8, and 10.5 NTU for

Celpure 100, 300, and 1000 respectively. Since the product

does not bind with Celpure media, product recovery with all

four Celpure grades was between 90% and 100%. Celpure

100 was selected for the remaining experiments as it yielded

the targeted turbidity and highest capacity.

Celpure 100 Concentration

Cake permeability depends on the concentration of filter aid

added. Lower amount of filter aid produces a cake in which

cells and cell debris are not well dispersed and cake is still

compressible and impermeable. Too much filter aid will lead

to formation of a permeable but thicker cake. The thickness

of the cake, as discussed later, negatively impacts the filter

area available in a cartridge. Different amounts of Celpure

100 were added to filtration feed in order to identify

the optimal concentration. Figure 6 illustrates a plot of

differential pressure versus throughput at different concen-

tration of filter aid. It appears that due to high amount of %

solids in the feed material (7%), the filter clogs immediately

without filter aid. Addition of Celpure 100 dramatically

improves the filtration capacity and the capacity increases

with the filter aid concentration. The resistance parameter of

Pichia pastoris cell was measured and is listed in Table III.

Experimental results at different Celpure 100 concentrations

were fitted with the resistance model (Eq. 6) and the values

for cake resistance and compressibility were estimated. As is

evident fromTable III, these estimated values are comparable

to those reported in the literature. Addition of filter aids in the

filtration process increases particle concentration (Cp), but

significantly reduces cake specific cake resistance (rc) and

cake compressibility (k), thus improving filtration through-

put.

Capacities (L of filtrate/m2 of filter area) at a differential

pressure of 30 psig for different% solids and Celpure amount

are summarized in Figure 7 with an error bar of þ/� one

standard deviation. As shown in Figure 7A, the addition of

Celpure 100 dramatically increases filtration capacity in

terms of filtrate volume per filter area, for example, for

feedstock containing 2% solids, addition of 15 g/L Celpure

100 increases filter capacity seven times from75 to 525L/m2.

However, as illustrated in Figure 7B, the filter cake height

also increases significantlywith increasing filter aid addition.

This results in need of an increased spacing between filter

layers and a dramatic decrease in the total available filter

surface area per housing with increasing amount of filter

aid addition. As shown in Figure 8, while the overall

filtration capacity per filter housing is improved by 1.5–

2 times, the volumetric flow rate per housing is substantially

reduced.

In summary, it is evident that addition of filter aid

significantly improves filter capacity (volume of filtrate/filter

surface area) while maintains high product recovery (>90%)

and low filtrate turbidity (<6 NTU). However, special filter

design with increased spacing between each filter layer is

required. The reduced packing density of filters offsets the

Figure 6. Effect of filter aid concentration on filtration performance.

Experimental conditions same asFigure 5 unless indicatedotherwise.Different

amounts of Celpure 100were added to feedstock containing 7% solids to reach

different concentrations (0, 18, 30, and 54 g/L). Data points are experimental

observations and lines are theoretical predictions based on Equation 6.

Table III. Comparison of resistance parameters with those reported in the literature and effect of Celpure 100 concentration on cake characteristics.

Baker’s yeast* P. Pastoris

P. Pastoris w Celpure 100

18 g/L 30 g/L 54 g/L

Resistance constant r0c (108) 9.49 3.92 6.3 11.3 9.7

Compressibility, k 0.7 0.75 0.442 0.32 0.25

Specific resistance at 100 kPa, rc (1012 m/kg) 3 2.2 0.102 0.045 0.017

Parameters, r0c and k are calculated basedon curve-fitting ofEquation6with experimental data and rc is calculatedby usingEquation 3.Viscosity offiltrate isassumed to be 0.00135 Pa � s at 48C.

*Data from Shimizu et al. (1993).


DOI 10.1002/bit

benefit of higher capacity of filter aid process. Furthermore,

there are other operational issues associated with handling of

the powder material in the manufacturing plant, as well as,

possibility of precipitation of filter aid in the filtration skid.

Microfiltration (Option 2)

Membrane Screening

Under permeate control mode, flux is maintained constant,

and the TMP is allowed to vary accordingly (Sheehan et al.,

1988). An examination of Equation 8 suggests that an

increase in TMP indicates higher resistance generated during

microfiltration due to higher cake resistance (higher Rc),

membrane pore plugging (higherRf) or a combination of both

mechanisms.

As mentioned earlier, the initial solids content in the

fermentation broth is about 50%. The undiluted fermentation

broth was processed using a microfiltration step and two

diafiltration volumes were performed at constant retentate

volume. Five membranes were operated under manufacturer

suggested crossflow and the permeate flux was controlled at

30–50 LMH. TMP for the Hydrosart membrane increased to

48 psi in 10 min and for Supor 0.2 mm membrane from 7 to

26 psi, indicating unstable processes. Millipore Prostak,

Pellicon II and GE-hollow fiber membranes yielded

acceptable performance under vendor suggested operating

conditions. Permeate turbidity was 2–3 NTU for all three

cases. After the microfiltration step was complete, all

membranes were flushed and regenerated using vendor

suggested cleaning agents and regimes, and NCWP recovery

of 50%–70% was obtained. Based on the screening results,

Millipore Prostak, Pellicon II and GE-hollow fiber were

selected for further investigation.

Optimization of Process Parameters

Figure 9A shows the determination of critical flux for Prostak

when processing feed with �50% solids at room tempera-

ture. At crossflow of 2.5 L/min/channel, the TMP increases

Figure 7. Dependence of filtration capacity (A) and cake height (B) on Celpure 100 concentration. Duplicate runs were performed for feed with 2%

and 3% solids. Error bar shows one standard deviation. Capacity and cake height were determined at flow rate of 350LMHand amaximumdifferential pressure

of 30 psi.

Figure 8. Effect of filter aid concentration on volumetric flowrate (A) and capacity per housing (B) at a maximum differential pressure of 30 psi with a

Millipore HC68 housing with 84.2500 height and 1800 diameter. [Color figure can be seen in the online version of this article, available at

www.interscience.wiley.com.]



exponentially when permeate flux reaches 25–30 LMH,

indicating the critical flux is 25 LMH. The critical flux at

crossflow of 8 L/min/channel increases to 45 LMH.

Critical flux during microfiltration is strongly dependent

on particle size and process parameter. For particles over

1 mm, the shear-induced diffusion lifts particles away from

the membrane surface (Bacchin et al., 1995). Under

our microfiltration operating conditions, the calculated

Reynold’s number is 150 at highest cross-flow rate, and

therefore laminar flow in the Prostak channel is expected

(Re <2,000). Since Prostak channel dimensions are known

and Pichia cell mean diameter was measured to be 2.8 mm,

the permeate flux can be calculated using shear-induced

diffusion model as expressed in Equation 10. It was not

possible to estimate cake thickness using resistancemodel, as

Rf is unknown and is expected to be significant due to the high

concentration of the Pichia feedstock. Assuming a cake

thickness of 0–0.1 mm, for effective channel height of 0.4

and 0.5 mm, flux and shear rate were calculated using

Equations 10 and 12 and presented in Figure 9B. It is seen that

the critical flux increases with cross-flow rate and then

saturates out. At low cross-flow rates the data suggests a

linear increase and is bracketed by the two theoretical lines.

However, as the cross-flow rate increases to 8 L/min/channel,

the critical flux enters a non-linear regime that is no more

defined by the model.

TMP and permeate flux are plotted as a function of process

volume for the three membranes in Figure 10. Once the

critical flux was identified, the membrane was operated at

80%–90% of critical flux and 2 diafiltration volumes were

performed at a loading of 100 L fermentation broth per m2

membrane area. Very stable TMP was observed during the

entire microfiltration step and permeate turbidity was �2

NTU for all three cases. Hollow fiber had the highest flux of

Figure 9. Determination of critical flux for Millipore Prostak (A) and comparison of experimental data with theoretical predictions from shear-induced

diffusion model at different crossflow rates (B). Prostak channel dimension: Length of 0.389 m, width of 0.198 m, and height of 0.5 mm. Pichia particle rs of

1.4mmwasmeasured byMalvern Laser LightDiffraction, volume fraction in the bulk stream,fb ismeasured to be 0.457, volume fraction atmembrane surface,

fw is assumed to be 0.85. Effective channel height was assumed to be 0.4 or 0.5 mm. [Color figure can be seen in the online version of this article, available at

www.interscience.wiley.com.]

Figure 10. TMP (*) and flux (&) during harvest of target protein by 2� diafiltration at constant retentate volume. Feed stream contains 46% solids.

Membrane loading is 100 L fermentation/m2membrane surface area. Crossflow for Pellicon II: 22 L/min/m2, Hollow fiber: 33 L/min/m2, and Prostak: 8 L/min/

channel. [Color figure can be seen in the online version of this article, available at www.interscience.wiley.com.]


DOI 10.1002/bit

55 LMH among three membranes. Pellicon II membranewas

not studied further as it yielded the lowest flux. Table IV

shows a comparison of permeate flux obtained in this

process with that in other yeast fermentation harvest

processes reported in the literature. It must be cautioned

that the quoted studies were done with a variety of feed

materials and experimental setups. However, it can be con-

cluded that performing microfiltration under permeate

control condition leads to a steady-state flux that is com-

parable to what has been reported with vibrating membrane

filtration (VMF) and much higher than with TMP controlled

processes.

Estimation of Step Yield

Target protein concentration in feed, permeate pools (every

0.5 DV), and final retentate were analyzed by SDS–PAGE

and densitometry techniques. As shown in Figures 11A and

B, the feed (lane 2) has the highest product concentration and

the product is washed out in the permeate (lanes 3–6) during

diafiltration with only a small amount remaining in the

retentate at the end of diafiltration (lane 7). Protein

transmission factor (sieving) is calculated to be 0.7 for both

membranes by comparing permeate and retentate protein

concentration (lanes 8 and 9). This protein transmission

factor is similar to that observed by Postlethwaite et al.

(2004) using vibrating membrane filtration at yeast concen-

tration of 500 g/L. We obtained 100% mass balance and

92.2% recovery after 2DVwith the hollowfiber cartridge and

111% mass balance and 93.5% recovery with the Prostak

membrane.

Equation 13 was used to calculate the microfiltration yield

from sieving coefficient and number of diavolumes. It should

be clarified that since the solids content is up to 50% in the

feed material, 1.5 DVand 2 DVare actually 3 DVand 4 DV

based on liquid volume. As illustrated in Figure 12,

experimental yield (calculated by total protein mass in the

permeate pool vs. that in the feed) matches very well with

theoretical yield calculated using Equation 13. Data in

Figure 12 indicates that the harvest process can be performed

with two options, 1.5 DV for 86% yield and shorter run time

or 2 DV for 92% yield and longer processing time.

Comparison of Different Harvest Approaches

Calculations were performed for linear scale-up for proces-

sing of 3,000 L fermentation broth using the different options

while keeping the filter or membrane loading as constant. For

microfiltration, several Prostak membranes or hollow fiber

cartridges were set-up in series to reduce total pumping

requirement. Permeate streams were controlled individually

using separate permeate pumps. Using this set-up, the

permeate pressure of upstream cartridge was higher than

downstream cartridge, but the TMPs across each cartridge

Table IV. Comparison of steady state flux for various yeast harvest processes.

Yeast concentration (g/L) Process conditions/scale Steady state flux (LMH) Reference

6 Constant TMP, pilot 20 Russotti et al. (1995)

30 Constant TMP, lab 15–25 Redkar and Davis (1993)

500 VMF*, lab 30–45 Postlethwaite et al. (2004)

450 Constant flux, lab 40–55 Our work

*VMF, vibrating membrane filtration.

Figure 11. Determination of protein concentration by SDS–PAGE for Hollow fiber (A) and Prostak (B) membranes. Lane 1: protein standard, lane 2: feed,

lane 3: permeate pool (0–0.5 DV), lane 4: permeate pool (0.5–1 DV), lane 5: permeate pool (1–1.5 DV), lane 6: permeate pool (1.5–2.0 DV), lane 7: final

retentate, lane 8: permeate at 1 DV, and lane 9: retentate at 1 DV.



wasmaintained to be the same. The comparison of these three

options is shown in Table V. The final pool volume is slightly

higher for the microfiltration step, which results in a longer

processing time of the following ultrafiltration/diafiltration

(UF/DF) step, but this effect is counterbalanced by the lower

processing time of the microfiltration step itself when

compared to that of the centrifugation and depth filtration

steps. Further, the microfiltration step offers the highest

product recovery. The pool turbidity is quite comparable for

all three options. The capital cost for Options 1A and 1B is

high, primarily due to the cost of the centrifuge. However,

due to the higher cost of the microfiltration membranes as

compared to the depth filters, the cost of consumables is

higher for Option 2. This would necessitate development and

validation of effective cleaning and sanitization methods to

allow for reuse of the membranes and result in higher

validation costs for Option 2. Finally, while scale-up of

microfiltration and depth filter steps is relatively simple to

perform (linear scale-up), scaling up of a centrifugation step

for high cell-density fermentation broth is more complex.

The overall comparison suggests that while all three

approaches are feasible, the optimal solution will depend

on the application under consideration.

CONCLUSIONS

In this study, we investigate and compare three different

harvest approaches, namely double pass centrifugation

followed by depth filtration, single pass centrifugation

followed by filter-aid enhanced depth filtration, and micro-

filtration. Based on the data presented here, it can be

concluded that under optimal conditions, all three options

can deliver the desired product recovery (>80%), harvest

process time (<15 h, including sequential UF/DF step), and

clarification (<6 NTU). However, as shown in Table V, there

are differences with respect to process performance (recov-

ery, processing time, development time), economics (capital

cost, cost of consumables, cost of validation) and manufac-

turability (ease of scale-up, robustness). Our analysis is based

on a case study involving recovery of a therapeutic protein

from Pichia pastoris fermentation broth and the results are

likely to be different for a cell culture or E. coli based

application. However, these considerations and the interplay

of the various attributes are likely to stay the same and would

need to be accounted for while developing an optimal harvest

solution.

Figure 12. Dependence of yield on diavolume. Experimental data (& and

*) are obtained by comparing protein mass in permeate versus total protein

mass in the feed. Modeling (solid line) is based on Equation 13 assuming

sieving coefficient of 0.7 as measured from SDS–PAGE gels in Figure 11.

Table V. Comparison of process performance, economics, and manufacturability criteria for Option 1A, Option 1B, and Option 2.

Option 1A Option 1Ba Option 2b

Process performance

Membrane/filter area (m2) 21.4 10 30

Processing time for filtration step (hr) 0.5 1 3

Processing steps 6 5 2

Total processing time for harvest (hr) 13 14 11

Harvest yield (%) 80%c 80%c 86%

Pool turbidity (NTU) 2–3 5–6 2–3

Economics

Capital cost High (centrifuge required) High (centrifuge required) Low

Consumables cost Low Low High (MF membrane cost)

Reuse validation cost Low (no reuse required) Low (no reuse required) High (reuse required)

Manufacturability

Ease of scale-up Medium (Centrifuge scale-up can be

challenging)

Medium (Centrifuge scale-up can be

challenging)

High (linear scale-up is

straight forward)

aAssuming feed w/3% solids and 20 g/L Celpure 100.bAssuming 1.5 DVat flux of 50 LMH.cLow harvest yield is mainly due to centrifugation step yield of 80–85%.


DOI 10.1002/bit

NOMENCLATURE

A filter surface area (m2)

b channel height (m)

Cp particle concentration in the feed (kg/m3)

d channel diameter (m)

J permeate flux (m/s)

k cake compressibility factor (�)

L channel length (m)

rc cake specific resistance (m/kg)

rc0

cake resistance constant (m/kg �Pa�k)

rs particle radius (m)

Rm membrane resistance (m�1)

Rc cake resistance (m�1)

Rf fouling resistance (m�1)

S sieving coefficient (�)

TMP transmembrane pressure (Pa)

v crossflow rate (m/s)

V filtrate volume (m3)

DP differential pressure (Pa)

r density (kg/m3)

gw wall shear rate (s�1)

dc cake thickness (m)

m permeate viscosity (Pa � s)ms suspension viscosity (Pa � s)fw particle volume fraction at membrane surface (�)

fb particle volume fraction in bulk stream (�)

The authors acknowledge many Amgen individuals in process

engineering, cellular process development, and pilot plant. In

particular, we would like to thank Steve Decker, Tina Kim, Raj

Krishnan, and Stephanie Tozer for providing Pichia fermentation

broth, Libby Russell for her work in centrifugation development and

Oliver Kaltenbrunner and Acke Stokelman for helpful discussions.We

would also like to extend our acknowledgement to Sarah Hove and

Kara Lounsbury of Millipore, Sharon Squires and Craig Robinson of

GEHealthcare,GlennHiroyasu of Pall andFredHutchison of Sartorius

for their technical assistance.

References

Bacchin P, Aimar P. Sanchez V. 1995. Model for colloidal fouling of

membranes. AICHE J 41(20):368–376.

Bailey SM, Meagher MM. 1997. Crossflow microfiltration of recombinant

E.coli lysates after high-pressure homogenization. Biotechnol Bioeng

56:304–310.

Belfort G, Davis R, Zydney A. 1994. The behavior of suspensions and

macromolecular solutions in crossflow microfiltration. J Membr Sci

96:1–54.

Bell DJ, Davies RJ. 1987. Cell harvesting of oleaginous yeast by crossflow

filtration. Biotechnol Bioeng 29:1176–1178.

Berthold W, Kempken R. 1994. Interaction of cell culture with downstream

purification: A case study. Cytotechnology 15:229–242.

Byrne MP, Titball RW, Holley J, Smith, LA. 2000. Fermentation, purification

and efficacy of a recombinant vaccine candidate against botulinum

neurotoxin type F from Pichia pastoris. Protein Expr Purif 18:327– 337.

Cheryan M. 1998. Ultrafiltration and microfiltration handbook. Lancaster:

Technomic Inc. 133p.

Clarkson A, Bulmer M, Titchener-Hooker NJ. 1996. Pilot-scale verification

of a computer-based simulation for the centrifugal recovery of

biological particles. Bioprocess Eng 14:81–89.

da Matta VM, Medronho RA. 2000. A new method for yeast recovery in

batch ethanol fermentations: Filter aid filtration followed by separation

of yeast from filter aid using hydrocyclones. Bioseparation 9:43–53.

Davis RH. 2001. Crossflowmicrofiltration with back pulsing. In:WangWK,

editor. Membrane separations in biotechnology. New York: Marcel

Dekker, Inc. pp 161–188.

Heertjes PM, Zuideveld PL. 1978a. Clarification of liquids using filter aids

Part I. Mechanisms of filtration. Powder Technol 19:17–30.

Heertjes PM, Zuideveld PL. 1978b. Clarification of liquids using filter aids

Part II. Depth filtration. Powder Technol 19:31–43.

Hurst B. 2004. Dynamic depth-filtration: Proof of principle, technical note

AMC06 v. 3.1, Advanced Minerals Corporation.

Jacob J, Pradanos P, Calvo JI, Hernandez A, Jonsson G. 1998. Fouling

kinetics and associated dynamics of structural modifications. Colloids

Surf A: Physicochem Eng Aspects 138:173–183.

Kwon DY, Vigneswaran S, Fane AG, Ben AR. 2000. Experimental

determination of critical flux in crossflow microfiltration. Separation

Purif Technol 19:169–181.

Lee SS, Burt A, Russotti G, Buckland B. 1995. Microfiltration of

recombinant yeast cells using a rotating disk dynamic filtration system.

Biotechnol Bioeng 48:386–400.

Li A, Crimmins DL, Luo Q, Hartupee J, Landt Y, Ladenson JH, Wilson D,

Anant S, Dieckgraefe BK. 2003. Expression of a novel regenerating

gene product, Reg IV, by high density fermentation in Pichia pastoris:

Production, purification, and characterization. Protein Expr Purif

31:197–206.

Luque S, Mallubhotla H, Gehlert G, Kuriyel R, Dzengeleski S, Pearl S,

Belfort G. 1999. A new coiled hollow-fiber module design for enhanced

microfiltration performance in biotechnology. Biotechnol Bioeng 65:

247–257.

Patel PN, Mehaia MA, Cheryan M. 1987. Crossflow membrane filtration of

yeast suspensions. J Biotechnol 5:1–16.

PerryR,GreenD. 1984. Perry’s chemical engineers’ handbook, 6th edn.New

York: McGraw-Hill, Inc. pp 19–66.

Postlethwaite J, Lamping SR, Leach GC, Hurwitz MF, Lye GJ. 2004. Flux

and transmission characteristics of a vibrating microfiltration system

operated at high biomass loading. J Membr Sci 228:89–101.

Rathore AR,WangA,MenonM, Riske F, Campbell J, Goodrich E,Martin J.

2004. Optimization, scale-up and validation issues in filtration of

biopharmaceuticals–Part I. Biopharm Int 17(8):50–58.

Redkar SG, Davis RH. 1993. Crossflow microfiltration of yeast suspensions

in tubular filters. Biotechnol Prog 9:625–634.

Reynolds T, Boychyn M, Sanderson T, Bulmer M, More J, Hoare M. 2003.

Scale-down of continuous filtration for rapid bioprocess design:

Recovery and dewatering of protein precipitate suspensions. Biotechnol

Bioeng 83:454–464.

RussottiG,GoklenKE. 2001.Crossflowmembrane filtration of fermentation

broth. In: Wang WK, editor. Membrane separations in biotechnology.

New York: Marcel Dekker, Inc. pp 85–160.

Russotti G, Osawa AE, Sitrin RD, Buckland BC, AdamsWR, Lee SS. 1995.

Pilot plant harvest of recombinant yeast employing microfiltration: A

case study. J Biotechnol 42:235–246.

Schlegel VL, Meagher M. 2001. Effects of different membrane

modular systems on the performance of crossflow filtration of

Pichia pastoris suspensions. In: Wang WK, editor. Membrane

separations in biotechnology. New York: Marcel Dekker, Inc. pp 189–

204.

Schutyser M, Rupp R, Wideman J, Belfort G. 2002. Dean vortex membrane

microfiltration and diafiltration of rBNDF E. coli inclusion bodies.

Biotechnol Progress 18:322–329.

Sheehan JJ, Hamilton BK, Levy PF. 1988. Pilot-scale membrane filtration

process for the recovery of an extracellular bacterial protease. ACS

Symp Ser 419:130–155.

Shimizu Y, Shimidera K, Watanabe A. 1993. Cross-flow microfiltration of

bacterial cells. J Ferment Bioeng 76(6):493–500.

Tanaka T, Kamimura R, Itoh K, Nakanishi K. 1992. Factors affecting the

performance of crossflow filtration of yeast cell suspension. Biotechnol

Bioeng 41:617–624.

Theodossiou I, Collins IJ, Ward JM, Thomas ORT, Dunnill P. 1997. The

processing of a plasmid-based gene from E. coli. Primary recovery by

filtration. Bioprocess Eng 16:175–183.



van Reis R, Zydney A, 2001. Membrane separations in biotechnology. Curr

Opin Biotechnol 12:208–211.

van Reis R, Leonard L, Hsu C, Builder S. 1991. Industry scale harvest of

protein from mammalian cell culture by tangential flow filtration.

Biotechnol Bioeng 38:413–422.

VargaEG,Tichener-HookerNJ,Dunnill P. 2001. Prediction of the pilot-scale

recovery of a recombinant yeast enzyme using integrated models.

Biotechnol Bioeng 74(2):96–107.

Winter C. 2004. Development, characterization, validation and implementa-

tion of Genentech’s CHO harvest operations. Abstracts of Paper, 227th

ACS National meeting. BIOT-156.

Yavorsky D, Mcgee S. 2002. Selection and sizing of clarification depth

filters. Genet Eng News 22(9):44–45.

Yavorsky D, Blanck R, Lambalot C, Brunkow R. 2003. The clarification of

bioreactor cell cultures for biopharmaceuticals. Pharm Technol March:

1–8.

Zeman L, Zydney A. 1996. Microfiltration and ultrafiltration, princi-

ples and applications. New York: Marcel Dekker, Inc. pp 372–

374.

Zydney AL, Colton K. 1986. A concentration polarization model for the

filtrate flux in crossflowmicrofiltration of particulate suspensions. Chem

Eng Commun 47:1–21.


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