DESign Space

ARTICLE

Defining Process Design Space for MonoclonalAntibody Cell Culture

Susan Fugett Abu-Absi, LiYing Yang, Patrick Thompson, Canping Jiang,Sunitha Kandula, Bernhard Schilling, Abhinav A. Shukla

Manufacturing Sciences & Technology, Bristol-Myers Squibb Co., 6000 Thompson Road,

East Syracuse, New York 13057; telephone: þ315-431-7926; fax: þ315-432-2343;

e-mail: [email protected]

Received 31 August 2009; revision received 16 February 2010; accepted 5 April 2010

Published online 16 April 2010 in Wiley InterScience (www.interscience.wiley.com).
DOI 10.1002/bit.22764
ABSTRACT: The concept of design space has been takingroot as a foundation of in-process control strategies forbiopharmaceutical manufacturing processes. During map-ping of the process design space, the multidimensionalcombination of operational variables is studied to quantifythe impact on process performance in terms of productivityand product quality. An efficient methodology to map thedesign space for a monoclonal antibody cell culture processis described. A failure modes and effects analysis (FMEA)was used as the basis for the process characterization exer-cise. This was followed by an integrated study of the inocu-lum stage of the process which includes progressive shakeflask and seed bioreactor steps. The operating conditions forthe seed bioreactor were studied in an integrated fashionwith the production bioreactor using a two stage design ofexperiments (DOE) methodology to enable optimization ofoperating conditions. A two level Resolution IV design wasfollowed by a central composite design (CCD). Theseexperiments enabled identification of the edge of failureand classification of the operational parameters as non-key,key or critical. In addition, the models generated from thedata provide further insight into balancing productivity ofthe cell culture process with product quality considerations.Finally, process and product-related impurity clearance wasevaluated by studies linking the upstream process withdownstream purification. Production bioreactor parametersthat directly influence antibody charge variants and glyco-sylation in CHO systems were identified.

Biotechnol. Bioeng. 2010;106: 894–905.

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KEYWORDS: cell culture; design space; quality by design;monoclonal antibody

Introduction

The quality by design (QbD) paradigm for biopharmaceu-tical manufacturing processes has emerged from the cGMPs

Correspondence to: A. A. Shukla

894 Biotechnology and Bioengineering, Vol. 106, No. 6, August 15, 2010

for the 21st century initiative taken by the Food and DrugAdministration (FDA, 2004). QbD involves three primarycomponents (Rathore andWinkle 2009): process knowledgethat includes a thorough understanding of process inputsand their impact on performance, the relationship betweenthe process and a product’s critical quality attributes (CQAs)and the association between CQAs and a product’s clinicalproperties. The QbD approach is expected to ‘‘build qualityinto the process’’ rather than ‘‘testing quality into the pro-duct.’’ As such, the QbD philosophy is anticipated to beimplemented throughout the lifecycle of a product—starting with process design and definition, moving on toprocess characterization and validation and finally to moni-toring and control of the commercial manufacturing process(Kozlowski and Swann, 2006; Rathore and Winkle, 2009).Some of the anticipated benefits of QbD for the biopharma-ceutical industry include a reduction in the number ofpost-commercial filings for process changes and a modernapproach to biopharmaceutical product quality leading togreater flexibility for the manufacturer.

The concept of ‘‘design space’’ for the manufacturingprocess is inherent as one of the three primary componentsof the QbD paradigm (Rathore et al., 2007). The ICH Q8guidance document defines design space as ‘‘the multi-dimensional combination and interaction of input variablesthat have been demonstrated to provide an assurance ofquality’’ (FDA, 2006, 2009). The expectation of flexibilitywithin the design space is inherent in the guidance; ‘‘workingwithin the design space is not considered a change.’’ Theguidance goes on to state ‘‘design space is proposed by theapplicant and is subject to regulatory assessment andapproval.’’ The design space concept is better established insmall molecule pharmaceutical manufacturing (Lipsanenet al., 2007; Nail and Searles, 2008). A systematic approachtoward the creation of a design space understanding of abiopharmaceutical manufacturing process is essential for thecreation of a robust and well-controlled process in-line withthe QbD paradigm (Rathore et al., 2008, 2009).

� 2010 Wiley Periodicals, Inc.

Figure 1. Strategy used to establish the process design space for the unit

operations of a biopharmaceutical manufacturing process.

Process characterization studies are performed at labora-tory scale (Seely and Seely, 2003) using a qualified scale-down model (Li et al., 2006) to define the design space. Theoperational parameters that will be studied during processcharacterization are prioritized by a risk analysis approachsuch as failure modes and effects analysis (FMEA) (FDA,2009; Seely and Haury, 2005). Process characterizationexperiments are typically conducted using a design of exp-eriments (DOE) approach. This has been applied towardthe characterization of individual downstream processingsteps (Kelley et al., 1997, 1998; Shukla et al., 2001). Thesetechniques have also been applied for the definition ofdesign space for microbial fermentation products (Harmset al., 2008; Rathore et al., 2008).

During the process characterization exercise, each oper-ational parameter is studied over a wide range called thecharacterization range. The aim is to understand processoperation in a range wider than that used for routineprocess operation. The acceptable range for any parameteris typically between the operating range and the character-ization range. The use of a design of experiments (DOE)methodology for process characterization enables a studyof parameter interactions and classification of parametersbased on their impact to productivity and product quality.

A tiered classification of operational parameters as non-key, key or critical and their operating and acceptable rangesform the in-process control (IPC) strategy for that product.The IPC strategy also utilizes in-process operating ranges,action limits and acceptance criteria to ensure the consistentmonitoring and control of the manufacturing process. Non-key operational parameters are easily controlled and/orhave wide operating ranges. Key operational parametersare essential for process performance considerations, inclu-ding productivity and scheduling. If varied outside theiraction limit, key operational parameters may affect processperformance, but not CQAs. Critical process parameters(CPPs) are operational (input) or performance (output)parameters that could affect the CQAs of the drug substanceif varied outside their acceptable range.

Figure 1 shows a flow diagram for the strategy usedto define the design space for a process step. The FMEAexercise enables operational parameters with low riskpriority number (RPN) scores to be eliminated from furtherstudy. Next, low resolution DOE studies using Resolution IIIor IV factorial designs are used for screening operationalparameters and eliminating non-key parameters. A higherresolution response surface methodology (RSM) design isused to study the remaining key process parameters. Forworst-case experiments, key operational parameters arecombined to create a worst-case outcome with respect to aparticular performance parameter for that step. This worst-case material is passed on to subsequent steps to determineif the proposed key operational parameter ranges are acc-eptable. Worst-case studies also enable critical processparameters to be identified. If a characteristic of the proteindoes not change significantly in subsequent steps, opera-tional parameters that affect it may be defined as CPPs in the

upstream step. For example, if the N-Linked oligosaccharideprofile does not change through the purification steps, theoperational parameters that affect it in the productionbioreactor would be defined as CPPs.

A typical mammalian cell culture process, as shown inFigure 2A, is comprised of a series of steps starting with vialthaw and proceeding through several stages of inoculumexpansion. The inoculum is used to initiate the productionbioreactor culture. The cells in the production bioreactor,typically operated in fed-batch mode, express and secretethe protein product. At the end of the production bioreactorstep, the cells and cell debris are separated from the cellculture supernatant, which is taken through downstreamprocess steps designed to remove impurities. Given itsmulti-stage nature, process characterization for the upst-ream process can be a time and resource intensive process. Astreamlined approach to upstream process characterizationis needed to provide a rapid understanding of the processdesign space and enable the creation of an appropriate IPCstrategy. However, the current literature has a paucity of casestudies describing the development of a design space forproducts produced by mammalian cell culture. Moreover,the linkage between cell culture process conditions andproduct quality has not been adequately explored for mam-malian cell culture.

This paper describes a holistic approach for defining theprocess design space for a monoclonal antibody derived bymammalian cell culture. A streamlined experimental designwas used to link steps in the inoculum stages with theproduction bioreactor. A linkage between upstream process

Abu-Absi et al.: Defining Cell Culture Design Space 895

Biotechnology and Bioengineering

Figure 2. A: Upstream steps of the monoclonal antibody cell culture process. B: Failure mode and effects analysis (FMEA) Ishikawa diagrams for the seed bioreactor and

production bioreactor steps. Underlined parameters were studied.

conditions and the downstream process was created. Acomprehensive understanding of raw material variabilityrequires significant at-scale manufacturing experience andwill be dealt with in a future report.

Materials and Methods

Cell Culture

The monoclonal antibody studied was expressed in ChineseHamster Ovary (CHO) cells. Cells were grown in suspensionusing proprietary cell culture media. Shake flasks (CorningIncorporated, Corning, NY) were cultured in VWR,International (West Chester, PA) CO2 incubators usingThermo Scientific, Inc. (Waltham, MA) MaxQ shaker plat-forms. The shake flask studies were conducted at the samescale and in the same disposable flasks used in manufactur-ing. The seed and production bioreactor steps were studiedin 7-L stirred-tank bioreactors (Sartorius, Goettingen,Germany). The 7-L bioreactors were operated as scale-down models of the manufacturing bioreactors. Specific


growth rate in the seed bioreactor was calculated by linearregression to determine the slope of the natural log of viablecell density versus culture duration.

Downstream Processing

Culture from the 7-L production bioreactors was harvestedusing centrifugation followed by a series of depth andabsolute filters. CUNOMaximizer EXT filters (3M, St. Paul,MN) were loaded at 200 L/m2 followed by sterile filtrationusing Acrodisc filters (Pall, East Hills, NY). The harvest poolwas subsequently purified via lab-scale MabselectTM (GEHealthcare, Piscataway, NJ) Protein A chromatography.

Analytical Methods

A Bioprofile Analyzer 400 (Nova Biomedical, Waltham,MA) was used to monitor pH, dissolved oxygen andmetabolite concentrations. Cell count measurements werecarried out with a Cedex automated cell counter (Innovatis

AG, Germany). Titer was measured by HPLC analysis ofbinding to Protein A. Size exclusion chromatography wasutilized to quantify the amount of high molecular weight(HMW) species. Charge variants were measured with ananalytical cation exchange chromatography (CEX) HPLCmethod. N-Linked glycosylation was measured by HighPerformance Anion-Exchange Chromatography with PulsedAmperometric Detection (HPAEC-PAD).

Results and Discussion

Risk Assessment

The first step toward mapping the process design space is toidentify factors (operational parameters) and to assess the

Table I. FMEA results.

Responses considered Parameter

Effects on seed bioreactor

growth rate and final viability

Seed bioreactor initial viable cell d

Seed bioreactor temperaturea

Shake flask incubator carbon

dioxide concentrationa

Seed bioreactor pHa

Shake flask initial viable cell densi

Shake flask final viable cell density

Shake flask incubator temperatureb

Shake flask agitation speed

Shake flask working volume

Seed bioreactor DO

Seed bioreactor aeration

Seed bioreactor agitation speed

Vial thaw waterbath temperature

Vial thaw duration

Effects on production bioreactor

productivity and product quality

Production bioreactor temperature

Production bioreactor initial

viable cell densitya

Seed bioreactor temperaturea

Production bioreactor pHa

Production bioreactor temp shift t

Production bioreactor durationc

Production bioreactor DOa

Seed bioreactor pHa

Seed bioreactor final viable cell den

Seed bioreactor durationc

Production bioreactor feed timing a

Seed bioreactor agitation speed

Seed bioreactor aeration

Seed bioreactor DO

Seed bioreactor initial viable cell den

Production bioreactor aeration

Production bioreactor agitation spee

Severity of the excursion: S¼ 1: negligible impact, 2: potential impact to mproduct quality, 5: potential impact to yield and product quality. Occurrence(high)–3 (low). Risk Priority Number (RPN)¼ S�O�D. Factors chosen for

aParameters evaluated in screening study.bShake flask incubator temperature was not studied although it received an RP

temperature on cell growth and viability was assessed only in the seed bioreaccDuration in the seed bioreactor and production bioreactor were not includ

evaluated by examining final viable cell density in the seed bioreactor. Durationquality from days 12 to 17.

risk each of them poses to process robustness. A formal riskassessment exercise that includes the development history ofthe process and an understanding of manufacturing controlsis highly recommended. This exercise enables identificationof factors that could influence important outcomes fromthe cell culture process (culture health, product titer andproduct quality). It also enables a reduction in the numberof factors that require study during process characterization.

An FMEA exercise was conducted for the inoculumexpansion, seed bioreactor and production bioreactor stepsto identify process variables that could influence cell cultureperformance. Figure 2B shows the Ishikawa diagrams thathelped identify factors for further study. Table I shows thefactors ranked based on the RPN scores. The threshold RPNscore was determined based on an acceptable level of risk,taking into consideration the amount of process knowledge

S (1–5) O (1–3) D (1–3) RPN (1–45)

ensitya 2 2 2 8

2 2 2 8

2 2 1 4

2 2 1 4

tya 2 1 2 4a 2 2 1 4

2 2 1 4

2 1 1 2

2 1 1 2

2 1 1 2

2 1 1 2

2 1 1 2

1 1 1 1

1 1 1 1a 5 2 2 20

3 3 2 18

3 2 2 12

5 2 1 10

iminga 5 1 2 10

5 2 1 10

4 2 1 8

4 2 1 8

sitya 3 2 1 6

3 2 1 6

nd volume 3 1 1 3

2 1 1 2

2 1 1 2

2 1 1 2

sity 1 1 2 2

2 1 1 2

d 2 1 1 2

anufacturing schedule, 3: potential impact to yield, 4: potential impact toof the excursion: O¼ 1 (low)–3 (high). Detection of the excursion: D¼ 1study are shown in bold font.

N score above the threshold. For efficiency in experimentation, the effect oftor.ed as factors in the studies. Duration in the seed bioreactor was indirectlyin the production bioreactor was indirectly evaluated by examining product



Table II. Factors studied to establish the IPC.

Code Factor

Range (coded)

evaluated during

screening studies

Range (coded)

evaluated during

CCD studies

A Incubator carbon dioxide �1 to þ1

B Shake flask initial viable cell density �1 to þ1

C Shake flask final viable cell density �1 to þ1

D Seed bioreactor temperature �1 to þ1

E Seed bioreactor pH �1 to þ1

F Seed bioreactor initial viable cell density �1 to þ1

G Seed bioreactor final viable cell density �1 to þ1

H Production bioreactor initial viable cell density �1 to þ1 �1 to þ1.5

I Production bioreactor temperature �1 to þ1 �2 to þ2

J Production bioreactor temperature shift timing �1 to þ1

K Production bioreactor pH �1 to þ1 �1 to þ1

L Production bioreactor dissolved oxygen �1 to þ1

gained during development. As an example, the effects of thefeed scheme (amount and timing of feeds) were notevaluated in the production bioreactor screening study sincefeeds are well controlled and were examined and optimizedduring process development. The process utilizes severalfixed volume feeds that are administered at specific culturedurations. The thresholds for the inoculum screening studyand production bioreactor screening study were 4 and 6,respectively. Factors chosen for study are underlined inFigure 2B and shown in bold font in Table I. Table IIdescribes the factors studied and the code that identifiesthem in the subsequent figures.

Figure 3. The qualification of the 7-Lscale-down model included a comparison o


Scale-Down Model Qualification

The 7-L scale-down production bioreactor model wasqualified by comparing profiles of viable cell density,viability, metabolites and titer in addition to antibodycharacteristics measured by SEC and N-Linked oligosac-charide profiles to results from commercial scale. Results forviable cell density, viability and normalized titer are shownin Figure 3. The viable cell density and viability trends at thetwo scales were comparable. The titer was slightly lower atcommercial scale than in the 7-L scale-down bioreactors.The protein produced by the 7-L scale-down bioreactors had

f (A) the viable cell density and viability profiles and (B) normalized titer results.

comparable HMW and N-Linked oligosaccharide profile tocommercial scale.

Inoculum Screening Studies

The purpose of the vial thaw and inoculum steps insuccessive shake flasks is to produce the minimum numberof viable cells required to inoculate the subsequent culturevessel. To achieve an efficient design, combinations ofoperating conditions in the shake flask stage were evaluatedtogether with those in the seed bioreactor to study theircollective impact on the entire inoculum expansion process.A DOE approach was utilized to study the impact ofinoculum expansion and seed bioreactor parameters on cellculture performance at the seed bioreactor stage. A two-levelResolution IV factorial design was conducted. A total of 24runs from vial thaw through 7-L scale-down seed bioreactorwere conducted in two blocks. Factors A through F inTable II were studied and evaluated for their effect on seedbioreactor growth rate and final viability.

Multiple seed bioreactors are often utilized in cell culturemanufacturing processes. The function of the first seedbioreactor(s) is to expand culture volume. The final seedbioreactor (the n� 1 seed bioreactor) is used to inoculatethe production bioreactor and has a greater potential toimpact the performance of the production bioreactor. Inthis report, the seed bioreactor step was evaluated in twostudies. During the inoculum screening study, the effects ofseed bioreactor parameters on cell growth and viability inthe seed bioreactor were evaluated. In the productionbioreactor screening study described below, the effects of then� 1 seed bioreactor parameters on the performance of theproduction bioreactor were evaluated.

The shake flasks were cultured at different incubatorcarbon dioxide setpoints and were seeded at varying initialviable cell densities and passaged when the target final viable

Table III. ANOVA models for seed bioreactor and production bioreactor res

Response Model

Seed bioreactor

specific growth

rate (day 1)

0.512þ 0.049E� 0.044EFþ 0.034B� 0.031Cþ 0.0

0.027Aþ 0.026DE� 0.022BFþ 0.019BC� 0.019B

0.018BE� 0.014Dþ 0.013BEFþ 0.012DF

Seed bioreactor final

viability (%)

96.65� 1.19F� 1.18D� 1.14EF� 1.10BEþ1.03B

1.03DEþ 0.63B� 0.60BDE� 0.56BFþ 0.51Aþ 0.4

0.16C� 0.05E

Titer (normalized) 1.00� 0.18D� 0.13DHþ 0.12J� 0.10Lþ 0.09Hþ 0

0.08HKþ 0.06Gþ 0.06K

HMW (%) 1.51þ 0.24Jþ 0.16K� 0.14L

G0F (%) 60.68þ 5.37Kþ 3.40H� 2.42GE� 2.17EK� 1.5

1.50Gþ 0.40E

G1F (%) 29.14� 3.87K� 3.18Hþ 2.32HJþ 1.99IJþ1.43G� 1.34IK� 0.83Jþ 0.47I

CEX Peak 1 (%) 7.88þ 0.86Iþ 0.43HKþ 0.34Hþ 0.11K

CEX Peak 6 (%) 3.07þ 0.32Kþ 0.22Lþ þ 0.20Dþ 0.19HL�0.14I� 0.14DH� 0.12H� 0.10E

Coded factor values range from �1 to þ1.

cell density was achieved. The average culture duration forthe shake flask steps ranged from 35 to 126 h. To ensuresufficient cell doublings for the culture to show effects fromsub-optimal conditions, shake flasks were repeatedly pass-aged to at least 10 cell doublings prior to inoculation of theseed bioreactors. The seed bioreactors were inoculated atvarying initial viable cell densities and operated at differenttemperature and pH setpoints. The viability and growth ratein the seed bioreactors were determined after 4 days.

Regression models for the specific growth rate and finalviability in the seed bioreactor are shown in Table III. Theequations are described in terms of coded factors. Duringthe execution of the studies, factors were studied at low,midpoint and high levels. In the model equations, the valuesof the coded factors at the midpoint condition are zero andthe values at the low and high conditions are �1 and þ1,respectively. The R-Squared term (R2) is an indication ofhow well the model fits the experimental data. However, thisvalue will inherently increase as more factors are included inthe model. The adjusted R2 is also an indication of how wellthe model fits the experimental data, taking into account thenumber of factors evaluated. The predicted R2 is calculatedusing regression analysis and is used to indicate how wellthe model will predict responses for new combinations offactors within the �1 to þ1 ranges evaluated to developthe model. The P-value indicates the overall significance ofthe model. Since the study was a Resolution IV design,interactions of two factors (e.g., CD) were aliased with othertwo parameter interactions. Three parameter interactions(e.g., BCF) were also aliased with other three parameterinteractions. To determine which interaction to include inthe model, a comparison of the R2 values and F-valueswas conducted. The aliased terms were chosen so that theresulting model had the maximum F-value as well asadjusted R2 and predicted R2 values that were withinreasonable agreement with each other.

ponses from screening studies.

R2 Adjusted R2 Predicted R2Model

P-value

31F�CF�

0.99 0.97 0.62 <0.0001

Dþ4BCþ

0.99 0.97 0.73 <0.0001

.08GJ� 0.88 0.77 0.61 0.0008

0.54 0.46 0.47 0.0035

0I� 0.89 0.83 0.66 <0.0001

0.94 0.90 0.74 <0.0001

0.59 0.49 0.22 0.0047

0.93 0.89 0.79 <0.0001



All six factors studied and several interactions hadsignificant effects (P< 0.01) on specific growth rate in theseed bioreactor. Key process parameters were selected bycomparing the magnitude of the effects to the results ofthe centerpoint conditions. The relative standard deviationof specific growth rate for the centerpoint runs was 5%.Therefore, significant model factors with coefficients greaterthan 5% of the model-predicted centerpoint growth rate,(>0.05� 0.512¼>0.026), were considered for designationas key process parameters. These factors included E, EF, B,C, F, and A. Shake flask carbon dioxide concentration (A)was not designated as a key process parameter since it iseasily controlled within the range studied and the inter-action EF was not designated since it was aliased with othertwo-parameter interactions. Shake flask initial viable celldensity (B), shake flask final viable cell density (C), seedbioreactor pH (E) and seed bioreactor initial viable celldensity (F) were selected as key process parameters due totheir effects on growth rate in the seed bioreactor.

The final viability in the seed bioreactors was found to behigh throughout the design, as shown in Figure 4. Themodelterms with significant effects (P< 0.01) on seed bioreactorfinal viability included B, D, F, and several interactions.However, no process parameters were designated as key forimpact to final viability since the magnitude of the effectswas small and the variability of the response was negligible.The relative standard deviation of final viability for thecenterpoint runs was less than 1%.

The inoculum studies enabled a rapid assessment of theshake flask and seed bioreactor steps. They confirmedthe robustness of the inoculum expansion steps and theappropriateness of the operating ranges.

Figure 4. Summary of final viability results from the seed bioreactor study. All comb

robustness of the inoculum expansion process.


Production Bioreactor Screening Studies

Factors D, E, and G through L were studied for their effecton the performance of the production bioreactor. Operatingconditions in the final seed bioreactor and in the productionbioreactor were studied in combination to enable an effi-cient experimental design. In addition, the study enabled anassessment of the impact of seed bioreactor operationalparameters on the production bioreactor. A two-levelResolution IV factorial design was conducted in 24 runs intwo blocks. The operating ranges for A, B, C, and F were inalignment with the control strategy defined by the inoculumstudy. The performance of the production bioreactors wasassessed by examining final product titer, percentage ofhigh molecular weight (HMW) aggregate species, N-Linkedoligosaccharide profile and analytical CEX charge variantprofile.

In addition to the desired protein, process-relatedimpurities and product variants are produced in cellculture. Controlling these species is crucial for achievingthe desired product quality in drug substance. This isparticularly important for the demonstration of productcomparability with a previous version of the manufacturingprocess. HMW aggregate levels represent a key area ofconcern for biopharmaceuticals due to their potentialimmunogenicity. This product-related impurity poses achallenge for clearance in the downstream purificationprocess. Therefore, it is important to quantify the impactof production bioreactor operating conditions on HMWlevels.

While glycosylation has not been directly linked toefficacy for this product, N-Linked glycosylation patterns

inations of conditions resulted in cell viabilities greater than 90%, demonstrating the

Figure 5. Trends in N-Linked oligosaccharide profile with culture duration for

factorial and centerpoint conditions. A: Factorial points for G0F trended lower than the

centerpoint results and (B) factorial points for G1F trended higher than the centerpoint

results.

have been shown to impact effector functions for mono-clonal antibodies (Jefferis, 2005). Glycosylation is known tobe directly influenced by the cell culture process conditionsand is typically not significantly altered through down-stream purification for monoclonal antibodies. Hence, con-trol of the upstream process is important to ensure thecorrect ratio of glycoforms. The N-Linked oligosaccharideprofile was quantified in terms of percent distribution betweenthree isoforms, G0F, G1F, and G2F. The isoforms are desi-gnated as GXF, where X represents the number of terminalgalactose units on the biantennary structure (Jefferis, 2005).

Charge variants are monitored to assess the heterogeneityof the product. Methods such as isoelectric focusing (IEF)are typically included in the product release analyticaltesting for biopharmaceuticals. An analytical CEX methodwas developed to characterize the charge variants for thisproduct. The profile results in several peaks. Peak 1 has beenidentified as deamidated forms of the antibody. Peak 6 hasbeen shown to correlate with increased basic banding in IEF.Use of the CEX method provided quantitative results bywhich to characterize the production bioreactor step. Inaddition, the CEX profile remains unchanged throughthe downstream process (not shown), so control at theproduction bioreactor step is necessary.

The centerpoint 7-L production bioreactor runs attaineda day 14 normalized titer range of 1.4–1.8 and a HMW rangeof 1.7–2.3%. Titer results were normalized to the model-predicted centerpoint titer. The factorial runs attained aday 14 normalized titer range of 0.5–1.8 and a day 14 HMWrange of 1.0–2.6%. The amount of HMW remained low(<3.1%) for the duration of each of the runs (up to 17 days).The overall N-Linked oligosaccharide profile showed littlevariation with culture duration as shown in Figure 5. Ingeneral, an overall minor increase was observed for the G0Fpeak, while an overall minor decrease was observed for theG1F peak and G2F peak (not shown) between days 12 and17. The G0F results for the factorial conditions trendedlower than the centerpoint conditions and G1F factorialresults trended higher than centerpoint conditions.Therefore, when interpreting the results of the studies,low G0F and high G1F were considered unfavorable.

The models generated from this dataset for normalizedtiter, HMW, N-Linked oligosaccharide profile and CEXprofile are shown in Table III. Most of the factors studied forthe production bioreactor had an influence on the finalproduct titer. In contrast, only a few parameters had aninfluence on the high molecular weight aggregate level andCEX Peak 1 at harvest. Several parameters influenced theN-Linked oligosaccharide profile and CEX Peak 6.

The model terms with significant (P< 0.05) effects ontiter were D, J, L, H, DH, and GJ. The relative standarddeviation of day 14 titer from the centerpoint runs was 8.6%.Therefore, significant model factors with coefficients greaterthan 8.6% of the model-predicted centerpoint titer,(>0.086� 1.0¼>0.086), were selected as key process para-meters. The interaction of DH was not identified askey due to the fact that it was aliased with other two-

parameter interactions. Seed bioreactor temperature (D),production bioreactor temperature shift timing (J), produc-tion bioreactor pH (L) and production bioreactor initialviable cell density (H) were selected as key process para-meters due to their individual effects on titer. The seedbioreactor temperature and timing of the temperature shiftin the production bioreactor had a significant influence ontiter due to their effect on peak viable cell density. Lowerseed bioreactor temperatures and later temperature shiftscorrelated with higher peak viable cell density (not shown).Likewise, higher titer correlates with higher peak viable celldensity.

HMW levels did not vary significantly for the factorialconditions compared to the centerpoint conditions, andhence the correlation to operational parameters was weak.This is an important result since it shows that this product-related impurity level was not significantly affected by theseed bioreactor or production bioreactor parameters. Themodel terms with significant (P< 0.05) effects on HMWwere J and K. However, no process parameters were desi-gnated as key for impact to HMW because the magnitude ofthe effects was small and the variability of the response wasnegligible. The relative standard deviation of HMW for thecenterpoint runs was 10%.

The model terms with significant (P< 0.05) effects on theG0F peak were K, H, GE, and EK. The relative standarddeviation of the G0F results for the centerpoint runs was 3%.



Therefore, significant model factors with coefficients greaterthan 3% of the model-predicted centerpoint G0F peak,(>0.03� 60.7¼>1.8), were selected as key process para-meters. The interactions GE and EK were not identified askey due to the fact that they were aliased with other two-parameter interactions. Production bioreactor pH (K) andinitial viable cell density (H) were selected as key processparameters due to their individual effects on G0F.

For the G1F peak, the model terms with significant effects(P< 0.05) were K, H, G, HJ, IJ and IK. The relative standarddeviation of the day 14 G1F peak from the centerpoint runswas 5%. Therefore, significant model factors with coeffi-cients greater than 5% of the model!predicted centerpointG1F peak, (>0.05� 29.1¼>1.5), were selected as key processparameters. The interactions HJ and IJ were not identified askey due to the fact that they were aliased with other two-parameter interactions. Production bioreactor pH (K) andinitial viable cell density (H) were selected as key processparameters due to their individual effects on G1F.

The relative standard deviation of the day 14 CEX resultsfor the centerpoint runs was 8%. Therefore, factors witheffects greater than 8% of the model-predicted centerpointvalue, (>0.08� 7.9¼>0.63; >0.08� 3.1¼>0.25), wereconsidered to be key. Production bioreactor temperature (I)was the only model term with significant (P< 0.05) effecton CEX Peak 1. It was designated as a key parameter, sincethe magnitude of its effect was 11% of the model-predictedcenterpoint value. Factors with significant (P< 0.05) effecton CEX Peak 6 were K, L, D, HL, I, DH, H and E. Productionbioreactor pH (K) was designated as a key parameter, sincethe magnitude of its effect was 10% of the model-predictedcenterpoint value.

Figure 6. Overlay plots showing results of production bioreactor study. A: Production

Shaded areas represent regions of concern in terms of N-Linked oligosaccharide and CE


Figure 6 shows overlay plots summarizing the results ofthe production bioreactor screening study. Regions of con-cern in terms of high levels of CEX Peaks 1 and 6 andunfavorable trends in N-Linked oligosaccharide profile areshaded dark gray. The normal operating ranges are indicatedin the plots and are a safe distance from the regions ofconcern. Of the key parameters identified (B, C, D, E, F, H, I,J, K, and L) three parameters (H, I, and K) were chosen forfurther study due to their potential to impact the criticalquality attributes of the drug substance. An RSM study wasdesigned to verify the designation of these parameters asCPPs and to confirm their acceptable ranges.

Impurity Clearance Through the Downstream Process

In addition to HMW levels described earlier, it is importantto determine if the levels of other process-related impurities,such as DNA and host cell protein (CHOP) from theChinese hamster ovary cells, can be correlated to upstreamoperational parameters. This can help assess the burden onthe downstream process to clear impurities. For this process,only a weak correlation was observed between in the overalllevel of CHOP in the cell culture harvest and the final non-viable cell density in the production bioreactor (not shown).However, a better correlation was observed between theCHOP levels after the first chromatography step in thedownstream purification process (Protein A chromatogra-phy) and the cell culture performance in the productionbioreactor (not shown). A small percentage of cells in thebioreactor lyse prior to and during harvest, releasing hostcell protein impurities into the cell culture fluid. Increased

bioreactor pH versus temperature and (B) initial viable cell density versus temperature.

X profiles. The normal operating ranges are also shown.

final non-viable cell density implies a larger release of CHOPfrom dying cells. It has been shown that monoclonalantibodies can bind CHOP from cell culture harvest fluidand carry these proteins through Protein A purification(Shukla and Hinckley, 2008). Higher levels of CHOP in theharvest fluid are often not independently observed due tothe high overall CHOP level and the relative insensitivity ofCHOP assays when high levels are present. However, thiscan be manifested through higher levels of CHOP in theProtein A eluate pool due to co-purification with theproduct. The downstream process was shown to be capableof clearing the extreme levels of CHOP and host cell DNAproduced during the production bioreactor characterizationexperiments. As a result, no critical operational parameterswere identified in the upstream process based upon effectsto these impurities. However, since a correlation was obser-ved between final viability and CHOP levels, and to ensurethat longer culture duration does not impact CHOP levels,harvest viability was designated as a CPP. The acceptable

Figure 7. Contour plots showing results of CCD study of the production bioreactor

acceptable ranges for temperature and initial viable cell density were constrained to ensure

in IEF gels) and final viability (correlates to CHOP levels).

limit for harvest viability was set based upon the lowestharvest viabilities generated during the study.

Production Bioreactor RSM Study

A face-centered central composite design (CCD) was con-ducted to study production bioreactor temperature (I), pH(K) and initial viable cell density (H), due to the demon-stration of their impact to glycosylation and charge variants,described earlier. The goal of the CCD study was to confirmthe previous results and ensure that the CPP ranges wereappropriately set. The CCD design enabled the inclusion ofcurvature in the regression models. The inclusion of severalcenter point runs also confirmed the robustness of theoperating conditions selected for the production process.

To explore the edge of failure, the ranges for temperatureand initial viable cell density were expanded from what wasevaluated in the screening study. The temperature rangestudied in the CCD was twice as wide as the range in the

for (A) Normalized Titer, (B) CEX Peak 1, (C) CEX Peak 6, and (D) Final viability. The

acceptable levels of CEX Peak 1 (deamidation), CEX Peak 6 (correlates to basic banding



Figure 8. Prediction profiler results for the effect of temperature on N-Linked

oligosaccharide profile. Constraint of temperature range ensures acceptable levels of

G0F and G1F.

screening study, and the upper range of the initial viable celldensity was increased. Figure 7 shows contour plots of theresults of the RSM study with coded values for the threefactors. During development, the process had beenoptimized for titer, as demonstrated by the bulls-eye nearthe centerpoint conditions for temperature and pH on theplot of normalized titer. The results of the CCD study shownin Figure 7A also corroborated the finding in the screeningstudy that production bioreactor temperatures between �1and þ1 do not significantly affect titer. The CEX resultsindicated high Peak 1 percentages at high temperature andhigh Peak 6 levels at high initial viable cell density combinedwith high pH. The effect of high initial viable cell density onPeak 6 was not identified in the screening study because ofthe more narrow range evaluated. In addition, final viabilitywas lower at high temperature, especially when combinedwith high pH.

Table IV. Factors of statistical significance and designations of key and critic

Result Response

No key or critical process

parameters identified

Seed bioreactor

final viability

HMW

Key parameters

identified

Seed bioreactor

growth rate

Titer

Critical process


CEX profile Peak 1

Peak 6 E,

N-Linked

oligosaccharide profile

G0F

G1F

aThe upper limit of H, production bioreactor initial viable cell density, evascreening study. It was not identified as a key parameter for effects on Peak 6 in thfailure for the upper limit of H, so it was deemed as a CPP for its effect on P

bThe range of I, production bioreactor temperature, evaluated in the CCD studnot identified as a key parameter for effects on N-Linked oligosaccharide profileedge of failure for the upper limit of I, so it was deemed as a CPP for its effe


Temperature, pH and initial viable cell density wereconfirmed to impact N-Linked oligosaccharide profiles.Temperature was not identified as a key parameter for itseffect on N-Linked glycosylation in the screening study.However, the evaluation of a wider temperature range ena-bled a demonstration of the edge of failure with respect tothe N-Linked oligosaccharide profile and resulted in thedesignation of this parameter as a CPP. Figure 8 shows theprediction profiler for the G0F and G1F species versusproduction bioreactor temperature. G0F decreases and G1Fincreases with increasing temperature. At a lower tempera-ture set point, the effects of pH and initial viable cell densityon glycosylation are minimized (not shown), thus improv-ing overall process robustness. Of course, a lower produc-tion bioreactor temperature also decreases product titer.The prediction models revealed an optimization of producttiter in conjunction with maintenance of comparable gly-cosylation patterns.

The factors found to impact glycosylation for the mono-clonal antibody are in general agreement with processparameters indicated in the literature to be important forglycosylation. Production bioreactor temperature, durationof the cell culture run and timing of inducer addition werefound to be important for N-Linked glycosylation in CHOcell culture (Andersen et al., 2000). Studies on other gly-coproteins indicate that culture pH (Gawlitzek et al., 2009)and culture temperature (Ahn et al., 2008) are the mostsignificant factors that impact glycosylation.

Due to the results of the CCD study, the upper limits fortemperature and initial viable cell density were constrained.A second CCD study was then conducted to ensure that therefined edges of the design space were acceptable (results notshown). Production bioreactor temperature (I), pH (K) andinitial viable cell density (H) were designated as CPPs.Table IV summarizes the factors that were studied and their

al operational parameters.

Factors of

statistical significance

in screening studies

Key process


in screening studies

Critical process

parameters

identified in

CCD studies

B, D, F, BC, BD, BE,

BF, DE, EF, BDE

J, K

A, B, C, D, E, F, BC,

BE, BF, DE, EF, BCF

B, C, E, F

D, H, J, L, GJ, DH D, H, J, L

I I I

D, H, I, K, L, DH, HL K Ha, K

H, K, GE, EK H, K H, Ib, K

G, H, K, HJ, IJ, IK H, K H, Ib, K

luated in the CCD study was higher than the upper limit evaluated in thee screening study. However, the results of the CCD study revealed the edge ofeak 6.y was two times wider than the range evaluated in the screening study. It wasin the screening study. However, the results of the CCD study revealed the

ct on N-Linked oligosaccharide profile.

designation as key or critical operational parameters. Inaddition to the factors in the study, harvest viability wasdesignated as a CPP based on the ability of the downstreamprocess to clear CHOP.

Conclusions

A tiered approach to process characterization provided astreamlined mechanism for studying the design space for acell culture process. The FMEA exercise was followed bycharacterization of the inoculum and production bioreactorstages of the process. An integrative study of shake flask andseed bioreactor parameters and of seed bioreactor and pro-duction bioreactor parameters minimized the total numberof experiments while yielding valuable information forparameter classification. The impact of worst-case cell cult-ure process operation on product quality after downstreampurification was considered. Production bioreactor para-meters that impacted product glycosylation included pH,temperature and initial VCD. Production bioreactor tem-perature also impacted deamidation. Charge variants indica-tive of basic banding in IEF gels were influenced by productionbioreactor initial viable cell density and pH. Upstreamoperating conditions were shown to have no other impacton product quality, but harvest viability correlated to CHOPlevels in the Protein A eluate. The development of predictivemodels for the cell culture process demonstrated optimiza-tion of process operating conditions to balance high producttiter with control of product glycosylation.

References

Ahn WS, Jeon JJ, Jeong YR, Lee SJ, Yoon SK. 2008. Effect of culture

temperature on erythropoietin production and glycosylation in a

perfusion culture of recombinant CHO cells. Biotechnol Bioeng

101(6):1234–1244.

Andersen DC, Bridges T, Gawlitzek M, Hoy C. 2000. Multiple cell culture

factors can affect the glycosylation of Asn-184 in CHO-produced

tissue-type plasminogen activator. Biotechnol Bioeng 70(1):25–31.

US Food and Drug Administration. 2004. Pharmaceutical cGMPs for the

21st century; a risk based approach.

US Food and Drug Administration. 2006. Guidance for industry; Q8

pharmaceutical development.

US Food and Drug Administration. 2009. Guidance for industry; Q8

pharmaceutical development (Revision 1).

Gawlitzek M, Estacio M, Furch T, Kiss R. 2009. Identification of cell culture

conditions to control N-glycosylation site-occupancy of recombinant

glycoproteins expressed in CHO cells. Biotechnol Bioeng 103(6):1164–

1175.

Harms J, Wang X, Kim T, Yang X, Rathore AS. 2008. Defining process

design space for biotech products: Case study of Pichia pastoris

fermentation. Biotechnol Prog 24(3):655–662.

Jefferis R. 2005. Glycosylation of recombinant antibody therapeutics.

Biotechnol Prog 21(1):11–16.

Kelley B, Jennings P, Wright R, Briasco C. 1997. Demonstrating process

robustness for chromatographic purification of a recombinant protein.

Biopharm Int 10(10):36–47.

Kelley B, Shi L, Bonam D, Hubbard B. 1998. Robustness testing of a

chromatographyic purification step used in recombinant Factor IX

manufacture. In: Kelly B, Ramlemeier A, editors. Validation of

biopharmaceutical manufacturing processes. Washington, DC: Amer-

ican Chemical Society. p 93–113.

Kozlowski S, Swann P. 2006. Current and future issues in themanufacturing

and development of monoclonal antibodies. Adv Drug Deliv Rev

58(5–6):707–722.

Li F, Hashimura Y, Pendleton R, Harms J, Collins E, Lee B. 2006.

A systematic approach for scale-down model development and char-

acterization of commercial cell culture processes. Biotechnol Prog

22(3):696–703.

Lipsanen T, Antikainen O, Raikkonen H, Airaksinen S, Yliruusi J. 2007.

Novel description of a design space for fluidised bed granulation. Int J

Pharm 345(1–2):101–107.

Nail S, Searles J. 2008. Elements of quality by design in development and

scale-up of freeze parenterals. Biopharm Int 21(1):44–52.

Rathore AS, Winkle H. 2009. Quality by design for biopharmaceuticals. Nat

Biotechnol 27(1):26–34.

Rathore A, Branning R, Cecchini D. 2007. Design space for biotech

products. Biopharm Int 20(5):36–40.

Rathore A, Saleki-Gerhardt A, Montgomery S, Tyler S. 2008. Quality by

design: Industrial case studies on defining and implementing design

space for pharmaceutical processes—Part 1. Biopharm Int 21(12):

37–47.

Rathore A, Saleki-Gerhardt A, Montgomery S, Tyler S. 2009. Quality by

design: Industrial case studies on defining and implementing design

space for pharmaceutical processes—Part 2. Biopharm Int 22(1):40–

44.

Seely R, Haury J. 2005. Application of FMEA to biotechnology manufactur-

ing processes. In: Rathore A, Sofer G, editors. Porcess validation in

manufacturing of biopharmaceuticals. Boca Raton, FL: Taylor and

Francis. p 31–68.

Seely J, Seely R. 2003. A rational step-wise approach to process character-

ization. Biopharm Int 16(8):24–35.

Shukla AA, Hinckley P. 2008. Host cell protein clearance during protein A

chromatography: Development of an improved column wash step.

Biotechnol Prog 24(5):1115–1121.

Shukla AA, Sorge L, Boldman J, Waugh S. 2001. Process characterization for

metal-affinity chromatography of an Fc fusion protein: A design-of-

experiments approach. Biotechnol Appl Biochem 34(Pt 2):71–80.



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