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Hochschule OffenburgFakultät Maschinenbau und Verfahrenstechnik
Fachbereich Bioverfahrenstechnik
A Downstream Processing Platform forMonoclonal Antibodies: Design
Considerations on A�nity, Ultrafiltration,and Polishing Operations
Thesis by
Pável Alejandro Marichal-Gallardo
submitted in fulfillment of the requirements
for the degree of
Master of Science Process Engineering
Supervisors
Prof. Dr. Christiane Zell (HS Offenburg)
Dr. Ali Nasser Eddine (UGA Biopharma GmbH)
February 2014
Declaration of Authorship
I, Pável Alejandro Marichal-Gallardo, declare that this thesis titled, ’A Down-stream Processing Platform for Monoclonal Antibodies: Design Considerations on Affinity,Ultrafiltration, and Polishing Operations’ and the work presented in it are my own. Iconfirm that:
⌅ This work was done wholly or mainly while in candidature for the degree at thisUniversity.
⌅ Where any part of this thesis has previously been submitted for a degree or anyother qualification at this University or any other institution, this has been clearlystated.
⌅ Where I have consulted the published work of others, this is always clearly attributed.
⌅ Where I have quoted from the work of others, the source is always given. With theexception of such quotations, this thesis is entirely my own work.
⌅ I have acknowledged all main sources of help.
⌅ Where the thesis is based on work done by myself jointly with others, I have madeclear exactly what was done by others and what I have contributed myself.
Signed:
Date:
i
Heredera intelectual del médico prodigio.Nieta de Aurora y del español con acento cubano.
A mi hermana Amaya, mi sangre,por siempre en mi memoria.
Pável Alejandro Marichal-Gallardo
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HOCHSCHULE OFFENBURG
Abstract
Fakultät Maschinenbau und VerfahrenstechnikFachbereich Bioverfahrenstechnik
Master of Science Process Engineering
A Downstream Processing Platform for Monoclonal Antibodies: Design
Considerations on Affinity, Ultrafiltration, and Polishing Operations
by Pável Alejandro Marichal-Gallardo
Today, monoclonal antibodies (mAbs) and Fc fusion proteins account for 35% of theentire market of therapeutic proteins and estimations are that 30% of the new drugsdeveloped in the next 10 years will be based on some antibody product. Bioreactor batchvolumes for mAbs at very large scale (VLS) can be as high as 25 m3, with routine titresof 3–6 g · L�1 , which have shifted the bottleneck of mAb production to downstreamprocessing (DSP). In this contribution, a purification approach for mAbs is studiedby means of screening critical operations such as Protein A affinity chromatography,ultrafiltration/diafiltration (UF/DF), and polishing.
A monoclonal human IgG in cell culture fluid (CCF) was used as a representativefeed-stock. For Protein A affinity chromatography, screening of elution pH and elutionbuffer composition was studied, along with column packing for scale-up and columnperformance testing. In the case of UF/DF, screening of feed flow rate and operatingtransmembrane pressure (TMP) was performed using flat sheet cassettes. Mass transferwithin the membrane module was analyzed and mathematical models were generatedwith MATLAB software for prediction and minimization of process time and membranearea, together with the estimation of optimal parameters during DF operated at constantconcentration at the membrane wall. For the final polishing, Capto® Adhere resin wasused in flow-through mode employing a design of experiments (DoE) in MODDE softwarefor the estimation of the most suitable load conditions for pH, conductivity, and IgG loadfor maximization of yield and minimization of impurities.
Under the optimized conditions for the unit operations analyzed, achieved globalrecovery of product was 85% with purity >99.5%. Based on the process conditions used,observations during the experiments, and recommendations in the literature, a base DSPplatform is suggested for the purification of gram quantities of mAb. This work can beuseful as a guide for optimization and implementation for pilot plant and industrial scalesseeing that all studied operations are fully suitable for VLS utilization.
Acknowledgements
The author wishes to thank Dr. Ali Nasser-Eddine for the opportunity of performingthe author’s Master of Science thesis work at UGA Biopharma GmbH, as well as for hissupervision and support.
The author greatly appreciates the financial assistance of Consejo Nacional de Ciencia yTecnología (CONACyT) throughout the author’s Master of Science studies at HochschuleOffenburg.
Special thanks to B.Sc. Iris González-Leal from Julius-Maximilians Universität Würzburgfor critical reviewing and discussion of the manuscript.
In addition, the author thankfully acknowledges:Prof. Dr. Christiane Zell for her insightful lectures, professional support, and supervisionof this thesis work.
The staff of UGA Biopharma GmbH for their support and supply of samples for carryingout this study, with special thanks to MSc. Othman Montacir for critical scientific dis-cussion.
Dr. Christian Rother and Dr. Dietrich Carlhoff from General Electric, and Gregor Richterand Dr. Anna Le-Bris from Life Technologies for their insightful comments, and scientificcontributions.
Mr. Noel Spare from Hochschule Offenburg for providing the author with a deeper under-standing of the world by means of the System of Profound Knowledge.
The professors and staff from Hochschule Offenburg, with special thanks to Izabela Sosnikand her e-mail of May 3rd, 2012.
Dr. Mario Álvarez, Dr. Manuel Zertuche, Dr. Sergio Serna, and Dr. Alicia Ramírez fromTecnológico de Monterrey for their wisdom, support, and teachings throughout the years.
The author’s classmates from the Master of Science Process Engineering (MPE) program:Pascal Ruf, Yulia Kudryashova, Sabrina Gil-Pascual, Kamila Świrkowska (honorary mem-ber), Bushra Ismail, Leonardo Díaz, Santiago Noguera, and Christoph Kramer for theirfriendship and a wonderful time in Germany and Poland.
v
Foreword
"Healthy citizens are the greatestasset any country can have."
Winston Churchill
I have been in close contact with the matter of healthcare inasmuch as my parents areboth physicians, and I have been lucky to meet men and women whose concern withpeople’s wellness has inspired myself and others to allot a great deal of effort to thisrighteous cause. My first job experience at the Pharmaceutical Biotechnology ResearchGroup in Mexico made me part of the noble ambition of making these modern drugsmore available.
A healthy individual both psychologically and physically is productive to society. Itis indeed in the best interest of all to pinpoint what needs to be done for this to happen.Biopharmaceuticals, the new generation of drugs, have brought numerous advantages inthe field of medicine and, along with other tendencies later discussed in this dissertation,they are reshaping the way healthcare is conducted, shifting treatments to personalizedone’s, greatly reducing the financial burden on our health institutions and helping minimizepatient frustration.
Industry is the engine that through mass production has made manufacturing costslow enough to make technology and products available around the world. With respect tobiopharmaceutical’s production, process development is firstly made in laboratory scaleand the shift to industrial scale is no easy task. Every aspect of the process developmentis based on the target protein’s intrinsic and production characteristics and ultimately,financial viability. The efficient bioprocess engineer has a complete understanding ofmanufacturing methods and purification alternatives. The true challenge of bioprocessdesign is, given a product, the smart configuration of unit operations to achieve a setgoal.
This work discusses the particular design and results for the recovery and purificationof monoclonal antibodies (mAbs), providing information about market, expression systems,
vi
state-of-the-art production, recovery and purification, in addition to brief discussions onrelevant topics, such as regulatory requirements.
Chapter 1 presents some statistics regarding the Biotechnology sector in Germany.Information about research institutes in the country as well as a short overview aboutthe private sector is disclosed. This chapter allows a quick understanding of the presentstate of biotechnology in Germany.
In Chapter 2 the reader is referred to every necessary aspect needed for the productionof biopharmaceuticals and is given information about commercially relevant molecules.The chapter is divided in two main parts: the first presents the history of biopharma-ceuticals in brief and discusses pertinent aspects of the most important categories ofbiopharmaceuticals and their actual standpoint. The second part provides a generaldescription of upstream processing (USP) (production technology, expression systems,scales) and downstream processing (DSP) (unit operations for recovery and purification).
Chapter 3 presents all the relevant theory on mAbs starting with their historiccontext and market, and proceeding with detailed information regarding their productionat industrial scale. USP is modestly described while DSP is approached in a much widerscope, presenting the reader with thorough discussion of the purification process by unitoperation. Chapter 5 discloses the methodology used for this dissertation and the rationalebehind the proposed setup. Results and discussions are presented in Chapter 6, andfinally, the conclusion of this work in Chapter 7. All experiments were performed at UGABiopharma GmbH (Hennigsdorf, Germany), a contract manufacturer offering cell-linedevelopment and process development services for its partners in the biopharmaceuticalindustry.
The reader can find a digital version of this document in A4 PDF format togetherwith figures and tables also in PDF at http://goo.gl/hfDWXA.
I earnestly hope this research can be of help to anyone interested in the subject andbe part of mankind’s global endeavor for longer and healthier living. I thank you and Iwish you happy reading.
Yours sincerely,
Pável Alejandro Marichal-Gallardo
UGA Biopharma GmbHNeundorfstraße 20a16761 Hennigsdorf
Table of Contents
Declaration of Authorship i
Abstract iv
Acknowledgements v
Foreword vi
List of Figures x
List of Tables xii
List of Symbols xiv
List of Acronyms xvii
1 Preface: Biotechnology Research in Germany 1
2 Global State of Biopharmaceutical Production 42.1 General Perspective of Biopharmaceuticals . . . . . . . . . . . . . . . . . . 4
2.1.1 The Need for Design Drugs . . . . . . . . . . . . . . . . . . . . . . 42.1.2 Dawn of Biopharmaceuticals and Current Scenario . . . . . . . . . 5
2.2 Upstream Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2.1 Expression systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2.2 Bioreactors for Mammalian Cell Culture . . . . . . . . . . . . . . . 12
2.3 Downstream Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3 Monoclonal antibodies 143.1 History and evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.2 Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.3 Production and purification . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.3.1 Harvest and primary recovery . . . . . . . . . . . . . . . . . . . . . 17
viii
Table of Contents ix
3.3.2 Capture by Protein A affinity chromatography . . . . . . . . . . . 193.3.3 Polishing operations . . . . . . . . . . . . . . . . . . . . . . . . . . 223.3.4 Viral clearance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.3.5 Ultrafiltration and Diafiltration . . . . . . . . . . . . . . . . . . . . 27
4 Objectives 34
5 Materials and Methods 365.1 Harvesting and primary recovery . . . . . . . . . . . . . . . . . . . . . . . 365.2 Packed-bed Chromatography . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.2.1 Affinity column packing . . . . . . . . . . . . . . . . . . . . . . . . 375.2.2 Protein A affinity chromatography . . . . . . . . . . . . . . . . . . 375.2.3 Size exclusion chromatography (SEC)-FPLC . . . . . . . . . . . . . 385.2.4 Polishing with Capto Adhere . . . . . . . . . . . . . . . . . . . . . 38
5.3 Ultrafiltration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395.3.1 System preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . 395.3.2 Clean membrane permeability . . . . . . . . . . . . . . . . . . . . . 395.3.3 Transmembrane pressure (TMP) excursion . . . . . . . . . . . . . 395.3.4 System cleaning and storage . . . . . . . . . . . . . . . . . . . . . . 405.3.5 Mathematical modeling . . . . . . . . . . . . . . . . . . . . . . . . 40
6 Results and Discussions 426.1 Protein A affinity chromatography . . . . . . . . . . . . . . . . . . . . . . 42
6.1.1 pH scouting for elution . . . . . . . . . . . . . . . . . . . . . . . . . 426.1.2 Column packing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446.1.3 SEC-FPLC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
6.2 Ultrafiltration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486.2.1 Mass transfer coefficient . . . . . . . . . . . . . . . . . . . . . . . . 486.2.2 Gel concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 506.2.3 Process time during concentration step . . . . . . . . . . . . . . . . 506.2.4 Diafiltration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
6.3 Polishing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 546.4 Mass balance at optimized process conditions . . . . . . . . . . . . . . . . 58
7 Summary, conclusions, and remarks 60
Appendix A Suggested buffers and solutions 61
Appendix B Matlab code for estimation of mass transfer parameters inultrafiltration (UF) 62
Bibliography 69
Index 79
List of Figures
1.1 Areas of activity, employee profile, and geographical distribution of ded-icated biotechnology companies in Germany as of 2012. Produced withdata from biotechnologie.de. . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.1 Estimated annual production and sales values of relevant biopharmaceuti-cals. Produced with data from Hagel, Jagschies, and Sofer. [5] . . . . . . . 7
2.2 Distribution of recombinant therapeutic proteins according to host mi-croorganism. Produced with data from Hacker, Chenuet, and Wurm. [23]
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.1 Immunoglobulin G (IgG) structure nomenclature. . . . . . . . . . . . . . 153.2 Blockbuster biologics 2012 sales (%) by product class and top 5 best sell-
ing monoclonal antibodies (mAbs). Produced with data from La MerieBusiness Intelligence. [58] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3 Downstream processing (DSP) platform for monoclonal antibodies (mAbs).Depending on target product and available facilities, this typical outlineexhibits minimal modifications at different levels. . . . . . . . . . . . . . . 19
3.4 Capto® Adhere ligand structure. The left side of the molecule is coupledto the column matrix. Structure drawn with MarvinSketch for Mac OSX. 22
3.5 Effect of transmembrane pressure (TMP) on permeate flux (J) duringtangential flow filtration (TFF). The slope of the clean water flux curveis defined as the membrane hydraulic permeability (Lp). Permeate flux isdependent both on TMP and bulk protein concentration (Cb). . . . . . . 28
3.6 Estimation of mass transfer coefficient k by plotting of limiting permeateflux versus solute bulk concentration Cb. The mass transfer coefficientrepresents the slope of the linear regression and the gelation concentrationCg the intersection with the x axis. Q = feed flow rate. . . . . . . . . . . 29
3.7 Driving force and resistance diagram during tangential flow filtration (TFF). 32
5.1 Sequence of unit operations performed for the suggested downstream pro-cessing (DSP) platform. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
6.1 Elution pH scouting in a MabSelect® 5 mL column. Loading of clarified cellculture fluid (cCCF) was made at 300 cm · h�1 and elution was performedat 250 cm · h�1. Yields with respect to loaded antibody were: A) 0%, B)22%, C) 95%, and D) 95%. . . . . . . . . . . . . . . . . . . . . . . . . . . 43
x
List of Figures xi
6.2 Effect of the elution buffer concentration on the yield during Protein Aaffinity chromatography. Yields with respect to loaded antibody were: A)95% and B) 95%. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
6.3 Tracer substance pulse for determination of column packing efficiency forMabSelect® column. Reduced plate height should be h 3 for optimalcolumn efficiency in bioprocess chromatography. An acceptable range forthe assymetry factor is 0.8 < AS < 1.8. . . . . . . . . . . . . . . . . . . . 45
6.4 A) Protein A affinity chromatography from stirred tank reactors (STR)cell culture. B) Size exclusion chromatography (SEC) for Protein A affinityeluate. Sample was incubated for 60 min in Buffer D (see Table 5.1 onpage 37) and filtered by 0.22 µm before injection into the SEC column. . 46
6.5 A) Protein A affinity chromatography from stirred tank reactors (STR)cell culture. B) Size exclusion chromatography (SEC) for Protein A affinityeluate. Sample was incubated for 60 min in Buffer D (see Table 5.1 onpage 37) and filtered by 0.22 µm before injection into the SEC column. . 47
6.6 A) Protein A affinity chromatography from wave bag reactor cell culture. B)Size exclusion chromatography (SEC) for Protein A affinity eluate. Samplewas incubated for 60 min in Buffer D (see Table 5.1 on page 37) and filteredby 0.22 µm before injection into the SEC column. . . . . . . . . . . . . . 48
6.7 A) Permeate flux versus transmembrane pressure (TMP) data set foran antibody purified with Protein A affinity chromatography in sodiumcitrate 50 mM pH 3.0 on a 0.02 m2 50 kDa polyethersulfone (PESU)cassette membrane. B) Limiting permeate flux versus ln(Cb). The slope oflinear regression represents the mass transfer coefficient k and the interceptwith the x axis the gel concentration Cg. C) Osmotic pressure versus wallconcentration Cw. The data was fitted to a second order polynomial forestimation of the virial coefficients ↵ and �. The permeate flux curve canbe modeled with the provided equation which must be solved iteratively.D) Permeate flux versus osmotic pressure for a second determination ofthe mass transfer coefficient k. . . . . . . . . . . . . . . . . . . . . . . . . 49
6.8 Membrane area required for a 1 h diafiltration (DF) process as a functionof Cw as determined in Equation (6.12). The optimal bulk protein concen-tration C⇤
b equals Cwe . At higher wall concentrations, changes in Cb can be
made without compromising the minimal required membrane area. . . . . 516.9 Membrane area required for a 1 h diafiltration (DF) process as a function
of J as determined in Equation (6.12). In a Cw controlled process, the opti-mum permeate flux J⇤ equals the mass transfer coefficient k, independentlyof the chosen Cw value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
6.10 Remaining components (%) during a diafiltration (DF) operation as afunction of retention (R) and number of diavlumes (N) as determined withEquation (6.13). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
6.11 A) Model coefficients for the yield response, B) model coefficients for theDNA response, C) observed versus predicted values for yield, and D) ob-served versus predicted values for DNA. . . . . . . . . . . . . . . . . . . . 55
6.12 Response surface plot (RSP) for A) Yield and B) DNA (ng/µL) for polishingstep with Capto® Adhere column. . . . . . . . . . . . . . . . . . . . . . . 56
List of Tables
2.1 Biopharmaceutical’s classification by biological functionality. . . . . . . . 52.2 Categories and main characteristics of the most important expression sys-
tems used for biopharmaceutical production. . . . . . . . . . . . . . . . . . 102.3 Process and product-related impurities found during downstream process-
ing (DSP) of biopharmaceuticals. Reproduced from Hagel et. al. [5] . . . . 112.4 Common unit operations in downstream processing (DSP). Reproduced
from Ghosh. [43] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.1 Generic names for monoclonal antibodies (mAbs) based on source andtarget. Modified from An. [51] . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2 Full monoclonal antibodies (mAbs) approved by the US Food and DrugAdministration (FDA) as of January 2014. . . . . . . . . . . . . . . . . . . 18
3.3 Commercially available multimodal/mixed mode chromatography (MMC)resins for polishing of monoclonal antibodies (mAbs). . . . . . . . . . . . 24
3.4 Properties of viruses for rodent-derived cell lines used for validation studies.Modified from Zhou. [110] . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.5 Commercially available virus filters. Modified from Phillips et al. [111] . . . 263.6 Characteristics of ultrafiltration (UF) membrane modules used in very large
scale (VLS) manufacturing of monoclonal antibodies (mAbs). Reproducedfrom van Reis and Zydney. [117] . . . . . . . . . . . . . . . . . . . . . . . . 30
5.1 Quick reference of buffers and solutions. . . . . . . . . . . . . . . . . . . . 375.2 Design of experiments (DoE) for polishing step with Capto® Adhere .
Full factorial of 3 factors at 2 levels plus 2 center points (23 + 2 = 10experiments) to resolve curvature effects. . . . . . . . . . . . . . . . . . . 38
6.1 Estimated modeling parameters for the ultrafiltration (UF) step. . . . . . 506.2 Response values for yield (%) and DNA (ng/µL) from the polishing step
with Capto® Adhere column. Responses correspond to the flow throughfraction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
6.3 Statistical model parameters for the yield and DNA responses obtainedfrom the polishing step with Capto® Adhere column. . . . . . . . . . . . 54
6.4 Typical impurity levels for end-product biopharmaceuticals. . . . . . . . . 576.5 Mass balance for the performed downstream processing (DSP) platform at
optimized conditions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
xii
List of Tables xiii
6.6 Suggested process parameters for all unit operations in the discussed down-stream processing (DSP) platform. For buffers and solutions refer to Ta-ble A.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
A.1 Recommended buffer solutions for the suggested downstream processing(DSP) platform. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
List of Symbols
Roman Letters
A m2 membrane areaA cm term related to eddy dispersion in Van
Deemter equationa � coefficient in Equation (3.13)a mL leading peak widthAS � assymetry factorB cm2 · h�1 term related to molecular diffusion in Van
Deemter equationb � coefficient in Equation (3.13)b mL tailing peak widthC g · L�1 solute concentration on the upper membraneC h�1 term related to mass transfer resistance in Van
Deemter equationc � coefficient in Equation (3.13)C⇤b g · L�1 optimum bulk protein concentration for diafil-
trationCb g · L�1 solute concentration in bulk feedCb,0 g · L�1 initial solute concentration in bulk feedCb,f g · L�1 final solute concentration in bulk feedCg g · L�1 gelation concentrationCp g · L�1 solute concentration in the permeate sideCw g · L�1 solute concentration at the membrane wallD m2 · s�1 solute diffusivity within the poresd m diameterd � coefficient in Equation (3.13)dH m equivalent hydraulic diameter of the module
xiv
List of Symbols xv
dp m mean size particle diameterh � reduced plate heightHETP m height equivalent to a theoretical plateJ⇤ L ·m�2 · h�1 optimum diafiltration fluxJ L ·m�2 · h�1 permeate fluxJ1 L ·m�2 · h�1 limiting permeate fluxk L ·m�2 · h�1 solute mass transfer coefficientL m length of flow channel` m height of packed bedLfm L · m�2 · h�1 ·
bar�1
fouled membrane permeability
Lp L · m�2 · h�1 ·bar�1
membrane hydraulic permeability coefficient
Mw Da molecular weightN � diafiltration volumesN m number of theoretical platesPF Pa pressure in the feed sidePP Pa pressure in the permeate sidePR Pa pressure in the retentate sideQ m3 · h�1 volumetric flow rateR � retention coefficientRg Pa · s ·m�1 gel layer resistanceRH nm hydrodynamic radiusRm Pa · s ·m�1 membrane hydraulic resistanceRsp Pa · s ·m�1 resistance due to surface polarizationtC min contact timetDF h contact timeTMP Pa transmembrane pressuretR min residence timeu cm · h�1 linear fluid velocityV m3 volumeV0 m3 initial volumeVR m3 retention volume corresponding to eluted vol-
ume at maximum peak heightWh m3 peak width at half peak height
Greek Letters
↵ � first virial coefficient� � second virial coefficient
List of Symbols xvi
� m surface polarization boundary�P Pa column pressure dropµ1 min first moment in residence time distribution
(RTD) curve derived from integration of tracersignal
µf min second moment in RTD curve derived fromintegration of tracer signal
�⇧i Pa osmotic pressure difference across the mem-brane
�2 � variance�i � osmotic reflection coefficient
List of Acronyms
AEX anion exchange chromatographyAF affinity chromatographyALCL anaplastic large cell lymphomaAML acute myeloid leukemia
BHK baby hamster kidneyBMBF Ministry for Education and ResearchBMBW Ministry for Education and Science
cCCF clarified cell culture fluidCCD centered composite designCCF cell culture fluidCEX cation exchange chromatographyCF compression factorCFF cross-flow filtrationcGMP Current Good Manufacturing PracticeCHC ceramic hydroxyapatite chromatographyCHO chinese hamster ovaryCIP cleaning-in-placeCLL chronic lymphocytic leukemiaCV column volumes
DBC Dynamic Binding CapacityDF diafiltrationDoE design of experimentsDP depth filtrationDSP downstream processingDV diavolumes
xvii
List of Acronyms xviii
EBC expanded bed chromatographyELISA enzyme-linked immunosorbent assayEPO erythropoietin
FDA US Food and Drug AdministrationFPLC fast protein liquid chromatographyFSH follicle stimulating hormone
GE General ElectricGMP Good Manufacturing PracticeGRAS generally recognized as safe
HA hydroxyapatiteHCCF harvested cell culture fluidhCG human chorionic gonadotropinHCP host cell proteinHEK human embryonic kidneyhGH human growth hormoneHIC hydrophobic interaction chromatographyHMWA high molecular weight aggregatesHPLC high-performance liquid chromatographyHPTFF high performance tangential flow filtration
IBs inclusion bodiesIEX ion exchange chromatographyIgG immunoglobulin G
LH luteinizing hormoneLPS lipopolysaccharideLRV log10 reduction value
mAbs monoclonal antibodiesMF microfiltrationMMC mixed mode chromatographyMMV mouse minute virusMWCO molecular weight cutoff
NFF normal flow filtration
List of Acronyms xix
NHL non-Hodgkin lymphomaNMWL nominal molecular weight limit
ODA Orphan Drug ActOPM osmotic pressure model
PESU polyethersulfonePF packing factorpI isoelectric pointPRV pseudorabies virus
qPCR quantitative polymerase chain reaction
RC regenerated celluloseReo-3 reovirus type 3rhSA recombinant human serum albuminRPC reverse phase chromatographyRSP response surface plotRTD residence time distribution
scFv small chain variable fragmentSEC size exclusion chromatographySFM stagnant film modelSLE systemic lupus erythematosusSP surface polarizationSTR stirred tank reactors
TFF tangential flow filtrationTMP transmembrane pressuretPA tissue plasminogen activator
UF ultrafiltrationUSP upstream processing
VLP virus-like particlesVLS very large scale
WHO World Health Organization
List of Acronyms xx
X-MuLV Xenotropic murine leukemia virus-relatedvirus
Chapter 1Preface: Biotechnology Research inGermany
The first synthetic drug was acetylsalicylate (aspirin, Bayer) and its production in 1895marks the beginning of the modern pharmaceutical industry. By 1900, there were only4 drugs available which had been scientifically proven to be effective in treating theirtarget indications: digitalis (various heart conditions), quinine (Malaria), ipecacuanha(dysentery), and mercury (syphilis). [1]
By 1950 remarkable improvements had been achieved and important biotechnologyproducts were manufactured at industrial scale, such as beer, cheese, citric acid andpenicillin, however, scientific knowledge (journals, books, conferences) wasn’t devoted tothis field.
During the early 1970s, some countries like Germany, UK, USA and Japan saw inbiotechnology the potential for innovation and economic growth. The first human proteinto be cloned and expressed was a polypeptide hormone, somatostatin, in 1977, based onHerbert Boyer’s work at the University of California. In 1974, the german Ministry forEducation and Science (BMBW) made a first approach for the funding of biotechnologyresearch and development. [2]
Today known as the german Ministry for Education and Research (BMBF) hastwo funding programmes: The Health Research Programme and the Research StrategyBioEconomy 2030, funded with e5.5bn until 2014 and e2.4bn until 2017, respectively.Additionally, the BMBF guarantees institutional funding for the four research societiesMax-Planck-Society, Leibniz Association, Helmholtz Association and the Fraunhofer-Gesellschaft, in which a wide range of researchers are working in biotechnology research.At Max Planck institutes, almost 90% of postdocs, half of all postgraduate students andmore than 40% of scientific directors recruited in the past decade came from abroad. [3]
The german biotech sector has above 35,000 people working in this field in around 600companies, from which 360 of them are in the biomedical sector. The companies which
1
CHAPTER 1. Preface 2
Hamburg
Bremen
Hannover
Düsseldorf
Wiesbaden
Dresden
Magdeburg
Mainz
Stuttgart
Kiel
89
91
10
10
11
29
33
17
23
31
43
51
18
17
23
17
565Dedicated Biotechnology
companies in 2012
5.1 %
31.5 %
10.8 %
4.3 %
48.3 %
Health /medicine
AgriculturalBiotechnology
Industrial Biotechnology
Non-specific services
Bioinformatics
17,430
35,190
€ 2.9 bn
€ 934 m
2012 Turnover of dedicated Biotechnology companies
2012 R & D Expediture ofdedicated Biotechnology companies
Employees in dedicated Biotechnology companies
Employees in commercialBiotechnology
44.1 %with 10 employees or less
42.8 %between 10 and 50 employees
33with more than 100 employees
companies
Figure 1.1. Areas of activity, employee profile, and geographical distribution of dedicated biotechnologycompanies in Germany as of 2012. Produced with data from biotechnologie.de. (This figureis available in full color at http://goo.gl/iA9h36)
fields of activity include exclusively biotechnology are defined as "dedicated biotechnologycompanies". The fields of activities and sizes of these companies are reported every year(see Figure 1.1), being the regions of Bavaria, Berlin, Brandenburg, Baden-Württembergand North Rhine-Westphalia the most prolific. According to Ernst & Young, in 2002 thenumber of biotechnology companies in Germany was 360 with 13,400 total employees,from which 7,300 worked in R&D. This numbers show the incredible growth of this sectorin the last 10 years.
Regarding employee structure, there were reported 17,430 employees in 565 dedicatedbiotechnology companies in 2012. North-Rhine-Westphalia employed the highest numberof people followed by Bavaria.
Almost half of the biotechnology sector in Germany is focused on health/medicine.This is not strange since the most important application of biotechnology is the search
CHAPTER 1. Preface 3
for new drugs, vaccines, and biomarkers, not only in Germany but worldwide.Small companies are still the main economic driving force in the german sector. 44.1%
of them have 10 of fewer employees and 42.8% have between 10 and 50 employees (seeFigure 1.1 on the preceding page). Surprisingly enough, only 33 companies (5.8%) havemore than 100 employees and only 7 (1.2%) have more than 250 employees.
In 2012, 20 start-ups were announced. Alltogether, dedicated biotechnology companiesreported a revenue of e2.9bn with a R&D expenditure of e934mn during 2012. Comparedwith 2011, revenue increased 10.9% while R&D expenditure decreased 4%. Since 2005,German science spending has increased about 60%, from e9bn to around e15bn in 2013,reaching 3% of the country’s GDP.
Chapter 2Global State of BiopharmaceuticalProduction
2.1 General Perspective of Biopharmaceuticals
2.1.1 The Need for Design Drugs
Despite astounding technological advances in all fields of science related to medical care,there is still a great amount of guesswork when it comes to diagnosis and treatment. Thisapproach is one of many factors causing the rising cost of medical care. As stated byWu-Pong (2008) [4] p.365:
"Once disease has been identified, the patient then usually receives the standardof care for their particular diagnosis. For example, according to the NationalHeart, Lung, and Blood Institute, the standard of care for uncomplicated StageI hypertension involves use of a thiazide diuretic with or without the additionof a second drug such as a beta blocker. The patient is then monitored forresponse to treatment or adverse reactions. Unsatisfactory therapeutic responseresults in a change in the dose or drug until a satisfactory clinical outcome isobtained."
This passage is an example of the statement above. This strategy also lays downs aphysical and psycological burden on the patient, especially for diseases that have a limitedarray of pharmaceutical treatment options, such as autoimmune diseases and cancer, toname two broad categories.
Efforts have been made and continue strongly to shift this trial-and-error approach,for instance with the use of molecular biology, gene therapy, and the development ofDNA-based and recombinant drugs that reduce most of the side effects of traditional phar-maceuticals. [4] Even with the first generation of biopharmaceuticals (non-recombinant)
4
CHAPTER 2. Biopharmaceutical Production 5
Table 2.1. Biopharmaceutical’s classification by biological functionality.
Category Subcategory
Blood products Blood clotting factorsAnticoagulantsThrombolytic agents
EnzymesHormonesHematopoietic growth factorsInterferons and interleukinsVaccinesMonoclonal antibodies Full mAbs
Fab, F(ab)2Fc fusion proteinsscFvPEG-ilated products
Abbreviations: monoclonal antibodies (mAbs), small chain variable fragment (scFv)
that were obtained from natural sources, medical issues were present. For example, insulin-dependent diabetics were treated with insulin preparations from either bovine or porcinesources. Human insulin was made from conversion of porcine insulin using a combinationof enzymatic and chemical treatment of the porcine product. Although the basic structureof the human, porcine, and bovine insulins is similar, the preparations derived from ani-mal tissues contained many impurities (proinsulin, arginine insulin, and desamidoinsulin),some of which elicited immune responses exacerbated by the dosing frequency.
2.1.2 Dawn of Biopharmaceuticals and Current Scenario
Biopharmaceuticals have been available commercially not more that 80 years. [5] They canbe classified by functionality (see Table 2.1), being most of them protein-based therapeuticagents. The discussion that follows excludes vaccines and includes only protein drugssince they are the most important kind of biopharmaceuticals, although the term ’vaccine’covers many different active ingredients, such as whole cells, virus, virus-like particles(VLP), antigens or fragments of antigens, plasmid DNA, mAbs and other proteins andconjugated molecules. [5] Some relevant biopharmaceuticals will be now described basedon their industrial and market relevance.
Insulin. The first modern protein drug was insulin isolated from animal pancreas in 1921by August Krogh. In 1972 Berg, Cohen, and Boyer constructed the first recombinantplasmids and introduced them in E. coli which could retain the modified plasmids whilegrowing. In 1978, Genentech (founded by Boyer) and Eli Lilly (with Krogh as a cofounderof the company known today as Novo Nordisk) developed the first industrial recombinantproduct: human insulin (Humulin). [2] Until today, production of recombinant insulinpresents with important challenges, especially during purification. Many consider thisprocess more an art than a science and few companies in the world possess the know-how
CHAPTER 2. Biopharmaceutical Production 6
for its production. Additionally, scientific literature regarding both production and purifi-cation is rather scarce, although good references can be found in journals, [6–9] books [10,11]
and patents. [12,13]
Blood products. Industrial production of human albumin, immunoglobulin fractions andcoagulation factors VIII and IX was possible thanks to the development of extraction-based methods by Edwin Cohn during the 1950s, [14,15] which uses ethanol to precipitateproteins at their isoelectric point (pI). This method is still used today for being simpleand economical and supplies a world market of intravenous IgG around 80 tons per year.Fractionation is cost effective and it has gained in complexity over the years, becoming awell-established industrial procedure used not only for IgG purification but also for thepurification of more than 20 different proteins. [16] Factor VIII, with a weight of 280 kDa,is the largest commercially produced recombinant protein.
Monoclonal antibodies. mAbs are throughly discussed in another section of this document.Please refer to Chapter 3.
Hormones. Excluding insulin which has already been described, there are several recom-binant hormones that have been manufactured industrially. In 1985, Protropin (humangrowth hormone (hGH)) (Genentech) was approved for the treatment of growth deficiencyin children. This particular case is interesting, because contrary to other molecules thatcontinued to be extracted from natural sources, in 1985 the extraction of human chorionicgonadotropin (hCG) from the pituitaries of deceased human donors was banned. Anotherrelevant category of hormones is gonadotropins. This group includes follicle stimulatinghormone (FSH), luteinizing hormone (LH), and hCG, prescribed for a variety of repro-ductive disorders. [1]
Other relevant biopharmaceuticals introduced after insulin include �-interferon (Bio-gen Idec), and a hepatitis B vaccine in 1986 (Chiron Coorporation), tissue plasminogenactivator (tPA) in 1987 (Genentech), and erythropoietin (EPO) in 1989 (Amgen). [1]
Although the top selling biopharmaceutical list is led by products indicated forafflictions found in a large number of people, biopharmaceuticals are well suited fortreating enzyme deficiencies and metabolic conditions in small patient populations. Forexample, since its passage in 1983, the Orphan Drug Act (ODA) has led to the approvalof 357 drugs for rare diseases and a pipeline of more than 2,100 additional products. [17]
It was predicted that global annual sales of biological medicines were to reach $US 100bln; the revenues are today > $US 165 bln total and over $US 110 bln just for recombinantantibodies and other proteins. The US Food and Drug Administration (FDA) granted
CHAPTER 2. Biopharmaceutical Production 7
Quantity per product (kg · year -1)
Sa
les
(US
$ ·
kg
-1)
100
1,000
2,000
50,000,000
5,000,000
50,000
10,000
50,000-500,0003,0002,000200
IgG, HSA
High-dose mAbs
Insulin
Low-dose mAbs
EPO, interferon,Factor IX, Factor VII
Factor VIII
151
Figure 2.1. Estimated annual production and sales values of relevant biopharmaceuticals. Producedwith data from Hagel, Jagschies, and Sofer. [5] (This figure is available in full color athttp://goo.gl/iA9h36)
18 new biopharmaceutical product approvals in 2012, from which 44% are manufacturedoutside of the US. [18]
The estimated actual production quantities and prices of important biopharmaceuti-cals is shown in Section 3.3. It is easily noticeable how quantity is related to price valueand thus the importance of industry-scale production for reductions of costs. In the caseof mAbs, the 10 TON annual production scenario is not far away and has been previouslydiscussed. [19]
Additional interesting developments in the last 10 years include: Heberprot-P R�, arecombinant human growth factor prescribed for diabetic foot ulcer, Racotomomab, theworld’s first lung cancer vaccine, Gardasil R�, a human papillomavirus vaccine, and Prevnar13 R�, a vaccine against most common strains of Streptococcus pneumoniae.
2.2 Upstream Processing
Upstream cell culture development is commonly defined by the steps encompassing cell line,media, and bioreactor process development. [20] In industry, stable cell line developmentis usually the first step in the entire bioprocess design. In recent years, however, transientcell lines have been used because they present some advantages that have been discussedelsewhere. [21]
CHAPTER 2. Biopharmaceutical Production 8
CHO cells
33%
Bacteria
30%
Other hosts
4% Yeast
16%
Other mammalian
cells
17%
Figure 2.2. Distribution of recombinant therapeutic proteins according to host microorganism. Pro-duced with data from Hacker, Chenuet, and Wurm. [23] (This figure is available in full colorat http://goo.gl/iA9h36)
2.2.1 Expression systems
Protein quality, functionality, production speed and yield are the most important factorsto consider when choosing the right expression system for recombinant protein produc-tion. [22] Table 2.2 on page 10 shows the categories and main characteristics of the mostimportant expression systems used for biopharmaceutical production. The distributionof therapeutic proteins according to the host production system is shown in Figure 2.2.
Mammalian. Mammalian cells have become the dominant expression system for recombi-nant proteins destined for clinical use due to their abilities for proper assembly, folding,and post-translational modifications (glycosylation, phosphorylation and acylation). [24]
Glycosylation in rodents is not identical to that of human, however, chinese hamster ovary(CHO) cells can produce human-like glycosylation. CHO cells are attractive as host for thefollowing reasons: 1) they can achieve high cell densities (> 1⇥ 107 cells ·mL�1) even atvolumes of 20,000 L, [25] 2) they are easily transfected with DNA using both chemical andphysical methods, 3) they do not produce infectious endogenous retroviruses and they nota permissive host for most pathogenic human viruses, therefore the risk of contaminationis low, [26] 4) under optimized cell culture conditions CHO cells can achieve up to 50pg · cell�1 · day�1. [23]
Other mammalian cell types include murine myeloma (NS0), baby hamster kidney(BHK), and human embryonic kidney (HEK). [5] The reader is referred to a completelist of mammalian cell types. [27] Important disadvantages of this category are that theyrequire 6–8 days (batch) and 10–21 days (extended batch) culture due to their slowgrowth rate, and their high associated costs. The latter has been addressed by serum- and
CHAPTER 2. Biopharmaceutical Production 9
protein-free synthetic media, which has had also a positive impact on DSP. It is commonto find mammalian product titres of 3–5 g · L�1 and as high as 10 g · L�1 reported forindustrial use. [22] The human retina-derived PER-C6 was reported to produce 26 g · L�1
of a monoclonal antibody. [28] An exhaustive review on biopharmaceuticals that havenproduced by CHO cells is provided. [27,29]
Bacteria. The first organism for recombinant production was E. coli and is still one of themost widely used hosts. Some advantages of E. coli include relatively inexpensive culturemedia, fast growth (hours to days), high product yields, easy promotor control, and easeof culture. However, most products accumulate in the cytoplasm as insoluble aggregatesknown as inclusion bodies (IBs); their recovery involve the rupture of the bacteria inorder to isolate and refold the IBs.
IBs can be quite problematic and purification yields are seldom above 10%. [30–32]
This issue has been overcome by engineering that allows secretion of the product into theperiplasmic space of the bacteria, which has a reducing environment and thus allows thepresence of soluble, correctly folded proteins. This approach is not without drawbacks,as secretion to the periplasmic space often results in high endotoxin levels. Efforts havebeen focused into secreting products to the culture fluid. [5,22,33]
Bacillus has lately gained more acceptance as expression system as it has strongsecretion to the culture media with no involvement of inclusion bodies and they haveno production of endotoxins and lipopolysaccharide (LPS). The genus Bacillus has beenhistorically used in the food industry, although succesful production of biopharmaceuticalshas been reported in B. subtilis, B. licheniformis, and B. brevis . [34]
Yeast. Proteins are produced in these single-celled eukaryotic fungal organisms when E. colicannot be used because of the need for folding or glycosylation. Two main species are used:S. cerevisiae and P. pastoris. Advantages of yeasts as expression systems include highyield, high productivity, the ability to handle S-S rich proteins, folding, and glycosylation.Additionally, they are less expensive and easier to handle than insect or mammalian cells.
Even though both Pichia and S. cerevisiae can secrete recombinant proteins intothe culture media with glycosylation, S. cerevisiae is often unacceptable for mammalianproteins because the O-linked oligosaccharides contain only mannose whereas highereukaryotic proteins have sialylated O- linked chains, which can cause immunologicalproblems. [22] Likewise, the major advantage of Pichia over E. coli is that the former iscapable of producing disulfide bonds and glycosylation of proteins. Proteins that requirechaperonins for proper folding cannot be expressed in P. pastoris. Despite their foldingcapabilities, refolding is sometimes necessary. [5,22] Yeast expression systems typically growfrom days to a week and their expression levels are usually higher than those of mam-malian cells, with as high as 15 g · L�1 reported. [35]
CHAPTER 2. Biopharmaceutical Production 10
Tabl
e2.
2.Ca
tego
ries
and
mai
nch
arac
teris
tics
ofth
em
ost
impo
rtan
tex
pres
sion
syst
ems
used
for
biop
harm
aceu
tical
prod
uctio
n.
Cat
egor
yO
rgan
ism
Pro
duct
titr
eG
lyco
syla
tion
Fold
ing
Secr
etio
nA
dvan
tage
sR
efer
ence
s
Mam
mal
ian
CH
O3–
10g·L
�1
Opt
imal
Opt
imal
Goo
dH
uman
-like
post
tran
slat
iona
lm
odifi
cati
ons
(gly
cosy
lati
on,
phos
phor
ylat
ion,
acyl
atio
n),l
ess
com
plic
ated
DSP
,hig
hpr
oduc
tivi
ty,
high
cell
dens
itie
sat
indu
stri
alvo
lum
es
5,22
–25,
27–2
9N
S0,B
HK
,HE
K3–
5g·L
�1
Opt
imal
Opt
imal
Goo
dP
ER
.C6
15–2
5g·L
�1
Opt
imal
Opt
imal
Goo
d
Bac
teri
alE.coli
3–15
g·L
�1
Non
eC
ytop
lasm
(Non
e)M
edia
(Poo
r)Fa
stgr
owth
,hig
hpr
oduc
tivi
ty,c
heap
5,22
,30–
34Per
ipla
sm(G
ood)
Per
ipla
sm(G
ood)
med
ia,w
ellc
hara
cter
ized
,eas
ym
anip
ulat
ion
Bacilus
spp.
3g·L
�1
Non
eO
ptim
alO
ptim
alN
oLP
Sas
inE.coli
,str
ong
secr
etio
n,no
IBs
invo
lvem
ent,
GR
AS
stat
usby
FD
A,c
ost
effec
tive
reco
very
Yea
stS.cerevisiae
9g·L
�1
Poo
r-G
ood
Goo
d-O
ptim
alO
ptim
alH
igh
dens
itygr
owth
,hig
hpr
oduc
tivi
ty,
good
hand
ling
ofS-
Sri
chpr
otei
ns,
stab
lepr
oduc
tion
stra
ins
5,22
,35
P.pastoris
1–15
g·L
�1
Goo
dG
ood-
Opt
imal
Opt
imal
Stro
ngpr
omot
ers,
expr
essi
onre
gula
ted
bym
edia
man
ipul
atio
n,gr
ows
inm
edia
wit
hon
lyon
eC
and
one
Nso
urce
Inse
ctS.frugiperda
11g·L
�1
Opt
imal
Opt
imal
Goo
dLa
ckof
limit
onpr
otei
nsi
ze,s
afet
y,hi
gh-d
ensi
tycu
ltur
eea
sysc
ale-
up,
post
tran
slat
iona
lmod
ifica
tion
s,ex
celle
ntfo
ldin
gm
achi
nery
,hig
hex
pres
sion
leve
ls,a
ble
toex
pres
sm
ulti
ple
gene
ssi
mul
aten
eous
ly
22,2
5,36
Fung
iA
.niger,
Fusarium
1–5g·L
�1
Opt
imal
Opt
imal
Opt
imal
Supe
rior
long
-ter
mge
neti
cst
abili
ty,
post
tran
slat
iona
lmod
ifica
tion
s,id
ealf
oren
zym
epr
oduc
tion
atla
rge
scal
e
22,2
5
CHAPTER 2. Biopharmaceutical Production 11
Table 2.3. Process and product-related impurities found during downstream processing (DSP) ofbiopharmaceuticals. Reproduced from Hagel et. al. [5]
Process-related impurities Product-related impurities
Cell-culture nutrients, chemicals Dimers, multimers, aggregatesHost cell protein Misfolded product and/or product with random
disulphide bridge formsProteolytic enzymes, other enzymatic activity Deamidated product variantsEndotoxins Product with oxidation of methionineCellular DNA, other nucleic acids Product with heterogeneity of post-translational
modifications such as glycosylation,phosphorylation and acylation
Virus Enzymatic degradation productsCell debris, lipidsAntifoams, antibioticsLeachage, e.g., from affinity columnsExtractables, e.g., from plastic surfacesWater, buffers
Insect. Insect cells have the best machinery for the folding of mammalian proteins and areoften chosen because of their fast production times and the ability for post translationalmodifications, which are more complex than those made by fungi. Insect cells have beenused for the production of vaccines, which can be made in less than 2 months. [22]
The most commonly used vector for recombinant protein expression in insect cells isthe baculovirus Autographa californica. One drawback of this system is that productionof heterologous protein is accomplished late in the viral infection, causing low yields andsometimes improperly folded proteins because of aggregation. However, high expressionlevels have been accomplished with this system, varying from few hundred milligrams to11 g · L�1. Larval culture is cheaper than mammalian culture, scale-up is easy, multiplegenes can be expressed simultaneously and the inability of baculovirus to infect plant andmammalian organisms make this system intrinsically safe. [25,36]
Filamentous fungi. Molds are used industrially for the production of enzymes. Fungican achieve protein production between 1-5 g · L�1, with as high as 35 g · L�1 reported,and produce bioactive proteins with post translational modifications. Their particularadvantage is superior long-term genetic stability due to vector integration as tandemrepeats, leaving as many as 100 gene copies. However, a high number of gene copies doesnot guarantee high levels of protein expression and thus low yields are attributed usuallyto transcription limitations. [22] One process limitation that can rise with recombinantprotein production in this system is the hampering of proteins due to fungal proteases. [25]
CHAPTER 2. Biopharmaceutical Production 12
Table 2.4. Common unit operations in downstream processing (DSP). Reproduced from Ghosh. [43]
Low resolution + High throughput High resolution + Low throughput
Cell disruption UltracentrifugationPrecipitation ChromatographyCentrifugation Affinity separationLiquid-liquid extraction ElectrophoresisLeachingFiltrationSupercritical fluid extractionMicrofiltrationUltrafiltrationAdsorption
2.2.2 Bioreactors for Mammalian Cell Culture
Industrial processes for suspended mammalian cells originated from the need to generatelarge amounts of products such as vaccines produced in BHK cells, developed in stirred tankreactors (STR) up to 3,000 L and, afterwards, interferon ↵ in the same expression systemfor up to 8,000 L. [37] Nowadays mammalian cell culture expands from approximately 10 Lat laboratory scale, to 100–500 for pilot plant, and typically in the range of 10,000–20,000L [23] for industrial scale. [38,39] CHO cells lead the industrial production with up to 70%of all licensed biopharmaceutical proteins. [40] Factors that cause cell death in large-scaleanimal cell culture include, but are not limited to: bubble bursting, nutrient depletion(glutamine, glucose, mitogenic factors), toxin accumulation (lactate, NH4+), hydrodynamicforces, pH variations, sub-optimal temperature, and high and low dissolved oxygen [41]
An ofter overlooked aspect is the abuse in the addition of antifoaming agents, which addan additional burden to DSP, decrease the oxygen transfer rate after the addition of theagent, and have toxic effects on cell physiology, that in turn can greatly affect productquality. [42]
Bioreactor systems used in commercial scale for biopharmaceutical production byanimal cells can be classified in 5 broad categories: 1) STR, 2) pneumatically agitated reac-tors, 3) membrane bioreactors, 4) packed and fluidized-bed reactors and 5) wave reactors.Detailed descriptions of these bioreactor systems have been reviewed elsewhere. [29,37,38]
2.3 Downstream Processing
DSP invokes the separation of a target species from a complex mixture. Bioseparationprocesses are based on multiple techniques that on their own would not deliver the expectedresults. A downstream process must combine high selectivity (or resolution) with highthroughput (or productivity) (see Table 2.4). DSP operations are usually classified inrecovery and purification. [43]
CHAPTER 2. Biopharmaceutical Production 13
Recovery steps isolate the product and prepare the process feed stream for purification,and include operations such as centrifugation, flocculation, and filtration. Purificationsteps remove the process and product-related impurities from the target product (seeTable 2.3 on page 11). The high-throughput, low-resolution techniques are first used tosignificantly reduce the volume and overall concentration of the material being processed.At the process development phase, the main challenges present include low producttitre, complex and poorly characterized feed, and product instability. [44] At larger scale,however, improvements in cell culture titres have shifted the attention to the technicaland economical optimization of the downstream process. DSP operations for mAbs willbe discussed with more detail on Chapter 3.
Chapter 3Monoclonal antibodies
3.1 History and evolution
The work of Köhler and Milstein [45] introducing hybridoma technology using tissue culturecell lines is considered as the onset of the modern antibody industry, paving the wayfor recombinant mAbs. In 1988, Better et al. [46] and Skerra and Pluckthun [47] reportedexpression of antibody genes in E. coli . In their early beginnings, mAbs were completelyproduced from mouse genes. As with the other biopharmaceuticals, an orientation fortheir development with completely human sequences took place. Table 3.1 provides adescription of antibody nomenclature; typically, the sole antibody generic name willdescribe its structure and target.
Murine mAbs (those with the suffix “-momab”) were not particularly effective inclinical studies or were immunogenic. In the subsequent years it became possible todesign and produce chimeric (“-ximab”) and humanized (“-zumab”) mAbs. A method formaking chimeric antibodies was reported by Morrison et al. in 1984. Chimeric mAbspossess approximately 25% murine sequence and 75% human sequence. (see Figure 3.1on the following page). This configuration results in a lower immunogenicity compared tomurine mAbs. [48] The first chimeric mAb to reach market was ReoProVR (Abciximab),a Fab molecule. RituxanVR (Rituximab) was the first complete immunoglobulin G (IgG)chimeric antibody to reach market. [49]
The first humanized monoclonal to reach market was prescribed for transplant rejec-tion licensed by the name of ZenapaxVR (Daclixumab) by Roche. State-of-the-art geneticengineering today focuses on producing fully human mAbs, (“-umab”) which have theadvantage of having practically negligible secondary effects on the host.
14
Chapter 3. Monoclonal Antibodies 15
scF
v
H
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ht
L
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vy
“Hin
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n
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ble
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iable
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[51]
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ical
ly,th
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afle
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ly4S
er)3
.[54
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ra
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ctur
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Therapeutic
MonoclonalA
ntibodies:From
Bench
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[55]
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lT
hera
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inForm
ulation
and
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ent
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for
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aceuticals.[
56]3D
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Chapter 3. Monoclonal Antibodies 16
Anti-TNF Antibodies31.8%
Insulin&
Insulin Analogs22.6%
Anti-InflammatoryAntibodies8.7%
CancerAntibodies 28.3%
RecombinantCoagulation
Factors8.6%
2,000
4,000
6,000
8,000
10,000
Tota
l sal
es
(US$
mln
)
Adalimumab Etanercept Infliximab Rituximab Trastuzumab
2012 2011
Figure 3.2. Blockbuster biologics 2012 sales (%) by product class and top 5 best selling monoclonalantibodies (mAbs). Produced with data from La Merie Business Intelligence. [58] (Thisfigure is available in full color at http://goo.gl/iA9h36)
Table 3.1. Generic names for monoclonal antibodies (mAbs) based on source and target. Modifiedfrom An. [51]
Prefix Target Antibody Source Suffix
Variable Nontumor target Viral -vir- -u- Human -mab-Bacterial -bac-Immune -lim- -o- MurineInfectious lesions -les-Antifungal -fung- -a- RatCardiovascular -ci(r)-Neurologic -ne(r)- -e- HamsterInterleukins -kin-Musculoskeletal -mul- -i- PrimateBone -os-Toxin as target -toxa- -xi- Chimeric
Tumor target Colon -col-Melanoma -mel- -zu- HumanizedMammary -mar-Testis -got- -axo- Rat/murine hybridOvary -gov-Prostate -pr(o)- -zixu- Chimeric+humanizedMiscellaneous -tu(m)-
3.2 Market
The worldwide consumption of human IgG increased almost 3-fold between the years1992 and 2003 from 19.4 to 52.6 tons. [50] Today, mAbs and Fc fusion proteins account for35% of the entire market of therapeutic proteins, [51] followed by erythropoietins (19%),insulins (14%), interferons (10%), and coagulation factors (6%), to name a few. [52] MAbsare the second largest class of drugs after vaccines. Estimations are that 30% of the newdrugs developed in the next 10 years will be based on some antibody product. [48]
Sales for the top 5 best selling mAbs in 2012 nearly reached US$ 40,000 mln (seeFigure 3.2). In 2014, the worldwide annual sales of biologic agents are expected to exceedthe annual sales of all other drugs combined. [57] La Merie Business Intelligence providesboth pay-per-view and free reports regarding the market of biopharmaceuticals. [58]
Chapter 3. Monoclonal Antibodies 17
3.3 Production and purification
Titres of 3–6 g/L are now routine at clinical phase Good Manufacturing Practice (GMP)manufacturing. [59] Bioreactor batch volumes at very large scale (VLS) can be as highas 25 m3, generating around 250 kg of harvest material. [60,61] The world demand forantibodies such as Rituxan and Enbrel is close to 1,000 kg per year, and with the growingdemands and higher productivities, an scenario of 10,000 kg annually has been predictedfor the near future. [19]
During the IgG first DSP development, the process consisted on plasma fractionationusing ethanol for the precipitation of proteins. This method was firstly performed foralbumin and was later modified for purification of IgG by Edwin Cohn during the 1950s. [14]
The setup of the modern DSP platform for mAbs at industrial scale was virtuallydesigned by Abhinav Shukla [62–66] and in contrast to plasma IgG purification, it mainlyuses packed-bed chromatography and membrane-based operations. MAbs DSP have globalyields in the range of 60–80%. [67]Even though there is an agreed consensus for the stepsinvolved in purification, there can be significant differences between products that givethis wide range in process yield. Each antibody product presents the design engineer withparticular and specific challenges, both product- and technology-related. For example, inVLS manufacturing, buffer consumption is in the range of 10,000 L at fermentation titreof 1 g · L�1 . In a scenario with an increase in bioreactor titre from 1 to 5 g · L�1 , thereis a 4 fold increase in buffer consumption. [59]
A DSP platform is a predefined sequence of unit operations that requires minimaldevelopment of critical parameters. Several authors have reviewed the recovery andpurification of mAbs. [48,61,63,68–70] Depending on target product and available facilities,this typical outline (see Figure 3.3 on page 19) exhibits modifications at different levels.Kelley [19] has reported a complete financial analysis for mAbs production.
3.3.1 Harvest and primary recovery
The DSP of mAbs begins with the separation of cells from the cell culture fluid (CCF). Usu-ally, the concentration of solids in the culture broth from mammalian cells is 40–50%. [71]
In a typical VLS process, cell separation is achieved by mechanical means. Harvestingoperations in terms of capital cost and energy consumption can account for up to 25% ofthe cost of the entire DSP. [19] Since harvesting operations were not studied in this work,their description will not be extensive.
Centrifugation. Centrifugation is used to separate product-containing liquids fromcells, cellular debris, and particulates. [72] At VLS the common practice is the use of con-tinuous centrifuges known as disk-stacks. [19,66,73] Although disk-stack centrifuges requiremore expensive peripheral equipment than compared to filtration and are not scale-up as
Chapter 3. Monoclonal Antibodies 18
Tabl
e3.
2.Fu
llm
onoc
lona
lant
ibod
ies
(mA
bs)
appr
oved
byth
eU
SFo
odan
dD
rug
Adm
inist
ratio
n(F
DA
)as
ofJa
nuar
y20
14.
#B
rand
nam
eP
rodu
ctM
olec
ule
Targ
etan
tige
nIn
dica
tion
App
rove
d
1O
rtho
clon
eO
KT
3M
urom
onab
-CD
3M
urin
eT
-cel
lCD
3R
ecep
tor
Tran
spla
ntre
ject
ion
1986
2R
eoP
roA
bcix
imab
Chi
mer
icgp
IIb-
gpII
Ia,a
lpha
vbet
ha3
Car
diov
ascu
lar
dise
ase
1994
3Ze
napa
xD
acliz
umab
Hum
aniz
edIL
-2re
cept
oral
pha
Tran
spla
ntre
ject
ion
1997
4R
itux
an,M
abT
hera
Rit
uxim
abC
him
eric
CD
20N
HL
1997
5H
erce
ptin
Tras
tuzu
mab
Hum
aniz
edE
rbB
2B
reas
tca
ncer
1998
6Sy
nagi
sPal
iviz
umab
Hum
aniz
edE
pito
peof
Fpr
otei
nof
RSV
Pre
vent
ion
ofR
SVin
fect
ion
1998
7R
emic
ade
Infli
xim
abC
him
eric
TN
F-a
lpha
Infla
mm
ator
ydi
seas
es19
988
Sim
ulec
tB
asili
xim
abC
him
eric
IL-2
rece
ptor
alph
aTr
ansp
lant
reje
ctio
n19
989
Myl
otar
gG
emtu
zum
abH
uman
ized
CD
33A
ML
2000
10C
ampa
thA
lext
uzum
abH
uman
ized
CD
52C
LL20
0111
Hum
ira
Ada
limum
abH
uman
TN
F-a
lpha
Infla
mm
ator
ydi
seas
es20
0212
Rap
tiva
Efa
lizum
abH
uman
ized
CD
11a
Pso
rias
is20
0213
Zeva
linIb
ritt
umom
abM
urin
eC
D20
NH
L20
0214
Bex
xar
Tosi
tum
omab
Mur
ine
CD
20N
HL
2003
15X
olai
rO
mal
izum
abH
uman
ized
IgE
Ast
hma
2004
16Av
asti
nB
evac
izum
abH
uman
ized
VE
GF
Col
orec
talc
ance
r20
0417
Erb
itux
Cet
uxim
abC
him
eric
EG
FR
Col
orec
talc
ance
r20
0418
Tys
abri
Nat
aliz
umab
Hum
aniz
edT
-cel
lVLA
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cept
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ple
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s20
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Hum
anE
GFR
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talc
ance
r20
0620
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ibiz
umab
Hum
aniz
edV
EG
FM
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arde
gene
rati
on20
0621
Solir
isE
culiz
umab
Hum
aniz
edC
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emen
tC
5In
flam
mat
ory
dise
ases
2007
22Si
mpo
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NF-a
lpha
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mm
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0923
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seas
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-12,
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asis
2009
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2009
26A
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raTo
ciliz
umab
Hum
aniz
edIL
-6R
heum
atoi
dar
thri
tis
2010
27P
rolia
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osum
abH
uman
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NK
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steo
poro
sis
2010
28Y
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test
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1129
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abve
doti
nC
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2011
30B
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imum
abH
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B-c
ella
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ctor
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2011
31Per
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ER
2B
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2012
32(p
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2012
33K
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2013
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LL20
13
Chapter 3. Monoclonal Antibodies 19
Protein Aaffinity chromatography
Low pH viralinactivation
Viralfiltration
FormulationSterile
filtration
UF/DF
UF/DF
Polishingstep 1
Polishingstep 2
Figure 3.3. Downstream processing (DSP) platform for monoclonal antibodies (mAbs). Depending ontarget product and available facilities, this typical outline exhibits minimal modificationsat different levels. (This figure is available in full color at http://goo.gl/iA9h36)
easily, their cleaning is more straight-forward, their validation is simpler, and the capitalinvestment is far from the prohibitive costs in the past. [74] Recently there has been a shiftwith the implementation of centrifuge-free processes that exploit the use of filtration ex-clusively for cell harvesting, and offer a more streamline integration with the introductionof single use technology in bioprocessing.
Depth Filtration. Also called "prefiltration", depth filtration (DP) consists of a porousmatrix that retains impurities. The use of depth filters after centrifugation is commonpractice in VLS because of the inefficient elimination of particles smaller than 1 µm bycentrifugation. [75] One particular advantage of DP is the possibility of using chargedmatrices. Positively charged depth filters have been used to effectively remove key con-taminants in mammalian cell streams such as viral particles, [76] DNA, [77,78] and host cellprotein (HCP). Depth filters often incorporate a microfiltration (MF) membrane at theend of system. [43]
Tangential Flow Filtration Microfiltration. MF as tangential flow filtration (TFF) is apopular harvesting technique for therapeutic products from mammalian cell cultures. [48]
Unlike centrifugation, MF generates a particle-free stream with no turbidity. Thoroughscreening and optimization for this operation is required since membrane fouling canmanifest itself as high costs due to frequent membrane replacement and loss of product.Typical pore sizes for MF are 0.1–0.8 µm. [43,66,71]
3.3.2 Capture by Protein A affinity chromatography
Protein A affinity chromatography is the step of choice for most industrial processes, withonly 30% using ion exchange chromatography (IEX) as capture step. [79,80] The objectiveof Protein A chromatography is mAb capture from harvested cell culture fluid (HCCF).This step provides a 5– to 10–fold concentration of the product.
Protein A is a polypeptide found in the cell wall of Staphylococcus aureus. Themolecular weight of the intact molecule is 54 kDa, and the traditional modified recombinantversion with the deleted cell wall domain is approximately 42 kDa. Protein A has 5homologous antibody binding domains, each with a molecular weight of 6.6 kDa andare themselves protease resistant. IgG attaches to Protein A by its Fc region by a
Chapter 3. Monoclonal Antibodies 20
binding mechanism that primarily consists of hydrophobic interactions as a function ofpH. Interactions with the Fab region have also been found in the literature. [81] Denizli [50]
reports IgG binding proteins from bacteria.Advantages of Protein A chromatography include high recovery (>95%) and high
purities in a single step (95–99 %), wide working pH range (2.0–11.0), possibility of cleaningwith reducing agents, and high Dynamic Binding Capacity (DBC). DBC is determinedas a function of flow rate and residence time, and for commercial resins available todaycan range between 30–60 mgIgG ·mL�1 resin. [79] Protein A may account for 50% of theDSP costs. [19]
Protein A binding is not affected by absence of glycosylation or its variations ifpresent. [82] IgG subclasses IgG1 and IgG4 bind more strongly than IgG2. Binding isusually performed at slightly alkaline pH (7.0–7.4) and HCCF requieres minimal or noprocessing at all prior to loading to the Protein A column. Typical HCCF from CHOand NS0 cells has a pH between 6.5 and 7.6 with a conductivity of approximately 10–20mS/cm. An UF/diafiltration (DF) step prior to loading may be considered if titres aretoo low or the DBC is unacceptable. [48]
Although an affinity resin, some groups will cause the adsorbent to act as an ionexchanger, and other compounds may bind by electrostatic interactions. Washing withthe equilibration buffer may not be enough for getting rid of nonspecifically boundcontaminants such as HCP, DNA, and media components. Typical concentrations ofadditives used are: 0.5–1.0M for NaCl and Na2SO4; 0.5–2.0M for CaCl2, MgCl2, andMgSO4; 0.002%–0.02% (v/v) for detergents such as Tween 20; 10%–20% for organicsolvents; 10%-20% for polyethylene glycol and polypropylene glycol; and 0.5M for aminoacids such as arginine and glycine. [83] Fully loaded or overloaded columns severely stressthe ability of chromatographic media to provide efficient fractionation of bound materials.This is an unavoidable limitation due to diffusive mass transport. [84] Overload effectsmay be avoided by restricting the load to approximately one third of the maximum loadcapacity of the column. For VLS, loading rarely exceeds 80% of DBC. [5] This higherloading is a tradeoff between product loss in flow through and unused column matrix.
At low pH, the histidyl residues involved in the bonding become protonated andmutually repellent, therefore allowing for the detachment of IgG from Protein A. Typicalmobile phases used for elution are: 100 mM Glycine-HCL pH 2.0, 20 mM HCl, 100 mMSodium Citrate pH 2.5, 1.0 M Propionic acid. [85] Depending on the IgG molecule, pHfor elution ranges between 2.7–4.5, and the highest pH that offers the maximum possibleproduct yield is chosen so that exposure to low pH is minimized. At the low pH usedfor elution, most molecules tend to form aggregates that can complicate the purificationprocedure as well as compromise drug safety. Regularly, antibodies can benefit from theaddition of additives to the elution buffer in order to increase conductivity for better
Chapter 3. Monoclonal Antibodies 21
product stability. High concentrations of NaCl, ethylene glycol, and arginine have beenadded to Protein A elution buffer to reduce aggregation.
Commercially available Protein A suppliers at industrial scales in the format of packedbed chromatography include General Electric (GE), Life Technologies, and Millipore.Normally resins can be classified by the nature of the Protein A ligand (natural wild vs.recombinant), immobilization chemistry, and bead characteristics. Recombinant ProteinA is expressed in E. coli as an extracellular variant lacking the wall associated region,as before explained. Sorbents based on porous glass and coated polystyrene materials(POROS® ) (Life Technologies) can be operated at high flow rates and are suitable forboth analytical formats such as the ones found for high-performance liquid chromatography(HPLC) quantification of IgG, and for large scale production. Most modern industrialscale processes use resins made of highly crossed-link agarose as matrix. Examples of thelatter are MabSelect® products by GE. In recent years there has been a shift to noveltechnologies such as the one offered by POROS® non-compressible media and membranechromatography (Sartorius Stedim Biotech). These formats offer major benefits over softmedia, for instance, the possibility to operate at higher flow rates. Non-compressible mediaand membrane chromatography rely more on convective than on diffusive transport.
One disadvantage of Protein A is the coelution of ligand with the antibody. LeachedProtein A fragments range between 6–40 kDa and originate from proteolytic activitypresent in the HCCF. This may be controlled by the addition of EDTA to the HCCF toinhibit metalloproteases although other kinds may be present. Lipids and DNA can arisefrom excessive cell lysis during harvesting operations, however, their fraction comparedto the proteinaceous fraction of HCP is very small. [80] Optimization can, in certain cases,reduce the HCP level below 100 ppm and DNA level below 3 pg ·mg�1. This implies thatat least one additional purification step is required to meet therapeutic-grade productrequirements. [86]
Typically after elution, a solution with a lower pH is used to eliminate tightly boundmaterial to the matrix bed, usually with a pH of 2.5–3.0 Native or recombinant ProteinA are stable on slightly alkaline conditions, typically with the use of <100mM NaOH.Cleaning and sanitization can also be performed with high concentrations of chaotropes(e.g. 6M urea, 6M guanidine HCL), however, the use of NaOH proves to be much lessexpensive, with the added benefit that its clearance from the column is easy to monitor.The use of 100mM NaOH has shown to give yields of nearly 100% over 200 cycles of columnuse, dropping to 50% after 300 cycles. A cleaning solution of 50mM NaOH/1.0M NaClgave only 11% drop of DBC after 300 cycles compared to a drop 16% after only 61 cycleswith use of NaOH alone. MabSelect® Sure has been engineered to withstand strongeralkaline conditions, such as 0.1–0.5M NaOH repeatedly for cleaning and sanitization. Anadditional advantage of this resin entirely composed of Protein A B domains, is elution
Chapter 3. Monoclonal Antibodies 22
+O
OH C
OH OH
OH
CH
N3
3
Figure 3.4. Capto® Adhere ligand structure. The left side of the molecule is coupled to the columnmatrix. Structure drawn with MarvinSketch for Mac OSX. (This figure is available in fullcolor at http://goo.gl/iA9h36)
at a less extreme pH aiding in minimization of aggregations issues. [87] provide a plot ofelution pH with MabSelect® and MabSelect® Sure for 14 different mAbs.
The main restrictive parameter during packed-bed chromatography is column pressuredrop (�P ), which is primarily a function of the volumetric flow rate (Q); Q is betterexpressed in terms of linear fluid velocity (u), which is constant at all scales and a keyparameter for scaling chromatography. The chosen u is a trade-off between the minimalresidence time (tR) for acceptable yield and the maximum �P the column that withstand.Common residence times for Protein A chromatography range from 2 to 6 min. [88] tR canbe expressed as:
tR =`
u=
⇡ · d2 · `4Q
=V
Q(3.1)
where ` and V are the height of the packed bed, and the volume of the packed bedrespectively. As u cannot be arbitrarily increased due to the pressure drop limit, and tR
needs to be kept constant, the parameter to modify is the diameter of the column (d). Thisis the main reason why in VLS manufacturing, Protein A columns are typically between10–30 cm in height with as much as 3 m in diameter. [19,80,89] Column volumes (CV) arekept constant between scales although slight adjustments might be made. Efficient andreproducible column packing becomes critical regarding predictable and linear scale-up.
3.3.3 Polishing operations
Polishing operations are meant for the final removal of trace contaminants to achieve finalpurity requirements for the target product. The entire train of unit operations duringDSP has a dramatic impact on the design of the polishing steps. For instance, in thecase of mAbs, the optimization of DP, Protein A affinity chromatography, and UF canreduce the contaminant burden in the stream prior to polishing with a final outcome ofperhaps only one polishing step. Nowadays in VLS mAbs manufacturing, a maximum oftwo steps of polishing are used, including a relatively narrow choice of operations such ashydrophobic interaction chromatography (HIC), IEX, and multimodal (a.k.a. Mixed-modechromatography), being the last two the most frequently implemented. [90]
Chapter 3. Monoclonal Antibodies 23
IEX is one of the most common operations for mAbs polishing at VLS. The usedformat involve cation exchange chromatography (CEX), anion exchange chromatography(AEX), multimodal ion exchange, or combinations between these three. Typical DBC ofion exchangers range between 70–100 g · L�1 , with bead sizes of 60–90 µm, and tR of2–5 min with bed heights of 20–40 cm. Linear flow rates fall between 100–500 cm · h�1
with as high as 700 cm · h�1 reported for agarose-based media. [48] In the case of rigidmatrices described before, such as POROS® , load capacities can exceed 500 g · L�1 andlinear flow rates of up to 1000 cm · h�1 can be applied without increased pressure orloss of resolution. Pressure drop limits for this kind of resins are in the range of 3.0 bar,making the linear flow rate and pressure limitation more equipment dependent ratherthan column dependent.
As mentioned before, the most common contaminants found in the product stream areHCP, DNA, high molecular weight aggregates (HMWA), low molecular weight degradationproducts, and leached Protein A. [89]
HCP contaminant clearance is a significant concern during DSP development forbiopharmaceuticals. Residual HCP in the final product can present potential safety risksto patients or compromise product stability. During purification, it is not clear whetherthe majority of HCP impurities bind to the resin and co-migrate with the product, [91] orif they are in some way associated with the product species. [83] HCP levels in the affinitypool are typically in the range of 300-3,000 ppm, but as high as 50,000–70,000 have beenreported. [92,93] Most approved FDA products measure less than 100 ppm of HCP.
Protein aggregation is a common phenomenon during biopharmaceutical manufactur-ing. Aggregation can well be the main issue during production processes and it has beenextensively researched. However, despite great efforts, the mechanisms of aggregation arepoorly understood and remain highly unpredictable. Aggregates can initially exist as smalldimers or fragments and progress toward larger structures, such as micron-sized particles,if these are thermodynamically favorable. There is no uniform terminology for differentkinds and sizes of aggregates, although they haven classified according to solubility, cova-lent bonding and native state, to mention a few. [60] Differences in biological activity ofthe aggregates compared to monomer species can significantly impair the potency of atherapeutic protein, [60] as well as eliciting immune responses. [94–96] Levels of aggregatesin HCCF range between 0.5%–25% for mAbs. [80] Protein A affinity chromatography haspoor resolving power for aggregates and they are typically eluted along with the mainproduct, and are major concern for polishing operations in order to have monomerichighly-pure antibody.
Leached Protein A fragments weighty typically 6–40 kDa, and their levels rangebetween 3 to 40 ppm depending on the used resin. [97] At VLS, this particular contaminantis of no major concern since because of its size, can be eliminated in the UF operations
Chapter 3. Monoclonal Antibodies 24
Table 3.3. Commercially available multimodal/mixed mode chromatography (MMC) resins for polish-ing of monoclonal antibodies (mAbs).
Company Product Ligand Matrix Particlename size
(µm)
GE Healthcare Capto MMC Carboxylic andphenyl group
Highlycross-linkedagarose
75
Capto® Adhere N-benzyl-N-Methylethanolamine
Highlycross-linkedagarose
75
Pall HEA HyperCel N-Hexylamine Cross-linkedcellulose
80–100
PPA HyperCel PPA Cross-linkedcellulose
80–100
MEP HyperCel 4-Mercapto-ethylpyridine
Cross-linkedcellulose
80–100
Blue Trysacryl M Cibacron BlueF3GA
Polymeric 40–80
following affinity chromatography, additional to the clearance offered by traditional packed-bed chromatography during polishing.
Except for DNA, there are no official guidelines that stipulate the level of tracecontaminants in the final formulation of mAbs. FDA requirements state an upper limitof 100 pg per therapeutic dose or up to 10 ng per dose for large-dose biopharmaceuticals,such as mAbs. Other guidelines from the World Health Organization (WHO) are alsoavailable. [98] As a rule of thumb, expected levels of impurities should be <100 ppm forHCP, <1.0% for HMWA, Protein A below detection [90,99,100]
AEX is usually run in flow-through mode, in which process conditions are set so thatthe antibody washes away from the column while the impurities bind to the stationaryphase, prior to their elution. [101] This mode allows for smaller column sizes since the columnbed is dimensioned with respect to the impurities, which after affinity chromatographyshould present only in trace amounts. Most humanized antibodies type IgG1 and IgG2have pIs of 7.5–9.0. [86] In-flow through mode, the antibody is protonated below its pI, whilenegatively charged impurities such as DNA, endotoxin, HCP, and even viruses. [102–104]
Commonly used AEX resins are reported by Ghose. [105]
In contrast to AEX, CEX is usually operated in bind-elute mode. HCP removal ismodestly better using CEX than using AEX, however, leached Protein A and HMWAclearance can be significantly better using CEX. [102,106] Excellent separation can beachieven even at process scales using gradient elution, if necessary. Fine-tuning is requiredfor the loading conditions of any IEX operation, since conductivities higher than 10–20mS/cm significantly reduce binding capacity, but lower values may result in binding andcoelution of undesired impurities [107] Complete and thorough guidelines for the selectionof an industrial scale cation exchanger are found in the literature. [84,108] Commonly used
Chapter 3. Monoclonal Antibodies 25
Table 3.4. Properties of viruses for rodent-derived cell lines used for validation studies. Modified fromZhou. [110]
Virus Size Envelope Genome Family Resistance
Mouse minute virus (MMV) DNA No Parvo 20–25 HighPseudorabies virus (PRV) DNA Yes Herpes 150–250 MediumReovirus type 3 (Reo-3) RNA No Reo 60–80 HighXenotropic murine leukemia
virus-related virus (X-MuLV)RNA Yes Retro 80–110 Low
CEX resins are reported by Ghose. [105]
Mixed mode chromatography (MMC) was introduced in the late 1950s as an anti-body purification operation. Nowadays, multimodal chromatography is mainly used asa polishing application with a more or less stream line of charge/hydrophobic ligands.Three of the most used ligands at industrial scale are 4-Mercaptoethyl pyridine (MEP),hydroxyapatite (HA), and Capto resins. Capto® Adhere is a strong ion exchanger withmultimodal functionality, designed for the intermediate purification and polishing stepsof mAbs. Polishing results with Capto® Adhere can be quite unpredictable and they areantibody dependent. For this reason, in order to find the optimal conditions, the use ofdesign of experiments (DoE) is highly recommended. An experimental design that is ableto assess interaction between the factors studied should be used. Typically, protein loadis in the range of 100–300 mgIgG ·mL�1. In the case of conductivity, an screening between5–50 mS/cm should be enough for an initial assessment. With Capto Adhere, in general,high pH and low conductivity favor the best clearance of HCP and leached Protein A.However, as mentioned before, this should be determined by every particular antibody.Cleaning protocols for each product should also be tested. The media can withstandexposure to 1M NaOH, 1M acetic acid, and 70% EtOH, nonetheless, oxidizing agents oranionic detergents should be avoided. [105,109] A list of commercially available multimodalresins is provided (see Table 3.3 on the previous page).
3.3.4 Viral clearance
When protein therapeutics are produced in animal cell lines, the risk of contaminationwith viruses from the cell line or with constituents of the culture media is a criticalconsideration. [110] Virological safety is one of the most important challenges during theproduction process of biopharmaceuticals and an issue that is widely discussed. [5] Ad-ditional to the potencial risk of viral propagation during cell culture, which must beassessed, manufacturers are required to demonstrate that the purification process is ableto remove or inactivate viruses. Several documents provide guidance on this matter. [111]
These guides specify model viruses and assays for viral clearance studies. [110]
The ICH Q5A requires the use of at least two dedicated orthogonal steps for viralreduction in addition to the clearance achieved by chromatography to ensure the safety
Chapter 3. Monoclonal Antibodies 26
Table 3.5. Commercially available virus filters. Modified from Phillips et al. [111]
Company Format Product Name Virus Sizes
Millipore NFF NFP >4 log X-174bacteriophage
Scale down 3.5 cm2; processmodules 0.08–1.5 m2
NFF NFR >6 log retrovirusTFF Viresolve®
70>4 log polio;
>7 log retrovirusScale down 150 and 1000
cm2; process modules0.75–1.4 m2
TFF Viresolve®
180>3 log polio;
>6 log retrovirusSartorius NFF Virosart
CPV>4 log PP7 bacteriophage;
>6 log retrovirusScale down 5 and 180 cm2;
process modules 0.2–2.1m2
Pall NFF DV20 >3 log PP7 bacteriophage;>6 log PR772bacteriophage
Scale down 14 and 140 cm2;process modules 0.07–6m2
NFF DV50 >6 log PR772bacteriophage
Asahi TFF/NFF Planova®
15N>6.2 log parvovirus;
>6.7 log poliovirusScale down 10 and 100 cm2;
process modules 0.12–4m2
Planova®
20N>4.3 log parvovirus;
>5.4 logEncephalomyocarditis
Planova®
35N>5.9 log Bovine viral
diarrhea virus;>7 log HIV
of products produced by mammalian cell culture. [26] Relevant viruses, defined as thoseviruses known to contaminate or likely to contaminate the starting materials and reagents,are used for clearance studies whenever possible, and if not available, a model virus canbe used for this purpose. For rodent-derived cell lines there are 4 different viruses used inviral clearance (see Table 3.4 on the preceding page). For a complete set of viruses formultiple hosts please refer to the publication from Kelley and Petrone. [112]
Typically, the objectives of a viral clearance study are 1) to demonstrate that the cellculture and purification processes are capable of removing or inactivating viruses, 2) todetermine the clearance of each process step and the clearance for the entire process, 3)to demonstrate the kinetics of inactivation in those process steps in which inactivation isthe primary method of clearance, and 4) to demonstrate that the column chromatographyregeneration solutions and processes can inactivate model viruses. [99]
Depending on the antibody titre and the amount of antibody in a dose, the virusesin the single-dose-equivalent are estimated in the ranges from 1010 to 1015 particles/mL.Therefore, a purification process must be capable of eliminating more than 15 to 20 logsof viruses. [113] On the basis of the log10 reduction value (LRV), the viral clearance stepcan be classified as effective (LRV >4), moderately effective (1< LRV <4), and ineffective(LRV <1). A lower limit of three is an acceptable factor for manufacturing processes. [48]
Several operations can be incorporated to a process for viral clearance. These include
Chapter 3. Monoclonal Antibodies 27
heat treatment, use of detergents, extreme pH, chromatography and virus filtration. AtVLS operation the most implemented are low pH inactivation, which takes advantage ofProtein A affinity chromatography, and viral filtration. The concept of “bracketed genericclearance” has been proposed for these steps if it could be prospectively demonstratedthat viral LRV "is not impacted by operating parameters that can vary, within a reasonablerange, between commercial processes." [114]
Low pH inactivation typically has an LRV greater than 6 and virus filtration has a LRVranging from 4 to 6. [111,114] Low pH inactivation must be assessed at the optimal elutionconditions. For most mAbs inactivation times are usually in the range of 30–120 minabove pH 3.0. The inactivation time must be determined by the kinetics of the inactivationitself. [80] Virus filtration is nowadays a very robust operation, usually performed withdisposable filters. Classification of filters can be made by filters made for retaining viruses50 nm or larger (retroviruses), or for retaining viruses 20 nm or larger (parvoviruses). Thedetermination of the viral model is quite important since 20 nm filters can cost as muchas 4 times more than 50 nm filters. Viral filtration is typically more expensive than sterilefiltration because of lower product throughput.
Typical volumetric loads for this operation range between 200–500 L/m2, at whichtime the filter should be replaced. Brough et al. [115] and Higuchi et al. [116] provide studiesof membrane fouling during virus filtration. A typical feed for VLS of viral filtration isaround 20 mgIgG · mL�1. A list of commercially available filters for virus filtration isprovided (see Table 3.5 on the previous page).
3.3.5 Ultrafiltration and Diafiltration
UF is a pressure-driven process in which a mixture of solutes is passed through a membranewith a narrow size distribution of pores. The separation of the solutes is mainly based onmolecular size: high molecular weight solutes such as proteins will be retained (retentate)by the membrane, while low molecular weight species such as salts and buffers will passtrough the membrane (filtrate or permeate). UF has been available in its current formatssince the 1970’s. The Loeb and Sourirajan’s phase inversion process created the firstanisotropic membranes and is considered a major breakthrough since it continues to bethe basis of today’s UF membranes. These membranes comprise a thin skin approximately0.5 µm thick that provides selectivity and a macroporous structures that offers mechanicaland structural integrity. [117,118]
Membrane-based operations are typically performed in two different configurations:normal flow filtration (NFF) also known as dead-end filtration and TFF also known ascross-flow filtration. Most UF devices use TFF; in this configuration the feed flow isparallel to the membrane and thus perpendicular to filtrate flow. The principal advantageof TFF over NFF is that the retained molecules can be swept along the membrane surface,
Chapter 3. Monoclonal Antibodies 28
Perm
eate
Flu
x, J
(L m
-2 h
-1)
Transmembrane Pressure , TMP (Pa)
Clean Water Flux
Pressure independent region
Pressure dependent region
Cb1
Cb2
Cb3
Cb1<C
b2<C
b3
Figure 3.5. Effect of transmembrane pressure (TMP) on permeate flux (J) during tangential flowfiltration (TFF). The slope of the clean water flux curve is defined as the membranehydraulic permeability (Lp). Permeate flux is dependent both on TMP and bulk proteinconcentration (Cb). (This figure is available in full color at http://goo.gl/iA9h36)
exiting the device and being recirculated thereafter. This allows a significant increasein process flux compared to NFF. [117] UF current uses in the purification and recoveryof mAbs involve tree main operations: 1) Product Concentration, 2) Buffer exchangeand desalting and 3) Purification. UF is the industry standard for the production ofhighly concentrated mAbs solutions and for solvent exchange operations. [119–123]In thecontext of polishing, UF is used for desalting, further purification of elution pools andfor solvent exchange operations leading to product storage/stabilization in formulationbuffers. [49,124,125] Buffer exchange and desalting are typically accomplished using a DFmode in which the low molecular weight components are washed away from the proteinby simultaneously adding fresh buffer (or solvent) to the feed during UF. [117] UF can beparticularly important in the removal of aggregates from mAbs and for the substitutionof steps that cannot be used at industrial scales such as SEC. [120] UF membranes poresizes are typically available in the range of 1 to 20 nm in diameter to separate soluteswith molecular weight from 5 to 500 kDa. [43] TFF pore sizes are reported as nominalmolecular weight limit (NMWL) or molecular weight cutoff (MWCO). The pore sizesused for each objective during monoclonal’s purification depends on the step particularobjectives. A rule of thumb for selecting membrane NMWL is to take 0.2 to 0.3 of theproduct’s molecular weight. [120] Frequently, 30 to 50 kDa membranes are used for highantibody retention at reasonable flux rates. The performance of membranes of 100 kDahas also been studied. [126,127]
The most used membrane formats for UF are flowsheet cassettes, hollow fiber, tubular,and spiral wound. Detailed descriptions and advantages of each of them can be found in
Chapter 3. Monoclonal Antibodies 29
Lim
itin
g P
erm
eate
Flu
x, J∞
(L m
-2 h
-1)
ln(Cb)
ln(Cg) = b / k
ln(Cg)
y(x) = b + mx
m = k
Q1<Q
2<Q
3
Q3
Q2
Q1
J∞ = k lnCg− k lnC
b
J∞ = k lnC
g
Cb
⎡
⎣⎢
⎤
⎦⎥
Figure 3.6. Estimation of mass transfer coefficient k by plotting of limiting permeate flux versus solutebulk concentration Cb. The mass transfer coefficient represents the slope of the linearregression and the gelation concentration Cg the intersection with the x axis. Q = feedflow rate. (This figure is available in full color at http://goo.gl/iA9h36)
the literature. [43,117,118,128,129] Some characteristics of these formats are listed in Table 3.6.Commercially available cassettes range in surface areas from about 20 cm2 to > 3 m2. [118]
Driving force. Analysis of solute and solvent transport through UF membranes isusually developed using either the Kedem-Katchalsky or Stefan-Maxwell descriptions ofirreversible thermodynamics. [117,130]
The driving force through the membrane is determined by the TMP defined as: [71]
TMP =PF + PR
2� PP (3.2)
where PF is the feed pressure, PR the retentate pressure and PP the permeate pressure.It is important to note that this equation is an approximation because the pressure dropalong the feed channel is not truly linear; this is due to conversion of feed to permeate. [117]
Nevertheless the equation is generally accepted for the estimation of TMP .The flow of solvent through an UF membrane is given by:
J = Lp · TMP (3.3)
where J is the volumetric permeate rate normalized by the membrane surface area and Lp
is the membrane hydraulic permeability coefficient. J can be easily obtained by measuringthe permeate volume per time (L · h�1) and normalizing with the filter membrane area(m2), which is provided by the manufacturer (giving (L · m�2 · h�1), also expressed asLMH). If J is plotted against TMP, a typical behavior for TFF is obtained (Figure 3.5).
Chapter 3. Monoclonal Antibodies 30
Table 3.6. Characteristics of ultrafiltration (UF) membrane modules used in very large scale (VLS)manufacturing of monoclonal antibodies (mAbs). Reproduced from van Reis and Zyd-ney. [117]
Module Channel Packing Energy costs Particulate Ease ofconfiguration spacing (cm) density (m2 ·m�3) (pumping) plugging cleaning
Spiral wound 0.03–0.1 600 Low Very high Poor–fairFlat sheet 0.03–0.25 400 Moderate Moderate GoodTubular 1.0–2-5 60 High Low ExcellentHollow fiber 0.02–0.25 1200 Low High Fair
Lp is equal to the reciprocal of the overall resistance:
Lp = (Rm +Rsp +Rg)�1 (3.4)
where Rm is the membrane hydraulic resistance, Rsp the resistance due to surface polariza-tion (SP) , and Rg the gel layer resistance. System performance in an UF process is usuallydefined on terms of permeate flux. [43] When no SP is present the only resistance foundis that of the membrane and therefore permeate flux increases linearly with increasingTMP (Figure 3.5).
Mass transport and surface polarization There are to kinds of mass transfer: 1) purelydiffusive mass transfer (molecular diffusion) and 2) convective mass transfer. Moleculardiffusion occurs when molecules moving randomly are transported from a region of higherconcentration to one of lower concentration. For macromolecules and submicron particles,Brownian diffusion is dominant, [43] which is expressed mathematically with Fick’s law.Convective transport takes place with flowing fluids, particularly in turbulent conditions.When a liquid flows past a solid surface a stagnant boundary liquid layer is formed closeto the surface, and within this layer, solute transport will take place by molecular diffusionif flow is laminar. However, if flow is turbulent in nature, will take place by a combinationof molecular and eddy diffusion. This is referred as convective mass transfer. [43]
As pressure drives solvent flow through the membrane, convection transports solutesto its upstream surface, causing solutes that do not pass through the membrane tobuild up near the surface. [43,131,132] This phenomenon is known as surface polarization orconcentration polarization (see Figure 3.5 on page 28).
Figure 3.5 shows how as TMP increases, the permeate flux deviates from the waterprofile, this being due to SP. Understanding SP and controlling its effects are essentialto implementing a good process. [120] Some common mathematical models for expressingpermeate flux in TFF are: [43,133,134] 1) stagnant film model (SFM), 2) osmotic pressuremodel (OPM), 3) Hagen-Poiseuille equation, and 4) shear-induced diffusion. Since theexperimental methods of this work exclude the last two models, they will not be discussedhere.
Chapter 3. Monoclonal Antibodies 31
Stagnant Film Model. The SFM model explains SP based on the following mass balance:
J · C � J · Cp +DdC
dx= 0 (3.5)
where C is the solute concentration on the upper membrane at a distance x, Cp soluteconcentration in the permeate side and D is the solute diffusivity within the pores. Atthe steady state, Brownian diffusion making the solute move away from the membraneand back to the feed (third term) is equal to the solute moving toward the membraneby convection (first term) and the solute passing through the membrane by convection(second term). [43] Integrating over the boundary layer thickness C = Cw at x = 0 andC = Cb at x = �, the following equation is obtained: [43,135]
J = k ln(Cw � Cp)
(Cb � Cp)(3.6)
where Cw is the solute concentration at the wall, Cb the solute concentration in the bulkfeed and k the solute mass transfer coefficient. When the solute is completely retained,Cp = 0, giving: [43,118]
J = k ln
Cw
Cb
�(3.7)
If SP becomes extensive, a gel layer may be formed on top of the membrane. Atthis point, Cw cannot longer increase its value and it’s expressed as Cg, which is thegelation concentration. Once the gel is formed, permeate flux reaches its maximumpossible value [118,132] (limiting permeate flux), becoming J1 (Figure 3.6) and being TMPindependent:
J1 = k ln
Cg
Cb
�(3.8)
For a concentration operation, membrane area and process time can be determinedwith the following equation: [128]
A · tV0
=
Cb,fZ
Cb,0
exp
"�
Cb,fR
Cb,0
dCb(Cb�Cp)
#dCb
J (Cb � Cp)(3.9)
where A is the membrane area, V0 the initial volume of sample, Cb,0 the initial bulkprotein concentration, and Cb,f the final bulk protein concentration. If Cp is assumed tobe negligible as before, we get:
A · t = V0 · Cb,0
2
664
1/Cb,0Z
1/Cb,f
J�1d
✓1
Cb
◆3
775 (3.10)
Chapter 3. Monoclonal Antibodies 32
Bulk Feed
Permeate
Retentate
PolarizationLayer
ΔΠ TMP
kBack diffusion
Cp
Membrane
Convection
Gel layer
Cw
(at x = 0)
Membrane
x = 0
x = δ
Cb (at x = δ)
Figure 3.7. Driving force and resistance diagram during tangential flow filtration (TFF). (This figureis available in full color at http://goo.gl/iA9h36)
Osmotic Pressure Model. OPM explains permeate flux decline by the buildup of solute inthe upper membrane surface which creates osmotic pressure (�⇧) that counteracts theapplied TMP (see Figure 3.7). The OPM model is basically equation J with an addedcorrection for the TMP parameter, resulting in:
J = Lp [TMP � ⌃ (�i�⇧i)] (3.11)
Because of SP, Lp value accounts at least for Rm+Rsp; if a gel layer is already formeddue to extensive SP, the Rg parameter must be added as in Equation (3.4). The osmoticreflection coefficient (�i) provides a measure of the membrane selectivity; �i = 1 for asolute that is completely permeable and �i = 0 for a solute that is completely retained.The osmotic pressure difference across the membrane (�⇧i = ⇧w � ⇧p) is a functionof Cw and Cp. Although protein osmotic pressures are small for C < 10g · L�1, �⇧i
can be comparable to TMP during UF because of the buildup of retained solutes at themembrane surface. It has been shown that retained antibodies at a wall concentrationof 191 g · L�1 have an osmotic pressure of 30 psig. That is, an elevated pressure of 30psig must be applied to the protein-rich retentate side of a water permeable membranecontaining 191 g ·L�1 of antibody in order to prevent water back-flow from the permeateside of the membrane containing water at 0 psig. [120] As experimental data suggests,osmotic pressure can be expressed as a polynomial equation: [135,136]
�⇧ = ↵Cw + �Cw2 (3.12)
Determination of the mass transfer coeffcient. Estimation of mass transfer coefficient k
Chapter 3. Monoclonal Antibodies 33
can generally be made in 3 different ways: 1) theoretical estimation, 2) determinationfrom experimental scouting data, and 3) estimation from sieving data. Theoretical deter-mination is based on correlation from heat and mass transfer and involves the Sherwook(Sh), Schmidt (Sc), and Reynolds (Re) numbers. All theoretical estimations are basedon the following:
Sh = aRebScc✓dHL
◆d
(3.13)
where dH is the equivalent hydraulic diameter of the module, and L the length of theflow channel. The coefficients a, b, c and d depend on module design and can they can beobtained to model very specific modifications and complex fluid dynamics. [118] Theoreticalestimations will not be discussed further here. As well, determination of k by sieving datais a broad subject that has been described elsewhere. [43,118,120]
Chapter 4Objectives
This work will focus on the set-up of a purification platform for a biosimilar monoclonalantibody produced in the supernatant of CHO cells in STR and wave bag bioreactors.The unit operations that will be studied are: 1) Protein A affinity chromatography, 2)UF, and 3) polishing.
For Protein A affinity chromatography the main objectives are:
• Determination of the maximum elution pH that gives acceptable yield (>90%)with no negative effect in product quality
• Optimization of elution buffer concentration to find the lowest possible con-centration with no negative effects on product quality
• Column packing in order to be able to elute IgG in quantities above 1 g percycle
• Determination of column packing performance• Determination of aggregate content in eluted fraction by analytical SEC
column• Comparison of aggregate content in eluted fraction between STR and wave
gab bioreactor cultures
For the UF step the main objectives are:
• Screening of process parameters such as TMP and feed flow rate• Determination of the mass transfer coefficient k by two different mathematical
models.• Determination of mathematical model robustness by comparison of measured
and estimated process time, and used and estimated membrane area.• Estimation of optimal process parameters for the DF step such as bulk protein
concentration and permeate flux.
34
Chapter 4. Objectives 35
For the polishing step the main objectives are:
• Set up for a DoE for the screening of loading conditions (pH, conductivityand load) for the polishing step
• Determination of response models for yield, aggregate content, and residualDNA
• Analysis of factors affecting yield and aggregate content through a statisticalmodel
• Determination of optimal loading conditions
Chapter 5Materials and Methods
After preparation, all buffers and solutions were filtered with a vacuum unit using cellulosenitrate filters with a NMWL of 0.45 µm from Sartorius Stedim Biotech (Göttingen, Ger-many) into previously sterilized borosilicate glass flasks, followed by a brief degasificationfor 5–10 min. All buffers were stored at 4�C.
The complete sequence of unit operations used is shown in Figure 5.1.
5.1 Harvesting and primary recovery
CCF containing recombinant antibody was kindly provided by the USP department ofUGA Biopharma GmbH (Hennigsdorf, Germany). The antibody was produced by CHOcells in chemically defined medium. Bioreaction was performed either in STR or wavebag reactors, both with a volume of of approximately 5 L. The IgG concentration in theCCF was 590 g · L�1 as determined by enzyme-linked immunosorbent assay (ELISA).CCF from bioreactors was centrifuged either in a Sorvall RC Bios centrifuge with aswing-bowl from Thermo Scientific (Berlin, Germany) or a bench top Heraeus MultifugeX1R centrifuge with a fixed-angle rotor from the sample supplier. Centrifugation wasperformed at 10,000 g for 20-30 min at 4�C.
HCCF was filtered afterwards in NFF format using regenerated cellulose (RC) filterswith a NMWL of 0.45 µm from Sartorius Stedim Biotech (Göttingen, Germany), followedby an additional filtration at the same conditions with a NMWL of 0.22 µm from thesample supplier.
Clarified cell culture fluid (cCCF) was stored at 4�C until loading into Protein Aaffinity chromatography.
36
Chapter 5. Materials and Methods 37
Table 5.1. Quick reference of buffers and solutions.
Identifier Buffer/Component [Conc] (mM) pH
A PBS 7.2KCl 2.7KH2PO4 1.5NaCl 136.9Na2HPO4·H2O 8.9
B Sodium Citrate 100.0 2.7–4.0C Sodium Citrate 50.0 3.0D Sodium Citrate 50.0 2.7D2 NaOH 50.0
NaCl 1000.0
5.2 Packed-bed Chromatography
Chromatographic runs were performed on a fast protein liquid chromatography (FPLC)Äkta Purifier system from GE Healthcare (Uppsala, Sweden). Absorbance measurementswere made at 280 nm for protein detection. All pre-packed columns and materials forin-house column packing were purchased from GE Healthcare (Uppsala, Sweden).
5.2.1 Affinity column packing
In order to be able to purify antibody in amounts close to 2.0 g, an XK 26/40 emptycolumn was packed with bulk MabSelect® resin. DBC of MabSelect® is reported tobe 30 g · L�1 at 10% breakthrough. Slurry was adjusted to a final concentration of 50%prior to packing. Compression factor (CF) and packing factor (PF) were 1.07 and 1.05respectively. Final volume of slurry was approximately 205 mL. Packing was performedaccording to manufacturer’s suggestions considering pressure and flow rate limits. Thelength of the consolidated bed-height was 190 mm. Theoretical bed height after axialcompression was 180 mm. The final calculated bed volume was 95.6 mL with a totalbinding capacity at 80% saturation of 2.29 g IgG.
Efficiency testing is the analysis of the residence time distribution for a tracer sub-stance passing through the column. Typical test signals applied to the column are pulseor step signals. Assessment of column packing performance was made with a 1% v/vacetone pulse. Sample volume was 1% of packed-bed volume. Linear velocity µ was set to30 cm · h�1 (Q = 2.65 mL ·min�1) according to manufacturer’s specifications. After therun was finished, packing parameters were calculated to determine packing performance.
5.2.2 Protein A affinity chromatography
Protein A affinity chromatography was performed in more than one column size. Allexperiments were run at 4�C. Protein A affinity columns were washed with 5 CV ofwater or until absorbance and conductivity showed a flat baseline. Following the water
Chapter 5. Materials and Methods 38
Table 5.2. Design of experiments (DoE) for polishing step with Capto® Adhere . Full factorial of 3factors at 2 levels plus 2 center points (23 + 2 = 10 experiments) to resolve curvatureeffects.
No. experiment Buffer [NaCl] (mM) pH Conductivity Load(mM) (mS/cm) (mgIgG ·mL�1)
1 BIS-TRIS 25 mM 50 5.5 5 502 BIS-TRIS 25 mM 50 5.5 5 2003 BIS-TRIS 25 mM 500 5.5 45 504 BIS-TRIS 25 mM 500 5.5 45 2005 BIS-TRIS 25 mM 50 7.0 5 506 BIS-TRIS 25 mM 50 7.0 5 2007 BIS-TRIS 25 mM 500 7.0 45 508 BIS-TRIS 25 mM 500 7.0 45 2009 BIS-TRIS 25 mM 300 6.25 25 125
10 BIS-TRIS 25 mM 300 6.25 25 125
wash, column equilibration was made with 5 CV of Buffer A (Table 5.1). Washing andequilibration were made at a linear velocity of 300 cm · h�1. cCCF was loaded at 300cm · h�1 to either 5 mL pre-packed MabSelect® column or the XK 26/40 column packedwith MabSelect® described before. Once sample loading had finished, column was washedwith Buffer A until absorbance and conductivity were stable. Elution of bound antibodywas made with a step gradient of either Buffer B or C at 250 cm · h�1. Elution peak wasautomatically collected by programming peak detection into the method. After elution,column was stripped with Buffer D for a contact time of at least 15 min. Cleaning andsanitization were performed with Buffer D2 for a contact time of at least 15 min. Elutedsample was left in acidic conditions for at least 60 min at 4�C before further processing.
5.2.3 SEC-FPLC
For determination of aggregate and monomer composition of pure samples, an analyticalSuperdex 200 10/300 column was used. A maximum sample volume of 500 µL was injectedaccording to manufacturers recommendations. Briefly, column was washed with 2 CV ofwater, followed by equilibration with 2 CV of Buffer A (Table 5.1). After sample injection,column was further washed with 2 CV of Buffer A. The flow rate used for the entirechromatographic run was 0.5 mL ·min�1.
5.2.4 Polishing with Capto Adhere
A DoE approach was used for screening of the best conditions for the polishing step. A fullfactorial design with 3 factors at 2 levels plus 2 center points (23 + 2 = 10 experiments) wasused to account for curvature effects . The experimental setup is summarized in Table 6.2.Two responses were introduced into the model, product yield (%) and aggregate content(%). Data processing was made using software package MODDE version 10 from Umetrics(Umea, Sweden).
Chapter 5. Materials and Methods 39
Protein Aaffinity chromatography
Bioreactor
Low pH viralinactivation
Centrifugation MF 0.45 µm MF 0.22 µm
MF 0.22 µmMF 0.22 µm
UF/DF50 kDa
PolishingCapto Adhere
MF 0.22 µm
Figure 5.1. Sequence of unit operations performed for the suggested downstream processing (DSP)platform. (This figure is available in full color at http://goo.gl/iA9h36)
5.3 Ultrafiltration
UF was performed with a Sartoflow® Slice 200 Benchtop System from Sartorius StedimBiotech (Göttingen, Germany) mounted with a polyethersulfone (PESU) membrane witha MWCO of 50 kDa and area of 20 cm2 from the same supplier.
5.3.1 System preparation
The pump feed rate was set to 150 mL ·min�1. The entire system was rinsed with 400mL of water, followed by an ionic equilibration with 400 mL of Buffer A (see Table 5.1on page 37). All experiments were performed at 20�C.
5.3.2 Clean membrane permeability
The clean membrane permeability was determined at a feed flow rate Q of 250 mL ·min�1.The retentate port was connected to the waste to avoid recirculation and the permeateport was connected to a receiving vessel. First, the retentate valve was completely openand after 1 min the TMP was recorded, starting with a value of 0.3 bar. These steps wererepeated by closing the retentate valve to achieve higher TMP in steps of 0.1 bar. The lastrecording was made at a TMP of 1.4 bar. Values of permeate flux J were plotted againstTMP. A linear regression was made in which the slope represents the clean membranepermeability Lp.
5.3.3 TMP excursion
Previous experiments showed that the best Q to work with the available membrane at theconditions described before was 250 mL ·min�1. A TMP excursion was made in order toestimate the TMP value of the system that gives the highest possible J without riskingproduct quality and premature membrane fouling. Briefly:
Chapter 5. Materials and Methods 40
1. An antibody solution previously purified with Protein A affinity chromatographywere added to the reservoir (concentration and pH).
2. Retentate valve was open fully and the system was set in total recycle mode(retentate and permeate ports connected to the reservoir tank).
3. Product was recirculated for 2–5 min.4. Permeate port was connected to receiver tank. Approximately 15–25 mL or permeate
were collected and flux measurements were recorded.5. Collected permeate from last step was returned to reservoir. System was set in total
recycle mode. Retentate valve was further closed to achieve a higher TMP.6. Steps 3 to 5 were repeated until a plateau was observed in the plotted values of J
vs TMP.7. System was set to a TMP close to the plateau of J to avoid membrane fouling and/or
product loss. Permeate port was connected to a receiver tank and the solution wasconcentrated to achieve a volume reduction of 25% in the reservoir vessel. IgGconcentration was measured.
8. Steps 3 to 7 were repeated for the actual product concentration. A total of 4concentrations were scouted.
9. Product was diafiltered with Buffer A (see Table 5.1 on page 37) by adding 7 timesthe final solution volume (diavolumes (DV)). Retentate volume was kept constant.
10. The product was retrieved from the system adding afterwards a small volume ofBuffer A to wash the product remaining inside the lines. IgG concentration wasmeasured and the solution was filtered with a 0.22 µm RC filter and stored at 4 �C.
5.3.4 System cleaning and storage
Permeate and retentate ports were connected to a waste tank. The entire system waswashed with 400 mL of water, followed by 400 mL of NaOH 1.0M. Once finished, thesystem was rinsed with 400 mL of water, and finally with 400 mL of EtOH 70% v/v.Membrane was removed from the system and stored separately in a sealed bag with EtOH70% to avoid drying of the membrane.
5.3.5 Mathematical modeling
Estimation the mass transfer coefficient was made using the SFM and the OPM. Plottingand determination of parameters was made using MATLAB software v2012. The code isprovided in Chapter B.
For the SFM, permeate flux was plotted against TMP. Limiting flux was extractedusing Equation (3.8) for every protein concentration. The mass transfer coefficient andthe gelation concentration were estimated from the linear regression of limiting permeatefluxes versus ln(Cb).
Chapter 5. Materials and Methods 41
For the OPM, the fouled membrane permeability Lfm was assumed to the 30% ofthe value of the clean membrane permeability Lp. The osmotic pressure at each pointwas determined from the following equation:
TMP =
✓J
Lfm
◆+�⇧ (5.1)
The mass transfer coefficient was obtained from a plot of permeate flux versus osmoticpressure. The solute concentration at the wall was calculated solving for Cw in Equa-tion (3.7). A polynomial fit of second order was used to determine virial coefficients ↵
and � from Equation (3.12).For the concentration step, process time was measured and it was compared to a
prediction made with Equation (3.10).
Chapter 6Results and Discussions
6.1 Protein A affinity chromatography
6.1.1 pH scouting for elution
The results for elution pH scouting are shown (see Figure 6.1 on the following page). Ascan be observed from the figure, even at pH 3.5 yield is quite low (22%). Integrated areasfor pH 3.0 and 2.7 showed no difference, and thus pH 3.0 was selected seeing that theyield was around 95%, and to avoid more extreme conditions. Since the elution is quitesensitive to pH, a narrower range could be studied between pH 3.0–3.5 in order to assessif even a slighter higher pH can be used.
Alternatively to running these experiments separately, a pH gradient can be madeby switching from 0%–100% elution buffer in 10–15 CV. The selected pH for elutionwould be the pH at maximum peak height in the chromatogram. Fractionation of gradientpeak can give helpful insights about the nature of the impurities in the elution pool. Ifimpurities such as HMWA of HCP can be separated in the gradient, it is possible to doso even at VLS as an alternative to the step elution.
Although not assessed in this work, the washing step after sample loading into theaffinity column is of high relevance. It has been shown that contaminant binding along theaffinity matrix is rather an IgG-impurity interaction than a matrix-impurity interaction. [83]
The washing step can profit from both the addition of certain substances (such as NaCl,isopropanol, urea) [83] –additionally from the ones already mentioned in Section 3.3.2– andfrom a reduced pH. For instance, from Figure 6.1 on the next page, it can be concludedthat a pH as low as 4.0 can be used in the washing step. The use of a high salt wash,such as NaCl 500 mM is recommended.
Once the lowest pH for elution was found to be 3.0, the effect of buffer concentrationin the elution buffer was studied. Two chromatographic runs were performed only varyingthe composition of the elution buffer (sodium citrate 100 mM and sodium citrate 50
42
Chapter 6. Results and Discussions 43
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S2
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S2
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ffe
r
Sodium Citrate 100 mM pH 3.0HiTrap MabSelect 5mL
0 10 20 30 40 50 60 70 800
200
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1400
AB
S2
80 (
mA
U)
VOL (mL)
0
20
40
60
80
100
%E
luti
on
Bu
ffe
r
Sodium Citrate 100 mM pH 2.7HiTrap MabSelect 5mL
A B
C D
Figure 6.1. Elution pH scouting in a MabSelect® 5 mL column. Loading of clarified cell culture fluid(cCCF) was made at 300 cm · h�1 and elution was performed at 250 cm · h�1. Yields withrespect to loaded antibody were: A) 0%, B) 22%, C) 95%, and D) 95%. (This figure isavailable in full color at http://goo.gl/iA9h36)
mM respectively). The results are shown in Figure 6.2 on the following page. At theacidic conditions for elution, a reduced salt concentration can promote the formationof aggregates even beyond the affinity step. For this particular set-up, no particulatesor turbidity were observed while elution, and after incubation in the acidic buffer, thussodium citrate 50 mM pH 3.0 was selected as elution buffer.
The use of neutralizing agents such as TRIS pH 9.0 was discouraged because additionof solutions with extreme pH causes local changes in the product pool that can leadto the formation of aggregates, either soluble or insoluble. As mentioned before, thereis no consistent definition of what a "soluble" or "insoluble" aggregate is. Generally,a soluble aggregate is referred to those that are not visible as discrete particles andcannot be removed by 0.22 µm filtration. On the other hand, insoluble aggregates arethose that visible tot he unaided eye (particulates, turbidity) and that can be removedby 0.22 µm filtration. [137] Oligomerization can arise from non-covalent interactions orfrom covalently linked species. [138] Detailed mechanisms of aggregate formation have beendiscussed elsewhere. [60,137]
The inherently unpredictable nature of this precipitation phenomenon can lead toclogging during the sterile filtration step that follows neutralization of the Protein Aeluate. [80] For this reason, direct neutralization of the eluate pool at small scales withstrong solutions is discouraged and whenever possible, incubation at acidic pH can befollowed by UF/DF for buffer exchange and milder neutralization. On the other hand, inVLS due to long process times, neutralization of the elution pool is common practice.
Chapter 6. Results and Discussions 44
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S2
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0 10 20 30 40 50 60 70 800
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S2
80 (
mA
U)
VOL (mL)
0
20
40
60
80
100
%E
luti
on
Bu
ffe
r
Sodium Citrate 50 mM pH 3.0HiTrap MabSelect 5mL
A B
Figure 6.2. Effect of the elution buffer concentration on the yield during Protein A affinity chromatog-raphy. Yields with respect to loaded antibody were: A) 95% and B) 95%. (This figure isavailable in full color at http://goo.gl/iA9h36)
One option as described before is the use of elution buffer stabilizers, such as arginine.This approach is one of the most practical ones since some substances including chaotropesand hydrophobic competitors can have a unexpected effect and cause unfolding which maylead to aggregation. [139] The possible mechanism through which arginine may contributeto reduce aggregation have been discussed in the literature. [140]
Nevertheless, insoluble aggregate formation might not always be an undesirablecircumstance. During Protein A affinity chromatography , turbidity may also be causedby HCP contaminants or a small fraction of misfolded product that precipitate underlow pH conditions. [92,139] Whatever the nature of the aggregates, If clogging of 0.22 µmfilters is too serious, DP has been used to eliminate aggregates in Protein A elution pools.Additionally, mild centrifugation can also be used. [80]
Due to of observations made while performing UF that will be discussed in thatparticular section, the use of the following elution buffer is recommended for furtherexperiments: sodium citrate 50 mM, NaCl 150 mM, arginine 150 mM (see Table A.1 onpage 61).
6.1.2 Column packing
Column packing was performed as described in Section 5.2.1. Column efficiency testingplays a central role in the qualification and monitoring of packed bed performance. Thetest can be used between runs to check for changes in column integrity; column efficiency istypically defined in terms of two paramters: 1) peak broadening over the column describedby a number of theoretical plates, and 2) peak symmetry described by the assymetryfactor AS , defined as:
AS = a/b (6.1)
Chapter 6. Results and Discussions 45
50 60 70 80 90 100 110 120 130
0
10
20
30
40
50
60
VOL (mL)
AB
S280 (
mA
U)
1% Acetone v/v @ 30 cm/h
a b
50%
10%
Peak width at half peak height
Wh
VR Retention Volume
ε = 7 .35%
Sample volume: 1% of packed bed volumeResin: MabSelect
Consolidated bed height=190 mmBed height after axialcompression=180mm
Packed bed volume=95.56 mL
N = 747 .96 Nm
H E T P = 0 .24(10)− 3mh = 2 .83AS = 1 .74
N =µ f
2
σ 2≈ 5 .54
!
VR
Wh
"2
H E T P =L
N
h =H E T P
d p=
L
d p·σ
2
µ 12≈
L
d p(5 .54)− 1
!
Wh
VR
"2
AS =b
a
Figure 6.3. Tracer substance pulse for determination of column packing efficiency for MabSelect®column. Reduced plate height should be h 3 for optimal column efficiency in bioprocesschromatography. An acceptable range for the assymetry factor is 0.8 < AS < 1.8. (Thisfigure is available in full color at http://goo.gl/iA9h36)
The relative peak width is defined as number of (theoretical) plates (N), height equivalentof a theoretical plate (HETP ) or preferably as reduced plate height (h): [5]
N =u2f�2
⇡ 5.54
✓VR
Wh
◆2
(6.2)
HETP =L
N(6.3)
h =HETP
dp=
L
dp
�2
u21⇡ L
dp(5.54)�1
Wh
VR
�2(6.4)
Finally, the relationship between peak broadening and liquid velocity is described theo-retically by the Van Deemter equation: [5]
HETP = A+B
u+ C · u (6.5)
With regard to the peak asymmetry, an asymmetry factor close to AS = 1 is ideal. Atypical acceptable range could be 0.8 < AS < 1.8 when working towards a reduced plateheight of h 3. [141]
In theory, the best column efficiency achievable in terms of reduced plate heightis typically h = 1.5 to 2 when using porous chromatography media used in bioprocessapplications. [141] Optimal column efficiency typically corresponds to an experimentallydetermined reduced plate height of h 3 for the porous media employed in bioprocess
Chapter 6. Results and Discussions 46
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200
400
600
800
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1200
1400
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S2
80 (
mA
U)
VOL (mL)
0
20
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80
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% E
luti
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Bu
ffe
r
Sodium Citrate 50 mM pH 3.0HiTrap MabSelect 5mL
0 5 10 15 20 25 30 350
10
20
30
40
50
60
70
AB
S2
80 (
mA
U)
VOL (mL)
Superdex 200 10/300Originator IgG
Monomeric IgG
HMWAIgG t
R
B
A
Figure 6.4. A) Protein A affinity chromatography from stirred tank reactors (STR) cell culture. B)Size exclusion chromatography (SEC) for Protein A affinity eluate. Sample was incubatedfor 60 min in Buffer D (see Table 5.1 on page 37) and filtered by 0.22 µm before injectioninto the SEC column. (This figure is available in full color at http://goo.gl/iA9h36)
chromatography. Reduced plate height facilitates the comparison of column efficiencyirrespective of column length and particle diameter of the medium. At test volocities wellover 20 cm · h�1, peak broadening increases as a result of shorter residence time and thuslimiting intraparticle diffusion (term C in Equation (6.5)).
The calculated test values for the packed column are shown in Figure 6.3. The h valueof 2.83 and AS of 1.74 give indication of good column performance, and thus re-packingwill not be necessary.
6.1.3 SEC-FPLC
Purification of samples from STR. Aggregate content from elution pools of 5 mL andpacked 95 mL column were analyzed as described in Section 5.2.3. As can be observed fromFigure 6.4, the 5 mL affinity column does not provide resolving power for separation ofaggregates, as expected. Since the elution pool presented no turbidity and the sample wasfiltered through 0.22 µm before loading into the SEC column, the HMWA in Figure 6.4.Bare supposed to be of soluble nature; judging by the retention time of these species, whichis very close to the column’s dead volume, their molecular weight most certainly exceeds400 kDa. Aggregate content for this experiment was 5%.
On the other hand, the same sample injected into the 95 mL packed column showsa different profile with regard to the affinity chromatogram (Figure 6.5.A). As can beobserved from the SEC analysis of both fractions, the small peak leading the main peak
Chapter 6. Results and Discussions 47
3500 3750 4000 4250 4500 4750 5000 52500
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Sodium Citrate 50 mM pH 3.0XK 26/40 w/MabSelect 95 mL
0 10 20 30 400
5
10
15
20
25
30
VOL (mL)
AB
S2
80 (
mA
U)
Superdex 200 10/300 GL
0 10 20 30 400
100
200
300
400
500
600
VOL (mL)
AB
S2
80 (
mA
U)
Superdex 200 10/300 GL
Fraction 2 HMWA
HMWA
Monomeric IgG
IgG tR
A B
C
Fraction 1
Fraction 2
Fraction 1
Figure 6.5. A) Protein A affinity chromatography from stirred tank reactors (STR) cell culture. B)Size exclusion chromatography (SEC) for Protein A affinity eluate. Sample was incubatedfor 60 min in Buffer D (see Table 5.1 on page 37) and filtered by 0.22 µm before injectioninto the SEC column. (This figure is available in full color at http://goo.gl/iA9h36)
contains only HMWA. The main peak contains also these high molecular weight speciesas can be seen from the chromatogram of Fraction 2. Aggregate content in the main peakwas calculated to be 5%. In this particular case, the HMWA eluted with more ease thanthe main peak. With optimization of the washing step, it may be possible to eliminatethem before elution by the use of high salt wash and/or decreased pH.
Isolation of IgG monomer should be possible with the subsequent polishing steps.Although the analytical SEC column provides a good resolution in this case, their usein VLS operation is seldom executed because of the lower resolution it gives comparedto analytical-size columns. Contrary to general believe, SEC resolves species not bymolecular weight differences but by variation in their hydrodynamic radii (RH). Forspherical proteins, the relationship between the two is given by: [142]
RH =⇣3V/4⇡
⌘1/3= 0.066Mw
1/3 (6.6)
Equation (6.6) states that an increase in molecular weight (Mw) of 2-fold will only increaseRH by ⇠ 25%. This physical constraint is the reason why baseline resolution is oftenvery complicated to achieve between monomeric and dimeric species. [137] An additionalcomplication for large scale SEC is the loss of resolution due to the large amounts of proteinloaded, when compared to analytical columns, not to mention high buffer consumptionand slow process times.
Purification of samples from wave bag reactors. The Protein A chromatogram of thewave bag culture purification had a similar profile as the 5 mL affinity runs, showingonly one main elution peak. When eluate was analyzed with SEC, only the monomericIgG peak was found (Figure 6.6). These results may indicate that the aggregate contentseen in the other experiments was not clone-dependent but rather process-dependent.
Chapter 6. Results and Discussions 48
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VOL (mL)
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S2
80 (
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1000 1200 1400 1600 1800 20000
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6000
AB
S2
80 (
mA
U)
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0
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100
%E
luti
on
Bu
ffe
r
Sodium Citrate 50 mM pH 3.0XK16/40 MabSelect 95 mL
Superdex 200 10/300 GL
IgG tR
A B
Figure 6.6. A) Protein A affinity chromatography from wave bag reactor cell culture. B) Size exclusionchromatography (SEC) for Protein A affinity eluate. Sample was incubated for 60 min inBuffer D (see Table 5.1 on page 37) and filtered by 0.22 µm before injection into the SECcolumn. (This figure is available in full color at http://goo.gl/iA9h36)
The difference could be due to agitation variations between the two systems, involvingperhaps sheer stress and air-liquid interphases. The use of wave bag reactors has beenreported in the growth of cultures at VLS which ends up with the 20,000 L scale beforediscussed. The use of wave bag reactors could have an important influence in the profileof aggregates since these often follow nucleation kinetics. [143] This requires an additionaldiscussion that will not be pursued here.
6.2 Ultrafiltration
6.2.1 Mass transfer coefficient
Scouting was performed as described in Section 5.3. The solute mass transfer coefficientis a measure of the hydrodynamics conditions within a membrane module. k is definedas:
k = D/� (6.7)
where � is the surface polarization boundary. On both laminar and turbulent conditions,the mass transfer coefficient has a strong dependency on the protein diffusion coefficient,which depends on protein charge, buffer conductivity and protein concentration. Theoret-ical estimations of the mass transfer coefficient should be made carefully, since there canbe variation by as much as a factor of 2 in different buffers associated with electrostaticinteractions, protein charge, and hydrodynamic volume due to the diffuse ion cloud. [117]
Nonetheless, there are quite specific correlations for the estimation of the mass transfercoefficient that take into account multiple variables of the process as well as complexsystem geometries and various module formats [118].
Chapter 6. Results and Discussions 49
0.4 0.6 0.8 1
20
30
40
50
60
70
Perm
eateFlu
x,J(L
m2h−
1 )
Tran sm embran e p re ssu re , T M P (b ar )
1.758 gL − 1
3.088 gL − 1
5.96 g L − 1
10.95 gL − 1
0 1 2 340
45
50
55
60
65
70
75
Lim
itin
gPerm
eate
Flu
x,J(L
m2h
−1 )
ln (C b)
0 50 100 150 2000.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Osm
oticPre
ssure,�⇧(b
ar)
C w(gL− 1)
0.1 0.2 0.3 0.425
30
35
40
45
50
55
60
65
70
75
Perm
eate
Flu
x,J(L
m2h−
1 )
O sm otic Pre ssu re , �⇧(b ar )
1.758 gL − 1
3.088 gL − 1
5.96 g L − 1
10.95 gL − 1
� =4.9694e - 07
↵ =0.0014602
J l im= k lnC g
C b
k =14.8915 LMH
A B
C D
C g =198.5431 gL − 1
J
Lf m= T M P − ↵C bexp
J
k− � C be x p
J
k
2
k =J 2 − J 1
lnC b ,1− lnC b , 2
k =12.84 LMH
�⇧ = ↵C w+ �C w2
Figure 6.7. A) Permeate flux versus transmembrane pressure (TMP) data set for an antibody purifiedwith Protein A affinity chromatography in sodium citrate 50 mM pH 3.0 on a 0.02 m2
50 kDa polyethersulfone (PESU) cassette membrane. B) Liminting permeate fllux versusln(Cb). The slope of linear regression represents the mass transfer coefficient k and theintercept with the x axis the gel concentration Cg. C) Osmotic pressure versus wallconcentration Cw. The data was fitted to a second order polynomial for estimation of thevirial coefficients ↵ and �. The permeate flux curve can be modeled with the providedequation which must be solved iteratively. D) Permeate flux versus osmotic pressure fora second determination of the mass transfer coefficient k. (This figure is available in fullcolor at http://goo.gl/iA9h36)
The mass transport coefficient values calculated for the SFM and OPM were 14.89LMH and 12.84 LMH respectively (see Table 6.1 on the next page). Evaluation of masstransfer by experimental scouting data is an accurate practical method for the evaluationof k. [118,135] This approach requires minimal experimentation but has its drawbacks: [117]
k value is only valid under high-polarization conditions, since Equation (3.8) accounts forfull retained solute and gel formation. If k is estimated under lower degrees of SP, thevalue can vary considerably because of solution viscosity and protein diffusivity. [117,120]
It was inferred from Figure 6.7.A that the optimal TMP at all concentrations was0.8 bar, in order to avoid membrane fouling.
Chapter 6. Results and Discussions 50
Table 6.1. Estimated modeling parameters for the ultrafiltration (UF) step.
Model
Parameter Stagnant film model (SFM) Osmotic pressure model (OPM)
k (L ·m�1 · h�1) 14.89 12.84Cg (g · L�1) 198
6.2.2 Gel concentration
The gelation concentration Cg was estimated to be 198 g·L�1. Gel concentrations for mAbssolutions in typical UF procedures range between 100–250 g·L�1. If Figure 3.6 is generatedfor multiple feed flow rates, some assumptions can be made about the “gelification” on themembrane. Non-convergence of flux decay plot to a common end point provides evidencethat no membrane fouling is occurring. When concentration dependent flux decay plotsconverge to a common end point regardless of the recirculation rate, one can concludethat the protein solute is forming a fouling protein gel. [118] Although this gel model hasbeen used quite extensively, independent measurements of protein solubility are often inpoor agreement with calculated values of Cw, which are additionally device dependent.This suggest than an actual “gel” does not form on top of the membrane. [117]
6.2.3 Process time during concentration step
Process time for the concentration of the IgG solution was made using Equation (3.10).Calculated and process times were 11.93 min and 13.25 min respectively. The error wasnearly 10 %. Discrepancies could be due to hydrodynamic conditions within the moduleand from unconsidered effects such as aggregation. At the end of the concentration stepthe protein solution became turbid. Removal of certain ionic species can lead to theformation of precipitates, happening when the quaternary protein structure is lost, asthe result of the removal of cations like magnesium, which serve to bridge the proteinsubunits. [118] However, the opposite effect can be observed if salts in the solution areconcentrated together with the protein and results in salting out. Both of these scenarioscan lead to fouling of the system.
Additionally, as discussed before, pH neutralization from acidic affinity eluates cancause the solution to become turbid. The use of additives that inhibit protein aggregationsuch as arginine can be used to reduce this effect upon pH neutralization by any means,as well as from additional effects such as microcavitation, and shear during pumping.Typically, lobular pumps are used in VLS for product loading into chromatography, UF,and final filling. [144] Other factors influencing aggregate formation are membrane materialsand stirring speed of the retentate tank.
There are typically 3 ways of controlling an UF operation: 1) constant TMP, 2)constant permeate flux J , and 3) constant wall concentration Cw. The first option is often
Chapter 6. Results and Discussions 51
0 10 20 30 40 50 60 70 80 90 1000
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
C b(gL− 1)
Cw = 100g · L − 1
Cw = 200g · L − 1
Cw = 30g · L − 1
L oc al m in imumC b
* = Cw · e− 1
A =NC 0V0
Cb · k ln Cw
CbtDF
Membra
neAre
a,A
(m2)
Figure 6.8. Membrane area required for a 1 h diafiltration (DF) process as a function of Cw asdetermined in Equation (6.12). The optimal bulk protein concentration C⇤
b equals Cwe . At
higher wall concentrations, changes in Cb can be made without compromising the minimalrequired membrane area. (This figure is available in full color at http://goo.gl/iA9h36)
chosen because of its simplicity and for manual systems is the easiest way of control byadjusting the retentate valve to achieve an specific TMP value. Constant permeate fluxis widely used in MF because it is not always possible to keep constant TMP withoutdrastic reductions in the permeate flux. [118,122] This approach can be achieved by usinga pump on the permeate side. In the third form of control, product yield is maximized,product quality is ensured and consistent, membrane area is minimized, and process timeis independent of membrane permeability, which can vary significantly between differentlots. Unfortunately, for a concentration step, an automated system is needed: a controlloop measures flux and controls the retentate valve to achieve constant wall concentration.This control loop can be programmed in 3 different ways: [136]
TMP =
✓J
Lfm
◆+ ↵Cw + �Cw
2 (6.8)
J = k ln
✓CwV
Cb,0V0
◆(6.9)
TMP =
✓k
Lfm
◆ln
✓CwV
Cb,0V0
◆+ ↵Cw + �Cw
2 (6.10)
where Cb,0 is the initial bulk protein concentration,Lfm the fouled membrane permeability,and V0 the initial volume.Equation (6.9) has the advantage of only requiring knowledgeof the mass transfer coefficient. This technique has been implemented in many automaticsystems. On the other hand, Equation (6.8) and Equation (6.10) require knowledge of theosmotic pressure virial coefficients ↵ and �. There is reported work on determining the
Chapter 6. Results and Discussions 52
0 10 20 30 40 50 60 70 800
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
Pe rm eate flux , J (L · m − 2· h − 1 )
Membra
neAre
a,A
(m2)
L oc al m in imum
J * = k
Cw = 200g · L − 1Cw = 100g · L − 1
Cw = 30g · L − 1
A =NC 0V0
Cw · eJ/k
· J · tDF
Figure 6.9. Membrane area required for a 1 h diafiltration (DF) process as a function of J as determinedin Equation (6.12). In a Cw controlled process, the optimum permeate flux J⇤ equals themass transfer coefficient k, independently of the chosen Cw value. (This figure is availablein full color at http://goo.gl/iA9h36)
osmotic pressure of concentrated protein solutions in the literature. [145] In this study, thefouled membrane permeability could not be determined experimentally because it wasthe only membrane available, and a previously fouled useless membrane should be usedfor the estimation of Lp. The fouled membrane permeability was assumed to be 30% ofthe value of Lfm. Combining Equation (3.7) and Equation (6.8) we get:
J = Lfm
(TMP � ↵Cb exp
✓J
k
◆� �
Cb exp
✓J
k
◆�2)(6.11)
Equation (6.11) can be used to calculate permeate flux values and it must be solvediteratively. A benefit of this approach is that the entire curve of permeate flux can bemodeled, in contrast with the SFM, that is only valid in the pressure independent regionunder high SP conditions.
6.2.4 Diafiltration
For a DF procedure at constant volume, the wall concentration can be controlled toremain constant with a manual system. In this kind of process, the minimal requiredmembrane area can be calculated as follows:
A =NC0V0
Cb · k lnhCwCb
i· tDF
(6.12)
During a constant Cw controlled DF process, the optimal bulk concentration C⇤b
equals Cwe as can be observed in Figure 6.8. As the wall concentration increases, the
Chapter 6. Results and Discussions 53
0 5 10 150.001
0.01
0.1
1
10
100
Diavolumes (N)
Co
nta
min
an
t R
eta
ine
d(%
of
ori
gin
al)
R = 0
R = 0.2
R = 0.4
Figure 6.10. Remaining components (%) during a diafiltration (DF) operation as a function of retention(R) and number of diavlumes (N) as determined with Equation (6.13). (This figure isavailable in full color at http://goo.gl/iA9h36)
function becomes flatter for the solved area and thus changes in Cb can be made withouta significant increase in the minimal required membrane area. For this process, Cb valuesof around 10–15 g · L�1 fit the membrane cassette of 0.02 m2 used for the experiments. Inthe case of the permeate flux for the process, the optimal value equals the mass transfercoefficient and is independent of the wall concentration value used (see Figure 6.10). Giventhe membrane area, Equation (6.12) can be used to determine the minimal process timefor a DF operation.
Regarding DF, the product retention is a critical parameter for estimation of thenumber of diavolumes (N) that can be made without compromising the yield. Even athigh yields, the multiple passes of the product through the pump and membrane act asseparate operations reducing the yield after each step. For the 50 kDa membrane used,there was no evidence of product in the permeate side. The remaining impurities can becalculated with the following equation:
Impurities(%) = 100 · e(R�1)N (6.13)
In order to have only 1% of original solution components, it can be estimated withEquation (6.13) that at least 4.5 DV are needed. For proteinaceous solutions, it is commonpractice to perform DF with 7–10 DV, especially for the formulation step.
Chapter 6. Results and Discussions 54
Table 6.2. Response values for yield (%) and DNA (ng/µL) from the polishing step with Capto®
Adhere column. Responses correspond to the flow through fraction.
# Buffer [NaCl] pH Conductivity Load Yield (%) DNA(mM) (mS/cm) (mgIgG ·mL�1) (ng/µL)
1 BIS-TRIS 25 mM 50 5.5 5 50 98.9 135.12 BIS-TRIS 25 mM 50 5.5 5 200 79.1 176.53 BIS-TRIS 25 mM 500 5.5 45 50 88.6 71.34 BIS-TRIS 25 mM 500 5.5 45 200 75 139.75 BIS-TRIS 25 mM 50 7.0 5 50 100 101.26 BIS-TRIS 25 mM 50 7.0 5 200 95.9 140.57 BIS-TRIS 25 mM 500 7.0 45 50 85.3 658 BIS-TRIS 25 mM 500 7.0 45 200 91.8 139.29 BIS-TRIS 25 mM 300 6.25 25 125 94.4 123.7
10 BIS-TRIS 25 mM 300 6.25 25 125 94.7 125.7
Table 6.3. Statistical model parameters for the yield and DNA responses obtained from the polishingstep with Capto® Adhere column.
Response
Statistical parameter Yield DNA
R2 0.957 0.974Q2 0.460 0.727
Model validity 0.200 0.385Reproducibility 0.999 0.998
6.3 Polishing
Polishing was performed as described in Section 5.2.4. Typically for polishing steps,impurities such as HCP, DNA, and leached Protein A are measured by ELISA. Theseimmunoaffinity methods were not yet developed at the time this work was performed. SinceHMWA was below detection levels in all samples before and after polishing, aggregateconcentration was not included as a response. DNA was measured by absorbance at 260nm for an initial and quick assessment of its clearance.
Prior to polishing experiments, sample can be subjected to multiple cycles of freezing-thawing in order to induce the formation of aggregates. The relatively low concentrationof impurities after Protein A affinity chromatography can make their quantification afterpolishing analysis challenging. As with viral clearance, spiking of the Protein A affinityeluate with HCP is also common practice for the estimation of its clearance duringpolishing. [100]
Statistical parameters of the obtained model for both yield and DNA responses arelisted in Table 6.3. R2 is the percentage of the variation of the response explained bythe model, describing how well the model fits the data. Although a high R2 value isa necessary for a good model, it is not enough. It is perfectly possible to have a highR2 value with a model that cannot predict data. On the other hand, a low R2 is anindication of poor reproducibility (poor control over experimental error) or poor model
Chapter 6. Results and Discussions 55
−8
−6
−4
−2
0
2
4
6
8
10
Yie
ld (
%)
pHCon
dLo
ad
pH*C
ond
pH*L
oad
Con
d*Lo
ad
−30
−20
−10
0
10
20
30
40
DN
A (
ng⋅µ
L−
1)
pHCon
dLo
ad
pH*C
ond
pH*L
oad
Con
d*Lo
ad
70 75 80 85 90 95 100 10570
75
80
85
90
95
100
105
Predicted (%)
Ob
se
rve
d (
%)
60 80 100 120 140 160 18060
80
100
120
140
160
180
Predicted (ng⋅µL−1)
Ob
se
rve
d (
ng⋅µ
L−
1)
R2 = 0.96 R2 = 0.97
Yield DNA
A
D
B
C
Figure 6.11. A) Model coefficients for the yield response, B) model coefficients for the DNA response,C) observed versus predicted values for yield, and D) observed versus predicted valuesfor DNA from polishing step with Capto® Adhere column. (This figure is available infull color at http://goo.gl/iA9h36)
validity, which will be further explained. For both yield and DNA content, the statisticalmodel shows acceptable R2 values. [146]
The second parameter, Q2, indicates how well the model predicts new data, and isdefined by the percent of the variation of the response predicted by the model accordingto cross validation. As with R2, a low Q2 value can be due to poor reproducibility (poorcontrol over experimental error) or poor model validity. A design with a good R2 and apoor Q2 may mean that there are insignificant terms in the model. For more than oneresponse, as in the work here performed, insignificant terms can be removed from themodel only when they are so for all the responses.
With a model validity value >0.25, the model error is in the range of the pure error.Notwithstanding, if the model validity is <0.25, there is a significant lack of fit. TheDNA response shows an acceptable model validity of 0.385, however, this is not the casefor the yield. Although the model validity is below the recommended 0.25, a true lackof fit is evidenced when both R2 and Q2 are quite small. In this case the low model
Chapter 6. Results and Discussions 56
Yield [%]
DNA [ng/µL]
76.7
93.4
110.2
126.9
143.7
160.45
78
81
90
87
84
93
99
96
102
96
93
90
87
84
81
78
99
90
99
96
93
90
93
96
87
110.2110.2110.2
126.9143.7
143.7
126.9
160.4
126.9
93.493.493.4
6.26.05.85.6 6.4 6.6 6.8pH
6.26.05.85.6 6.4 6.6 6.8pH
6.26.05.85.6 6.4 6.6 6.8pH
6.26.05.85.6 6.4 6.6 6.8pH
6.26.05.85.6 6.4 6.6 6.8pH
6.26.05.85.6 6.4 6.6 6.8pH
Co
nd
uct
ivit
y [m
S/cm
]
5
10
15
20
25
30
35
40
45
Co
nd
uct
ivit
y [m
S/cm
]
5
10
15
20
25
30
35
40
45Load [mg IgG/mL] = 200Load [mg IgG/mL] = 125Load [mg IgG/mL] = 50
Load [mg IgG/mL] = 200Load [mg IgG/mL] = 125Load [mg IgG/mL] = 50A
B
Figure 6.12. Response surface plot (RSP) for A) Yield and B) DNA (ng/µL) for polishing step withCapto® Adhere column. (This figure is available in full color at http://goo.gl/iA9h36)
validity is probably caused by the high reproducibility, which in turn makes the lack offit artificial. [146]
Finally, the reproducibility is the variation of a response under the same conditions(pure error), often at the center points, compared to the total variation of the response.For both yield and DNA responses the reproducibility approaches a value of 1, whichmeans the pure error is 0. To make this parameter more robust, a design with 3 centerpoints instead of 2 as performed here could be made.
Model coefficient plots for the responses are shown (see Figure 6.11 on the previouspage). The values represent the regression coefficients with their confidence intervals.For this analysis, coefficient plots are scaled and centered so that the coefficients can becomparable. The observed versus predicted values show good behavior for both responses.The size of an individual coefficients represents the change in the yield or DNA content,when a factor varies from 0 to 1 while other factors are kept at their average values. Acoefficient is only significant when its confidence interval does not cross zero. For instance,in the yield model, the pH*Cond and the Cond*Load interactions are not significant tothe response. In the case of DNA, load and the pH*Cond and Cond*Load interactionsare not significant.
For polishing with Capto® Adhere resin, it has been reported for some antibodies that
Chapter 6. Results and Discussions 57
Table 6.4. Typical impurity levels for end-product biopharmaceuticals.
Impurity Level/concentration in final product References
Host cell protein (HCP) <100 ppm 83,98,100DNA <10 ng/dose for large-dose
biopharmaceuticals98,99,148
High molecular weight aggregates(HMWA)
<1.0%, typically <0.5% 90,149
Leached Protein A below detection 90,99,100
Table 6.5. Mass balance for the performed downstream processing (DSP) platform at optimizedconditions.CF = Concentration Factor. * theoretical estimation for total IgG
Operation Volume CF [IgG] Total IgG Purity Yield (%)(mL) (fold) (mg ·mL�1) (mg) (%) (%)
Bioreactor 3400 1 0.59 2000.6 30 100Centrifugation 3400 1 0.59 2000.6 30 100Microfiltration 0.45 µm 3400 1 0.59 2000.6 30 100Microfiltration 0.22 µm 3400 1 0.59 2000.6 30 100Protein A affinity 140 24.28 13.53 1894.2 98 94Microfiltration 0.22 µm 140 1 13.53 1894.2 98 100UF/DF 50 kDa 150 0.93 12.37 1855.5 98 98Microfiltration 0.22 µm 200 0.75 9.04 1808 98 97.4Capto® Adhere * 220 0.90 7.74 1703 99.5 94.21Microfiltration 0.22 µm* 220 1 7.74 1703 99.5 100
84.52
higher loading and low pH result in improved yield. Likewise, the interaction pH*Loadplays a similar role at high values. [147] It should be noted, however, that outcomes fromthis sort of experiments are antibody-dependent.
The response surface plot (RSP) for yield and DNA content show the effect of thefactors levels in the design (see Figure 6.12 on the preceding page). As can be observedin the figure, yield becomes more conductivity dependent at higher IgG loading onto thecolumn. At a load = 200 mgIgG ·mL�1, yields >96% are achieved in the entire pH rangestudied.
In the case of DNA content, the opposite effect is observed: as loading increases,DNA content in the sample becomes more pH dependent. When normalizing DNA withthe protein concentration of the analyzed samples, a parameter ng DNA/mg antibody isobtained (data not shown). For this particular antibody, a single dose of 100 mg was usedto determine the maximum amount of DNA per dose (1 dose = amount of drug given over24 h [99]) according to regulatory requirements, [148] which gives an upper limit of 0.1 ngDNA/mg antibody. The DNA content in the analyzed samples falls above the maximumestimated by two orders of magnitude. It is important to note that the determination ofsuch small amounts of DNA requires analytical methods that are sensitive and robust.Additionally to ELISA, quantitative polymerase chain reaction (qPCR) has been usedfor this goal. [98] Expected final levels of impurities for biopharmaceutical products are
Chapter 6. Results and Discussions 58
listed in Table 6.4 on the previous page.For this design an optimization was made with the software in order to find the
conditions for maximization of the yield and minimization of the measured DNA contentin the flow through. The suggested loading parameters for the Capto® Adhere resin are:pH = 5.5, conductivity = 24 mS/cm, and load = 200 mgIgG ·mL�1. The residence timeshould not be less than 2 min. For optimization experiments other design models canbe used such as centered composite design (CCD). CCD includes quadratic interactionsof the analyzed factors, that in combination with the distribution levels of the factors,provides a more robust model. Because this kind of model gives more information aboutthe analyzed design, a higher number of experiments is required. [146]
If HCP presents a serious problem during polishing, analysis tools such as two-dimensional gel electrophoresis can be used to study the diversity of HCP present in theculture media and their dynamic behavior for earlier considerations of their eliminationduring the recovery and purification process. [150,151] Buffer exchange after polishing forformulation purposes was not performed. Formulation is a complicated and long processthat is time-demanding. Although formulation is a critical step for assuring long termstability and product quality, it is often disregarded and ignored in DSP discussions. If afinal buffer exchange is to be made for the goal of storing the sample, some useful advicesare to sterile filter the sample and reduce its exposure to light if the intent is liquid storage.If instead the sample is to be frozen, sodium phosphate is discouraged, since the dibasicsalt reportedly has a lower solubility than the monobasic sodium phosphate salt and,hence, tends to crystallize during freezing, reducing the pH of the sample and increasingthe risk of protein damage due to the formed crystals. [125,152–154]
6.4 Mass balance at optimized process conditions
A mass balance for the suggested DSP platform at the best conditions determined in thiswork is presented in Table 6.5. Although the polishing step with Capto® Adhere wasperformed, the mass balance is a theoretical estimation based on the selected conditionsfor that particular step, since the total amount of sample was not processed at theseconditions. The product loss for the MF steps, with the exception of the one following UFwas negligible. Even at VLS manufacturing, these operations working at optimal levelshave yields >99%. As mentioned earlier in Section 3.3, mAbs DSP have global yields inVLS falling in the range of 60–80%. [67] Overall, a recovery of 85% was purity >99.5%(see supplementary material online). Considering all the estimated optimized parameterstogether with literature suggestions based on issues and observations during the entireprocess, a complete list of process variables and conditions is provided for the suggestedDSP platform (see Table 6.6 on the following page).
Chapter 6. Results and Discussions 59
Tabl
e6.
6.Su
gges
ted
proc
ess
para
met
ers
for
allu
nit
oper
atio
nsin
the
disc
usse
ddo
wns
trea
mpr
oces
sing
(DSP
)pl
atfo
rm.F
orbu
ffers
and
solu
tions
refe
rto
Tabl
eA
.1.
#O
pera
tion
Step
Buff
er/s
olut
ion
Pro
cess
para
met
ers
1C
entr
ifuga
tion
--
10,0
00·g
,4�C
,30
min
2M
icro
filtr
atio
n-
-0.
45µm
,reg
ener
ated
cellu
lose
(RC
)3
Mic
rofil
trat
ion
--
0.22
µm
,RC
4P
rote
inA
affini
tych
rom
atog
raph
y;re
sin:
Mab
Sele
ct®
Was
hW
ater
10C
V,3
00cm
·h�1
Equ
ilibr
atio
nB
uffer
E5
CV
,300
cm·h
�1
Sam
ple
load
300cm
·h�1
Was
haf
ter
sam
ple
load
Buff
erF
5–10
CV
,300
cm·h
�1
Elu
tion
Buff
erG
5–10
CV
,250
cm·h
�1
Stri
ppin
gB
uffer
Hat
leas
t15
min
Cle
anin
g-in
-pla
ce(C
IP)
Buff
erI
atle
ast
15m
inW
ash
Wat
er5–
10C
V,3
00cm
·h�1
Stor
age
Buff
erL
5C
V,3
00cm
·h�1
5V
iral
inac
tiva
tion
-B
uffer
G30
–60
min
,20�C
6U
ltra
filtr
atio
n,50
kDa,
20cm
2po
lyet
hers
ulfo
ne(P
ESU
)W
ash
Wat
erQ
=15
0mL·m
in�1,4
00m
LE
quili
brat
ion
Buff
erE
Q=
250m
L·m
in�1,4
00m
LC
once
ntra
tion
Q=
250mL·m
in�1,T
MP
=0.
8ba
rD
iafil
trat
ion
Buff
erE
Cw=
100g·L
�1
,tD
F=
1h,N
=10
DV
Was
hW
ater
Q=
150m
L·m
in�1,4
00m
LC
IPB
uffer
MQ
=15
0mL·m
in�1,8
00m
L,50
�C
Was
hW
ater
Q=
150m
L·m
in�1,4
00m
LSt
orag
eB
uffer
LQ
=15
0mL·m
in�1,4
00m
L7
Mic
rofil
trat
ion
--
0.22
µm
,RC
8Pol
ishi
ng;r
esin
:Cap
to®
Adh
ere
Was
hW
ater
10C
V,2
00cm
·h�1
Equ
ilibr
atio
nB
uffer
J5
CV
,200
cm·h
�1
Sam
ple
load
200mg I
gG·m
L�1,C
olle
ctflo
wth
roug
h,do
not
was
hw
ith
mor
eth
an15
CV
Elu
tion
Buff
erK
5–10
CV
,200
cm·h
�1
Stri
ppin
gB
uffer
Hat
leas
t15
min
CIP
Buff
erM
atle
ast
15m
inW
ash
Wat
er5–
10C
V,2
00cm
·h�1
Stor
age
Buff
erL
5C
V,2
00cm
·h�1
8M
icro
filtr
atio
n-
-0.
22µm
,RC
Chapter 7Summary, conclusions, and remarks
MAbs are nowadays the most important category of biopharmaceuticals and will continueto be in the foreseeable future due to their great therapeutic potential and ever-evolvingengineering aspects of their production and purification. The wide spread use of mAbsboth in industry and academy makes it clear that bioprocess engineers should possessmore than a general understanding of their production for delivering high quality productsat low costs.
Although there is growing globalization of mAbs production, the development foreach antibody should be assessed individually; due to the complexity of the process bynumerous factors, only general guidance can be provided.
The work here presented achieves this goal of general guidance regarding criticalunit operations found in antibody manufacturing, rather than delivering a completelyoptimized process with fully regulatory compliance, which is a complex activity thatrequires considerable effort in both process and analytical development. Thorough processcharacterization may add as much as a year to the overall process development time andrequires a fully integrated process characterization team (⇠8–12 people) including USPand DSP personnel and analytical departments. [155]
The performed process in this study yielded a product with high purity (>99.5%)and good recovery (⇠85%), that can be further improved by analyzing the unit operationsthat were not in the scope of this work. Along with the already mainstream technologiesused and presented here, the DSP platform for mAbs will benefit from the development ofnew materials and adoption of novel technologies such as membrane chromatotraphy, highperformance tangential flow filtration (HPTFF), and the use of non-compressible media,to name a few, for the establishment of a robust, scalable, and cost-effective process.
60
Appendix ASuggested buffers and solutions
The Table A.1 below lists buffers and solution for the entire DSP platform suggested inthis work.
Table A.1. Recommended buffer solutions for the suggested downstream processing (DSP) platform.
Identifier Buffer/Component [Conc] (mM) [Conc] (% v/v) pH
E PBS 7.2KCl 2.7KH2PO4 1.5NaCl 136.9Na2HPO4·H2O 8.9
F PBS 6.0KCl 2.7KH2PO4 1.5NaCl 300Na2HPO4·H2O 8.9
G Sodium Citrate 50.0 3.0NaCl 150.0Arginine 150.0
H Sodium Citrate 50.0 2.7NaCl 1000.0
I NaOH 50.0NaCl 1000.0
J BIS-TRIS 25.0 5.5NaCl 300.0
K Sodium Citrate 50.0 3.0L EtOH 20.0M NaOH 1000.0
61
Appendix BMatlab code for estimation of masstransfer parameters in UF
The following code was used with Matlab v2012 software for determination of masstransfer parameters in the ultrafiltration (UF) step as described in Section 5.3. Withimported sets of TMP , J and Cb as a matrix from experimental data, the provided codegenerates automatically figures and parameters.
Three sets of code are provided:
1. For finding the mass transfer coefficient k with stagnant film model (SFM) andosmotic pressure model (OPM).
2. For finding local minimums in the membrane area function (Equation (6.12) onpage 52) plotting against permeate flux J when working at constant membraneconcentration Cw. Optimal J is found. A previous determination of mass transfercoefficient k is required, as well as input for process variables such as initial volumeV0, diafiltration volumes N and diafiltration time t.
3. For finding local minimums in the membrane area function (Equation (6.12) onpage 52) plotting against bulk product concentration Cb when working at constantmembrane concentration Cw. Optimal Cb is found. A previous determination ofmass transfer coefficient k is required, as well as input for process variables such asinitial volume V0, diafiltration volumes N and diafiltration time t.
Set number 1:
1 %Import data as matrix: 'TMP', 'J', and 'Cb'
2 set(0, 'defaultTextInterpreter', 'latex');
3 Lp=362.17;
4 Lfm=0.3*Lp; % LMH %Input Fouled membrane permeability
5 lnCb=log(Cb(1,:));
6 limJ=max(J,[],1); %Extract limiting Flux from 'J' matrix.
62
Appendix B. Matlab code 63
7
8 figure1=figure('Color',[1 1 1],'Position', [100, 100, 1000, 1000]);
9 aspect_ratio_x=1; aspect_ratio_y=1; aspect_ratio_z=1;
10
11 % Plot 1: Permeate Flux vs Cb)
12 subplot1=subplot(2,2,1,'Parent',figure1); %rows, columns
13 box(subplot1,'on'); hold(subplot1,'all');
14 pbaspect([aspect_ratio_x aspect_ratio_y aspect_ratio_z]);
15 marcador=['s' 'o' '+' '^'];
16 j=[0 1 0 1];
17 for i=1:4
18 plot(TMP(:,i),J(:,i),'MarkerSize',8,'Marker',marcador(1,i),...
19 'LineStyle','none',...
20 'Color',[0 0 0],'MarkerFaceColor',[j(1,i) j(1,i) j(1,i)]);
21 end
22 %Axis Limits
23 axis([0.3 1.1 15 75]);
24 ylabel('Permeate Flux, $$J (L m^2 h^{�1})$$','FontWeight','bold');25 xlabel('Transmembrane pressure, $$TMP (bar)$$');
26 %Legends
27 legend([num2str(Cb(1,1)),' $$g L^{�1}$$'],...28 [num2str(Cb(1,2)),' $$g L^{�1}$$'],...29 [num2str(Cb(1,3)),' $$g L^{�1}$$'],...30 [num2str(Cb(1,4)),' $$g L^{�1}$$'],...31 'Location','SouthEast');
32
33 %Annotations Plot 1
34 annotation(figure1,'textbox',...
35 [0.195584959290841 0.437114060614664,...
36 0.953954954954955 0.0725388601036269],...
37 'String',['$${J \over {{L_{fm}}}} = TMP � \alpha {C_b}\exp',...
38 '\left[ {{J \over k}} \right] � \beta {\left( {{C_b}\exp ',...
39 '\left[ {{J \over k}} \right]} \right)^2}$$'],...
40 'FitBoxToText','off',...
41 'LineStyle','none');
42
43 % Plot 2: Plot Limiting Flux vs ln(Cb)
44 subplot2=subplot(2,2,2,'Parent',figure1); %rows, columns
45 box(subplot2,'on');
46 pbaspect([aspect_ratio_x aspect_ratio_y aspect_ratio_z]);
47 hold(subplot2,'all');
48 %Regression analysis
49 p = polyfit(lnCb,limJ,1);
50 f = polyval(p,lnCb);
51 % Extract k and Cg
52 k=abs(p(:,1));
53 Cg=exp(p(:,2)/k);
54
Appendix B. Matlab code 64
55 plot(lnCb,limJ,'MarkerSize',8,'Marker','square','LineStyle',...
56 'none','Color',[0 0 0]);
57 ylabel('Limiting Permeate Flux, $$J (L m^2 h^{�1})$$',...58 'FontWeight','bold')
59 xlabel('$$ln(C_b)$$');
60 plot(lnCb,f,'��','Color',[0 0 0]);
61 % Annotations Plot 2
62 annotation(figure1,'textbox',...
63 [0.694963482198776 0.83904109589041,...
64 0.0840026497085322 0.0773475761232163],...
65 'String',['$${J_{\lim }} = k\ln \left[ {{{{C_g}}',...
66 '\over {{C_b}}}} \right]$$'],...
67 'FitBoxToText','off',...
68 'LineStyle','none');
69 annotation(figure1,'textbox',...
70 [0.669625498007 0.642363046420 0.110374501992 0.0300657894736],...
71 'Interpreter','latex',...
72 'String',['$$k =$$',num2str(k),' LMH'],...
73 'FitBoxToText','off',...
74 'LineStyle','none');
75 annotation(figure1,'textbox',...
76 [0.665625498007 0.613285890596 0.156374501992 0.0300657894736],...
77 'Interpreter','latex',...
78 'String',['$$C_g =$$',num2str(Cg),' $$g L^{�1}$$'],...79 'FitBoxToText','off',...
80 'LineStyle','none');
81
82 % Plot 3: Osmotic Pressure vs Cw
83 subplot3=subplot(2,2,3,'Parent',figure1); %rows, columns
84 box(subplot3,'on');
85 pbaspect([aspect_ratio_x aspect_ratio_y aspect_ratio_z]);
86 hold(subplot3,'all');
87 o = 1;
88 p = 7;
89 cplot = 2;
90 OsmoticP=TMP�J/Lfm; %Calculate Osmotic Pressure
91 Cw=Cb.*exp(J/k); %Calculate wall concentration
92 plot(Cw(o:p,2),OsmoticP(o:p,cplot),'MarkerSize',8,'Marker','o',...
93 'LineStyle','none','Color',[0 0 0],...
94 'MarkerFaceColor',[0 0 0]);
95 p2 = polyfit(Cw(o:p,cplot),OsmoticP(o:p,cplot),2); %2nd order pol
96 f2 = polyval(p2,Cw(o:p,cplot)); % Apply model and assess fit
97 alpha=p2(:,2);
98 beta=p2(:,1);
99 plot(Cw(o:p,cplot),f2,'��','Color',[0 0 0]); % Compare data and fit
100 ylabel('Osmotic Pressure, $$\Delta\Pi (bar)$$','FontWeight','bold')
101 xlabel('$$C_w (g L^{�1})$$');102 % Annotations Plot 3
Appendix B. Matlab code 65
103 %EQUATION box
104 annotation(figure1,'textbox',...
105 [0.238892847028 0.405054443028 0.122107152971 0.0278018250916],...
106 'Interpreter','latex',...
107 'String',{'$$\Delta \Pi = \alpha {C_w} + \beta {C_w}^2$$'},...
108 'FitBoxToText','off',...
109 'LineStyle','none');
110 %Paramters box
111 annotation(figure1,'textbox',...
112 [0.200892847028 0.26284566844 0.0727605394254 0.0278018250916],...
113 'Interpreter','latex',...
114 'String',['$$\alpha =$$',num2str(alpha)],...
115 'FitBoxToText','off',...
116 'LineStyle','none');
117 annotation(figure1,'textbox',...
118 [0.199892847028 0.241665638187 0.0961071529713 0.0278018250916],...
119 'Interpreter','latex',...
120 'String',['$$\beta =$$',num2str(beta)],...
121 'FitBoxToText','off',...
122 'LineStyle','none');
123
124
125 % Plot 4: Flux vs Osmotic Pressure
126 subplot4=subplot(2,2,4,'Parent',figure1); %rows, columns
127 box(subplot4,'on');
128 pbaspect([aspect_ratio_x aspect_ratio_y aspect_ratio_z]);
129 hold(subplot4,'all');
130 for i=1:4
131 plot(OsmoticP(:,i),J(:,1),'MarkerSize',8,...
132 'Marker',marcador(1,i),'LineStyle','none',...
133 'Color',[0 0 0],'MarkerFaceColor',[j(1,i) j(1,i) j(1,i)]);
134 end
135 axis([0.04 0.4 25 75]);
136 %axis([0 0.4 15 50]); %Axis limits
137 ylabel('Permeate Flux, $$J (L m^2 h^{�1})$$','FontWeight','bold');138 xlabel('Osmotic Pressure, $$\Delta\Pi (bar)$$');
139 %Legends
140 legend([num2str(Cb(1,1)),' $$g L^{�1}$$'],...141 [num2str(Cb(1,2)),' $$g L^{�1}$$'],...142 [num2str(Cb(1,3)),' $$g L^{�1}$$'],...143 [num2str(Cb(1,4)),' $$g L^{�1}$$'],...144 'Location','SouthEast');
145
146 % Annotations Plot 4
147 annotation(figure1,'textbox',...
148 [0.649990701931878 0.328172687912556,...
149 0.366567567567568 0.116580310880829],...
150 'String',...
Appendix B. Matlab code 66
151 ['$$k={{{J_2}�{J_1}}\over{\ln {C_{b,1}}�\ln{C_{b,2}}}}=',...152 '8.94$$ LMH'],...
153 'FitBoxToText','off',...
154 'LineStyle','none');
Set number 2:
1 % Declare variables
2
3 N0=10; % Diavolumes
4 V0=0.5; % L
5 C0=2; %g/L
6 t=1; % h
7 k=14.89 % LMH
8 syms A J
9 %hold off;
10 figure1=figure('Color',[1 1 1]);
11 set(0, 'defaultTextInterpreter', 'latex');
12
13 % Graph
14 for i=1:3
15 if i==1;
16 Cwi=30;
17 elseif i==2;
18 Cwi=100;
19 elseif i==3;
20 Cwi=200;
21 end
22 Cbii=Cwi/exp(J/k);
23 A=(N0*C0*V0)/(Cbii*J*t)
24 ezplot(A,[0 80]); ylim([0 0.1])
25 hold on;
26
27 A1=diff(A); % Differentiating A function...
28 A1=simplify(A1); % Reducing...
29 crit_pts=solve(A1); % Finding local minimum...
30 plot(double(crit_pts), double(subs(A,crit_pts)),'ro','MarkerSize',...
31 10,'Color',[1 0 0],...
32 'MarkerFaceColor',[1 0 0]);
33 xlabel('Permeate flux, $$J$$ ($$L \cdot m^{�2} \cdot h^{�1}$$ )',...
34 'FontWeight','bold'); ylabel('Membrane Area, $$A$$ $$(m^2)$$',...
35 'FontWeight','bold')
36 title('')
37 end
38
39 annotation(figure1,'textbox',...
40 [0.538562604340568 0.688571428571429 0.316195325542571 0.16],...
Appendix B. Matlab code 67
41 'Interpreter','latex',...
42 'String',{'$$A = {{N{C_0}{V_0}} \over {\left[ {{C_w} \cdot '...
43 '{e^{\left( {{\raise0.5ex\hbox{$\scriptstyle J$}'...
44 '\kern�0.1em/\kern�0.15em'...45 '\lower0.25ex\hbox{$\scriptstyle k$}}} \right)}}} \right] \cdot J '...
46 '\cdot {t_{DF}}}}$$'},...
47 'FontSize',12,...
48 'FitBoxToText','off',...
49 'LineStyle','none');
50 annotation(figure1,'textbox',...
51 [0.570282136894825 0.581645021645022 0.182639398998331 0.0800000],...
52 'Interpreter','latex',...
53 'String',{'$${J}^ * = k$$'},...
54 'FontSize',12,...
55 'FitBoxToText','off',...
56 'LineStyle','none');
57 annotation(figure1,'textarrow',[0.246190265025042 0.221875],...
58 [0.627272727272727 0.688783570300158],'TextEdgeColor','none',...
59 'Interpreter','latex',...
60 'String',{'Local minimum'});
Set number 3:
1 % Declare variables
2
3 N0=10; % Diavolumes
4 V0=0.5; % L
5 C0=2; %g/L
6 t=1; % h
7 k=14.89 % LMH
8 syms A Cbii
9 %hold off;
10 figure1=figure('Color',[1 1 1]);
11 set(0, 'defaultTextInterpreter', 'latex');
12
13 % Graph
14 for i=1:3
15 if i==1;
16 Cwi=30;
17 elseif i==2;
18 Cwi=100;
19 elseif i==3;
20 Cwi=200;
21 end
22
23 A=(N0*C0*V0)/(Cbii*k*log(Cwi/Cbii)*t)
24 ezplot(A,[0 160]); ylim([0 0.1])
Appendix B. Matlab code 68
25 hold on;
26 % Find local minimum
27 A1=diff(A);
28 A1=simplify(A1);
29 crit_pts=solve(A1);
30 plot(double(crit_pts), double(subs(A,crit_pts)),'ro',...
31 'MarkerSize',10,'Color',[1 0 0],...
32 'MarkerFaceColor',[1 0 0]);
33 xlabel('Cb $$(gL^{�1})$$','FontWeight','bold');34 ylabel('Membrane Area $$(m^2)$$','FontWeight','bold')
35 title('')
36 end
37
38 annotation(figure1,'textbox',...
39 [0.538562604340568 0.688571428571429 0.316195325542571 0.16],...
40 'Interpreter','latex',...
41 'String',{'$$A = {{N{C_0}{V_0}} \over {{C_b} \cdot k\ln \left[',...
42 '{{{{C_w}} \over {{C_b}}}} \right]{t_{DF}}}}$$'},...
43 'FontSize',12,...
44 'FitBoxToText','off',...
45 'LineStyle','none');
46 annotation(figure1,'textbox',...
47 [0.570282136894825 0.581645021645022 0.182639398998331...
48 0.0800000000000002],...
49 'Interpreter','latex',...
50 'String',{'$${C_b}^ * = {C_w} \cdot {e^{ � 1}}$$'},...
51 'FontSize',12,...
52 'FitBoxToText','off',...
53 'LineStyle','none');
54 annotation(figure1,'textarrow',[0.246190265025042 0.221875],...
55 [0.627272727272727 0.688783570300158],'TextEdgeColor','none',...
56 'Interpreter','latex',...
57 'String',{'Local minimum'});
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Index
Aacetic acid, 25anion exchange chromatography (AEX), 23, 24antibiotics, 11antifoaming, 12arginine, 21, 44, 50aspirin, 1
Bbaby hamster kidney (BHK), 8, 12Bacillus, 9baculovirus, 11biopharmaceuticals, 5
annual production quantities, 7classification by biological functionality, 5DNA-based, 4extraction, 6levels of impurities in final product, 58recent approvals, 7sales price, 7
bioreactorsbatch volumes, industrial scale, 17wave bag, 12, 47
Brownian diffusion, 30, 31
Ccation exchange chromatography (CEX), 23, 24cell culture fluid (CCF), 17, 36cell debris, 11cell disruption, 12centered composite design (CCD), 58centrifugation, 12chaotropes, 21, 44chaperonins, 9chinese hamster ovary (CHO), 8, 9, 12, 20, 36chromatography, 12
intraparticle diffusion, 46number of theoretical plates, 44peak broadening, 44reduced plate height, 45resolution, 47
clarified cell culture fluid (cCCF), 36, 38, 43cleaning-in-place (CIP), 59coagulation factors, 6Cohn, Edwin, 17column sanitization, 21column volumes (CV), 22, 37compression factor (CF), 37concentration polarization, see surface
polarization (SP)convective mass transfer, 21, 30cross-flow filtration, see ultrafiltration (UF)culture media, 9
excretion to, 9protein-free, 9
Ddepth filtration (DP), 19, 22, 44design of experiments (DoE), 25, 35, 38
model coefficients, 56model validity, 55
diabetic foot ulcer, 7diafiltration (DF), 20, 28, 34, 51–53
constant volume, 52diavolumes (DV), 40, 53diffusive mass transfer, 21, 30digitalis, 1disk-stack centrifuges, 17downstream processing (DSP), vii, 9, 11–13, 17,
19, 20, 22, 23, 39, 57–61buffer consumption, 17
79
Index 80
Dynamic Binding Capacity (DBC), 20, 21, 23, 37
EE. coli, 9eddy diffusion, 30electrophoresis, 12endotoxin, 9, 11enzyme-linked immunosorbent assay (ELISA), 36,
54, 57erythropoietin (EPO), 6ethylene glycol, 21extractables, 11
FFab, 5fast protein liquid chromatography (FPLC), 37Fc fusion proteins, 5Fick’s law, 30filamentous fungi, 11
proteases, 11filtration, 12
filter fouling, 44membrane fouling, 50
flocculation, 13follicle stimulating hormone (FSH), 6
GGeneral Electric (GE), 21generally recognized as safe (GRAS), 10glycosylation, 9gonadotropins, 6Good Manufacturing Practice (GMP), 17
Hharvested cell culture fluid (HCCF), 19–21, 23, 36high molecular weight aggregates (HMWA), 23,
24, 42, 46, 47, 54, 57high performance tangential flow filtration
(HPTFF), 60high-performance liquid chromatography (HPLC),
21hormone, 5host cell protein (HCP), 19–21, 23, 24, 42, 44, 54,
57, 58clearance, 23
human chorionic gonadotropin (hCG), 6human embryonic kidney (HEK), 8human growth hormone (hGH), 6hydrophobic interaction chromatography (HIC),
22
hydroxyapatite (HA), 25
Iimmunoglobulin G (IgG), 14–16, 21, 34, 40, 47,
50, 57pH for elution, 20, 22subclasses, 20
inclusion bodies (IBs), 9, 10insect cells, 11insulin, 5
human, 5isolation and purification, 6
ion exchange chromatography (IEX), 19, 22–24isoelectric point (pI), 6, 24
Llipids, 11lipopolysaccharide (LPS), 9log10 reduction value (LRV), 26, 27luteinizing hormone (LH), 6
Mmagnesium, 50malaria, 1mammalian cells
in suspension, 12product titres, 9types of, 8
mass transfer coefficient, 48, 51methods for estimation, 32
membrane hydraulic permeability coefficient, 29metalloproteases, 21methionine, 11microcavitation, 50microfiltration (MF), 12, 19, 51, 58Ministry for Education and Research (BMBF), 1Ministry for Education and Science (BMBW), 1mixed mode chromatography (MMC), 22, 24, 25molds, see filamentous fungimolecular weight cutoff (MWCO), 28, 39monoclonal antibodies (mAbs), vi, vii, 5–7, 13,
14, 16–19, 22–25, 27, 28, 30, 50, 58, 60annual production, 17approved by the FDA, 18chimeric, 14expression, 14harvest and primary recovery, 17murine, 14nomenclature, 14polishing operations, 22, 54
Index 81
mouse minute virus (MMV), 25multimodal chromatography, see mixed mode
chromatography
Nnominal molecular weight limit (NMWL), 28, 36normal flow filtration (NFF), 27, 28, 36
OOrphan Drug Act (ODA), 6orthogonal steps, virus clearance, 25osmotic pressure model (OPM), 30, 40, 41, 49, 50,
62
PP. pastoris, 9packing factor (PF), 37plasmid, 5polyethersulfone (PESU), 39, 49, 59post-translational modifications, 8
glycosylation, 11prefiltration, see depth filtration (DP)protein
precipitation by ethanol, 17aggregates, 11, 43, 54aggregation, 11, 22, 50diffuse ion cloud, 48diffusivity, 49folding, 9fractionation, 6hydrodynamic radii, 47measurements of solubility, 50nucleation kinetics, 48precipitation, 44storage, freezing, 58
Protein A, 19leached from affinity column, 21, 23, 54
Protein A affinity chromatography, 19binding conditions, 20column packing performance, 37commercially available resins, 21key contaminants after, 23non-compressible media for, 21residence time, 22scale-up, 22
pseudorabies virus (PRV), 25
Qquantitative polymerase chain reaction (qPCR),
57
quinine, 1
Rrecombinant
drugs, see also biopharmaceuticalsexpression system, 8
regenerated cellulose (RC), 36, 40, 59reovirus type 3 (Reo-3), 25residence time distribution (RTD), xviresponse surface plot (RSP), 56, 57
SS. cerevisiae, 9Shukla, Abhinav, 17size exclusion chromatography (SEC), 28, 34, 38,
46–48small chain variable fragment (scFv), 5somatostatin, 1stagnant film model (SFM), 30, 31, 40, 49, 50, 52,
62boundary layer, 31, 48
stirred tank reactors (STR), 12, 34, 36, 46, 47surface polarization (SP), 30, 31, 49, 52synthetic media
protein free, 9
Ttangential flow filtration (TFF), 19, 27, 28, 30, 32tissue plasminogen activator (tPA), 6transient cell line, 7transmembrane pressure (TMP), 28, 39, 40, 49–51
Uultracentrifugation, 12ultrafiltration (UF), 20, 22, 23, 27–30, 34, 39, 44,
50, 58, 62clean membrane permeability estimation, 39fouled membrane permeability, 52gelation concentration, 31limiting permeate flux, 31, 40membrane area, 31modes of process control, 50module and membrane formats, 28, 48optimal bulk protein concentration, 51pore sizes, 28solute concentration at the wall, 31, 51
upstream processing (USP), vii, 36, 60US Food and Drug Administration (FDA), 6, 10,
18, 23, 24, 80
Index 82
Vvaccine
hepatitis B, 6vaccines, 5van Deemter equation, 45vector, see plasmidvery large scale (VLS), 17, 19, 20, 22, 23, 27, 30,
42, 43, 47, 48, 50, 58virus
clearance, 54commercially available filters, 26filtration, 27human pathogenic, 8
inactivation by low pH, 27
rodent derived, 25
validation guidelines for clearance, 25
virus-like particles (VLP), 5
W
World Health Organization (WHO), 24
X
Xenotropic murine leukemia virus-related virus(X-MuLV), 25
Y
yeasts, 9
