High cell density perfusion process development producing ...1097269/FULLTEXT01.p… ·...
Transcript of High cell density perfusion process development producing ...1097269/FULLTEXT01.p… ·...
High cell density perfusion process development for antibody producing Chinese Hamster Ovary
cells
Ye Zhang
Doctoral Thesis in Biotechnology Stockholm, Sweden 2017
©Ye Zhang School of Biotechnology Royal Institute of Technology AlbaNova University Center SE-106 91 Stockholm Sweden Printed by Universitetsservice US-AB TRITA-BIO Report 2017:14 ISSN 1654-2312 ISBN 978-91-7729-450-4
Abstract
Perfusion operation mode is currently under fast expansion in mammalian cell based
manufacturing of biopharmaceuticals, not only for labile drug protein but also for
stable proteins such as monoclonal antibodies (mAbs). Perfusion mode can
advantageously offer a stable cell environment, long-term production with high
productivity and consistent product quality. Intensified high cell density culture
(HCDC) is certainly one of the most attractive features of a perfusion process due to
the high volumetric productivity in a small footprint that it can provide. Advancements
in single-use technology have alleviated the intrinsic complexity of perfusion processes
while the maturing in cell retention devices has improved process robustness. The
knowledge for perfusion process has been gradually built and the “continuous” concept
is getting more and more acceptance in the field.
This thesis presents the development of robust perfusion process at very high cell
densities in various culture systems. Four HCDC perfusion systems were developed
with industrial collaborators with three different mAb producing Chinese Hamster
Ovary (CHO) cell lines: 1-2) WAVE Bioreactor™ Cellbag prototype equipped with cell
separation by hollow fiber filter utilizing Alternating Tangential Flow (ATF) and
Tangential Flow Filtration (TFF) techniques; 3) Fiber matrix based CellTank™
prototype; 4) Glass stirred tank bioreactor equipped with ATF. In all the systems,
extremely high viable cell densities above 130 million viable cells per milliliter
(MVC/mL) up to 214 MVC/mL were achieved. Steady states were maintained and
studied at 20-30 MVC/mL and 100-130 MVC/mL for process development. Perfusion
rate selection based on cell specific perfusion rate (CSPR) was systematically
investigated and exometabolome study was performed to explore the metabolic
footprint of HCDC perfusion process.
Keywords: WAVE Bioreactor, Cellbag, ATF, TFF, hollow fiber, CSPR, CellTank, SUB,
single-use-bioreactor, disposable, perfusion rate
Sammanfattning Perfusionssystem för produktion av biobaserade proteinläkemedels, via cellodling, är
ett område som just nu expanderas och utvecklas i snabbt takt, inte bara med avseende
på labila proteiner men också mer stabila produkter så som monoklona antikroppar
(mAb). Den stora fördelen med perfusionssystem, över andra odlingstekniker, är dess
kompakta formfaktor, en kontinuerligt hög produktivitet under lång tid, konsekvent
hög produktkvalitet och stabila odlingsförhållanden. Perfusionsteknikens främsta fördel
är möjligheten att erhålla extremt hög celltäthet, vilket resulterar i mycket hög
volymetrisk produktivitet. Utveckling inom engångsodlingssystem och
cellretentiontekniker har skapat möjligheter för att underlätta nyttjandet av
perfusionsprocesser samt ökat robustheten. Allt eftersom kunskapen om
perfusionssystem har ökat, har kontinuerliga produktionsystem blivit mer accepterade
inom forskningsområdet.
Den här avhandlingens fokus är utveckling av en robust perfusionsprocess som kan nå
oerhört höga celltäthet, i flertalet odlingssystem. Fyra olika system för hög celltäthet
har utvecklats, med industriella samarbetspartners, för produktion av tre olika mAb
genom olika Chinese Hamster Ovary (CHO) cellinjer: 1-2) WAVE Bioreactor™ Cellbag,
vågbaserad reaktor prototyp med cellseparation genom varierande tangentialt flöde
(ATF) och tangential flödes filtrering (TFF); 3) Fiber matris baserad CellTank™
prototype; 4) Glasbioreaktor med ATF, för cellretention. I samtliga system nås extremt
höga celltäthet, över 130 miljoner viabla celler per milliliter (MVC/mL), och som högst
214 MVC/ml. ”Steady state” förhållanden upphölls under processutvecklingen på
cellkoncentrationer motsvarande 20-30 MVC/ml respektive 100-130 MVC/ml. En
systematisk studie för optimering av perfusionsflöden utfördes, baserad på cellspecifika
perfusionshastigheter (CSPR), samt en exometabolomestudie för att utreda
metaboliska avtryck under höga cellmassor i perfusionssystem.
To my parents
List of publications This thesis is based on the following publications, which are referred to in the text by
their roman numerals:
I Clincke M-F, Mölleryd C, Zhang Y, Lindskog E, Walsh K, Chotteau V. Very High
Density of CHO Cells in Perfusion by ATF or TFF in WAVE BioreactorTM. Part I. Effect of
the Cell Density on the Process. Biotechnology Progress. 2013; 29(3): 754-767
II Zhang Y, Stobbe P, Orrego-Silvander C, Chotteau V. Very high cell density perfusion
of CHO cells anchored in a non-woven matrix-based bioreactor. Journal of
Biotechnology, 2015; 213: 28-41
III Zhang Y, Zhan CJ, Girod P-A, Martiné A, Chotteau V. Optimization of the cell
specific perfusion rate in high cell density perfusion process. (Manuscript)
IV Zamani L, Zhang Y, Aberg M, Lindahl A, Mie A, Chotteau V. Metabolic footprinting
of CHO cell culture bioprocess data in fed-batch and perfusion mode using LC-MS data
and multivariate analysis. (Manuscript)
The following publication is not included in this thesis:
V Al-Khalili L, Gillner K, Zhang Y, Åstrand C, Shokri A, Hughes-Brittain N, McKean
R, Robb B, Chotteau V. Characterization of Human CD133+ Cells in Biocompatible
Poly(l-lactic acid) Electrospun Nano-Fiber Scaffolds. Journal of Biomaterials and Tissue
Engineering. 2016; 6(12): 959-966(8)
Author's contributions Paper I: Took part in cultivation and sample analysis. Performed viscosity study.
Paper II: Experimental design and operation. Wrote the manuscript.
Paper III: Experimental design and operation. Wrote the manuscript.
Paper IV: Participated in the work related to the bioprocessing and cell biology. The
manuscript was collectively written.
List of abbreviations
ACD Average Cell Diameter
Amm 1) Ammonia; 2) Ammonia concentration in the culture †
API Active Pharmaceutical Ingredients
ATF Alternating Tangential Flow
ATF Wave bioreactor equipped with ATF perfusion system
CE Capillary Electrophoresis
CHO Chinese Hamster Ovary
CMO Contract Manufacturing Organization
COGS Costs Of Goods
CSPR Cell Specific Perfusion Rate
CSPR_min minimum CSPR
CT CellTank perfusion system
CV Viable Cell density
CVMAX Maximum Viable Cell density
D Perfusion rate
DHFR Dihydrofolate Reductase
DO Dissolved Oxygen
DSP Down Stream Process
ESI-QTOF Electrospray Ionization-Quadrupole Time-Of-Flight (MS)
Glc* 1) Glucose; 2) Glucose concentration in the culture †
GlcNAc N-acetyl-glucosamine
Gln* 1) Glutamine; 2) Glutamine concentration in the culture†
GS Glutamine Synthetase
GSH Glutathione
HCDC High Cell Density Culture
HCP Host Cell Protein
HF Hollow Fiber
HPLC High-Performance Liquid Chromatography
HTtot Accumulated production in harvest
IgG Immunoglobulin G
Lac 1) Lactate; 2) Lactate concentration in the culture †
mAb Monoclonal Antibody
MCB Master Cell Bank
MWCO Molecular Weight Cut Off
MVC/mL Million Viable Cells per milliliter
MS Mass Spectrometry
OUR Oxygen Uptake Rate
PBR Product concentration in bioreactor
PCA Principal Component Analysis
PHT Product concentration in harvest
PLS Partial Least Square
PLS-DA Partial Least Squares-Discriminant Analysis
qamm Cell specific ammonia production rate
qglc Cell specific glucose uptake rate
qgln Cell specific glutamine uptake rate
qlac Cell specific lactate production rate
qp Cell specific productivity
Redox Reduction-oxidation reaction
rdegr Degradation rate (of glutamine)
ROS Reactive Oxygen Species
RV/day Reactor Volume per day
Smedium Substrate concentration in fresh medium
Sstock Substrate stock solution concentration
SUB Single Use Bioreactor
SDS-PAGE Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis
ST Stirred Tank bioreactor equipped with ATF perfusion system
TCA TriCarboxylic Acid
TFF Tangential Flow Filtration
TFF Wave bioreactor equipped with TFF perfusion system
TNF Tumor Necrosis Factor
t-PA Tissue Plasminogen Activator
USP Up Stream Process
V (Working) Volume
Vbleed Volume of (daily) cell bleed
VHT Volume of (daily) harvest
Vshoot Volume of (daily) supplement
WCB Working Cell Bank
µ Growth rate †In Table 9
Table of Contents 1 Introduction………...…………………………………………….......................... 1
1.1 Outline of this thesis………………………………………………………….......... 1
1.2 Recombinant protein production in mammalian cell systems……………......... 2
1.2.1 Monoclonal Antibodies (mAbs) manufacturing………………………….......... 4
1.2.2 Immunoglobulin G (IgG)…………………………………………………........... 7
1.2.3 CHO cell cellular metabolism………………………………………………........ 8
1.2.3.1 Energy and amino acid metabolism…………………………………….......... 8
1.2.3.2 Metabolomics profiling of extracellular metabolites………………….......... 11
1.2.4 Cultivation techniques………………………………………………………........ 12
1.3 Perfusion process………………………………………………………………........ 14
1.3.1 Perfusion systems……………………………………………………………........ 16
1.3.2 High Cell Density Culture (HCDC)………………………………………........... 20
1.3.3 Single-Use-Bioreactor (SUB) systems……………………………………........... 23
1.4 Perfusion process development and optimization…………………………......... 26
1.4.1 Perfusion rate D……………………………………………………………........... 27
1.4.2 Cell Specific Perfusion Rate (CSPR)………………………………………......... 28
2 Present investigation………………………………………………….................. 30
2.1 Aim of investigation…………………………………………………………........... 30
2.2 HCDC perfusion system development (Paper I-III)……………………............. 31
2.2.1 Perfusion system general set-up…………………………………………........... 32
2.2.2 WAVE Bioreactor™ Cellbag prototype perfusion systems with ATF and TFF
(Paper I) ………………………………………………………………………............... 37
2.2.3 CellTank™ prototype perfusion system (Paper II)……………………............. 38
2.2.4 Glass stirred tank bioreactor with ATF (Paper III)…………………................ 40
2.3 HCDC perfusion process development (Paper I - IV) ………………….............. 40
2.3.1 Maximal viable cell density (Paper I - III)…………………............................. 41
2.3.2 HCDC established and stabilized in the perfusion systems (Paper I-III)......... 44
2.3.3 Perfusion rate optimization (Paper III) …………………................................ 46
2.3.4 Product quality at HCDC (Paper II, III) …………………................................ 48
2.3.5 Cellular metabolism in HCDC (Paper I-IV) …………………........................... 51
2.3.5.1 Energy metabolism (Paper I-III) …………………........................................ 51
2.3.5.2 Metabolomics profiling of extracellular metabolites (Paper IV).................. 52
3 Concluding remarks…………………………………………………................... 55
Acknowledgements…………………………………………………........................
.
57
References…………………………………………………........................................
.
59
1
1 Introduction
1.1 Outline of this thesis
This thesis begins with an introduction to the background of current cultivation
techniques in recombinant protein production by mammalian cell-based systems.
Using Chinese Hamster Ovary (CHO) cells producing monoclonal antibodies (mAbs) as
model system, the thesis focuses on High Cell Density Culture (HCDC) perfusion
process development and covers mainly two parts.
The first part is HCDC perfusion system development with our industrial collaborators.
The second part addresses the HCDC perfusion process development and highlights
investigations of high cell densities and perfusion rate tuning from a more general
process perspective. The cell metabolism and product quality at HCDC perfusion
process are also discussed.
2
1.2 Recombinant protein production in mammalian cell
systems
Following on the recombinant DNA technique breakthrough by biochemist Herbert
Boyer and geneticist Stanley Cohen in the early 1970s, recombinant therapeutic
proteins have been under fast development. Before 1994 the approved
biopharmaceutical products were 25 in United States and European Union markets,
and this number increased significantly to 246 products by 2014 (including 34
products that have been withdrawn after approval in both markets)1.
Host cells are genetically engineered so that the gene encoding for a protein of interest
is incorporated and expressed during cultivation of the host cells. To be fully biological
functional, the expressed target proteins have to be correctly folded and processed with
proper post-translational modifications2, 3. There has been an ongoing shift from non
mammalian-based to mammalian-based expression system: from 50%/50% used in
biologics manufacturing in 1990-1994, to mammalian cell systems occupying 60% for
biologics approved between 2010-20141. Expression based on mammalian cells have
been favored due to the increase of products that require post-translational
modifications, particularly glycosylation. Microbial systems such as prokaryotic cells
Escherichia coli lack such machinery, but still dominate the production of approved
products quantitatively.
The CHO cells were first used in laboratory work in 1919 but the culture technique was
established in 1957, when Dr. Theodore T. Puck identified conditions for good viability
and fast growth. After that, CHO cells began to be intensively used as host cells. They
grow fast with good viability and are easy to culture. The post-translational
modifications of CHO cells provide folded proteins close to human proteins. They can
3
be genetically manipulated to reach high expression level and obtain other attributes
such as metabolic modification. Robust and effective gene amplification systems are
available for CHO cells such as dihydrofolate reductase (DHFR) and glutamine
synthetase (GS). They are also a safe choice with low risk for virus contamination4.
Since the recombinant biopharmaceutical t-PA (tissue plasminogen activator)
expressed in CHO cells was approved in 1987, CHO cells as host cells have
cumulatively occupied 35.5% of the total biopharmaceutical product approvals in US
and EU markets by 20141.
There are also other mammalian host cells available such as human cell lines HEK293
and Per C6 cells, mouse myeloma cell lines NS0 and Sp2/0. Human cell lines are
mainly used in products demanding specific post-translational modifications1. NS0 cells
have competitive high cell specific productivities similar to CHO cells, but have
exhibited less “human glycosylation pattern” compared to CHO cells, with potential
risk of non-human glycosylation and sialylation5. In addition, NS0 cells require
cholesterol supplementation6.
From a bioprocess point of view, CHO cells are relatively easier to be adapted to
suspension growth in serum-free and animal components free media compared to
other mammalian expression systems. Suspension cell culture is advantageous in
homogenous culture environment allowing easy scaling up of the process6, 7.
4
1.2.1 Monoclonal Antibodies (mAbs) manufacturing
Among the 54 recombinant biologics (biologically produced pharmaceuticals)
approved from 2010 to 2014 in US and EU markets, 17 were mAbs. The others were
hormones, enzymes, vaccines etc. and one gene therapy product1. Therapeutic
indications for mAbs range from cancers, inflammatory diseases to autoimmune
diseases. Table 1 presents the top 6 prescription drug biopharmaceuticals in 2015
showing that 5 of them are mAbs. The expiring patents of many of these blockbusters
makes them very attractive targets for biosimilars, copies of approved original mAbs
out of patents.
Table 1 Top 6 Best-selling biologics of 2015
Rank Biologics Expression
System Company
2015
Revenues
billion$
Patent expiry
(EU)
Patent expiry
(US)
1 Humira®
(adalimumab) CHO AbbVie 14.01 2018 2016
2 Rituxan®
(rituximab) CHO Roche 7.33 2013 2016
3
Lantus®
(insulin
glargine)
Escherichia
coli Sanofi 7.09 2014 2014
4 Avastin®
(bevacizumab) CHO Roche 6.95 2019 2017
5 Herceptin®
(trastuzumab) CHO Roche 6.8 2014 2019
6 Remicade®
(infliximab) CHO Janssen 6.56 2015 2018
5
Taking Remicade® as example: Remicade® is the trade name for infliximab, an anti-
TNFα (Tumor Necrosis Factor alpha) chimeric mAb drug for autoimmune diseases
treatment. It was originally approved by the US Food and Drug Administration (FDA)
in 1998. In 2016, two biosimilars of Remicade®, Inflectra® (Pfizer) and Remsima®
(Celltrion) have been registered by FDA. Another biosimilar, Flixabi® (Biogen), has
been approved in a few countries in EU. The race about titer, time, and cost is no
longer the privilege of larger pharmaceutical corporations, but many small companies
are present with a lot of efforts devoted to process development and optimization8.
There are three production technologies commonly used in mAb manufacturing9:
transient expression, stable cell pool, and stable clonal cell line. All three methods
involve getting the foreign target gene into the cells. In the transient expression
method the foreign DNA does not integrate into the host genome. Therefore the target
gene does not replicate and eventually gets lost over several days. It yields ≈ 50 µg to
gram per liter level expression in a short time (6-10 days from the point of
transfection) depends on the host cell line and it is suitable for milligrams to grams of
mAb production under short time constraints, for instance in preclinical development10-
12. As an alternative for rapid protein production, stable cell pool refers to a
heterogeneous pool of transfected host cells where the foreign gene was integrated into
the host genome and reproduce along when the host cells proliferate. This method
yields ≈ 300 mg to over gram per liter and the turnaround time is also relatively fast as
early as 8 weeks13, 14. It is ideal for antibody reagents generation and has certain
advantages over the transient expression: it can be frozen down to working stock for
production to avoid repeated transfections; the medium change that is difficult to
operate at large scale in transient expression is eliminated.
Compared to cell pool method that contains cells with different expression levels, the
stable clonal cell line method screens and selects high producers in the pool, leading to
6
cell clones with multiple gram-level expressions. A single clone is then used to establish
a cell line that is then amplified and cryopreserved. The cryopreservation is called cell
banking, usually is a two-tiered system - a master cell bank (MCB) and a working cell
bank (WCB). A MCB usually contains 50-400 ampoules and a WCB is derived from one
ampoule of the MCB15. The establishment of stable production cell lines usually takes
14 weeks up to a whole year and they are used over many manufacturing cycles for
consistent manufacturing of the mAb products.
The mAb manufacturing is a process taking several weeks that can be separated into
upstream process (USP) and downstream process (DSP). In the USP cells are thawed
from the WCB and expanded in shake flasks placed in CO2 supplemented incubators.
The culture is then amplified in seed train in bioreactors in increasing sizes. The
production cultivation takes place in the production bioreactor by one of the following
modes: batch, fed-batch or perfusion. In batch/fed-batch processes, the harvested cell
broth is processed in DSP that usually starts with centrifugation to collect the cell-free
supernatant where the mAb product has been excreted, while in perfusion the cell-free
supernatant is available in the harvest tank. A series of filtration and chromatography
steps are carried out to purify, concentrate and finally put the product in an adequate
formulation buffer.
The current thesis focuses on USP only.
7
1.2.2 Immunoglobulin G (IgG)
IgG is the most abundant immunoglobulin in human serum (70-75%). Figure 1
illustrates IgG and its Fc N-glycosylation. Immunoglobulin glycosylation is one of the
most important post-translational modifications. For this, sugar moieties are covalently
added to specific amino acid residues of the protein. Correct glycosylation is critical to
protein solubility, functionality and stability to generate biologically effective
biotherapeutics. For IgG the N-glycosylation site on the Fc fragment is highly conserved
at CH2 domain Asn-297 of each heavy chain. It is a biantennary structure that is often
core-fucosylated. Figure 1 lists the most common four structures G0F, G1F, G2F, and
Man5 based on the degree of galactosylation. Variation in glycosylation profiles mainly
focus on the degree of fucosylation, N-acetyl-glucosamine (GlcNAc), galactosylation,
and sialylation.
Figure 1 IgG and Fc N-glycosylation
Fab
Fc
VL
C L
V H
CH1
CH2
CH3
N297
N297
N297
N297
G0F
G1F
G2FS1
Man5
GlcNAc Man Galactose Fucose Sialic acid
8
1.2.3 CHO cell cellular metabolism
Mammalian cells are complex systems with a large amount of biochemical reactions
taking place simultaneously. To be able to improve recombinant cell performance in
culture, it is important to understand the cellular metabolism.
1.2.3.1 Energy and amino acid metabolism
Figure 2 illustrates the energy metabolism pathways in CHO cells focusing on
glycolysis, TriCarboxylic Acid (TCA) cycle, and glutaminolysis. Glucose serves as the
main energy source for the cells and the aerobic complete oxidation (via TCA cycle) of
glucose generates 32 molecules of ATP per mole of glucose. However cultured cells
also utilize the anaerobic glycolysis that generates only 2 ATP known as Warburg
effect16, producing lactate in the cytosol before it is excreted. Lactate acidifies the
culture and is considered one of the most critical byproducts.
Previous studies have shown that the cells utilize glucose more efficiently when a low
glucose level is present in the culture medium17, 18. At a low environmental glucose
concentration, the cells tend to use the TCA cycle pathway to generate more energy,
while at a high environmental glucose concentration, the cell metabolism switches to
anaerobic glycolysis and produces mainly lactate17, 19, 20.
The other main byproduct ammonia comes from amino acids catabolism, mainly from
glutamine. Glutamine is an unstable essential amino acid that is the major source of
nitrogen, carbon and energy for the cells. Its oxidation (glutaminolysis) leads to
ammonia and lactate, as well as non-essential amino acids such as Ala and Asn21-24.
Glutamine produces ammonia from the metabolic degradation in the mitochondria but
a cell-free spontaneous degradation in culture medium is also taking place due to
9
instability at physiological pH. Both lactate and ammonia are known to be toxic to
mammalian cell growth above certain levels, but the actual inhibitory concentrations
vary with cell lines and processes25-28.
Figure 2 Simplified metabolic pathways for CHO cells showing mostly amino acids catabolism
To minimize the adverse effect of lactate and ammonia to the culture, controlled
feeding strategies of glucose and glutamine have been adopted29, 30. As an alternative,
the introduction of GS gene in host cell genome allows the cells to grow without
glutamine supplement31-33. This is now widely used as a highly efficient selection
system in different mammalian cell lines34-37. Alternatively dipeptides containing
glutamine, such as glutamax (AlaGln), are used.
10
Amino acids are essential for biomass building up, energy metabolism and also protein
products formation. They are important building blocks of cellular components such as
glutathione. Glutathione (GSH) is a tripeptide generated from glutamate, cysteine, and
glycine, a key survival antioxidant that protects cells from damaging caused by reactive
oxygen species (ROS) such as free radicals and peroxides. It maintains cellular
reduction-oxidation reaction (redox) balance and alleviates cellular oxidative stress38-40.
11
1.2.3.2 Metabolomics profiling of extracellular metabolites
Nowadays various omics analytical techniques/platforms e.g. transcriptomics,
genomics, proteomics, metabolomics and statistical tools have become available for
mammalian cell engineering application41-50. This has largely enhanced the
understanding of mammalian cell metabolism. Metabolomics profile of the culture
provides knowledge about numerous extracellular metabolites and has a great
potential in discovery and biomarker identification for cell engineering purpose51-60.
Compared with other omics based tools, metabolomics reveals the phenotype defining
processes that pinpoint the exact metabolites directly responsible for cell behavior61.
Metabolomics profiling is usually performed on High Performance Liquid
Chromatography Mass spectrometry (HPLC/MS) analytical platform and huge data sets
are generated. Intensive data-mining is then needed to obtain useful information and
interpret the data, which is still very technically challenging in reality62-65. Different
multivariate data analysis tools are therefore been employed to extract information
from the data, both the metabolomics analysis results and their correlation with the
bioprocess parameters in cultures65, 66. Among all the available tools Principal
Component Analysis (PCA), Partial Least Square (PLS) regression and Partial Least
Squares-Discriminant Analysis (PLS-DA) are the most commonly used in bioprocess
field65, 67, 68.
12
1.2.4 Cultivation techniques
There are four cultivation modes commonly used for mammalian cells based
recombinant protein manufacturing: batch, fed-batch, continuous and perfusion as
shown in Figure 3 below.
Figure 3 Four main cultivation techniques in recombinant protein production
Since the first approved recombinant therapeutic protein Activase (Genentech)
produced in CHO cells in 1987, the manufacturing techniques of recombinant proteins
have significantly evolved. Early manufacturing employed 7-day batch cultivation with
medium containing animal components such as serum. In batch process, the cells grow
in a nutrient-rich base medium with a fixed volume and the whole batch is harvested
when the production is terminated. The process usually lasts for a week and the whole
batch is harvested when the viability drops to below 30%69. Its simple setup makes it
relatively easy to perform compared with the other techniques, but there are several
drawbacks such as low productivity, low cell density, nutrients depletion, byproducts
accumulation, etc.
In fed-batch mode, a nutrient-rich feed medium is added to the cultivation with a
defined feeding profile to avoid nutrients depletion and to support higher cell densities
Batch Fed-batch Continuous Perfusion
Feed Feed Cell-free
Harvest
Cells
Feed Harvest
13
and longer period of production. The production processes are 10-21 days with an
average of 14 days. In the fed-batch processes the production bioreactors are reaching
25,000 liters. Drawbacks are however that byproduct accumulation is inevitable in fed-
batch mode and that the culture is changing with time, which can cause a product
quality variation with time70.
In chemostat, the feed medium is added at the same rate as part of the culture broth is
removed. In this mode a constant volume is maintained and steady state can be
maintained for a long time. This mode is sometimes adopted in process development
but not in production, due to intrinsic disadvantages of this technique, such as low
achievable cell densities due to the low growth rate of mammalian cells, and the
complex product recovery from collected cell broth71.
In perfusion process the cells are retained in the bioreactor, cell-free supernatant is
continuously collected and fresh medium is continuously added at the same rate. In
this cultivation mode, the cells are accumulated in the bioreactor, nutrients are
continuously fed and inhibiting waste products are continuously discarded so it is
possible to reach high cell densities with high cell viabilities and long duration of the
culture. Although process specific, protein synthesis is usually proportional to the
number of productive cells. Therefore a high cell density implies a high volumetric
productivity for both growth related and non-growth related products. Generally
speaking perfusion systems are able to achieve 4-10 times higher volumetric
production rates compared to fed-batch process due to potential high cell densities72.
The collected cell-free supernatant can be either stored at 4°C until preparation for
purification, or directly to a continuous DSP, depending on the facility infrastructure.
With the maturation of cell culture technology in the past decades, high titers with
desired quality have been successfully achieved in fed-batch processes. New progresses
14
in gene cloning and clone selection have largely enhanced the cell growth, viability,
cell specific productivity as well as stability. Chemically defined media can nowadays
support better culture performance with higher productivities, longer production time
and higher cell viability. Product titer of 10 g/L or even higher have been reported73.
Despite the high performances of fed-batch technique, there is today a shift of process
development motivated by cost reductions, process intensification and higher control of
the product quality and process consistency74.
1.3 Perfusion process
A perfusion process has a constant renewal of the culture medium, which provides a
stable environment to cells and the product as well as a short retention time to the
product. Due to the high cell densities, high volumetric productivities can be achieved
leading to higher space-time yields. Although the process is relatively more
complicated than fed-batch, perfusion technique has recently received an increasing
interest in the biopharmaceutical industry75 and is widely acknowledged nowadays as
an efficient cultivation technique for production, for seeding train, and/or for high cell
density banking76-78. Due to the shorter residence time of continuous renewal of the
culture medium, perfusion process is the ideal choice for unstable active
pharmaceutical ingredients (APIs), for example coagulation factor VIII Kogenate®
(Bayer), ReFacto® (Pfizer), and coagulation factor VIIa Novoseven® (Novo Nordisk).
Interestingly, although the majority of biopharmaceuticals are still manufactured using
fed-batch process, historically big pharmaceutical companies have successfully applied
perfusion process in their commercial biologics manufacturing7, 79. Some biologics
manufactured with perfusion systems are Protein C Xigris® (Eli Lilly), mAbs such as
15
Campath® (Sanofi), Simulect® (Novartis), ReoPro®, Remicade®, Simponi® and Stelara®
(Janssen Biotech, formerly Centocor).
Perfusion technology has several advantages over fed-batch and batch modes:
o Optimum and stable environment for the cells and protein products
o Low byproducts accumulation due to the continuous medium removal
o High cell density with potentially high viability
o High and consistent product quality
o Long-term production
o Reduced manufacturing facility cost capital expenditure and associated costs
o Much smaller size bioreactors to reach the same yearly yield of product
o Amenability to disposable equipment
o Manufacturing flexibility
o …
Some of these listed advantages correlate with each other, for instance an optimum
and stable environment for the cells leads to high viability and less apoptosis in the
culture, which minimizes the host cell protein (HCP) released into the culture that
simplifies downstream purification process.
Since a couple of years, there has been a paradigm shift towards continuous
manufacturing in biopharmaceutical industry80-82. Integration of perfusion process with
continuous downstream process has been explored and is under fast development83-85.
It has been described that compared with existing fed-batch production process, a fully
integrated continuous process of upstream and downstream can cut down the
recombinant protein manufacturing costs of goods (COGS) by an average of 55%, in
particular due to a smaller capital expenditure86.
16
1.3.1 Perfusion systems
The cell retention module is a unique feature in perfusion systems. Perfusion systems
can be generalized into two types (Fig.4A&B) based on the cell separation techniques:
i) cell separation ensured by a device and ii) cell immobilization/entrapment in a
system continuously perfused with circulation of medium.
Case i) is shown in Fig.4A: the cells are cultured in suspension in a bioreactor, and a
separate cell retention device is used to retain the cells in the bioreactor while the cell-
free supernatant is processed to the harvest tank. Such cell separation devices can be
connected with any kind of bioreactor to establish a perfusion system. The most
commonly used cell separation devices are filtration-based methods, acoustic settler,
gravity settler, centrifuge, etc. as in Fig.4A187.
Figure 4 Perfusion systems
A): Cell suspension with cell retention system outside (A1) and inside (A2) the bioreactor
B): Cell immobilized or entrapped in a matrix outside (B1) and inside (B2) the bioreactor
Feed Cell-free Harvest
Feed
Cell retention system
(A1) (B1)
Feed
Feed
(A2) (B2)
Cell suspension
Perfused tank
Cell loaded matrix
Cell loaded matrix
Cell suspension
Cell-free Harvest
Cell-free Harvest
Cell-free Harvest
17
The cell separation module can also be integrated into the bioreactor as shown in
Fig.4A2. This system has the advantages that there is no need to pump the cell broth
out, so that the potential pump shear stress is removed, and that the cells are less
subject to varying environment; for instance there is no temperature drop due to
circulation in the tubing and to/from the cell retention system. Some filtration-based
devices have been developed and are widely used for this case, for example internal
spin filters are used in the manufacturing of ReoPro® and Remicade® in 500 L
bioreactors in Janssen Biotech79. A summary of the cell retention devices is given in
Table 2.
Fig.4B demonstrates the other type of perfusion system (ii), where the cells are
anchored or entrapped in a support such as a membrane or a matrix/scaffold. The cell-
loaded matrix is often outside (Fig.4B1) of the perfused tank, but configurations inside
the tank have been developed (Fig.4B2). In this setting the homogenization of the
culture is performed by either magnetic stirrer or propellers in the perfused tank,
where the control of the culture parameters such as temperature, dissolved oxygen
(DO) and pH takes place. The concept is that as long as the medium circulation
between the cell-loaded matrix and the perfused tank is rapid and adequate, the
culture environment in the tank is similar to the environment of the matrix where the
cells are located.
Typical examples of such bioreactors are packed bed bioreactor and fibrous bed
bioreactors with different scaffolds for instance depth filter88, ceramics matrix89 and
fibrous matrix90. It can also be cells attached or captured on immobilized
microcarriers91, 92 or hollow fibers93. Gradients of nutrients, metabolites, gas exchange,
and dead cells accumulation are common drawbacks in such setting94, 95. Such
heterogeneous cell environment could potentially trigger cell population shift,
productivity and product quality variations, even necrosis in extreme cases. This
18
heterogeneity also implies scaling up limitation96, therefore this kind of perfusion
system has not yet been adopted in very large-scale production process, except for
Rebif®, an interferon β-1a by Merck-Serono, was reported to be manufactured using
multiple 75 L fixed bed bioreactors79. Table 2 Summary of cell retention devices
Type Principle Cell retention device Reference
Cells immobilized or
entrapped in a
matrix/scaffold
Packed bed
FibraCel™ nonwoven polyester
base carriers 97-99
iCELLis® bioreactor (polyester
microfibers)
Quantum hollow-fiber-based
(Beckman-Coulter)
Ceramic matrix 89
Porous glass sphere 92
3D annular porous scaffolds 100
Fibrous bed Polyester fabric sheet 90
Depth filter 88
Suspension cells with
individual separation
device
Centrifugation Centritech® 101-103
Sedimentation Inclined settler, gravity settler 104-109
High frequency resonant
ultrasonic waves Acoustic settler 110-118
Acceleration Hydrocyclone 119-121
Dielectrophoresis Dielectrophoretic separator 122
Filtration
Dynamic rotating disc filter 123
Floating membrane filter 124-128
Coiled porous fibers 129
Hollow fiber 130
TFF 77, 131, 132
ATF 77, 131, 133
Spin filter 134, 135
Vortex flow filtration 136, 137
Ceramic filter 138
19
Among all the available methods, filtration based systems have a very high retention
(100%) of the cells. A drawback of filtration based systems is the clogging and fouling
of filters, which may lead to retention of the product of interest or termination of the
cultivation, alternatively need of replacement by a new cartridge. The tangential flow
filtration (TFF) is also known as crossflow filtration, where the feed in flow travels
along the filter surface instead of passing through the filter (Fig. 5).
Figure 5 TFF and ATF process for IgG production
In this flow pattern the filter cake that is clogging the filter is largely removed, leading
to a substantially increased operation time of the filter. It uses hollow fiber (HF) that
packs numerous long porous filaments in parallel inside a cartridge. Crossflow
filtration alleviates fouling, but the increased flow rate in the filter leads to increased
shear rate. The alternating tangential flow filtration (ATF) shares the same principal as
TFF, except that the cell broth is drawn through the filter and pushed back to the
Permeate
Diaphragm
Controller
Feed/Retentate
Permeat
e
Feed
Retentate
Cell suspension Membrane
Permeate
TFF ATF
Bioreactor Bioreactor
Section
20
reactor by a diaphragm pump, which functions cyclically using vacuum and
compressed air. The rapid flow back and forth helps preventing fouling, and the
pressure variation over the membrane occurring during pump cycles leads to back flush
of the filtration system.
Hollow Fiber cartridges used in cell culture perfusion processes are generally divided
into two main types based on the membrane pore size: microfiltration 0.1 to 0.65 µm
and ultrafiltration 30 to 750 kDa of molecular weight cut off (MWCO). The selection of
the HF membrane depends on the application. In the case of a production process for
IgG with approximate molecular weight of 150 kDa, a microfilter allows the retention
of all the cells in the bioreactor and the product is collected in the permeate side of the
cartridge as shown in Fig.5.
An ultrafiltration filter with much smaller pore size retains both the cells and the
protein product inside the bioreactor. The product accumulation can reach very high
titer due to the fast increasing cell density and is suitable for stable protein products
with no or little degradation issues.
1.3.2 High Cell Density Culture (HCDC)
By implementing a cell retention device, a high cell density culture (HCDC) is
achievable and definitely one of the most advantageous features of a perfusion process
if accompanied by a high volumetric productivity. Compared to cell densities 5-25
MVC/mL reached in a typical fed-batch mode139, cell densities of 20 to 100 MVC/mL in
perfusion cultivations (homogenous suspensions) have been previously reported76, 123,
126, 127, 132, 135. HCDC is very challenging from both the culture system performances and
process techniques perspectives. Limiting oxygen and mass transfer are commonly
21
observed140 and excessive accumulation of CO2 in HCDC large bioreactors is also a
problem for the cell growth and product quality.
In the case of a non-growth related production, a perfusion process can be divided into
two phases: a growth phase where the cell density reaches a desired high number,
followed by a production phase where the high cell density is maintained by cell
bleed141-145 or hypothermia146-150. Cell bleed refers to partial removal of cell broth and
contains as well dead cells and cell debris. Hypothermia induces cell cycle arrest at the
G0/G1 phase151 and slows down the cell growth and cellular metabolism. In many
cases, cell arrest is accompanied by an enhanced productivity146-150.
As part of the perfusion system development, the feasible HCDC for production phase
needs to be determined. Earlier studies have recommended an optimum viable cell
density (Cv) of 40-80 MVC/mL to reach a high volumetric production152 but Wright et
al have reported that only very few processes were able to achieve such high Cv145.
It is unclear which cell density can be (or should be) called 'high cell density', and this
value is certainly evolving due to the progresses of perfusion processes enabling higher
cell densities. In this context, the present thesis reports extremely high cell densities
well above 100 MVC/ml. Table 3 summarizes published data of HCDCs restricted to ≥
40 MVC/mL and using commonly used cell lines. These HCDCs have been mainly
established during process development and not in manufacturing.
It is worth pointing out that some of the high cell densities reported were not from
direct cell sample analysis. For example Oh et al. reported a density of 187 MVC/mL
hybridoma cells achieved in a hollow fiber bioreactor, calculated from the production
data by assuming a constant cell specific productivity qp. This is however not always
correct due to cellular metabolism variation. The same group compared two
22
approaches for CV calculation, one based on a constant cell specific glucose
consumption rate qglc and another based on a constant qp and obtained 60 MVC/mL
using the first method and 30 MVC/mL using the second method on exactly the same
culture153.
Table 3. HCDC of mammalian/human cells above 40 MVC/mL reported in the literature
Cell line Product CV
(MVC/mL)
D
(RV/day)
Cell retention device Reference
S2 mAb 104 9.9 Floating filter 126
Sf21 55 6 HF
154
CHO mAb 68.3 1 ATF 155
CHO mAb 132 6 ATF 131 present thesis
CHO mAb 214 10 TFF 131 present thesis
CHO mAb 2001 10 Fibrous bed 156 present thesis
CHO mAb 50-60 ATF 85
CHO TNK-tPA 60 6 Acoustic filtration
system
157
Hybridoma mAb 1872 39 Dual hollow fiber
bioreactor
153, 158
Hybridoma mAb 1003 Packed bed 99
Hybridoma mAb 101 Fibrous bed 159
SP20 IgM 45 11 Vortex flow filter 136
HEK293 100 ~6.4 HF 160
1cell density given by online biomass probe 2cell density calculated from culture productivity 3cell density per cm3 of packed bed volume
23
1.3.3 Single-Use-Bioreactor (SUB) systems
Despite several obvious advantages, perfusion operation mode has still limited
application due to its inherent technical complexity. Disposable systems can help to
alleviate the technical operational complexity and decrease the risk of contamination.
Disposable technologies are under fast development in the biopharmaceutical industry,
covering the whole manufacturing process from vial thaw to the final product. The
disposable systems offer benefits such as161:
o Elimination of contamination risk due to pre-sterilization
o Minimization of turnover time between cultivations due to absence (or high
reduction) of cleaning or sterilization
o Elimination of clean validation
o Reduction of staff training and simplified set up
o The process can be replicated or transferred between different facilities due to small
footprint and close systems
o Low risks of cross contamination. Various products can be produced at the same
facility, a factor critical for contract manufacturing organizations (CMOs).
o …
Three kinds of SUB systems based on different agitation principles can be
distinguished: 1) disposable culture bags on a rocking table; 2) conventional stirred
tank bioreactors where the stainless steel or glass vessel is replaced by polymer-based
containers, including plastic stirred tanks and stirred bags that are placed in a
supporting tank; 3) Hollow fiber bioreactors. Table 4 summarizes the SUB systems
used in mammalian cell based upstream processes for scale ≥ 1 L. Here traditional
shake flasks, roller bottles and (multi-layer) culture dishes are not included.
24
Table 4 SUB systems used in mammalian cell based upstream processes for scale ≥ 1 L
Type Name Vendor Scale Stirring Ref.
Plastic stirred tank or stirred
bag
UniVessel® SU Sartorius-Stedim 2.6 L
Impeller
162, 163
Mobius® CellReady Applikon/Merck
Millipore 3 L 164
Brunswick™ CelliGen® BLU
Eppendorf 5L, 14L 165
CellVessel™ Cercell 500 mL - 30
L
Bio Bench SUB Solida Biotech 250 mL - 75
L
BIOne Distek HyClone S.U.B. Thermo Fisher 50 - 2000 L 166
Xcellerex XDR-50 - 2000 GE Healthcare 50, 200, 500, 1000, 2000L
167
Biostat® STR Sartorius-Stedim 12-1000 L 168
BaySHAKE® Bayer 50 L - 1000 L Rotary
oscillation 169
SB10/50/200-X (OrbShake)
Kühner 10L, 50L,
200L Orbital shaking
Nucleo™ Bioreactor/PadReactor®
ATMI/PALL 25 - 1200 L Paddle 163
Disposable bags on a rocking
table
WAVE Bioreactor™ GE Healthcare mL - 1000 L
Rocking
124-127,
170
Biostat® RM Sartorius-Stedim 100 mL - 300
L
162, 171-
173 AppliFlex Applikon 10 L - 50 L
Cell-tainer® Celltainer biotech 20 L, 200 L 2-dimensional
movement 174
Hollow fiber bioreactor
FiberCell Systems FiberCell Systems 175 CellMax Spectrum labs
Others
PBS PBS 3, 15, 80,
500 L Vertical-Wheel™
176
iCellis Applikon/PALL 1 - 70 L Magnetic
drive impeller
CellMaker Plus Cellexus
Biosystems 1 - 50 L
Airlift+magnetic stirring
177
25
Since the introduction of the disposable cell culture bag178, this idea has been rapidly
adapted to existing technologies and platforms. One of the most successful SUB, the
wave bioreactor, has a wave like rocking motion instead of traditional mechanical
mixing. It facilitates the gas and mass transfer, and generates less damaging bubbles
compared to a sparged bioreactor, leading to a significantly reduced shear stress
environment for the cells. It is widely used mainly for the seeding train but also as
production bioreactor in various biologics manufacturing from different cell lines124, 179-
182as well as for cell banking.
To provide a closer mimic of conventional stirrer tank bioreactors and to address the
oxygen transfer challenge at high cell densities in large-scale cultivation, large
disposable stirred tank bioreactors are made of a culture bag placed in a support tank,
and are called “stirred bag”. They are available in different scales up to 2000 L, and
reported to have comparable performance to their stirred tank counterparts183-186 with
all the advantages offered by disposable systems.
The benefits from SUBs make them the ideal support for perfusion process.
Development of both perfusion process and disposable techniques are currently
pushing the industry towards a next-generation continuous manufacturing process.
26
1.4 Perfusion process development and optimization
Upstream process (USP) development includes different aspects, is commonly
platform-based and preferably performed in-house. The ultimate goal is to reduce the
cost per product9, 187, 188 and obtain a robust process ensuring a safe product with the
targeted quality. The factors involved in perfusion process optimization are described
in Table 5. Table 5 Perfusion process optimization
Target Optimization goal Candidates
Cell line Gene cloning Clone selection
o Cell growth o Productivity o Cell line stability o Product quality o …
Medium formulation
Media/additives
o Cell growth and survival o Productivity o Byproducts o Product quality o …
o Carbon sources o Amino acids o Vitamins o Inorganic salts o Trace metals o Lipids
Equipment Cell retention set up
o Cell retention efficiency o Product retention o Viability o …
o Selection of device o Flow rate o Shear stress o Specific operational
parameters o …
Process control and scale up
o Temperature o pH o Perfusion rate o Working cell density o Dissolved oxygen o pCO2 o …
Analytical methods
o Cell density/Biovolume o Metabolites o Product titer o Product quality o HCP o DNA
27
1.4.1 Perfusion rate D
The perfusion rate is one of the most important parameters in a perfusion process. An
optimal perfusion rate is vital in delivering nutrients and removing byproducts in
accordance to the cell needs to support high productivity and product quality. It is also
essential for the product cost, since large media expenses in perfusion mode is always a
big concern. In some applications where the protein quantity is prioritized within a
short time restraint, such as research and preclinical studies, excessive medium feeding
can be preferred to minimize the risk. However in most cases, the medium
consumption needs to be taken into consideration, and mainly three strategies are
considered for the selection of the perfusion rate: nutrients/metabolites based, cell
density based and in-house experience based, depending on the experience of the
company or the scientist developing the process (Table 6).
Table 6 Perfusion rate selection strategies
Adjustment principle Based on References
Cell density-based Cell specific perfusion rate (CSPR) 131, 189, 190
Metabolite-based Residual nutrients/metabolites 191-194
OUR 160, 195
Experience-based Previous know-how 98
Among all the metabolite-based strategies, although oxygen uptake rate (OUR) is the
most accurate online analysis for cellular metabolism196, the residual glucose
concentration in the culture is often used approach due to its simple implementation.
28
1.4.2 Cell specific perfusion rate (CSPR)
A strategy for the perfusion rate selection based on CSPR (unit nL/cell/day) assumes
that the medium is exchanged at a rate proportional to the cell density. It provides a
consistent microenvironment to the cells in the culture, regardless of the cell density. It
is a straightforward strategy that links the perfusion rate to the cell density, a culture
parameter that is easily accessible and highly reliable. This strategy does not take
potential metabolic variations in consideration.
The CSPR is the ratio of the perfusion rate D (unit Reactor volume/day, RV/day) to the
viable cell density CV (unit MVC/mL), as shown in eq. 1:
CSPR = ! !!
eq. 1
For a defined CSPR, D is given as a function of CV. CSPR based feeding strategy can be
applied automatically by the control station with online biomass measurement. It
allows minor and steady regulation of D in response to CV variations and can be
preferred instead of step-wise change of D.
A key factor is the minimum CSPR (CSPR_min) that delivers nutrients meeting cell
needs and supports high productivity. Not to mention that the lower CSPR is, the lower
medium amount is spent, which becomes substantial at high cell densities.
Konstantinov et al. suggested that a CSPR below 50 pL/cell/day offers batch-like titer
and that CSPR of 40-50 pL/cell/day has been successfully used to support HCDC 85, 131.
However the CSPR value is determined by the “richness” of the culture media, also
known as “medium depth”. Fortified media are able to sustain HCDC at much lower
CSPR, for example Xu S et al. maintained steady states at 42.5 and 68.3 MVC/mL at
CSPR 23 and 15 pL/cell/day respectively in two processes155.
29
To determine the CSPR_min for a process it is usual to employ a steady state or
pseudo-steady state screening strategy. A perfusion culture at a reference CSPR is first
stabilized, then the CSPR is stepwise varied and at each step a new steady state (or
pseudo-steady state) is established. The culture performances are monitored to decide
whether the CSPR needs to be further varied 152, 197. During this procedure, the
perfusion medium can be optimized to improve the performances.
Steady states investigation is rather time-consuming as it usually takes over a week to
establish steady states152, 198. This not only raises development cost, but also brings in
concerns for potential cell population shift due to the long time span. Transient
responses to process parameter variation have been reported previously to show a
qualitative prediction similar to steady states198. Therefore in our study a strategy based
on pseudo-steady states was adopted for process parameter screening and
optimization. Each process parameter variation was usually maintained for minimum 5
days and the first 2 days of each variation was considered as "transient" and thus was
not taken into data analyses. For simplicity in the text, pseudo-steady state is simply
named steady state.
30
2 Present investigations
2.1 Aim of investigation
Monoclonal antibodies are used intensively in biomedical science and as effective
therapeutic treatments against autoimmune disorders, cancers. Since the first use of
CHO cells in recombinant protein production, the manufacturing techniques have been
steadily developed. There has been a changing processing paradigm towards
continuous manufacturing and leading pharmaceutical companies such as Janssen,
Genzyme, Novartis have already adopted perfusion process in their mAb/recombinant
protein products pipeline. As one of the key advantages that perfusion process offers,
high achievable cell density is attractive but very demanding in equipment advances
and process knowledge.
The aim of this thesis is to contribute to the understanding and evolution of
mammalian cell based perfusion process in general. We target at high cell density
perfusion process and intend to provide know-how for its establishment as well as
optimization.
By working closely with our industrial collaborators, we develop high cell density
perfusion systems for antibody producing CHO cells that includes both equipment
development from industrial prototypes and process knowledge advancement. The
present investigation thus starts with HCDC perfusion system development, followed
by HCDC perfusion process development.
31
2.2 HCDC perfusion system development
The thesis includes three projects and four perfusion system configurations (Table 7):
WAVE bioreactor™ with a Cellbag prototype connected with ATF or TFF, named 'ATF'
and 'TFF' respectively; CellTank™ prototype named 'CT' ; stirred tank connected with
ATF named 'ST' . Three cell lines, exhibiting different productivities, were used in the
thesis. Cell line #1 (used in ATF and TFF runs) was a research cell line CHO DHFR-
producing IgG1, named K4. Cell line #2 (used in CT and ST#1) was CHO DP-12 clone
#1934 (ATCC), a research cell line with low productivity. Cell line #3 (used in ST) was
an industrial high producer CHOM provided by Selexis (Switzerland). All the cell lines
were adapted to suspension growth in serum-free and medium free of animal derived
component. Table 7 Summary of the perfusion runs
Perfusion system Exp. Cell line Collaborations Paper
WAVE™ + ATF
'ATF'
ATF#4
K4 GE Healthcare
(Sweden, USA) I, IV
ATF#5
ATF#8
ATF#9
ATF#15A
ATF#15B
WAVE™ + TFF
'TFF'
TFF#6
TFF#10
Fiber matrix based – CellTank™
'CT'
CT#1
DP-12 Belach (Sweden)
PerfuseCell (Denmark) II CT#2
CT#3
Glass stirred tank + ATF
'ST'
ST#1 DP-12 Belach (Sweden)
Iprabio (Belgium)
Selexis (Switzerland)
III ST#2 CHOM
ST#3
32
2.2.1 Perfusion system general set-up
The perfusion was performed by either the control systems WAVEPOD™ for ATF&TFF,
and CytoSys™ for some ST runs, or peristaltic pumps with manual tuning. Except one
run (ST#2) where the CytoSys™ was used to perform automatic bleeding at given cell
density according to online biomass sensor, the cell bleeding was done with peristaltic
pump based on daily cell growth information. The hollow filter cartridges were
ReadyToProcess™ filters (GE Healthcare) with 0.2 micron pore size and surface area
850 cm2 for ATF&TFF runs and 420 cm2 for ST runs.
Figure 6 Perfusion system general set-up
x
Harvest
Cell bleeds
bubble flask
CO2 O2 Air/N2
Alkali
Feed
Intermediate flask
Glucose
Glutamine
50 mL syringe 10 mL syringe
Sampling
Bioreactor
Sampling
Antifoam
Controller
33
A general perfusion system scheme is given in Figure 6, showing the bioreactor
connected with ATF system as an example. The bioreactor was a stirred tank equipped
with propellers or magnetic stirrer, or a disposable culture bag on a rocking table.
Alternatively a CellTank™ prototype system was used where a matrix was immersed in
a reservoir (Paper II). Daily feed was prepared in an intermediate flask with feed
medium, glutamine and glucose fulfilling cells’ daily needs, calculated from the cell
consumption. Antifoam was supplemented if needed. The intermediate flask helped to
deliver a homogenized and accurate feed according to the needs of the cells, as well as
minimize glutamine degradation in the feed.
In the case of suspension culture, daily samples were taken via the sampling line,
which was also used as cell bleeding line. Cell bleed was performed manually to reach
the target CV or continuously via a peristaltic pump that was calibrated to keep a given
CV. An alternative was that online biomass probe was installed in the system, with
control based on the biomass signal to carry out the cell bleed according to a CV set
point. This bleeding set up was used in experiment ST#2 in in stirred tank with
CytoSys® control system from Iprabio, Belgium. For simplicity, the inoculation line is
not represented in Figure 6. The inoculation was performed using the feed or bleeding
line depending on the connections available and experimental settings. A “bubble
flask” is a sterile flask, half filled with water, where the exhaust air enters and the
bubbles are monitored. This is a practical setting to watch the aeration of the whole
system, as well as to maintain a slight overpressure of the bioreactor system to lower
the risk of contamination. In the configurations involving ATF and TFF systems, the HF
cartridges (GE Healthcare, Uppsala, Sweden) were mounted vertically on an ATF-2
station (Refine Technology) or installed with a Watson-Marlow 620S pump as the
recirculation pump. Cell free harvest was pumped out continuously from the permeate
side of the HF and collected in a harvest tank. The perfusion was performed by the
34
control station with assigned perfusion rates or manually through two peristaltic
pumps, one for feed in and one for the harvest.
In all the experiments of ATF, TFF and ST, cell samples were taken twice a day from the
bioreactor and harvest. For CT runs, samples from the matrix, reservoir and harvest
were daily taken. The cell density, viability, average cell diameter (ACD), pH, pCO2,
pO2, osmolality, concentrations of glucose, lactate, glutamate, glutamine, ammonia
were analyzed offline with Bioprofile FLEX (Nova Biomedical). In CT runs, cell samples
were not available, so the viable cell number was monitored by on online biomass
sensor and the culture viability was calculated from the activity of lactate
dehydrogenase (LDH, Promega) in the supernatant. The perfusion rate was calculated
from daily weight of feed medium, harvest and cell bleeds.
The process parameters dissolved oxygen (DO), pH, biomass etc. were on-line
monitored and controlled as listed in Table 8.
Table 8 Monitoring and feedback control of the bioreactor
Control Set-point Up regulation Down regulation ATF TFF CT ST
Agitation -
Propeller �
Rocking on a rocking table � �
Magnetic stirring �
Tempera
ture 37°C
Heating jacket around the bioreactor � �
Heating element on the rocking table � �
DO 40%
O2, Air to headspace � � � �
Open tube/ porous sparger � �
N2 to headspace �
pH 7.0
Addition of NaCO3 � � � �
CO2 to
headspace � � � �
The product IgG concentration was quantified by high-performance liquid
chromatography (HPLC) protein A method (Waters). The IgG concentrations in the
35
bioreactor and the harvest line – or the matrix, the reservoir and the harvest in CT
experiments - were analyzed to study the product retention by the filter. Reducing
sodium dodecyl sulfate-polyacylamide gel electrophoresis (SDS-PAGE) was performed
on products and glycan analysis was performed using capillary electrophoresis (CE)
method for some of the experiments to investigate the product quality.
The calculation of the cell specific perfusion rate (CSPR), cell specific
consumption/production rates of glucose (qglc), lactate (qlac), glutamine (qgln), ammonia
(qamm), cell growth rate (µ), cell specific productivity (qp) are given in table 9.
Table 9 Main process parameters
Parameter Equation
µ µ = 1 C!(dC!dt
+V!"##$V ∆T
C!)
qglc q!"# = 1 C!(D Glc!"#$%! − Glc −
dGlcdt
+V!"##$ Glc!"#$%
V ∆T)
qgln q!"# = 1 C!(D Gln!"#$%! − Gln −
dGlndt
+V!"##$ Gln!"#$%
V ∆T−r!"#$Gln)
qlac q!"# = 1 C!(D Lac +
dLacdt
)
qamm q!"" = 1 C!(D Amm − Amm!"#$%! +
dAmmdt
−r!"#$Gln)
qp q! = 1 C!(dP!"dt
+ D −V!"##$V
P!" +V!"##$
VP!")
Accumulated
production in
harvest
HT!"! = P!"V!"dt!
!
For abbreviations, see List of Abbreviations.
For the metabolomics study, UHPLC was performed on low molecular weight (<10
kDa) fractions of the culture media and each sample was analyzed by both positive and
36
negative ionization mass spectrometry to increase the range of ions detected. The
components in the elutes were then analyzed by an ESI-QTOF MS in both positive and
negative ion modes.
After synchronizing the data from different experiments using peak detection and
alignment, peaks found in all the samples were chosen and multivariate statistical
analyses were performed using Unscrambler-X (CAMO, Norway). PCA was used in
dimensionality reduction and visualization of the complex dataset. PLS regression
models were used to identify the correlation between LC-MS data and bioprocess
variables in cultures.
37
2.2.2 WAVE Bioreactor™ Cellbag prototype perfusion system
with ATF and TFF (Paper I)
Figure 7 shows part of the perfusion systems developed for the Cellbag prototype.
Perfusion system with ATF (Figure 7A) and TFF (Figure 7B) were established through
a series of experiments and process parameters were gradually established and
optimized. Before the perfusion cultivations listed in Table 7, water model ATF#1 was
carried out using the first version of Cellbag prototype to identify basic parameters
such as working volume, rocking speed and rocking angle etc. Supplement of antifoam
at 0/10/50 ppm were screened to determine working volume that eliminated bubble
occurrence in the dip tube, which was detrimental to both cells and filters. ATF#2 and
ATF#3 were two batch cultures to build fundamental understanding of the cells,
medium, and the system. Several fed-batch runs were also carried out in the same
experimental setting to gain knowledge of the system from comparative studies
(Paper IV).
Figure 7 Perfusion system development (part) for the Cellbag prototype
(B) (A)
38
Through a series of HCDC perfusion runs, various technical obstacles were identified
and addressed accordingly. Here take ATF system for example, more details can be
found in Paper I. In ATF system the ATF alternated flow stopped after CV reached 132
MVC/mL in ATF#15A. This CV was then confirmed by ATF#15B, where the ATF
function was interrupted at 123 MVC/mL again. By pressurizing the Cellbag with 0.02-
0.03 bar the ATF function was successfully restored and the culture was continued.
This clearly suggested the vacuum limit in present settings to pull the highly viscous
cell broth at high CV. Potential answers to that were: 1) lower the vacuum effect to <-
0.53 bar; 2) pressurize the bioreactor to facilitate the vacuum; 3) increase the total
lumen section area. These solutions were verified experimentally and thus contributed
to the perfusion system understanding. Furthermore several other bottlenecks were
overcome such as oxygen transfer limitation at extremely high cell densities and
rocking conditions suitable for ATF operations.
2.2.3 CellTank™ prototype perfusion system (paper II)
In the CT system, suspension cells were entrapped inside a non-woven polyester matrix
that was sealed in a cassette. The cassette was immersed in a 2 L tank (reservoir)
where the actual perfusion took place. Figure 8A shows the perfusion system
configuration and Figure 8B is a closer look of the system design and how the cells are
located in the fibrous matrix observed under microscope. Limited access to the cell
sample necessitated the implementation of an online biomass probe based on dielectric
spectroscopy. Unlike other cell immobilized or entrapped fibrous bed bioreactor
systems that always suffer from nutrients/metabolites gradient and gas transfer
heterogeneity, CellTank™ advantageously provides a very homogeneous environment.
Furthermore no product retention was observed.
39
Figure 8 Perfusion system for the CellTank™ prototype
The CellTank™ prototype design, stirrer table prototype, as well as the control system
were optimized during the perfusion system development.
(A)
(B)
40
2.2.4 Glass stirred tank bioreactor with ATF (Paper III)
HCDC perfusion system was developed in a bench top
glass stirred tank bioreactor using HF cartridge and
ATF as cell separation module. Figure 9 is a close look
at the connection between the bioreactor and HF
mounted on an ATF station. CSPR based perfusion
rate selection strategy was systematically studied at
steady states. A CSPR optimization strategy was
experimentally proposed and two CSPR adjustment
approaches, D variation and CV variation, were
performed and compared. The critical role of the
glucose concentration per cell was emphasized and
discussed (Section 2.3).
2.3 HCDC perfusion process development (Paper I - IV)
As described in Section 2.2, four HCDC perfusion systems were developed. The
maximal CV (CVMAX) supported by each system configuration was achieved in one of the
runs. The goal was to explore the system limitations in our perfusion process settings
and to overcome various challenges during this operation by optimizing industrial
prototype design as well as process parameters. Other than pushing the system limit by
reaching the CVMAX, steady states at 20-30 MVC/mL and at 100-130 MVC/mL were also
established and studied for process development. Steady state at 20-30 MVC/mL is
known as a highly suitable system to screen the process parameters152, 197 and we were
Figure 9 ST Perfusion system
development (part)
41
able to perform the same study at 100-130 MVC/mL to explore further the potential of
perfusion processes. Table 10 summarizes the experiments in various perfusion systems
for HCDC perfusion process development and more details are presented separately.
Table 10 Summary of the perfusion runs
Perfusion
system Exp. CVMAX
Steady state study at
20-30 MVC/mL
Steady state study at
100-130 MVC/mL
ATF
ATF#4 �
ATF#8 �
ATF#9 �
ATF#15A �
TFF TFF#6 �
TFF#10 � � �
CT
CT#1 �
CT#2 �
CT#3 �
ST
ST#1 � �
ST#2 � �
ST#3 � � �
2.3.1 Maximal viable cell density (Paper I-IV)
A summary of the CVMAX achieved in all the perfusion systems and some process details
are given in Table 11. The perfusion rate selection was based on a CSPR of 50-55
42
pL/cell/day, except in ST experiments where the CSPR effect was systematically
studied. Table 11 CVMAX achieved in the different perfusion systems
Perfusion system ATF TFF CT ST
Cell line K4 K4 DP-12 DP-12 CHOM
Experiment no. ATF#15A TFF#10 CT#1 ST#1 ST#3
Working volume (L) 4 4 150 mL 1 1
Culture duration (day) 11 44 27 37 30
CVMAX (MVC/mL) 132 214 ≥200 (200
pF/cm) 188 165
CtotMAX (106 cells/mL) 137 224 - 198 174
Cell viability (%) 96.3 - 99.2 85.1 - 98.3 87.4-97.9 91-99.3 92.9-98.2
ACD (µm) (a) 20-30
MVC/mL; (b) 100 MVC/mL;
(c) above 150 MVC/mL
16.4a; 17.3b 17.1a;
17.1b; 16.7c -
16.7a;
16.5b;
17.7c
18.5a;
17.3b;
17.9c
Average CSPR
(pL/cell/days) 50 55 50 varied varied
Maximal D (RV/day) 6 10 10 3.7 6.5
Average µ during perfusion
(day-1) (a) 20-30 MVC/mL;
(b) 100 MVC/mL; (c) above
150 MVC/mL
0.57a; 0.26b 0.35a;
0.28b; 0.15c
0.17a; 0.26b;
0.14c
0.54a;
0.26b;
0.11c
0.38a;
0.34b;
0.14c
Average qp during perfusion
(pg/c/d) (a) 20-30
MVC/mL; (b) 100 MVC/mL;
(c) above 150 MVC/mL
6.0a; 7.8b 9.6a; 16.5b;
13.6c
2.5a; 1.6b;
1.3c
2.8a;
1.5b; 0.9c
39.2a;
32.7b;
26.7c
pCO2 (kPa) 2.4 - 21.9 3.1 – 28.8 2.7-8.6 4.9-10.6 2.5-13.8
CtotMAX = maximal total cell density; ACD = average cell diameter; qp = cell specific IgG productivity
In ATF#15A, CV reached 132 MVC/mL but no higher value due to the vacuum power
limitation of the diaphragm pump to pull highly viscous cell broth from the bioreactor
into the HF. This maximum CV was later confirmed with a new HF in run ATF#15B
43
(Paper I). In a TFF system we were able to reach a higher CV of 214 MVC/mL, which
is the currently highest CHO cell density ever reported according to our knowledge.
This cell density is definitely approaching the theoretical limit of biovolume using cells
with am average cell diameter of 17 µm (both K4 and DP-12). However this high cell
density above 200 MVC/mL lasted only for 2 days before the system collapsed due to
high pressure in the recirculation loop caused by high viscosity and a very high pCO2 of
31 kPa that was deleterious for the cells (Paper I).
In the CT the cells were entrapped in the matrix and, therefore, it was not possible to
take cell samples for offline cell density analysis. An independent experiment was
carried out in Paper II to compare the online biomass probe analysis with offline
image-based cell density analysis of Bioprofile instrument. It was confirmed that the
biomass sensor reading of 1 pF/cm equaled the offline measurement of 1 MVC/mL up
to CV 160 MVC/mL, above which the online sensor gave lower cell densities and
saturated at around 203 pF/cm. Therefore we believe that CV above 200 MVC/mL was
reached in CT#1 when the online biomass sensor indicated 200 pF/cm (Paper II).
In ST experiments, high CV 188 MVC/mL was achieved in ST#1 at ultra low CSPR 26
pL/cell/day however HF fouling and clogging led to termination of the run. 165
MVC/mL was achieved in ST#3 using another cell line with slightly larger size, at
CSPR 34 pL/cell/day. Compared with the ATF runs performed in the disposable
Cellbag, the working volume was smaller relatively to the filter area, resulting in higher
CV (Paper III).
All the perfusion cultures had high viabilities despite the extremely high cell densities.
None of the runs were terminated due to cell death, but intrinsic limitation due to the
cell retention were encountered such as high viscosity and excessive bubble formation.
44
2.3.2 HCDC established and stabilized in the perfusion systems (Paper I-III)
Table 12 gives a summary of HCDC at steady states in all 4 perfusion systems.
Table 12 HCDC at steady states
Sys. Exp.no.
Days at
20-30
MVC/mL
Days at
100-130
MVC/mL
Approach to
maintain cell
density
CSPR
(pL/cell/
day)
Cell
viability
(%)
Purpose of study
ATF
ATF#4 4
Bleeding
80 92.6-98.9
Perfusion system
development,
Process consistency
and reproducibility
ATF#8 9 60 89-98
ATF#9 16 60 88.3-98
TFF TFF#6 16 60 88.5-98
TFF#10 14 19 60 85.1-98.3
CT CT#2 14
Hypothermia 60 90.6-97.1
CT#3 15 60 71-97.9
ST
ST#1 44
Bleeding
40/60/80 92.7-99.3 Perfusion system
development, CSPR
optimization
strategy
ST#2 12+8* 5 45/65/85 91-98.1
ST#3 11 9 45/65 92.4-98.2
* Cv was maintained at 15 MVC/day (12 days) and 35 MVC/days (8 days) respectively.
Cell arrest by hypothermia is known to improve the DP-12 cell productivity. In CT#2
we studied the possibility of arresting the cell growth and maintain the high CV at 100-
130 MVC/mL to explore the potential of operation at such high CV with elevated IgG
production. The culture temperature was gradually lowered from 37°C to 32°C, 31°C,
30°C, finally to 29°C which led to a complete cell growth arrest (Figure 10A). By doing
so we were able to maintain the culture at 100-130 pF/cm for 14 days and the viability
was ≥90% during the whole run. The cell specific productivity at hypothermia
increased by 47% compared to 37°C. CT#3 was a high CV production process based on
45
information collected from CT#1&2. The culture temperature was decreased directly to
31°C on day 13 when CV reached 100 MVC/mL (Figure 10B)C. After a stable
production phase at 100-130 pF/cm for 15 days, the temperature was set back to 37°C
and the cell growth successfully resumed.
Figure 10 Hypothermia applied to CellTank cultures for cell growth arrest
Packed bed bioreactor and fibrous bed bioreactor have intrinsic drawback of culture
heterogeneity as presented in Section 1.3.1, but the CellTank was able to overcome this
nutrients/metabolites gradient due to its very efficient recirculation system. The
successful implementation of hypothermia enables its application in fast product
manufacturing and the collected product is completely cell free, which alleviates the
downstream processing as well.
40
80
120
160
0 5 10 15 20
Cv (
MVC
/mL)
20
25
30
35
40
0 10 20 30
Tem
pera
ture
(˚C
)
Time (Days)
CT#2 CT#3
46
2.3.3 Perfusion rate optimization (Paper III)
CSPR based perfusion rate selection was applied in all the studies. In ATF, TFF and CT
runs, a constant CSPR 50-60 pL/cell/day was applied. It supported all the cultures very
well and extremely high cell densities were achieved. In ST runs this approach was
reviewed and investigated in details.
CSPR is calculated from D and CV (eq.1). This indicates that the regulation of CSPR can
be done via varying either of the two values and the outcome should be the same for a
given CSPR from a mathematical point of view. This assumption was investigated in
ST#1 by implementing the same CSPR using both approaches. According to discussion
in the previous section, a perfusion culture stable at 20-30 MVC/mL is a perfect tool to
study various process parameters. The idea was that starting from a steady state at 30
MVC/mL, the CSPR was varied by changing D at constant CV or changing CV at
constant D. Steady states were then established for each variation and the average cell
growth rate µ in each state was monitored to determine whether another variation was
needed (in case an improvement/deterioration was observed). The µ was used as the
optimization indicator because it was easily accessible and highly reliable. µ could be
replaced by another important process parameter such as qp to be used as the
optimization target.
As part of the results, Figure 11 shows the culture performances obtained for CSPR
increase by increasing D (from 1.5 RV/D) at constant CV of 30 MVC/mL or decreasing
CV (from 30 MVC/mL) at a constant D 1.5 RV/mL. Both approaches suggested the
same optimum CSPR 60 pL/cell/day for cell growth or culture maintenance while
minimizing the medium consumption. The highest qp was observed at 60 pL/cell/day
when increasing D, but at 80 pL/cell/day when decreasing Cv. As mentioned above, if
qp had been selected as the reference parameter instead of µ, the CV would have been
47
lowered further attempting to reach even higher qp. The optimum of 60 pL/cell/day
CSPR should however be put in regards to the increased medium cost brought by the
relatively high resulting perfusion rate.
Figure 11 CSPR variation study by (A) increasing D at constant CV of 30 MVC/mL;
(B) decreasing CV at constant D = 1.5 RV/day
0
1
2
3
D (
RV/d
ay)
0
0.2
0.4
0.6
µ (d
ay-1
)
2
4
6
0 10 20 30
q p (
pg/c
/d)
Time (days)
0
20
40
60
80
100
CSPR
(pL
/cel
l/da
y)
(A)
I II III IV
0
10
20
30
40Cv
(M
VC/m
L)
2
4
6
8
0 5 10 15 20
q p (
pg/c
/d)
Time (days)
0.1
0.2
0.3
0.4
µ (d
ay-1
)
0
20
40
60
80
100
CSPR
(pL
/cel
l/da
y) (B)
I II III
48
It is worth noticing that these two approaches do not deliver identical effect in terms of
productivity and cell metabolism (Paper III). A potential explanation to this
observation is the glucose metabolism switch between TCA cycle and anaerobic
glycolysis, determined by the instantaneous glucose concentration available per cell,
and not by the glucose mass available per cell, which relates only to the stoichiometric
consumption. The same calculated CSPR from both D and CV variation by eq. 1 gives
the same stoichiometric glucose availability (mass per cell), not the same glucose
concentration per cell. Based on this we suggest that glucose concentration per cell is
used to analyze the influence of a component.
2.3.4 Product quality at HCDC (Paper II, III)
As the cell productivity is highly cell line specific, here we take a closer look at DP-12
cells because they have been used both in CT and ST runs. The average qp of DP-12
cells in a 7-day fed-batch process is 1.5 pg/c/d and the average qp at different cell
densities in perfusion cultures CT#1 and ST#1 are presented in Table 11. At 20-30
MVC/mL the average qp was 67%-87% higher in perfusion processes and the average
qp at 100 MVC/mL was almost identical as in fed-batch cultures, where the peak cell
density hardly reaches 5 MVC/mL in the same medium. From this, one can easily
figure out the volumetric productivity benefit that HCDC perfusion process provides,
not to mention the additional 47% increased qp when hypothermia was applied as
discussed in section 2.3.2.
At the same time, product quality is certainly as important as productivity. As
presented in the introduction, perfusion process generally provides a stable
environment to have a more consistent product. Despite that it still raises concern for
product quality at very high CV due to limited publishing of such information.
49
Reduced SDS-PAGE was performed in CT experiments to study the product quality in
HCDC (Paper II). The product was very consistent in CT#1 for all the CV's, even when
CV reached 200 x106 cells/mL. However fragment variant of the light chain appeared in
CT#2 after hypothermia was applied. It was postulated that the light chain variation
appeared as a result of different glycosylation pattern, potentially due to lower
temperature.
An industrial high producer CHOM cell was used in ST runs and N-glycosylation
related product quality profiles were analyzed (Paper III). Figure 12 shows the main
glycan of G0F, G1F, G2F and Man-5 with corrected peak area in percentage. ”Other”
refers to other structures such as afucosylated G0, G1, etc. Only the steady states are
shown for better visualization. In ST#2, the cell density was maintained at 15, 35 and
100 MVC/mL at CSPR 45, 65 and 80 pL/cell/day respectively (Figure 12A). G0F, G1F,
G2F and Man-5 were very consistent at CV 15 and 35 MVC/mL at both CSPR 45 and 65
pL/cell/day, but obvious decrease of G1F and G2F and increase of Man-5 and G0F
could be seen at 92 MVC/mL under 80 pL/cell/day CSPR. This could be due to the
elevated concentration of lactate with this CSPR. A different glycosylation profile has
been also observed by the company Selexis in fed-batch cultivations with high lactate
concentrations.
The same glycan analysis was applied to ST#3, where the CV was maintained at 20 and
100 MVC/mL and a much lower CSPR of 45 pL/cell/day was applied at 100 MVC/mL
(Fig.12B). After the CSPR variation was studied at 100 MVC/mL the cell bleeding was
stopped and the CV was maximized at low CSPR. It is very clear that the glycan pattern
was very consistent at different CV, indicating that the process was very robust with no
product quality shift from beginning to the end at these different CV's. As this
commercial cell line is a cell line for the manufacturing of a biosimilar, it is very
important that the product quality closely resemble the originator drug.
50
Figure 12 Glycosylation profiles of CHOM cells. (A) 5 steady states in ST#2: CV 15MVC/mL (i&ii), CV 35
MVC/mL (iii&iv), at CSPR 45 (i&iii) & 65 (ii&iv) pL/cell/day respectively. CV 92 MVC/mL at CSPR 85
pL/cell day (v). (B) ST#3, where CV was 15, 20, 88, 106, 86, 131, 165 MVC/mL at low CSPRs.
20
40
60
80
100
-40
0
40
80
120
160
5 10 15 20 25 30 35
Viab
ility
(%
)
Cv (
MVC
/mL)
CS
PR (
pL/c
ell/
day)
Cultivation time (days)
20
40
60
80
100
-40
-20
0
20
40
60
80
100
Viab
ility
(%
)
Cv (
MVC
/mL)
CS
PR (
pL/c
ell/
day)
i
ii
iii
iv v
b
(A)
(B)
51
2.3.5 Cellular metabolism in HCDC 2.3.5.1 Energy metabolism
A CSPR around 50 pL/cell/day was applied in all the perfusion runs other than the ST
experiments where the CSPR strategy was investigated. In all the runs the cell specific
uptake rates of glucose and glutamine, as well as cell specific production rates of
lactate and ammonia were calculated and monitored as part of the daily routine. The
medium used contained 25 mM pre-mixed glucose so it was impossible to lower the
glucose concentration in the feeding. Here we take DP-12 cells for example because
they have been used in different systems. In CT experiments, the maximum residual
lactate and ammonia concentrations were 86 mM and 8 mM respectively (CT#2). Due
to the high and probably inhibiting concentrations of both byproducts, CT#3 was
performed with a low substrate feeding strategy that led to moderate production of
lactate (40 mM) and ammonia (5 mM) with very low residual levels of glucose and
glutamine. In ST, by applying lower CSPR's, the residual glucose and glutamine in the
cultures were maintained at 1-2 mM and ≈ 0 respectively. The lactate was mainly
maintained below 10 mM with peaks at 21.9 mM at high CSPR during CSPR variation
study. The ammonia concentration was comparable to the other experiments with a
maximum value of 4.9 mM in ST#1.
Our results are consistent with previous reports describing that at low glucose
concentrations, the cells consume glucose more efficiently and utilize mainly the TCA
cycle pathway. We have also demonstrated that qglc was linear to CSPR independently
of the cell density, and that the cells unnecessarily consumed more glucose when more
glucose was fed, leading to lactate accumulation in the culture. This speaks against the
alternative method to tune the perfusion rate based on the residual glucose level in the
culture in the development of a perfusion process.
52
2.3.5.2 Metabolomics profiling of extracellular metabolites (Paper IV)
An exometabolome study, i.e. metabolomics of the extra-cellular components in the
culture, was performed in perfusion culture TFF#10 (Figure 13A) where the CV
reached 214 MVC/mL in TFF system, in comparison to two fed-batch runs (Figure 13B)
FB#11 (37°C) and FB#16 (37°C à 35.5°C on day 7). The cell growths in both process
modes are shown in Figure 13. Both fed-batch cultures at different temperatures were
compared to HCDC perfusion process, especially at extremely high cell densities.
Figure 13 Growth profiles of TFF#10 and two fed-batch runs, illustrating the samples selected for
metabolomics profiling study. (A) Sample events (red), at different cultivation stages in TFF#10
perfusion run and cell density (blue). (B) Sample events in fed-batch runs FB#11 (37°C) and FB#16
(37°C à 35.5°C on day 7).
Figure 14 shows the PCA score plots for the perfusion run TFF#10 (red) and for both
fed-batch runs (blue). Figure 14A is a score plot where the model has been calibrated
and validated with the data from the three runs. Figure 14B is a score plot showing a
model calibrated with the data from the fed-batch runs only, and then used for the
predictions of the perfusion run and fresh medium. Figure 14A clearly exhibits that fed-
batch samples are mostly defined by PC1 while perfusion samples are mostly defined
by PC2 with much less spreading than fed-batch samples. This indicates that the
0 5 10 150
4
8
12
16
20
Cv (
MVC
/mL)
FB#11
FB#16
50
100
150
200
250
0 10 20 30 40
Cv (
MVC
/mL)
Time (days)
(A) (B)
53
extracellular metabolites profile was more constant in the perfusion run despite the
huge difference in cell density and the much longer cultivation time. In Figure 14B it is
also very interesting to observe that the data from the perfusion run process cluster
with fresh medium samples and are close to fed-batch at day 3 to 5. It suggests that
throughout the whole perfusion process regardless of the cell density, the extracellular
metabolites might be growth-related similarly to day 3 to 5 in a fed-batch process.
Figure 14 PCA score plot for TFF#10 and FB#11&16. (A) A model calibrated and validated with the
data from three runs. (B) A model calibrated with the data from the fed-batch runs only.
!""#$%&
'(&
)*+&,-&
)*+&-&
)*+&.&)*+&$/& "0#$$&1&"0#$2&
*&
34567&859:*&
!"#$%&'#()*&
(*+&,-(*+&.&
/&
(A)
(B)
54
In perfusion mode, almost all the detected metabolites were highly consistent
irrespective of the cell density due to the fact that the same CSPR had been applied
throughout the culture. However we identified some potential extracellular biomarkers
that varied with the cell density. They were all involved in glutathione metabolism and
potentially imply an escalation of oxidative stress at cell density above 110 MVC/mL
even though a constant CSPR was applied (Paper IV). This hypothesis needs further
investigation in the future.
55
3 Concluding remarks HCDC perfusion process is very attractive due to the high volumetric productivity it can
offer, which allows much smaller production footprints with improved productivity and
product quality. However outcome can be uncertain and the process development can
be complex due to many unknowns at high cell densities. This thesis is about HCDC
perfusion process development and it is divided into two main parts.
The first part of the work presents four HCDC perfusion systems developed with our
industrial collaborators: WAVE Bioreactor™ Cellbag prototype coupled with ATF and
TFF respectively, a non-woven polyester matrix based CellTank™ prototype, and a
bench top stirred tank bioreactor equipped with ATF. In the HCDC perfusion system
development, industrial prototypes were optimized and perfusion processes were
gradually established.
The second part of the work focuses on HCDC perfusion process characterization and
optimization. As part of the perfusion system development, maximal viable cell
densities were achieved in all the systems to identify the capacity of the different
system configurations to support HCDCs. In WAVE Bioreactor™ coupled with TFF and
ATF, the CVMAX was 214 MVC/mL and 132 MVC/mL respectively. 214 MVC/mL is
definitely approaching the higher physical limit of the possible CV where the cells are
compressed against each other with no liquid between cells131. In the ATF equipped
system, the achieved cell density was lower due to the limitation of vacuum pump to
draw the highly viscous cell broth at such high cell density. This was later alleviated by
a filter size larger relatively to the culture volume, and the extremely high CV of 188
MVC/mL was achieved in the stirred tank bioreactor equipped with ATF. In the
CellTank™ system the maximum CV was 200 pF/cm measured from online biomass
56
probe based on dielectroscopy principle. This has been verified to be at least 200
MVC/mL during the study156.
During HCDC perfusion process development, steady states at 20-30 MVC/mL and 100-
130 MVC/mL were established and maintained by cell bleeding or hypothermia. These
were used to screen the key process variables, as well as to verify the process
consistency and reproducibility.
As one of the key process variables in perfusion processes, perfusion rate is important
not only for the culture performances but also for the process cost. CSPR based
perfusion rate selection has been widely used because it ensures a constant
environment to the cells, and it has been successfully applied to all our perfusion
processes to support the HCDCs. Two approaches to vary the CSPR via CV and D do not
deliver the same effect despite the identical value of CSPR. This is probably due to the
glucose concentration per the cell, resulting in different glucose consumption kinetics
despite similar glucose mass available per cell.
Note that a medium containing 25 mM glucose was used in all our experiments.
Balanced feeding of glucose and amino acids is very important regarding the cell
growth, productivity and glycosylation related product quality. The byproducts
accumulation observed in all the runs is related to sub-optimally balanced feed, which
is addressed in this thesis by perfusion rate variation. Medium development, outside
the focus of the current thesis, would definitely be helpful as part of the process
development, and associated to the perfusion rate strategy. We foresee that our studies
will add value to the development of perfusion technology in recombinant protein
production in mammalian cell systems.
57
Acknowledgements Without the support and guidance that I received from many people it would not have
been possible to do this PhD. I would like to thank all the people who have helped,
listened, encouraged, and pushed me throughout this study.
Foremost, I would like to express my deepest gratitude to my supervisor Véronique
Chotteau for her continuous support of my PhD study and research, for her
immeasurable guidance, advice and patience. I would not have imaged having a better
mentor and advisor for my PhD study.
I would like to thank my supervisor, Vincent Bulone for regular meetings to keep me
on the right track, and valuable advices.
My sincere thanks to all members of the Biotechnology School at Albanova, especially
the division of industrial biotechnology, both past and present, for support, discussion
and a nice work environment.
Thank you to VINNOVA for financial support.
I would like to acknowledge all collaborators for the hard work. Thank you to GE
Healthcare for sponsoring the Wave Bioreactor™ Cellbag project. Special thanks to Eva
Lindskog, Kieron Walsh, Craig Robinson and Eric Fäld from GE Healthcare. Thank you
to PerfuseCell for sponsoring the CellTank bioreactor. Special thanks to Per Stobbe and
Jacob Hiob Thilo for being always so supportive J. Thank you to Belach Biotechnology
for their support in the CellTank project and equipment for Stirred Tank project.
Special thanks to Christian Orrego Silvander. Thank you to David Sergeant from
Ipratech for lending us the CytoSys process control system and helping with all
troubleshooting. Thank you to Selexis for lending us the CHOM cell line and Pierre-
58
Alain Girod for our nice collaboration. Special thanks to Alexandra Martiné from
Selexis for glycosylation analysis.
Thank you Gen Larsson, for all best suggestions and discussions about research,
education and much more beyond that.
Thank you Andres Veide, as my internal advance reviewer, much more as a mentor for
bioprocess. Doesn’t matter if it is upstream or downstream, E. coli or CHO cells, it’s the
process that matters J
Thank you Atefeh Shokri and Carolina Åstrand for pushing me J, and for valuable
suggestions of course.
I would like to thank all colleagues and lab mates, both past and present, not only for
the support and inspiration, but also for your kindness and friendship. Especially Johan
Norén, Lubna Al-Khalili, Erika Hagrot, Marie-Françoise Clincke, Carin Mölleryd, Lena
Thoring, Caijuan Zhan, Mattias Leino, Karin Gillner, Fredrik Rönnmark, Jenny
Rönnmark, Hubert Schwarz, Nils Arnold Brechmann, Liang Zhang, Martin Gustavsson,
Gustav Sjöberg, Johan Jarmander, Magnus Lindroos, Antonius Van Maris, Gunaratna
Kuttuva Rajarao, Mónica Guevara-Martínez, Mariel Pérez-Zabaleta, Caroline
Bramstång, Berndt Björlenius, Eric Björkvall, Kaj Kauko, Anna Klara Lindgren. Special
thanks to David Hörnström, who was always supportive and agreed to revise the
Swedish abstract at the last minute. I am very grateful to all of you and it is my great
honor to be among the bioprocess family.
I would like to thank my family, especially MaMa, BaBa, Chao, and my parents-in-law
who have been by my side through this PhD, for your guidance, encouragement and
support. It is your loves that give me power, confidence and the most precious thing,
time. Finally to darling Tiana for being such an angel baby that makes it possible for
me to complete what I started. Happy 1st birthday!
59
References 1. Walsh, G., Biopharmaceutical benchmarks 2014.Naturebiotechnology2014, 32,
(10),992-1000.
2. Wurm, F. M., Production of recombinant protein therapeutics in cultivated
mammaliancells.Naturebiotechnology2004,22,(11),1393-8.
3. Walsh, G., Biopharmaceutical benchmarks 2010.Naturebiotechnology2010, 28,
(9),917-24.
4. KarthikP.Jayapal,K.F.W.,Wei-ShouHu,MirandaG.S.Yap,RecombinantProtein
Therapeutics from Cho Cells - 20 Years and Counting. CHO Consortium: SBE Special
Edition2007,40-47.
5. Muchmore, E. A.; Milewski, M.; Varki, A.; Diaz, S., Biosynthesis of N-
GlycolyneuraminicAcid-thePrimarySiteofHydroxylationofN-AcetylneuraminicAcidIs
theCytosolicSugarNucleotidePool.JBiolChem1989,264,(34),20216-20223.
6. Sato,J.D.;Kawamoto,T.;McClure,D.B.;Sato,G.H.,CholesterolrequirementofNS-
1mousemyelomacellsforgrowthinserum-freemedium.MolBiolMed1984,2,(2),121-
34.
7. Chu, L.; Robinson, D. K., Industrial choices for protein production by large-scale
cellculture.Currentopinioninbiotechnology2001,12,(2),180-7.
8. McCamish,M.;Woollett, G.,Worldwide experiencewith biosimilar development.
Mabs2011,3,(2),209-217.
9. Birch,J.R.;Racher,A.J.,Antibodyproduction.AdvDrugDelivRev2006,58,(5-6),
671-85.
10. Backliwal, G.;Hildinger,M.; Chenuet, S.;Wulhfard, S.;De Jesus,M.;Wurm, F.M.,
Rational vector design and multi-pathway modulation of HEK 293E cells yield
recombinantantibodytitersexceeding1g/lbytransient transfectionunderserum-free
conditions.NucleicAcidsRes2008,36,(15),e96.
60
11. Durocher, Y.; Perret, S.; Kamen, A., High-level and high-throughput recombinant
proteinproductionby transient transfectionof suspension-growinghuman293-EBNA1
cells.NucleicAcidsRes2002,30,(2),E9.
12. Geisse, S., Reflections onmore than 10 years of TGE approaches.ProteinExpres
Purif2009,64,(2),99-107.
13. Ye,J.X.;Alvin,K.;Latif,H.;Hsu,A.;Parikh,V.;Whitmer,T.;Tellers,M.;Edmonds,M.
C. D.; Ly, J.; Salmon, P.; Markusen, J. F., Rapid Protein Production Using CHO Stable
TransfectionPools.BiotechnolProgr2010,26,(5),1431-1437.
14. Hatton,D.;Forrest-Owen,W.;Dean,G.;Gibson,S.;Crook,T.;Lunney,A.;Ruddock,
S.;Davis,A.;Daramola,L.;Field,R.,High-yieldingCHOcellpoolsforrapidproductionof
recombinantantibodies.InCellsandCulture,Springer:2010;pp239-244.
15. Froud, S. J., Cell Bank Preparation and Characterization. In Animal Cell
Biotechnology:MethodsandProtocols, Jenkins,N., Ed.HumanaPress:Totowa,NJ, 1999;
pp99-115.
16. Warburg,O.,Ontheoriginofcancercells.Science1956,123,(3191),309-14.
17. Europa,A.F.;Gambhir,A.;Fu,P.C.;Hu,W.S.,Multiplesteadystateswithdistinct
cellular metabolism in continuous culture of mammalian cells. Biotechnology and
bioengineering2000,67,(1),25-34.
18. Zhou,W.;Rehm,J.;Europa,A.;Hu,W.-S.,Alterationofmammaliancellmetabolism
bydynamicnutrientfeeding.Cytotechnology1997,24,(2),99-108.
19. Cairns,R.A.;Harris,I.S.;Mak,T.W.,Regulationofcancercellmetabolism.NatRev
Cancer2011,11,(2),85-95.
20. Ronald Zielke, H.; Ozand, P. T.; Tyson Tildon, J.; Sevdalian, D. A.; Cornblath, M.,
Reciprocal regulation of glucose and glutamine utilization by cultured human diploid
fibroblasts.JournalofCellularPhysiology1978,95,(1),41-48.
21. Fitzpatrick, L.; Jenkins,H. A.; Butler,M., Glucose and glutaminemetabolism of a
murineB-lymphocytehybridomagrowninbatchculture.ApplBiochemBiotechnol1993,
43,(2),93-116.
61
22. Jenkins,H.A.;Butler,M.;Dickson,A.J.,Characterizationofglutaminemetabolism
intworelatedmurinehybridomas.Journalofbiotechnology1992,23,(2),167-82.
23. Ljunggren, J.; Haggstrom, L., Glutamine limited fed-batch culture reduces the
overflowmetabolismofaminoacidsinmyelomacells.Cytotechnology1992,8,(1),45-56.
24. Ozturk,S.S.;Palsson,B.O.,Growth,metabolic,andantibodyproductionkineticsof
hybridomacellculture:1.Analysisofdatafromcontrolledbatchreactors.Biotechnology
progress1991,7,(6),471-80.
25. Ozturk, S. S.; Riley, M. R.; Palsson, B. O., Effects of ammonia and lactate on
hybridoma growth, metabolism, and antibody production. Biotechnology and
bioengineering1992,39,(4),418-31.
26. Schneider, M.; Marison, I. W.; von Stockar, U., The importance of ammonia in
mammaliancellculture.Journalofbiotechnology1996,46,(3),161-85.
27. Xing, Z.; Li, Z.; Chow, V.; Lee, S. S., Identifying inhibitory threshold values of
repressing metabolites in CHO cell culture using multivariate analysis methods.
Biotechnologyprogress2008,24,(3),675-83.
28. Yang, M.; Butler, M., Effects of ammonia on CHO cell growth, erythropoietin
production,andglycosylation.Biotechnologyandbioengineering2000,68,(4),370-80.
29. Glacken, M. W.; Fleischaker, R. J.; Sinskey, A. J., Reduction of waste product
excretionvianutrientcontrol:Possiblestrategiesformaximizingproductandcellyields
onseruminculturesofmammaliancells.Biotechnologyandbioengineering1986,28,(9),
1376-89.
30. Wlaschin,K.F.;Nissom,P.M.;GattiMde,L.;Ong,P.F.;Arleen,S.;Tan,K.S.;Rink,A.;
Cham, B.; Wong, K.; Yap, M.; Hu, W. S., EST sequencing for gene discovery in Chinese
hamsterovarycells.Biotechnologyandbioengineering2005,91,(5),592-606.
31. Bebbington,C.R.;Renner,G.;Thomson,S.;King,D.;Abrams,D.;Yarranton,G.T.,
High-Level Expression of a Recombinant Antibody from Myeloma Cells Using a
Glutamine-SynthetaseGene as anAmplifiable SelectableMarker.Bio-Technol1992, 10,
(2),169-175.
62
32. Bell, S. L.; Bebbington, C.; Scott, M. F.;Wardell, J. N.; Spier, R. E.; Bushell, M. E.;
Sanders,P.G.,Geneticengineeringofhybridomaglutaminemetabolism.EnzymeMicrob
Technol1995,17,(2),98-106.
33. Birch,J.R.;Boraston,R.C.;Metcalfe,H.;Brown,M.E.;Bebbington,C.R.;Field,R.P.,
Selecting and designing cell lines for improved physiological characteristics.
Cytotechnology1994,15,(1-3),11-6.
34. Browne,S.M.;Al‐Rubeai,M.,AnalysisofanartificiallyselectedGS‐NS0variant
withincreasedresistancetoapoptosis.BiotechnolBioeng2011,108,(4),880-892.
35. Fan,L.;Kadura,I.;Krebs,L.E.;Hatfield,C.C.;Shaw,M.M.;Frye,C.C.,Improvingthe
efficiency of CHO cell line generation using glutamine synthetase gene knockout cells.
BiotechnolBioeng2012,109,(4),1007-1015.
36. Rendall, M.; Maxwell, A.; Tatham, D.; Khan, P.; Gay, R.; Kallmeier, R.; Wayte, J.;
Racher,A., Transfection tomanufacturing: reducing timelines for high yieldingGS-CHO
processes.AnimalCellTechnologyMeetsGenomics,Springer,Dordrecht2005,701-704.
37. Smales,C.M.;Dinnis,D.M.;Stansfield,S.H.;Alete,D.;Sage,E.;Birch,J.R.;Racher,
A. J.; Marshall, C. T.; James, D. C., Comparative proteomic analysis of GS‐NS0murine
myeloma cell lines with varying recombinant monoclonal antibody production rate.
BiotechnolBioeng2004,88,(4),474-488.
38. Anderson,M.E.,Glutathione:anoverviewofbiosynthesisandmodulation.Chem
BiolInteract1998,111-112,1-14.
39. Bray,T.M.;Taylor,C.G.,Tissueglutathione,nutrition,andoxidativestress.CanJ
PhysiolPharmacol1993,71,(9),746-51.
40. Lu,S.C.,Glutathionesynthesis.BiochimBiophysActa2013,1830,(5),3143-53.
41. Bailey, J. E.; Sburlati, A.; Hatzimanikatis, V.; Lee, K.; Renner, W. A.; Tsai, P. S.,
Inverse metabolic engineering: a strategy for directed genetic engineering of useful
phenotypes.Biotechnologyandbioengineering2002,79,(5),568-79.
42. Dinnis, D. M.; Stansfield, S. H.; Schlatter, S.; Smales, C. M.; Alete, D.; Birch, J. R.;
Racher,A.J.;Marshall,C.T.;Nielsen,L.K.; James,D.C.,Functionalproteomicanalysisof
63
GS-NS0 murine myeloma cell lines with varying recombinant monoclonal antibody
productionrate.Biotechnologyandbioengineering2006,94,(5),830-41.
43. Korke,R.;Gatti,M.D.;Lau,A.L.Y.;Lim,J.W.E.;Seow,T.K.;Chung,M.C.M.;Hu,W.
S.,Largescalegeneexpressionprofilingofmetabolicshiftofmammaliancellsinculture.J
Biotechnol2004,107,(1),1-17.
44. Korke,R.;Rink,A.;Seow,T.K.;Chung,M.C.;Beattie,C.W.;Hu,W.S.,Genomicand
proteomicperspectivesincellcultureengineering.Journalofbiotechnology2002,94,(1),
73-92.
45. Seow, T. K.; Korke, R.; Liang, R. C.M. Y.; Ong, S. E.; Ou, K.;Wong, K.; Hu,W. S.;
Chung, M. C. M., Proteomic investigation of metabolic shift in mammalian cell culture.
BiotechnolProgr2001,17,(6),1137-1144.
46. Seth,G.;Philp,R.J.;Lau,A.;Jiun,K.Y.;Yap,M.;Hu,W.S.,Molecularportraitofhigh
productivityinrecombinantNS0cells.BiotechnolBioeng2007,97,(4),933-951.
47. Smales, C. M.; Dinnis, D. M.; Stansfield, S. H.; Alete, D.; Sage, E. A.; Birch, J. R.;
Racher, A. J.; Marshall, C. T.; James, D. C., Comparative proteomic analysis of GS-NS0
murinemyeloma cell lineswith varying recombinantmonoclonal antibody production
rate.Biotechnologyandbioengineering2004,88,(4),474-88.
48. Stansfield, S.H.; Allen, E. E.; Dinnis, D.M.; Racher, A. J.; Birch, J. R.; James,D. C.,
DynamicanalysisofGS-NS0cellsproducingarecombinantmonoclonalantibodyduring
fed-batchculture.Biotechnologyandbioengineering2007,97,(2),410-24.
49. Tian,Q.;Stepaniants,S.B.;Mao,M.;Weng,L.;Feetham,M.C.;Doyle,M.J.;Yi,E.C.;
Dai,H.;Thorsson,V.;Eng,J.;Goodlett,D.;Berger,J.P.;Gunter,B.;Linseley,P.S.;Stoughton,
R. B.; Aebersold, R.; Collins, S. J.; Hanlon, W. A.; Hood, L. E., Integrated genomic and
proteomicanalysesofgeneexpressioninMammaliancells.MolCellProteomics2004,3,
(10),960-9.
50. Wlaschin, K. F.; Seth, G.; Hu, W. S., Toward genomic cell culture engineering.
Cytotechnology2006,50,(1-3),121-140.
64
51. Ahn, W. S.; Antoniewicz, M. R., Parallel labeling experiments with [1,2-
(13)C]glucose and [U-(13)C]glutamine provide new insights into CHO cellmetabolism.
MetabEng2013,15,34-47.
52. Carinhas,N.;Duarte,T.M.;Barreiro,L.C.;Carrondo,M.J.;Alves,P.M.;Teixeira,A.
P., Metabolic signatures of GS-CHO cell clones associatedwith butyrate treatment and
culturephasetransition.Biotechnologyandbioengineering2013,110,(12),3244-57.
53. Chong,W.P.;Reddy,S.G.;Yusufi,F.N.;Lee,D.Y.;Wong,N.S.;Heng,C.K.;Yap,M.
G.; Ho, Y. S., Metabolomics-driven approach for the improvement of Chinese hamster
ovary cell growth: overexpression ofmalate dehydrogenase II. Journalofbiotechnology
2010,147,(2),116-21.
54. Chong,W.P.;Yusufi,F.N.;Lee,D.Y.;Reddy,S.G.;Wong,N.S.;Heng,C.K.;Yap,M.
G.; Ho, Y. S., Metabolomics-based identification of apoptosis-inducing metabolites in
recombinantfed-batchCHOculturemedia.Journalofbiotechnology2011,151,(2),218-
24.
55. Dietmair,S.;Hodson,M.P.;Quek,L.E.;Timmins,N.E.;Chrysanthopoulos,P.;Jacob,
S. S.; Gray, P.; Nielsen, L. K., Metabolite profiling of CHO cells with different growth
characteristics.Biotechnologyandbioengineering2012,109,(6),1404-14.
56. Sellick, C. A.; Croxford, A. S.; Maqsood, A. R.; Stephens, G.; Westerhoff, H. V.;
Goodacre, R.; Dickson, A. J., Metabolite profiling of recombinant CHO cells: designing
tailored feeding regimes that enhance recombinant antibodyproduction.Biotechnology
andbioengineering2011,108,(12),3025-31.
57. Sellick, C. A.; Croxford, A. S.; Maqsood, A. R.; Stephens, G. M.;Westerhoff, H. V.;
Goodacre, R.; Dickson, A. J., Metabolite profiling of CHO cells: Molecular reflections of
bioprocessingeffectiveness.BiotechnolJ2015,10,(9),1434-45.
58. Selvarasu,S.;Ho,Y.S.;Chong,W.P.;Wong,N.S.;Yusufi,F.N.;Lee,Y.Y.;Yap,M.G.;
Lee, D. Y., Combined in silicomodeling andmetabolomics analysis to characterize fed-
batchCHOcellculture.Biotechnologyandbioengineering2012,109,(6),1415-29.
65
59. Sheikholeslami,Z.;Jolicoeur,M.;Henry,O.,Probingthemetabolismofaninducible
mammalian expression system using extracellular isotopomer analysis. Journal of
biotechnology2013,164,(4),469-78.
60. Templeton, N.; Dean, J.; Reddy, P.; Young, J. D., Peak antibody production is
associatedwithincreasedoxidativemetabolisminanindustriallyrelevantfed-batchCHO
cellculture.BiotechnolBioeng2013,110,(7),2013-+.
61. Čuperlović-Culf,M.,MetabolomicsinAnimalCellCulture.InAnimalCellCulture,Al-
Rubeai,M.,Ed.SpringerInternationalPublishing:Cham,2015;pp615-646.
62. Bajad,S.;Shulaev,V.,LC-MS-BasedMetabolomics. InMetabolicProfiling:Methods
andProtocols,Metz,T.O.,Ed.HumanaPress:Totowa,NJ,2011;pp213-228.
63. Idborg, H.; Zamani, L.; Edlund, P. O.; Schuppe-Koistinen, I.; Jacobsson, S. P.,
MetabolicfingerprintingofraturinebyLC/MSPart1.Analysisbyhydrophilicinteraction
liquidchromatography-electrosprayionizationmassspectrometry.JChromatogrBAnalyt
TechnolBiomedLifeSci2005,828,(1-2),9-13.
64. Idborg-Bjorkman, H.; Edlund, P. O.; Kvalheim, O. M.; Schuppe-Koistinen, I.;
Jacobsson,S.P.,ScreeningofbiomarkersinraturineusingLC/electrosprayionization-MS
andtwo-waydataanalysis.AnalChem2003,75,(18),4784-4792.
65. Chong,W.P.;Thng,S.H.;Hiu,A.P.;Lee,D.Y.;Chan,E.C.;Ho,Y.S.,LC-MS-based
metabolic characterization of high monoclonal antibody-producing Chinese hamster
ovarycells.Biotechnologyandbioengineering2012,109,(12),3103-11.
66. Devantier,R.;Scheithauer,B.;Villas-Boas,S.G.;Pedersen,S.;Olsson,L.,Metabolite
profiling for analysis of yeast stress response during very high gravity ethanol
fermentations.BiotechnolBioeng2005,90,(6),703-714.
67. Jackson,J.E.,Auser'sguidetoprincipalcomponents.JohnWiley&Sons:2005;Vol.
587.
68. Wold,S.;Sjöström,M.;Eriksson,L.,PLS-regression:abasictoolofchemometrics.
Chemometricsandintelligentlaboratorysystems2001,58,(2),109-130.
66
69. Mehra,S.;Chandrawanshi,V.;Prashad,K.,AdvancedBioprocessEngineering:Fed-
BatchandPerfusionProcesses.InAppliedBioengineering,Wiley-VCHVerlagGmbH&Co.
KGaA:2017;pp417-468.
70. Castilho, L.; Moraes, A.; Augusto, E.; Butler, M., Animal cell technology: from
biopharmaceuticalstogenetherapy.GarlandScience:2008.
71. Griffiths, J. B., Animal-Cell Culture Processes - Batch or Continuous. JBiotechnol
1992,22,(1-2),21-30.
72. Ozturk, S.; Hu, W.-S., Cell culture technology for pharmaceutical and cell-based
therapies.CRCPress:2005.
73. Huang, Y.-M.; Hu, W.; Rustandi, E.; Chang, K.; Yusuf-Makagiansar, H.; Ryll, T.,
Maximizing productivity of CHO cell-based fed-batch culture using chemically defined
mediaconditionsand typicalmanufacturingequipment.BiotechnolProgr2010,26, (5),
1400-1410.
74. Kelley, B., Industrialization of mAb production technology The bioprocessing
industryatacrossroads.Mabs2009,1,(5),443-452.
75. Langer, E. S., Trends in Perfusion Bioreactors.BioProcess International2011, 9,
(10),5.
76. Tao, Y.; Shih, J.; Sinacore, M.; Ryll, T.; Yusuf-Makagiansar, H., Development and
implementation of a perfusion-based high cell density cell banking process.Biotechnol
Progr2011,27,(3),824-829.
77. Clincke, M. F.; Molleryd, C.; Samani, P. K.; Lindskog, E.; Faldt, E.; Walsh, K.;
Chotteau,V.,VeryhighdensityofChinesehamsterovarycellsinperfusionbyalternating
tangential flowortangential flowfiltrationinWAVEBioreactor-partII:Applicationsfor
antibodyproductionandcryopreservation.Biotechnologyprogress2013,29,(3),768-77.
78. Heidemann,R.;Mered,M.;Wang,D.Q.;Gardner,B.;Zhang,C.;Michaels,J.;Henzler,
H.-J.; Abbas, N.; Konstantinov, K., A new seed-train expansionmethod for recombinant
mammaliancelllines.Cytotechnology2002,38,(1-3),99-108.
67
79. Pollock, J.; Ho, S. V.; Farid, S. S., Fed-batch and perfusion culture processes:
Economic, environmental, and operational feasibility under uncertainty. Biotechnol
Bioeng2013,110,(1),206-219.
80. Hernandez, R., Continuous manufacturing: a changing processing paradigm.
BioPharmInternational2015,28,(4).
81. Konstantinov,K.B.;Cooney,C.L.,Whitepaperoncontinuousbioprocessing.May
20-21,2014ContinuousManufacturingSymposium.JPharmSci2015,104,(3),813-20.
82. Langer,E.S.;Rader,R.A.,Continuousbioprocessingandperfusion:wideradoption
comingasbioprocessingmatures.BioProcessingJournal2014,13,43-9.
83. Godawat,R.;Konstantinov,K.;Rohani,M.;Warikoo,V.,End-to-endintegratedfully
continuous production of recombinant monoclonal antibodies. J Biotechnol2015, 213,
13-19.
84. Kamarck,M.E.,Buildingbiomanufacturingcapacity[mdash]thechapterandverse.
NatBiotech2006,24,(5),503-505.
85. Warikoo,V.;Godawat,R.;Brower,K.;Jain,S.;Cummings,D.;Simons,E.;Johnson,T.;
Walther, J.; Yu, M.;Wright, B.; McLarty, J.; Karey, K. P.; Hwang, C.; Zhou,W.; Riske, F.;
Konstantinov,K.,Integratedcontinuousproductionofrecombinanttherapeuticproteins.
BiotechnolBioeng2012,109,(12),3018-3029.
86. Walther, J.; Godawat, R.; Hwang, C.; Abe, Y.; Sinclair, A.; Konstantinov, K., The
businessimpactofanintegratedcontinuousbiomanufacturingplatformforrecombinant
proteinproduction.Journalofbiotechnology2015,213,3-12.
87. Voisard, D.; Meuwly, F.; Ruffieux, P. A.; Baer, G.; Kadouri, A., Potential of cell
retention techniques for large-scale high-density perfusion culture of suspended
mammaliancells.Biotechnologyandbioengineering2003,82,(7),751-65.
88. Lee, J. C.; Chang,H.N.; Oh,D. J., Recombinant antibody production by perfusion
culturesofrCHOcellsinadepthfilterperfusionsystem.Biotechnologyprogress2005,21,
(1),134-9.
89. Mitsuda, S.; Matsuda, Y.; Kobayashi, N.; Suzuki, A.; Itagaki, Y.; Kumazawa, E.;
Higashio,K.;Kawanishi, G., ContinuousProductionofTissuePlasminogen-Activator (T-
68
Pa) by Human Embryonic Lung Diploid Fibroblast, Imr-90 Cells, Using a Ceramic Bed
Reactor.Cytotechnology1991,6,(1),23-31.
90. Chen, C.; Huang, Y. L.; Yang, S. T., A fibrous-bed bioreactor for continuous
production of developmental endothelial locus-1 by osteosarcoma cells. Journal of
biotechnology2002,97,(1),23-39.
91. Bohak,Z.;Kadouri,A.; Sussman,M.V.;Feldman,A.F.,NovelAnchorageMatrices
forSuspension-CultureofMammalian-Cells.Biopolymers1987,26,S205-S213.
92. Looby,D.;Griffiths, J.B.,Fixedbedporousglasssphere(porosphere)bioreactors
foranimalcells.Cytotechnology1988,1,(4),339-46.
93. Knazek,R.A.;Gullino,P.M.;Kohler,P.O.;Dedrick,R.L.,Cell cultureonartificial
capillaries:anapproachtotissuegrowthinvitro.Science1972,178,(4056),65-6.
94. delaBroise,D.;Noiseux,M.;Massie,B.;Lemieux,R.,Hybridomaperfusionsystems:
acomparisonstudy.Biotechnologyandbioengineering1992,40,(1),25-32.
95. Piret, J. M.; Cooney, C. L., Mammalian-Cell and Protein Distributions in
UltrafiltrationHollowFiberBioreactors.BiotechnolBioeng1990,36,(9),902-910.
96. Piret, J. M.; Devens, D. A.; Cooney, C. L., Nutrient and Metabolite Gradients in
Mammalian-CellHollowFiberBioreactors.CanJChemEng1991,69,(2),421-428.
97. Kaufman,J.B.;Wang,G.;Zhang,W.;Valle,M.A.;Shiloach,J.,Continuousproduction
andrecoveryofrecombinantCa2+bindingreceptorfromHEK293cellsusingperfusion
throughapackedbedbioreactor.Cytotechnology2000,33,(1-3),3-11.
98. Meuwly, F.; von Stockar, U.; Kadouri, A., Optimization of themedium perfusion
rateinapacked-bedbioreactorchargedwithCHOcells.Cytotechnology2004,46,(1),37-
47.
99. Wang,G.;Zhang,W.; Jacklin,C.;Freedman,D.;Eppstein,L.;Kadouri,A.,Modified
CelliGen-packedbedbioreactorsforhybridomacellcultures.Cytotechnology1992,9,(1-
3),41-9.
100. Donato,D.; Labate,G. F.D.;Debbaut,C.; Segers,P.; Catapano,G.,Optimizationof
constructperfusion in radial-flowpacked-bedbioreactors for tissueengineeringwitha
2Dstationaryfluiddynamicmodel.BiochemEngJ2016,109,197-211.
69
101. Johnson, M.; Lanthier, S.; Massie, B.; Lefebvre, G.; Kamen, A. A., Use of the
Centritechlabcentrifugeforperfusioncultureofhybridomacellsinprotein-freemedium.
BiotechnolProgr1996,12,(6),855-864.
102. Kim, B. J.; Chang, H. N.; Oh, D. J., Application of a cell-once-through perfusion
strategy for production of recombinant antibody from rCHO cells in a centritech lab II
centrifugesystem.BiotechnolProgr2007,23,(5),1186-1197.
103. Kim, B. J.; Oh, D. J.; Chang, H. N., Limited use of Centritech Lab II Centrifuge in
perfusion culture of rCHO cells for theproductionof recombinant antibody.Biotechnol
Progr2008,24,(1),166-174.
104. Batt, B. C.; Davis, R. H.; Kompala, D. S., Inclined sedimentation for selective
retention of viable hybridomas in a continuous suspension bioreactor. Biotechnology
progress1990,6,(6),458-64.
105. Choo, C. Y.; Tian, Y.; Kim, W. S.; Blatter, E.; Conary, J.; Brady, C. P., High-level
productionofamonoclonalantibodyinmurinemyelomacellsbyperfusioncultureusing
agravitysettler.Biotechnologyprogress2007,23,(1),225-31.
106. Lipscomb, M. L.; Mowry, M. C.; Kompala, D. S., Production of a secreted
glycoproteinfromaninduciblepromotersysteminaperfusionbioreactor.Biotechnology
progress2004,20,(5),1402-7.
107. Nivitchanyong, T.; Martinez, A.; Ishaque, A.; Murphy, J. E.; Konstantinov, K.;
Betenbaugh, M. J.; Thrift, J., Anti-apoptotic genes Aven and E1B-19K enhance
performanceofBHKcellsengineeredtoexpressrecombinantfactorVIIIinbatchandlow
perfusioncellculture.Biotechnologyandbioengineering2007,98,(4),825-41.
108. Pohlscheidt,M.;Jacobs,M.;Wolf,S.;Thiele,J.;Jockwer,A.;Gabelsberger,J.;Jenzsch,
M.;Tebbe,H.;Burg,J.,Optimizingcapacityutilizationbylargescale3000Lperfusionin
seedtrainbioreactors.BiotechnolProgr2013,29,(1),222-229.
109. Searles,J.A.;Todd,P.;Kompala,D.S.,Viablecellrecyclewithaninclinedsettlerin
the perfusion culture of suspended recombinant Chinese hamster ovary cells.
Biotechnologyprogress1994,10,(2),198-206.
70
110. Coakley, W. T., Ultrasonic separations in analytical biotechnology. Trends
Biotechnol1997,15,(12),506-11.
111. Coakley,W. T.;Whitworth, G.; Grundy,M. A.; Gould, R. K.; Allman, R., Ultrasonic
manipulationofparticlesandcells.Ultrasonicseparationofcells.Bioseparation1994,4,
(2),73-83.
112. Dalm, M. C.; Jansen, M.; Keijzer, T. M.; van Grunsven, W. M.; Oudshoorn, A.;
Tramper, J.; Martens, D. E., Stable hybridoma cultivation in a pilot-scale acoustic
perfusion system: long-term process performance and effect of recirculation rate.
Biotechnologyandbioengineering2005,91,(7),894-900.
113. Dowd, J. E.; Kwok, K. E.; Piret, J. M., Glucose-based optimization of CHO-cell
perfusioncultures.Biotechnologyandbioengineering2001,75,(2),252-6.
114. Gaida,T.;Doblhoff-Dier,O.; Strutzenberger,K.;Katinger,H.;Burger,W.;Groschl,
M.; Handl, B.; Benes, E., Selective retention of viable cells in ultrasonic resonance field
devices.Biotechnologyprogress1996,12,(1),73-6.
115. Gorenflo,V.M.;Angepat,S.;Bowen,B.D.;Piret, J.M.,Optimizationofanacoustic
cellfilterwithanovelair-backflushsystem.Biotechnologyprogress2003,19,(1),30-6.
116. Gorenflo, V. M.; Smith, L.; Dedinsky, B.; Persson, B.; Piret, J. M., Scale-up and
optimization of an acoustic filter for 200 L/day perfusion of a CHO cell culture.
Biotechnologyandbioengineering2002,80,(4),438-44.
117. Ryll,T.;Dutina,G.;Reyes,A.;Gunson,J.;Krummen,L.;Etcheverry,T.,Performance
of small-scale CHO perfusion cultures using an acoustic cell filtration device for cell
retention: characterization of separation efficiency and impact of perfusion onproduct
quality.Biotechnologyandbioengineering2000,69,(4),440-9.
118. Trampler,F.;Sonderhoff,S.A.;Pui,P.W.;Kilburn,D.G.;Piret, J.M.,Acousticcell
filterforhighdensityperfusioncultureofhybridomacells.Biotechnology(NY)1994,12,
(3),281-4.
119. Elsayed,E.;Piehl,G.-W.;Nothnagel, J.;Medronho,R.;Deckwer,W.-D.;Wagner,R.,
Use of hydrocyclone as an efficient tool for cell retention in perfusion cultures.Animal
CellTechnologyMeetsGenomics2005,679-682.
71
120. Elsayed,E.A.;Wagner,R.InApplicationofhydrocyclonesforcontinuouscultivation
of SP-2/0 cells in perfusion bioreactors: Effect of hydrocyclone operating pressure, BMC
proceedings,2011;Springer:2011;pP65.
121. Jockwer,A.;Medronho,R.A.;Wagner,R.;Anspach,F.;Deckwer,W.-D.,Theuseof
hydrocyclones for mammalian cell retention in perfusion bioreactors. In Animal Cell
Technology:FromTargettoMarket,Springer:2001;pp301-306.
122. Docoslis,A.;Kalogerakis,N.;Behie,L.A.;Kaler,K.V.,Anoveldielectrophoresis‐
based device for the selective retention of viable cells in cell culturemedia.Biotechnol
Bioeng1997,54,(3),239-250.
123. Castilho, L. R.; Anspach, F. B.; Deckwer, W. D., An integrated process for
mammalian cell perfusion cultivation and product purification using a dynamic filter.
Biotechnologyprogress2002,18,(4),776-81.
124. Tang, Y. J.; Ohashi, R.; Hamel, J. F. P., Perfusion culture of hybridoma cells for
hyperproductionofIgG(2a)monoclonalantibodyinawavebioreactor-perfusionculture
system.BiotechnolProgr2007,23,(1),255-264.
125. Tao, Y.; Shih, J.; Sinacore, M.; Ryll, T.; Yusuf-Makagiansar, H., Development and
implementationofaperfusion-basedhighcelldensitycellbankingprocess.Biotechnology
progress2011,27,(3),824-9.
126. Wang,L.;Hu,H.;Yang,J.;Wang,F.;Kaisermayer,C.;Zhou,P.,Highyieldofhuman
monoclonal antibody produced by stably transfected Drosophila schneider 2 cells in
perfusioncultureusingwavebioreactor.Molecularbiotechnology2012,52,(2),170-9.
127. Yuk, I. H.; Baskar, D.; Duffy, P. H.; Hsiung, J.; Leung, S.; Lin, A. A., Overcoming
ChallengesinWAVEBioreactorswithoutFeedbackControlsforpHandDissolvedOxygen.
BiotechnolProgr2011,27,(5),1397-1406.
128. Wang,L.L.;Hu,H.X.;Yang,J. J.;Wang,F.;Kaisermayer,C.;Zhou,P.,HighYieldof
Human Monoclonal Antibody Produced by Stably Transfected Drosophila Schneider 2
CellsinPerfusionCultureUsingWaveBioreactor.Molecularbiotechnology2012,52,(2),
170-179.
72
129. Banik, G. G.; Heath, C. A., Hybridoma Growth and Antibody-Production as a
FunctionofCell-DensityandSpecificGrowth-RateinPerfusionCulture.BiotechnolBioeng
1995,48,(3),289-300.
130. Blasey,H.D.;Jäger,V.,StrategiestoIncreasetheEfficiencyofMembraneAerated
andPerfusedAnimalCellBioreactorsbyanImprovedMediumPerfusion.InAnimalCell
Culture and Production of Biologicals: Proceedings of the Third Annual Meeting of the
Japanese Association for Animal Cell Technology, held in Kyoto, December 11–13, 1990,
Sasaki,R.;Ikura,K.,Eds.SpringerNetherlands:Dordrecht,1991;pp61-73.
131. Clincke,M. F.;Molleryd, C.; Zhang, Y.; Lindskog, E.;Walsh, K.; Chotteau, V., Very
highdensityofCHOcellsinperfusionbyATForTFFinWAVEbioreactor.PartI.Effectof
thecelldensityontheprocess.Biotechnologyprogress2013,29,(3),754-67.
132. Cortin, V.; Thibault, J.; Jaeob, D.; Garnier, A., High-titer adenovirus vector
productionin293Scellperfusionculture.BiotechnolProgr2004,20,(3),858-863.
133. Genzel,Y.;Vogel,T.;Buck,J.;Behrendt,I.;Ramirez,D.V.;Schiedner,G.;Jordan,I.;
Reichl,U.,Highcelldensitycultivationsbyalternatingtangentialflow(ATF)perfusionfor
influenzaAvirusproductionusingsuspensioncells.Vaccine2014,32,(24),2770-2781.
134. Deo,Y.M.;Mahadevan,M.D.;Fuchs,R.,Practicalconsiderationsinoperationand
scale-up of spin-filter based bioreactors for monoclonal antibody production.
Biotechnologyprogress1996,12,(1),57-64.
135. Werner, A.; Lutkemeyer, D.; Poggendorf, I.; Lehmann, J.; Muthing, J., Serum-free
production of a chimeric E-selectin-IgG protein from 1 to 100 l scale: Repeated batch
cultivationversuscontinuousspinfilterperfusion.Cytotechnology2002,38,(1-3),47-56.
136. Mercille, S.; Johnson, M.; Lanthier, S.; Kamen, A. A.; Massie, B., Understanding
factors that limit the productivity of suspension-based perfusion cultures operated at
highmediumrenewalrates.BiotechnolBioeng2000,67,(4),435-450.
137. Mercille, S.; Massie, B., Apoptosis-resistant E1B-19K-expressing NS/0 myeloma
cells exhibit increased viability and chimeric antibody productivity under perfusion
cultureconditions.BiotechnolBioeng1999,63,(5),529-543.
73
138. Dong,H.;Tang,Y.J.;Ohashi,R.;Hamel,J.F.P.,Aperfusionculturesystemusinga
stirredceramicmembranereactorforhyperproductionofIgG(2a)monoclonalantibody
byhybridomacells.BiotechnolProgr2005,21,(1),140-147.
139. Li,F.;Vijayasankaran,N.;Shen,A.Y.;Kiss,R.;Amanullah,A.,Cellcultureprocesses
formonoclonalantibodyproduction.Mabs2010,2,(5),466-79.
140. Ozturk, S. S., Engineering challenges in high density cell culture systems.
Cytotechnology1996,22,(1-3),3-16.
141. Deschenes,J.S.;Desbiens,A.;Perrier,M.;Kamen,A.,Useofcellbleedinahighcell
density perfusion culture and multivariable control of biomass and metabolite
concentrations.Asia-PacJChemEng2006,1,(1-2),82-91.
142. Fenge, C.; Fraune, E.; Schiigerl, K., Perfusionbioreactor performance at different
cellbleedrates.AnimalCellTechnology1992,365-372.
143. Kuystermans,D.;Al-Rubeai,M.,Bioreactor systems forproducing antibody from
mammaliancells.InAntibodyExpressionandProduction,Springer:2011;pp25-52.
144. Sherman,M.;Lam,V.;Carpio,M.;Hutchinson,N.;Fenge,C.,ContinuousCellCulture
Operationat2,000-LScale.BioProcessInt2016,14,(7).
145. Wright,B.;Bruninghaus,M.;Vrabel,M.;Walther, J.; Shah,N.;Bae,S.; Johnson,T.;
Yin, J.; Zhou, W.; Konstantinov, K., A novel seed-train process: using high-density cell
banking, a disposable bioreactor, and perfusion technologies. BioProcess International
2015,13.
146. Ahn,W.S.;Jeon,J.J.;Jeong,Y.R.;Lee,S.J.;Yoon,S.K.,Effectofculturetemperature
on erythropoietin production and glycosylation in a perfusion culture of recombinant
CHOcells.Biotechnologyandbioengineering2008,101,(6),1234-44.
147. Chen,Z.L.;Wu,B.C.;Liu,H.;Liu,X.M.;Huang,P.T.,Temperatureshiftasaprocess
optimizationstepfortheproductionofpro-urokinasebyarecombinantChinesehamster
ovarycell lineinhigh-densityperfusionculture.Journalofbioscienceandbioengineering
2004,97,(4),239-43.
74
148. Ducommun,P.;Ruffieux,P.;Kadouri,A.;vonStockar,U.;Marison,I.W.,Monitoring
oftemperatureeffectsonanimalcellmetabolisminapackedbedprocess.Biotechnology
andbioengineering2002,77,(7),838-42.
149. Rodriguez,J.;Spearman,M.;Tharmalingam,T.;Sunley,K.;Lodewyks,C.;Huzel,N.;
Butler, M., High productivity of human recombinant beta-interferon from a low-
temperatureperfusionculture.Journalofbiotechnology2010,150,(4),509-18.
150. Tsai, Y. S.; Yoon, S. J.; Chuppa, S.; Konstantinov, K.; Naveh, D., Fermentor
temperatureasatoolforcontrolofhigh-densityperfusionculturesofmammaliancells.
AbstrPapAmChemS1996,211,128-BIOT.
151. Sunley, K.; Butler, M., Strategies for the enhancement of recombinant protein
production frommammalian cells by growth arrest.Biotechnology advances2010, 28,
(3),385-94.
152. Konstantinov, K.; Goudar, C.; Ng, M.; Meneses, R.; Thrift, J.; Chuppa, S.;
Matanguihan,C.;Michaels, J.;Naveh,D.,The“push-to-low”approachforoptimizationof
high-density perfusion cultures of animal cells. In Cell Culture Engineering, Springer:
2006;pp75-98.
153. Oh,D. J.;Choi,S.K.;Chang,H.N.,High-densitycontinuousculturesofhybridoma
cells inadepth filterperfusionsystem.Biotechnologyandbioengineering1994,44, (8),
895-901.
154. Deutschmann,S.M.;Jager,V.,OptimizationofthegrowthconditionsofSf21insect
cells for high-density perfusion culture in stirred-tank bioreactors. Enzyme Microb
Technol1994,16,(6),506-12.
155. Xu, S.; Chen,H.,High-densitymammalian cell cultures in stirred-tankbioreactor
withoutexternalpHcontrol.Journalofbiotechnology2016,231,149-59.
156. Zhang,Y.;Stobbe,P.;Silvander,C.O.;Chotteau,V.,Veryhighcelldensityperfusion
ofCHOcellsanchored inanon-wovenmatrix-basedbioreactor. JBiotechnol2015,213,
28-41.
157. Ryll,T.;Dutina,G.;Reyes,A.;Gunson,J.;Krummen,L.;Etcheverry,T.,Performance
of small-scale CHO perfusion cultures using an acoustic cell filtration device for cell
75
retention:Characterizationof separationefficiencyand impactofperfusiononproduct
quality.BiotechnolBioeng2000,69,(4),440-449.
158. Oh,D. J.;Chang,H.N.,HighDensityCultureofHybridomaCells inaDualHollow
FiberBioreactor.BiotechnolTech1992,6,(1),77-82.
159. Zhu,H.;Yang,S.T.,Long-termContinuousProductionofMonoclonalAntibodyby
HybridomaCellsImmobilizedinaFibrous-BedBioreactor.Cytotechnology2004,44,(1-
2),1-14.
160. Kyung, Y. S.; Peshwa, M. V.; Gryte, D. M.; Hu, W. S., High density culture of
mammalian cells with dynamic perfusion based on on-line oxygen uptake rate
measurements.Cytotechnology1994,14,(3),183-90.
161. Eibl,R.;Kaiser,S.;Lombriser,R.;Eibl,D.,Disposablebioreactors:thecurrentstate-
of-the-art and recommended applications in biotechnology. Applied microbiology and
biotechnology2010,86,(1),41-9.
162. Imseng,N.;Steiger,N.;Frasson,D.;Sievers,M.;Tappe,A.;Greller,G.;Eibl,D.;Eibl,
R.,Single-usewave-mixedversusstirredbioreactors for insect-cell/BEVS-basedprotein
expressionatbenchtopscale.EngLifeSci2014,14,(3),264-271.
163. RodriguezR,C.J.,GiraudS,Demonstratedperformanceofadisposablebioreactor
withananchorage-dependentcellline.BioProcessInternational2010,8,4.
164. Kaiser, S. C.; Eibl, R.; Eibl, D., Engineering characteristics of a single-use stirred
bioreactoratbench-scale:TheMobiusCellReady3Lbioreactorasacasestudy.EngLife
Sci2011,11,(4),359-368.
165. Gossain, V.; Mirro, R., Linear scale-up of cell cultures. BioProcess International
2010.
166. Valasek, C.; Cole, J.;Hensel, F.; Ye, P.; Conner,M.A.;Ultee,M.E., Production and
purification of a PER. C6-expressed IgM antibody therapeutic. BioProcess Int2011, 9,
(11),28-37.
167. Minow, B.; Rogge, P.; Thompson, K., Implementing a fully disposable MAb
manufacturingfacility.BioProcessInt2012,10,(6),48-57.
76
168. Noack,U.;DeWilde,D.;Verhoeye,F.;Balbirnie,E.;Kahlert,W.;Adams,T.;Greller,
G.;Reif,O.-W.,Single-UseStirredTankReactorBIOSTATCultiBagSTR:Characterization
andApplications.InSingle-UseTechnologyinBiopharmaceuticalManufacture,JohnWiley
&Sons,Inc.:2010;pp225-240.
169. Kauling,J.;Brod,H.;Jenne,M.;Waldhelm,A.;Langer,U.;Bodeker,B.,Novel,Rotary
Oscillated,ScalableSingle-UseBioreactorTechnologyfortheCultivationofAnimalCells.
Chem-Ing-Tech2013,85,(1-2),127-135.
170. Clincke,M. F.;Molleryd, C.; Zhang, Y.; Lindskog, E.;Walsh, K.; Chotteau, V., Very
highdensityofCHOcellsinperfusionbyATForTFFinWAVEbioreactor.PartI.Effectof
thecelldensityontheprocess.BiotechnolProgr2013,29,(3),754-767.
171. Hundt,B.;Best,C.;Schlawin,N.;Kassner,H.;Genzel,Y.;Reichl,U.,Establishmentof
aminkenteritisvaccineproductionprocessinstirred-tankreactorandWaveBioreactor
microcarrierculturein1-10Lscale.Vaccine2007,25,(20),3987-95.
172. Slivac, I.; Srcek, V. G.; Radosevic, K.; Kmetic, I.; Kniewald, Z., Aujeszky's disease
virusproductionindisposablebioreactor.JBiosciences2006,31,(3),363-368.
173. Weber,W.;Weber,E.;Geisse,S.;Memmert,K.,Optimisationofproteinexpression
and establishment of the Wave Bioreactor for Baculovirus/insect cell culture.
Cytotechnology2002,38,(1-2),77-85.
174. Zijlstra, G. M.; Oosterhuis, N., Cultivation of PER.C6® Cells in the Novel CELL-
Tainer™High-PerformanceDisposableBioreactor.InCellsandCulture:Proceedingsofthe
20th ESACT Meeting, Dresden, Germany, June 17-20, 2007, Noll, T., Ed. Springer
Netherlands:Dordrecht,2010;pp807-808.
175. Cadwell, J. J., New developments in hollow-fiber cell culture. AMERICAN
BIOTECHNOLOGYLABORATORY2004,22,14-19.
176. Lee,B.;Fang,D.;Croughan,M.;Carrondo,M.;Paik,S.-H.,Characterizationofnovel
pneumatic mixing for single-use bioreactor application.BMCProceedings2011, 5, (8),
O12.
77
177. Taylor,I.,TheCellMakerPlus™single-usebioreactor:anewbioreactorcapableof
culturing bacteria, yeast, insect and mammalian cells. Biotechnica, Hannover, Germany
2007.
178. Singh, V., Disposable bioreactor for cell culture using wave-induced agitation.
Cytotechnology1999,30,(1-3),149-58.
179. Hami, L. S.; Green, C.; Leshinsky, N.; Markham, E.; Miller, K.; Craig, S., GMP
productionandtestingofXcelleratedTCells(TM)forthetreatmentofpatientswithCLL.
Cytotherapy2004,6,(6),554-562.
180. Hollyman,D.;Stefanski,J.;Przybylowski,M.;Bartido,S.;Borquez-Ojeda,O.;Taylor,
C.;Yeh,R.;Capacio,V.;Olszewska,M.;Hosey, J.,Manufacturingvalidationofbiologically
functionalTcellstargetedtoCD19antigenforautologousadoptivecelltherapy.Journal
ofimmunotherapy(Hagerstown,Md.:1997)2009,32,(2),169.
181. Birch,J.R.;Racher,A.J.,Antibodyproduction.AdvDrugDeliverRev2006,58,(5-
6),671-685.
182. Chartrain, M.; Chu, L., Development and Production of Commercial Therapeutic
Monoclonal Antibodies in Mammalian Cell Expression Systems: An Overview of the
CurrentUpstreamTechnologies.CurrPharmBiotechno2008,9,(6),447-467.
183. DeWilde,D.;Noack, U.; Kahlert,W.; Barbaroux,M.; Greller, G., Bridging the gap
fromreusabletosingle-usemanufacturingwithstirred,single-usebioreactors.BioProcess
Int2009,7,(4).
184. Ozturk, S., Comparison of product quality: disposable and stainless steel
bioreactor.BioProduction,Berlin2007.
185. Valentine,P. In Implementationofasingle-usestirredbioreactoratpilotandGMP
manufacturing scale formammalian cell culture, ESACT 2009 Meeting, Dublin, Ireland,
2009;2009.
186. Zambaux, J., How synergy answers the biotech industry needs. BioProduction,
Berlin2007.
78
187. Butler, M.; Meneses-Acosta, A., Recent advances in technology supporting
biopharmaceutical production from mammalian cells. Applied microbiology and
biotechnology2012,96,(4),885-94.
188. Shukla, A. A.; Thommes, J., Recent advances in large-scale production of
monoclonalantibodiesandrelatedproteins.TrendsBiotechnol2010,28,(5),253-61.
189. Dowd,J.E.;Jubb,A.;Kwok,K.E.;Piret,J.M.,Optimizationandcontrolofperfusion
culturesusingaviablecellprobeandcellspecificperfusionrates.Cytotechnology2003,
42,(1),35-45.
190. Ozturk, S. S., Engineering challenges in high density cell culture systems.
Cytotechnology1996,22,(1),3-16.
191. Dowd,J.E.;Kwok,E.K.;Piret,J.M.,Off-lineglucose-basedcontrolandoptimization
ofperfusionCHOculturefeedrates.AbstrPapAmChemS2000,219,U181-U181.
192. Dowd, J. E.; Kwok, K. E.; Piret, J. M., Glucose-based optimization of CHO-cell
perfusioncultures.BiotechnolBioeng2001,75,(2),252-256.
193. Link,T.;Backstrom,M.;Graham,R.;Essers,R.;Zorner,K.;Gatgens, J.;Burchell, J.;
Taylor-Papadimitriou, J.; Hansson, G. C.; Noll, T., Bioprocess development for the
productionofarecombinantMUC1fusionproteinexpressedbyCHO-K1cellsinprotein-
freemedium.JBiotechnol2004,110,(1),51-62.
194. Wang,M.D.;Yang,M.;Huzel,N.;Butler,M.,ErythropoietinproductionfromCHO
cellsgrownbycontinuouscultureinafluidized-bedbioreactor.BiotechnolBioeng2002,
77,(2),194-203.
195. Galvez, J.;Lecina,M.;Sola,C.;Cairo, J. J.;Godia,F.,OptimizationofHEK-293Scell
cultures for the production of adenoviral vectors in bioreactors using on-line OUR
measurements.Journalofbiotechnology2012,157,(1),214-22.
196. Wlaschin, K. F.; Hu, W. S., Fedbatch culture and dynamic nutrient feeding. Adv
BiochemEngBiotechnol2006,101,43-74.
197. Chotteau,V.,ContinuousBiomanufacturing:CurrentPracticeandFuturePotential.
RefineTechnology,27-39.
79
198. Angepat, S.; Gorenflo, V. M.; Piret, J. M., Accelerating perfusion process
optimization by scanning non-steady-state responses.BiotechnolBioeng2005, 92, (4),
472-478.