advancing biopharmaceutical process control

8/12/2019 Advancing Biopharmaceutical Process Control

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Advancing Biopharmaceutical Process Control

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Introduction *

Deploying Smart Process Sensors

Sensibly

CLICK HERE p. 3

Multivariate Data Analysis forBiotechnology and Bioprocessing

CLICK HERE p. 9

Seeking Innovation to Solve

Persistent Problems

CLICK HERE p. 16

Bioreactor Operational Excellence:Best Practices from Scale-up to

Control

CLICK HERE p. 23

Additional Resources

CLICK HERE p. 31

CONTENTS

In biopharmaceutical development, scaleup and scaledown are critical to

developing a robust process and maintaining product quality. Technology

must be correctly transferred within the context of a multi-disciplinary

process that requires knowledge of real world materials and equipment,potential sources of variability, and critical quality attributes for the product.

The Pharmaceutical Quality by Design framework, ICH Q10, FDAs 2004

Process Analytical Technology (PAT) Guidance and 2011 Process Validation

Guidance can help manufacturers develop a strategy for process and

analytical method transfer, validation and continuous quality improvement.

However, for any biopharmaceutical process, the foundation for all of this

work is frequent and accurate process measurement, typically using dissolved

oxygen, pH and conductivity sensors.

This e-resource summarizes best practices for biopharmaceutical process

scaleup, as well as technology and analytical method transfer from experts

at pharmaceutical manufacturing and engineering companies, while it also

highlights the role that DO, pH and conductivity sensors play in ensuring

process robustness.

INTRODUCTION


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TODAYS DEMANDINGbiopharmaceutical

manufacturing operations require

careful control of process conditions,

whether during cell culture, purification

or drug product formulation. Process

sensors play a critical role in enabling

high-performance manufacturing by

providing real-time visibility into how a

process attribute is changing as well as

the ability to correlate that change with

the stage of the process.

In the pharmaceutical industry, it

is extremely valuable to see how

an attribute changes with time and

correlate that change with part of the

process, says L. Harry Lam, Ph.D.,

a biopharmaceutical manufacturing

industry expert. It is imperative to

have effective equipment that provides

reliable measurements.

With the critical role that process

sensors play in bioprocesses, selecting

the best sensors for the job is the key

to process success and understanding.

To successfully deploy sensors, pharma

manufacturers should consider following

these principles:

Dene objectives, and nd a sensor

technology that provides the

information needed to accomplish

your goals.

The sensible choice depends on the

situation; for example, the size and

age of the manufacturing operation

and relevant regulatory guidelines help

determine what is sensible in addition

to the particular product being

manufactured.

Successful sensor deployment requires

follow-through to implement the

system correctly.

MATCHING SENSOR TECHNOLOGY

WITH GOALS

The right choice of sensor begins with

the particular manufacturing challenge

being addressed. Different products

require different controls and possibly

different sensors. The first principle for

successfully deploying sensors is that

a manufacturer must clearly define the

problem to be solved and identify the

sensor technology that provides the

data required to solve it.

You must know what is important

about the process, and then you can

pick the sensor, says Tina Larson, a

Deploying Smart Process Sensors SensiblyAdvancing sensor technologies improve choices and reliability

BY AMBER RATCLIFF, ANALYTICAL SENSORS MARKET SEGMENT MANAGER, HAMILTON COMPANY

ADVANCING BIOPHARMACEUTICAL PROCESS CONTROL www.pharmamanufacturing.com
http://www.pharmamanufacturing.com/http://www.pharmamanufacturing.com/


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bioprocess industry expert who

heads up manufacturing operations

and engineering for a major

pharmaceutical company.

This principle is especially important

to remember for organizations

scouting new technologies. Although

new technology is exciting and

touted for its promise, focus must be

maintained on identifying the best

suited sensor technology to measure

the target critical process parameter.

The key question is, warns Larson,do the data give you what you need

to solve your problem? There is no

shortage of challenges that drive

manufacturing operations to invest in

new sensor technology. Investment

in new sensor technology is driven

by those manufacturing challenges

where there is the greatest amount of

uncontrolled variability, says Larson.

In biopharmaceutical manufacturing,

that is the cell culture stage.

Accurately measuring cell mass

in cell cultures, for example, is

an unsolved problem that still

eludes effective measurement.

Another area of uncontrolled

variability is glycosylation in cell

cultures; researchers need a better

understanding of the biological

control of glycosylation to develop

proper sensors for this complex

problem.

SUCCESS DEPENDS ON

THE SITUATION

In addition to seeking a sensor

technology that provides the

data needed to manage a critical

process attribute, regulatory and

business considerations also impact

bioprocess sensor implementation

decisions.

The recent U.S. Food and Drug

Administration Process Analytical

Technology (PAT) initiative is

driving a closer look at bioprocess

analytics by pharmaceutical

manufacturers. The PAT guidelines

urge manufacturers to update their

sensor technology as needed so

Figure 1. Hamilton Arc Sensor operation (left) with the wireless hand held (right) at the main 750 liter

fermenter of a microbial fermentation plant. The hand held eliminates the need for multiple transmitters,

handling data from as many as 31 sensors at a time.


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that they have data on those process parameters that

affect the quality of their products. For example, antibody

production is a core process in biopharmaceutical

manufacturing. Methods for purifying antibodies andproducing the antibody protein are well established, and

bioprocess sensors such as pH and DO are standard.

These sensors allow operators to manage critical process

parameters in real time. Further, having routinely collected

the data, they are now available for use in analyzing

issues when they do occur, perhaps even allowing

manufacturers to spot success trends not previously

recognized.

The PAT guidelines point to best practices that ensure

critical attributes are monitored and that provide

insurance against process failures. Other factors to be

considered in making a new sensor deployment decision

are the size and age of the manufacturing plant, which

affect the cost of making the change. There must be an

important business case for going through the change

process, states Larson.

Process sensors measure a range of attributes, from

traditional pH and dissolved oxygen (DO) to cutting-edge

attributes whose relevance is still being proven. Whether a

new sensor is state-of-the-art, a workflow innovation and/

or cutting edge affects the implementation decision.

State-of-the-art sensors are newer versions of well-

established sensors. They measure critical attributes

that facilitate high-performance plant operation and

biopharmaceutical production. State-of-the-art might

OPTICAL DO SENSORSOptical DO sensors replace traditional Clark cells, which

were based on electrochemical measurement within the

sensor itself. In the classical amperometric (electrochemical)

procedure described by Clark, oxygen diffuses through a

membrane and induces a chemical reaction with the elec-

trolyte behind it, creating a voltage differential propor-

tional to the amount of oxygen present. Optical measure-

ment, in contrast, is based on the fluorescent quenching

of oxygen on a luminophore. This optical system is located

inside the membra ne cap and is thus not affected by fac-

tors such as turbidity.

Because of pressure effects on the fluid-filled membranecap, classical amperometric sensors can give inaccurate

readings when exposed to pressure and temperature

changes. This makes it difficult to assess the status of the

culture and triggers alarm systems unnecessarily, disrupt-

ing daily operations. In contrast, optical DO sensors have a

more rugged sensor cap that easily handles broad perfor-

mance ranges, accommodating measurement temperatures

from -10C to 80C, pressures up to 12 bar/174 psi, and pres-

sure spikes up to 80 bar.

Optical DO sensors are also more robust for modern

biopharmaceutical processes that require sterilization-in-

place (SIP), cleaning-in-place (CIP) and autoclaving. Com-pared to optical sensors that do not rely on electrolytes,

amperometric sensors recover slowly after cleaning, and

their signal quality deteriorates with repeated sterilization

or long run times. Slow recovery extends downtime and

can lead to waste if sensors are not fully responsive before

restarting the process. Optical sensors have no electrolytes

that require polarization time and can therefore remain

stable over entire production runs and repeated steriliza-

tion cycles.


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sensing technology has allowed for innovations in DO

and pH measurement specifically, where the expensive

electronics, which do not need sterilization, are not

single use.

Cutting-edge bioprocess sensor technology brings

novel and unproven approaches to solving problems.

This might be a new engineering approach, a new assay,

or a new chemical measurement. Solid-state and near-

infrared (NIR) sensors are two examples of cutting-edge

technology for the biopharmaceutical industry. Solid-

state technology is an exciting, promising innovation

for bioprocess sensor engineering because of the

potential for low-cost manufacturing and low powerconsumption. Near-infrared data processing, although

not a new technology in itself, is experiencing renewed

interest, driven by advances in NIR data processing that

allow its use in aqueous situations. With these advances,

NIR sensors are now being reconsidered for bioprocess

monitoring. To what extent each type of cutting-edge

technology will be useful in bioprocess sensors remains

to be seen.

FOLLOW-THROUGH

It is hard to get new sensors into manufacturing at a

large pharmaceutical company, says Larson. You need

a lot of data to make the case that the new sensor will

be beneficial.

We test new sensor technology in our pilot plants,

she explains. Once we have experience with it, and

it proves robust and solves the problem that we are trying

SMART SENSORSBioprocess operational performance has also been hindered

by the difficulty of transmitting information to and from sen-sors. The introduction of smart sensors makes sensor and data

management easier. Smart sensors contain a memory chip

embedded in the electronic circuitry to store identification

and calibration information. The advantages of smart sensorsinclude:

Eliminating the need to calibrate sensors in line, reducing

system downtime;

Reducing incorrect estimations of sensor life, reducing batch

failure due to sensor failure;

Eliminating extensive manual documentation of sensor

performance to meet quality system reporting requirements,

reducing administration costs.

Smart sensors self-monitor measurement performance; an

intrinsic quality indicator predicts a failing sensor before a run

starts, reducing the risk of in-process failure and maximizing

useful life instead of requiring scheduled maintenance. Out-of-range results are reported immediately, triggering alarms and

prompting action as appropriate.

Because smart sensors store calibration data, pre-calibration

and configuration can be done right in the lab. Installation anddowntime costs are reduced by more than half withsmart sensors.

Smart sensors improve time to market, allowing less complex

setup and faster qualification, says Lam.

Finally, smart sensors report identification and calibration

information electronically, resulting in better quality system

adherence while saving time previously spent on manual

documentation.


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to solve, then we send the data

to manufacturing operations for

deployment consideration. For sensor

design engineers wondering how toget started with PAT, Larson advises a

sensible approach. The technologist

designing a sensor must understand the

manufacturing problem.

Once the decision is made, the

deployment itself requires careful

attention to ensure proper setup and

configuration. Thoroughly planning

implementation ensures that thebenefits of the new sensor technology

are realized, while helping to minimize

organizational and validation-related

questions. Training, data collection,

data analysis, integration into third-

party equipment, version management

and reporting are just a few of the

considerations that require careful

planning before implementation begins.

Another important question to answer

is how data acquisition technology will

be used to maintain control of all the

new data.

With the goal of any process

improvement being to initiate a new

approach, it is important to have

support, both internally and externally.

Partnerships with sensor vendors

extend beyond ensuring successful

deployment. Pharmaceutical

manufacturers get involved in sensordesign by surveying the landscape for

new sensor technology and disposables

and partnering with vendors in two

basic situations: when they see a

technology that they think is worth

developing, and when a vendor is close

to bringing to market a new technology

that is of interest to the company,

says Larson.

Regardless of the type of innovation

being introduced, successful

deployment of bioprocess sensors

requires that implementation of a new

technology follow PAT guidelines,

be suited for the manufacturing

process, answer the question it is

meant to address, and add enough

value to justify making a change. The

right choice varies with the situation;

different products require different

controls and possibly different

sensors, and the cost-benefit ratio

depends on the size and age of the

manufacturing plant. Adoption of new

technology often results in unexpected

improvements in process understanding

and performance.

ACKNOWLEDGEMENTS

The authors would like to thank the following

industry experts:

Tina Larson is a bioprocess engineer

who has spent her career in biochemical

manufacturing operations for major

pharmaceutical corporations. In her

current role, she is the head of technical

development operations and engineering

for a large biopharmaceutical manufacturing

organization.

L. Harry Lam, Ph.D., is a bioprocess

engineer who has spent his career in

biochemical manufacturing operations for

major pharmaceutical corporations, wherehe has been responsible for technology

implementation and many successful

development projects and manufacturing

campaigns. Currently, Dr. Lam works at

Shire Pharmaceuticals in La Jolla, Calif.

Lindsay Leveen, currently a consultant, is a

manufacturing process industry strategist

and early technology adopter focused

on sustainable development. He led

manufacturing technology implementation

for biopharmaceuticals, where he was anearly proponent of single-use methods.

Editors Note: For the complete list of refer-

ences associated with this article, visit:

www.pharmamanufacturing.com
http://www.pharmamanufacturing.com/http://www.pharmamanufacturing.com/http://www.pharmamanufacturing.com/http://www.pharmamanufacturing.com/


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THE MODERNbiopharmaceutical/

biotechnology manufacturing facility

contains many sophisticated control,

data logging and data archiving

systems. Massive amounts of data are

collected from sources such as raw

materials analysis, process outputs

and final quality assessments, which

are stored in data

warehouses.

The sheer volume of

data contained in these

warehouses makes it a

near impossible task to

extract the information

using simple charting and

univariate methods of

analysis. Such complex

data requires methods

of analysis that can cope

with multiple variables

simultaneously that not

only reveal influential

variables, but also reveal

the relationship such

variables have with

each other. This is where Multivariate

Analysis (MVA) is finding a much

greater role in the analysis of complex

bioprocess data.

Much more effort is being put into

the discovery and development

of biotherapies and personalized

medicines. Biopharmaceutical and

biotechnology companies are looking

for ways to accelerate drug discovery,

and through quality and compliance

initiatives such as the Food and Drug

Administrations (FDA) current Good

Manufacturing Practice (cGMP) and

Quality by Design (QbD) principles, and

Data Driven Knowledge

Discovery, reduce the

regulatory approval

time and be first to

market. This means

that data collected

throughout the entire

product lifecycle

must be analyzed and

interpreted in order

to gain extensive

product and process

understanding. This, in

turn, leads to improved

quality, greater

confidence in the

market for a companys

products and ultimately

market capitalization.

Multivariate Data Analysis for Biotechnology, Bio-processingPowerful MVA and DoE methods are giving biotech companies greater insights from complex data

By Brad Swarbrick, Vice President Business Development


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It is estimated that the time it takes to bring a new drug

or therapy to market is approximately 12 years. This

usually involves three phases: discovery, clinical trials

and registration.

Coupled with these phases is the development of a suitable

manufacturing process that can consistently produce the

highest quality product and be compliant with FDA current

Good Manufacturing Practice (cGMP) guidance. This

includes the development of a formulation that is robust

under processing conditions, scale up considerations and

technology transfer from facility to facility or even between

different types of manufacturing equipment. Each of these

phases can be improved and accelerated through the use of

the tools of MVA and Design of Experiments (DoE).

Even before data is analyzed, one of the biggest challenges

facing the industry is getting this data into a format that is

amenable to MVA. Many data collection and agglomeration

systems are commercially available for compiling various

forms of data and these can be seamlessly integrated

into MVA packages so that the vast array of graphical

and analytical approaches can be applied to reveal the

information it contains. The general statement is data is

only data until the information is extracted from it, and

from there, information leads to knowledge.

THE APPLICATION OF MVA AND DOE IN THE BIOTECH PRODUCT LIFECYCLE.

Figure 1: Biotechnology companies can realize significant benefits using MVA and DoE from product development through to manufacturing and quality control.

MVA for isolatingcandidate therapies

DOE for formulation

MVA for assistingwith theinterpretation ofclinical statistics &demographics

DOE for technologytransfer and scale-up

MVA for processcontrol and earlyevent detection

DOE for optimizinganalyticalprocedures

MVA for developingrobust quantitivemodels

Faster time to market

Reduced development timeframes and costs

Improved process under-standing

Increased product quality

Development &Discovery

Clinical Trials Quality Control

Manufacture &

ControlBusiness benets


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MVA IN THE PRODUCT LIFECYCLE

Unlike small molecule drug product development,

biotherapies are fundamentally more complex in terms

of structure and application and suffer greatly fromnatural biological variability. For example, isolating and

selecting cell cultures or bacterial strains to further

develop into future products is aided greatly by the tools

of MVA, including the monitoring of the processes (e.g.,

fermentation reactions) used to produce them. From

there, the tools of DoE can be used to devise formulations

that stabilize the active component(s) during manufacture

and are also useful in product scale-up studies.

Once the candidate therapy (cell cultures, antibody, virusstrain, etc.) has been formulated into a stable matrix,

MVA can be used to assist in the interpretation of clinical

trial data and can even lead to accelerating the lengthy

process through a much more comprehensive and overall

approach to data analysis, especially when combined with

the principles of adaptive designs and the Critical Path

Initiative endorsed by FDA.

When the candidate therapy has been approved for

market release, the tools of MVA are useful for assessing

the success of technology transfer from R&D to

production, or from one manufacturing facility to another.

In the production environment, MVA is useful for assessing

incoming or internally produced raw material quality and

characteristics. Combined with rapid spectroscopic (or

other characterization methods) control strategies for the

real-time monitoring and adjustment of processes within

the so-called design space can be devised so that

proactive quality control can be realized. DoE and MVA

are then used in developing robust analytical methods

for stability studies and other post-production analyses.

Data collected over time from a manufacturing facility

can be modeled to assess batch-to-batch consistency

and facilitate continuous improvement (CI) and preventive

maintenance and corrective action (CAPA) programs.

The entire process is summarized in Figure 1.

Candidate Therapy Discovery: During the initial

development of new therapies, there is usually much

information available on candidate cultures, antibodies,

etc., in respect to their chemical, biological andtoxicological properties. Combined with information

from origin and other background information, the

method of Principal Component Analysis (PCA) provides

a key data mining tool for the development scientist

to not only classify candidates of similar properties

and characteristics, but also to discover unique classes

that may be better suited to the treatment of specific

conditions.

PCA provides a visual map of the sample groupings,

allowing for the more efficient selection of real candidate

therapies, but it also provides a map of the input variables

and their relationships that cause the samples to group

the way they do. Figure 2 provides an example of the

outputs of a PCA in the form of the scores and loadings

plots. The scores provide a map of the samples and the

loadings provide a map of the input variables.


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PCA (or more generally MVA) applied to this kind of data

is sometimes referred to as Quantitative Structure Activity

Relationships (QSAR) and has helped some companies to

significantly reduce the time and effort required to isolatesuitable candidates for further development.

Formulation of suitable products: Stabilizing the candidate

into a suitable matrix for manufacturing and delivery is

best approached using DoE and, in particular, excipient

screening and mixture designs. Excipient screening

designs allow the formulation scientist to select the best

components that will preserve the nature of the candidate,

while mixture designs allow for the development of the

best combination that will not only stabilize the candidate,but also protect it during subsequent manufacturing

processes.

CLINICAL TRIALS

Clinical trials have traditionally been the domain of

univariate statistical approaches (in particular clinical

statistics) where statistical significance is assessed for

parameters such as efficacy and major side effects. The

tools of MVA can be used to compliment the findings

generated by clinical trial statistics to further confirm andaccelerate key findings through this phase of product

development.

The ability to incorporate demographic, age, sex and

patient history into predictive or exploratory models is

a unique feature of the MVA method, and approaches

such as the L-PLS model can provide an overall picture

of the patient groups, disease markers and the candidate

properties to better assess the effect of the therapy on

specific patient groups. Figure 3 provides an example ofthe L-PLS model structure and an example output.

Through the use of MVA tools for monitoring and

controlling bioprocesses, manufacturers worldwide have

realized significant cost savings through proactive quality

control. During the scale up and technology transfer

of a process from R&D to full scale manufacturing, the

use of DoE is a critical strategy for assessing the effect

SCORES AND LOADINGS PLOTS FOR A CANDIDATE SELECTION STUDY

Figure 2: In this example, Source 1 samples have high amounts of impurities

whereas Source 3 samples have the highest cell count. As a rule of thumb,

variables located outside the inner ellipse are regarded as being important in

interpretation of clusters in the Scores plot.


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of changing process and equipment variables. This

allows the denition of the Design Space, which denes

the most effective control strategy for the process.

Multivariate Statistical Process Control (MSPC) usesmultivariate exploratory and predictive models and

integrates them into the entire data collection and

process control system.

This allows manufacturers to be more innovative in their

approach to quality combining in-line process analytics

into single or holistic process models that better assess

the quality of production than single measurements in

isolation. Two particular processes that are commonly

used in biotherapy manufacture are fermentation andlyophilization. Some applications of MVA to these are

discussed in the following sections.

MVA FOR FERMENTATION MONITORING

For many years manufacturers have been challenged

with the development of suitable models for monitoring

the progress of batch processes, fermentation being

one such process. These batch models aim to establish

a process trajectory and associated limits around the

trajectory that define the bounds of acceptableproduct quality.

Methods exist that unfold batch data and use so-called

maturity indices to model the process. However,

the major drawback of these methods is that they

assume linear relationships in the processes, which are

fundamentally incorrect and have only partially solved

the batch problem. Other approaches use time warping

to distort the time scale and align batch trajectories.

Again, these approaches also suffer fundamentally as

they distort the chemistry or biology of the system and

hence do not describe the true state of the process.

Relative Time Mapping (RTM) addresses the

shortcomings of the previously defined methods by

keeping the chemistry/biology of the system intact,

while at the same time, providing the usual batch

trajectory plots and associated diagnostics that have

become synonymous with this type of analysis.

Whether batch models or traditional Statistical Process

Control (SPC) charts are used to assess the progress

of a bioprocess, there are many diagnostics available in

multivariate models that can be used to determine the

onset of process failure.

EARLY EVENT DETECTION

The term Early Event Detection (EED) is being

increasingly used to describe the application of

Multivariate Statistical Process Control for the detection

of process faults. The diagnostics from these models

can be fed back into the manufacturing control systemsusing protocols such as OPC to automate process

adjustments and therefore maximize the quality of the

final product.

An extension of MSPC is the use of Hierarchical Models

(HM). These models provide an excellent way of

classifying the state of discrete phases of processes

such as fermentation and adapt to changing conditions


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as they occur. HMs can be set up

as Classification Classification,

Classification Prediction

and Projection Predictionmodels which can be adapted to

applications such as analysis of

raw materials, process monitoring

and quality-control applications.

Near Infrared (NIR) spectroscopy

has been used for many years

with multivariate predictive and

exploratory models for the rapid,

non-destructive assessment ofproduct quality. One common

application of the NIR method

is the quantitative analysis of

residual moisture in lyophilized

products.

Lyophilization is a common

method used in the manufacture

of biopharmaceutical products

as it uses low temperatures toremove residual moisture, thus

preserving the structure of the

active components and allowing

their storage at room temperature.

The traditional method of analysis

for residual moisture in lyophilized

product is Karl Fischer (KF)

titration which is a destructive test

and can only be applied to a small

number of samples.

Replacement of the KF method

with NIR not only results in non-

destructive testing, but also allows

for 100% inspection systems to

be put in place. These systems

use MVA predictive models to

transform the NIR spectrum into a

single value for residual moisture

(or other properties) and are usedto accept and reject product as it

is being manufactured.

In one case, a biopharma

manufacturer saved about $1

million by using the NIR method

combined with PCA to validate

the performance of a new freeze

THE L-PLS MODEL AND ITS POTENTIAL FOR CLINICAL TRIAL DATA ANALYSIS.

Figure 3: In this example, the variables in green describe the background information of the patients, the

variables in blue are the side effects of the formulations (the actual formulations in light blue) and the red

dots indicate patient groups. This combined plot is the most informative way of displaying the relationship

between the three data tables depicted in the frame above.

Factor - 1

Factor-2

Correlation loadings from L-PLS regression


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dryer. They also developed a quantitative Partial Least

Squares Regression (PLSR) model to replace the KF

method in the laboratory. This method saves them

$1,000 per sample and provides more confidence whenreleasing the batch to market.

QUALITY CONTROL

Although initiatives such as Process Analytical

Technology (PAT) have been used by many

manufacturers globally to assess product and

process quality at the point of manufacture, not every

process measurement can be replaced at the point

of manufacture. Quality Control (QC) operations are

still vital in the final release stage of some, if not all,products.

Due to the high variability in many biological assays,

DoE and MVA can be used to design and refine the

analytical methods used in the QC laboratory and

has been successfully applied to the optimization of

chromatographic methods, the refinement of sampling

procedures and the analysis of complex data produced

by mass spectrometers.

Another advantage of combining spectroscopic

analysis with MVA methods is in stability studies. Since

the NIR method is non-destructive and is sensitive to

changes in the product and its matrix, the same sample

can be assessed over the entire timeframe of the

study. Where applicable, this avoids the destruction of

product, and the results are completely representative

as the same sample is being assessed each time.

MVA and DoE are fast becoming essential tools for

all process development and monitoring applications.

Bioprocesses provide an excellent but challenging

application area. Modern manufacturing executionsystems and control platforms produce a massive

amount of data that requires the tools of MVA to fully

data mine the most important information and make

real-time quality decisions.

From raw material analysis to final product release,

MVA models can be integrated into the total Quality

Management System (QMS), allowing manufacturers

to realize the benefits of the Quality by Design (QbD)

initiative.

Multivariate data analysis and DoE are powerful

tools ideally suited for understanding the complex

behavior and relationships in biological systems. These

methods can be used across the full biotech product

lifecycle, from discovery and development, to scale up,

production and quality control. Todays leading MVA

and DoE solutions can be seamlessly integrated with

other systems including process equipment, laboratory

and spectroscopy instruments, enabling faster andmore informed decision making.

Leading biotechnology companies that implement

and exploit the power of MVA and DoE can realize

substantial benefits including lower development

and production costs, improved product quality and

compliance, technology transfer, faster time to market

and ultimately increased business value.


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ITS PROBABLYfair to say that

innovation in the biopharmaceutical

manufacturing industry is a slow

cycle. But that would ignore the many

changes in biomanufacturing over the

past 5 to 10 years: Better expression

systems, widespread adoption of novel

single-use applications, re-emergence

of perfusion technologies, new modular

and flexible facilities, better sensors,

control systems and downstream

technologies.

The industry now finds itself very aware

of the promise new technologies carry

in terms of optimizing, and in some

cases revolutionizing existing processes.Results from our 10th Annual Report

and Survey of Biopharmaceutical

Manufacturers1, in which we surveyed

238 biomanufacturers, indicate that

end-users are still actively looking for

a range of new technologies to solve

persistent problems.

MORE FOR LESS

The driving factors today in

bioprocessing innovation, according

to our study, involve improving efficiency

and productivity. This equates to getting

more out of existing processes for less

money. For example, roughly two-thirds

of the industry attributes improvements

in manufacturing performance to single-

use systems and applications. But most

of the recognized benefits involve

improved efficiency, especially in clinical-

scale processes. Single-use devices

shorten the time getting facilities up and

running and reduce capital investments

necessary for new plants. New facilities

offer more flexibility, and modularapproaches along with faster campaign

turnaround times and lower annual

maintenance costs.

Similarly, the industry is demanding

better downstream processes

demands that are generally focused

on cheaper, equally effective

chromatography, protein-A and

purification steps. Again, innovations

need to be about cost eectiveness. So

as innovators and suppliers develop new

products, theyll need to demonstrate

their technologies are actually better

than current approaches.

PRODUCTIVITY INNOVATION 2013

In a separate survey Bioplan Associates

ran late last year, among the more than

450 global subject matter experts and

senior participants who make up our

Biotechnology Industry Council,2the

study found consistent expectations

regarding improvements in productivity.

Again, improvements in downstream

processing and single-use technologies

ranked as the top 3 trends for 2013,

these were followed by demands for

Seeking Innovation to Solve Persistent ProblemsStudy reveals biopharmas growing demand for effective technologies to drive efficiency and

productivity in and cost out of biopharmaceutical processing

BY ERIC S. LANGER, PRESIDENT AND MANAGING PARTNER, BIOPLAN ASSOCIATES INC.


17/31


better analytical methods. New analytical

methods are required for better process

monitoring and process improvements.

In addition, to develop biosimilars, theindustry needs better characterization

techniques, and better processes.

Otherwise, even if similarity with a

reference biologics could be shown,

the cost of producing a new biosimilar

might not be much lower than the

original; this could dramatically reduce

the attractiveness of any such high-cost

generic version.

Returning to the attractiveness of single-

use technologies, we found in our annual

survey that respondents today estimate

35% of their upstream clinical production

operations to be single-use. This compares

with 25% of respondents that said more

than 80% of their downstream clinical

production steps are now single-use.

The number is 16% of downstream

commercial-scale production. This wasntsurprising to see that the lowest use was

for downstream commercial production,

which remains mostly fixed stainless-steel

equipment. It was also fairly consistent

to see the highest adoption rate be for

downstream clinical production, likely

due to broader use of disposable tubing

and filters, buffer containers, etc.

FOCUS SUPPLIERS, FOCUS

Researchers asked respondents to

consider new product and services

developed by suppliers and to identify

the top areas they want suppliers to

focus their development efforts on. This

year, of the 21 areas the study listed,

the areas highlighted in prior years, and

other studies continue to occupy the

top position. Specically:

Disposable products, including bags,

connectors and other devices

Better probes and sensors

Process development services

(up-and down-stream)

Chromatography products

Biopharmaceutical manufacturers are seeking new modular and flexible facilities with better sensors, control

systems and downstream technologies.


18/31


Some of the specic new product development areas

being demanded by biopharmas operational managers are

included in Figure 1. Identification of these areas by end-users

is not a reflection of their need for technical advances toproduce drugs that otherwise wouldnt be possible. Rather

it mirrors their requirements to produce more efficiently and

less expensively. This shift in focus on manufacturing is a

maturation process that has been growing over the past

10 years.

TRENDS OVER TIME

Looking at responses from the past four years, the study

shows that interest in more innovative approaches to

bioprocessing, again, centers on process improvements. Forexample, demand for better upstream process development

services has increased five percentage points over the past

four years, while interest in downstream PD has decreased

from a high of 35% in 2008, down to 26% this year. Similar

declines in interest in new chromatography products are

evident. This suggests the acute bottlenecks created around

downstream operations have been abating, while the chronic

need for improved productivity has not.

Better disposable devices, including bags and connectors,has increased by about five percentage points from 2008.

Demand for disposable probes and sensors is up by roughly

10 percentage points, while interest in new bioreactors and

purification products has remained steady during the period.

Predictably, interest in various new technologies varies by

business model and even geography and the studys

results offer a window into how different parts of the

Disposable products, bags, connectors, etc.

Disposable product: probes, sensors, etc.

Disposable product: bioreactors

Disposable product: purification

Process development (upstream) services

Analytical assays

Process development (downstream) services

Analytical development

Chromatography products

Cell culture media

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

43.8%

39.6%

34.0%

34.0%

29.9%

27.1%

26.4%

26.4%

25.0%

25.0%

i i i

i l l

il i i

I li il i

i l i l

SELECTED NEW PRODUCT DEVELOPMENT FOCUS AREAS

i i l i i i

..

..

..

..

..

..

..

..

..

..

Source: 10th Annual Report and Survey, Biopharmaceutical Manufacturing and

Capacity, www.BioPlanAssociates.com, April 2013

Figure 1

Figure 2

i l , , , .

i l : , , .

i l : i

i l : i i

l i

l i l

l i

l i l l

ll l i

.

.

.

.

.

.

.

.

.

.

Use of high capacity resins

Single use filters

Buffer Dilution systems/skids

In-line Buffer dilution systems

Single use disposable TFF membranes

ADOPTING NEW DSP TECHNOLOGIES 2010 VS 2013

Downstream Purification (DSP) technologies being considered

54.0%41.2%

48.2%43.5%

44.4%

36.8% 43.0%28.7%

38.1%42.6%

36.8%35.7%

36.5%39.7%

47.4%45.2%

34.9%38.2%

37.7%27.8%


19/31


industry are looking at innovation.

When comparing biomanufacturing

developers to contract manufacturing

service providers (CMOs), for example,

the study found that while the former

were more interested in disposable

probes and sensors (41.3% vs. 27.8%),

the latter were twice as interested in

disposable purification products (61.1%

vs. 30.2%). Again, this is likely the result

of CMOs need for efficiency to remain

competitive.

THE INNOVATION PENDULUM

Although bioprocessing industry growth

isnt as radical as in semiconductors

(where Moores Law proposed that

the number of transistors on a chip

doubles every 18 months), demand for

innovation that improves efficiencyis similar in respect to how new

product developments swing from

one bottleneck to the next. This year,

single-use applications are clearly

the subject of much interest when it

comes to innovation; in prior years, and

likely in the future, other technology

areas will bounce back. Downstream

purification, new and better analytical

tools, and improved services offeringsfor example are likely to re-emerge as

urgent problems as the industry resolves

current, more acute, issues.

For example, separately in our study

we asked respondents which of 21

different, novel downstream purification

(DSP) technologies they were actively

considering to address bioprocessing

problems (see Figure 2). The responsesare indicative of potential future

adoption and do not take into account

respondents already having adopted these

technologies or those who are considering

but not actively pursuing them.

Topping the list of new downstream

processing solutions being considered

Among the technologies biopharmaceutical manufacturers want suppliers to focus innovation and

development on are single-use technologies including bags, connectors and other devices.


20/31


this year are high capacity resins, by

54% of respondents. Following are

single-use filters (44.4%), buffer dilution

systems/skids (38.1%), in-line buffer

dilution systems (36.5%) and single-use

disposable TFF membranes (34.9%).

Compared with years past, we see

a greater interest in the use of high

capacity resins (this years 54% being up

from 41% in 2010) and single-use filters

(44.4%, up from 28.7% in 2010). Some

downstream areas are showing a trend

toward decreased consideration. Forexample, the 36% actively considering

in-line buffer dilution systems is down

from 48% a couple of years ago. These

declines may also reflect greater adoption

of these technologies in the past few

years, with fewer respondents falling

into the actively considering column

as a result of following through on those

considerations. It may also be that

incremental improvements in processes,elimination of purification steps and a

diminished interest in alternatives for

current technologies (such as Protein A

alternatives) have weakened the urgency

for new solutions.

Our analysis finds that CMOs may be a

leading-edge indicator regarding plans

for adoption of alternative downstream

processing technologies. Results offersome insight into which markets are

likely to expand in the coming months

and years. For example, CMOs are far

more likely to be considering single-use

disposable TFF membranes (50% vs.

32%) and disposable UF systems (40%

vs. 28%).

The biopharmaceutical manufacturing industry is actively considering and adopting a range of new

technologies for all facets of the manufacturing process. (Photo: Boehringer Ingelheim)


21/31


We also found significant differences on

a regional basis, with U.S. respondents

generally more likely than Western

European respondents to be consideringa range of technologies. For example,

use of high capacity resins (71% U.S.

vs. 33% W Europe), a 37.3 point gap;

and In-line buer dilution systems (53%

U.S. vs. 11% W Europe). On the other

hand, Western Europeans demonstrated

significantly more active consideration

for membrane technologies, alternatives

to chromatography and precipitation.

WHATS AHEAD FOR NEW

TECHNOLOGIES

The biopharmaceutical manufacturing

industry is actively considering and

adopting a range of new technologies

for all facets of the manufacturing

process. Single-use devices continue to

crop up in any conversation about new

technologies and a move towards leaner,

more flexible and modular systemsseems likely in biopharmas future.

It is also important to note that

introduction of innovations is not at

all easy in this industry, and there are

many obstacles to new technology

introductions. Regulatory issues

force biomanufacturers to stabilize

bioprocessing systems early on, so

processes can remain largely unaltered

through a drug products lifetime. This

can make manufacturers less receptiveto improvements. Thus, manufacturing

strategy takes a long view when it

comes to adoption of innovation.

On the other side, vendors, larger ones

in particular, invest significantly in R&D

and product lines. They have a vested

interest in evaluating where future

adoptions will be needed, and how

rapidly they will be taken up.

Economic conditions can also play a

role. While budgets are again expanding,

tighter conditions continue to

discourage the financing and entrance

of smaller suppliers in the market,

reducing the pool of likely contributors

to innovation. Even so, biomanufacturers

are showing a renewed urgency to

improve productivity, reduce costs whileboosting quality. This is reflected in

increasing budgets over the past four

years supporting activities focused on

production efficiency.

Given the complexity, and the long

product development cycle, the only

way to ensure efficient process is

for industry suppliers and vendors

to continue to identify and meet the

demands of end-users. This will drive

future investing in the developmentof new technologies. And supporting

this, on the suppliers side, we find that

vendors R&D budgets for new product

development have also expanded over

the four years.

So, with increased budgets and interest

from both manufacturers and vendors

on innovation that boost efficiency,

its easy to visualize a robust futurefor the industry. For biomanufacturers

to truly push forward innovation and

remain competitive as cost pressures

increase, and biosimilars evolve, they

will continue to demand better ways

of evaluating new technologies to cut

downtime to market and streamline the

overall testing process.

Survey Methodology: The 201310th Annual Report and Survey of

Biopharmaceutical Manufacturing

Capacity and Production yields a

composite view and trend analysis

from 238 responsible individuals at

biopharmaceutical manufacturers

and contract manufacturing

organizations (CMOs) in 30 countries.


22/31


The methodology also included over 158 direct suppliers of

materials, services and equipment to this industry. This years

study covers such issues as: new product needs, facility

budget changes, current capacity, future capacity constraints,expansions, use of disposables, trends and budgets in

disposables, trends in downstream purification, quality

management and control, hiring issues, and employment.

The quantitative trend analysis provides details and

comparisons of production by biotherapeutic developers

and CMOs. It also evaluates trends over time, and assesses

differences in the worlds major markets in the U.S.

and Europe.

ABOUT THE AUTHOR

Eric S. Langer is president and managing partner at BioPlan Associates

Inc., a biotechnology and life sciences marketing research and publishing

firm established in Rockville, MD, in 1989. He is editor of numerous

studies, including Biopharmaceutical Technology in China, Advances in

Large-scale Biopharmaceutical Manufacturing, and many other industry

reports. Contact Eric at: [email protected]; 301-921-5979;

www.bioplanassociates.com.

REFERENCES

110th Annual Report and Survey of Biopharmaceutical Manufacturing Capacity and

Production: A Survey of Biotherapeutic Developers and Contract Manufacturing

Organizations, BioPlan Associates, April 2013.

2BioPlan Associates 2013 Biotechnology Industry CouncilTM Trends Analysis

Study

Demand for disposable probes and sensors is up by roughly 10 percent-

age points, while interest in new bioreactors and purification products

has remained steady.
http://www.pharmamanufacturing.com/mailto:[email protected]://www.bioplanassociates.com/http://www.bioplanassociates.com/mailto:[email protected]://www.pharmamanufacturing.com/


23/31


BIOREACTOR OPERATIONAL EXCELLENCE:

Best Practices from Scale-up to ControlIn biomanufacturing, knowledge of scale-up, processing and control should be shared and

documented in order to achieve and sustain operational excellence.

BY BRIAN J. STAMPER AND CILLIAN MCCABE, BIOPROCESS RESEARCH AND DEVELOPMENT, ELI LILLY AND COMPANY

THE MODERNcell culture bioprocess

has been successfully scaled up to

volumes greater than 25,000L through

sound engineering fundamentals and

thorough process understanding. This

hasnt happened by accident, but ratherby bioprocessing professionals taking a

systematic approach to characterizing

the bioreactors capabilities and

tendencies, developing robust and

reliable scale-up procedures, and

establishing and maintaining proper

control criteria. Those manufacturers

that identify and document operational

best practices for a cGMP cell culture

plant also tend to be those that sustainoperational success and deliver high-

quality biopharmaceuticals to patients

in a timely and reliable manner.

Identifying and documenting bioreactor

operation best practices allows for

more robust processing by helping

to properly educate the operations,

engineering and technical staff who

oversee the bioreactor processes.

Shared learning helps to reduce

the amount of tribal knowledge

that exists within a group and to

maintain high levels of operational

excellence even in times of employee

turnover, with the end result being

a sustainable and reliable supply of

biopharmaceuticals.

With these ideas in mind, we have set

out to document best practices thatwe have learned for bioprocessing,

most notably in the areas of equipment

design and overall process control.

BIOREACTOR DESIGN

Starting o with the proper bioreactor

design can resolve many process

issues before they arise. One key

bioreactor design issue that should not

be underestimated is the importanceof geometric similarity between

bioreactors: maintaining aspect ratios,

impeller sizing ratios, impeller spacing

ratios and baffle size and location will

greatly increase the probability of

success at scale. A properly designed

bioreactor can lead to reduced

qualification and process validation


24/31


timeframes, as well as increased apparent process

robustness and operational success.

Another key aspect to bioprocess scale-up success is thedesign of the sparger. Often, two spargers are installed

in the production bioreactors while only one sparger is

used for the seed bioreactors. While various types of

spargers have been utilized within the industry, we have

successfully implemented the use of drilled pipes and

sparge stones. The drilled pipe yields large bubbles and

a lower kLa (which will be discussed below), while the

sparge stone yields small bubbles and a very high kLa

such that a greater amount of oxygen can be delivered

into the cell broth for the same gas flow rate. Figures1 and 2 illustrate the sparger location and two sparger

types.

The ability of the bioreactor to deliver oxygen to the

cells is defined by the mass transfer relationship shown

in Equation 1. The change in oxygen concentration

is controlled by kLa, the average saturation oxygen

concentration of the bubbles,, dissolved oxygen

concentration ([O2]dissolved) and the oxygen uptake by

any cells present (OUR).

The kLacan be mapped as a power law function of the

power/volume and superficial gas velocity. Understanding

of the kLaallows for estimation of the OUR capacity of the

FIGURE 1.Illustration of the sparger location within a bioreactor(not drawn to scale).

Figure 2. Comparison of bubble sizes erupting from sparge stone and drilled

pipe (reproduced from www.mottcorp.com).

SPARGE STONE DRILLED PIPE
http://www.pharmamanufacturing.com/http://www.mottcorp.com/http://www.mottcorp.com/http://www.pharmamanufacturing.com/


25/31


bioreactor, prediction of required oxygen

flow rates and prediction of the time-

course profile of the dissolved carbon

dioxide levels.

Based on process needs and sparger

capabilities, the process engineer must

determine the preferred configuration for

the bioreactors. If process OURneeds are

sufficiently low, the default configuration

can be the use of one drilled pipe in

the seed bioreactors, and two drilled

pipes in the production bioreactors. The

combination of a drilled pipe and spargestone may also be used, but the oxygen

transfer ability afforded by the sparge

stone is typically not necessary. Use of

the sparge stone should be avoided if

possible due to the increased operational

complexity associated with bioreactor

set-up, manual changes in gas flows

during a process to maintain dissolved

carbon dioxide levels, increased foaming

and potential cleaning concerns.

MIXING CHARACTERIZATION

When scaling up a free suspension

cell culture bioreactor, a thorough

understanding of the mixing

characteristics is essential. If the mixing

inside the bioreactor is appropriately

controlled, then the cells will experience

an environment very similar to that of the

bench-scale bioreactor and will therefore

be much more likely to behave as they

did in the scale-down bioreactors.

The literature shows that many methods of

scale-up have been considered, including

matching power/volume, impeller blade

tip speeds, bulk mixing Reynolds numbers

and bulk mixing times. Due to the nature of

these various parameters, it is not possible

to maintain them all during scale-up under

one set of conditions.

Experience has shown that maintaining

a similar power/volume (P/V) at the

various bioreactor sizes greatly increases

the probability that the mixing within

the bioreactor will be appropriate. P/V

is a function of the impeller geometry,

the agitation rate and working volume,

as shown in Equation 2, where is the

density, n is the number of impellers,

Npis the impeller type power number,N is the agitation rate, Diis the impeller

diameter and Vis the liquid volume.

To further characterize the mixing,

the bulk mixing time at various

agitation rates can be measured via

pH or conductivity, or calculated using

commercially available models. This

is performed to determine the lengthof time required for the bulk liquid to

become 99% homogeneous with respect

to pH or conductivity.

Combining the results of the kLa

mapping, mixing time determination and

P/Vcalculations can lead the process

engineer to choose the appropriate

agitation setpoint to enable successful

scale-up. Once the agitation setpoint isdetermined, the sparging scheme can

be designed to ensure the dissolved

oxygen in the bioreactor is maintained

while carbon dioxide is effectively

stripped from the bioreactor and foam

accumulation is kept to a minimum.

BIOREACTOR CONTROL AND

ALARMING

Mammalian cell culture basedbioprocesses require monitoring and

control of the bioreactor environment

to ensure consistent bioprocess

performance. The parameters requiring

control include temperature, agitation,

dissolved oxygen (DO) and pH. Other

parameters such as cell density,

nutrient concentration, desired and


26/31


undesirable culture by-products are

controlled indirectly via medium and

feed formulation and can be greatly

affected by the physical and chemicalparameters. Neglecting control of

these parameters could potentially

impact final product quality, so

online measurements can be

employed to maintain the culture

in an optimal state. Bioreactor

control schemes entail a series of

steps as outlined below.

Parameter monitoring and control

requires the use of an appropriate

analytical device, an appropriate

sampling method and a control

system which can act appropriately

to the information it receives. Online

monitoring control systems are rapid,

non-invasive and minimize potential

for contaminant introduction and may

be performed inside or outside of the

bioreactor but must be connected

directly to the bioreactor interior.Conventional parameters subject

to online analyses include pH, DO

concentrations, agitation, backpressure

and temperature. Measurements

of additional chemical parameters

including cell density and viability,

waste metabolites, nutrients and

product concentration have historically

been measured offline, although many

technologies are becoming available

that enable online measurement.

Online control employs probes, each

of which have a sensor whose function

is to gather information relevant to

the biological state of the culture. This

information is then converted into an

electrical signal that can be amplified,

recorded and analyzed so as to drive

the applicable control scheme. Inlight of this, it is important that this

information is pertinent to the current

or future state of the bioprocess and

be quickly generated and processed

with minimal manual intervention.

The sensors should be selected based

on the following criteria: potential to

cause contamination, robustness and

reliability of sensor elements, specificity

for parameter being measured andinsensitivity to the harsh environment of

the bioreactor. Maintaining reproducible

and acceptable product quality

and productivity, while minimizing

downtime, are the primary business

drivers behind an effective bioprocess

monitoring and control strategy.

Effective bioreactor control may entail

monitoring more than just the primarycontrol parameters. For example, we

once encountered a situation in which

the pH stayed within the control range,

but caustic was being fed to the tank

even though the base controller had an

output of zero such that the controller

was not trying to feed caustic. Our

investigation led to the discovery that

Measure of response variable

Comparison of measured value to process setpoint

Activation of control scheme


27/31


the tubing from the caustic vessel to the

bioreactor was not installed properly in

the peristaltic pump, and caustic was

leaking past the pump and into thebioreactor.

Another problem we experienced

entailed hyperoxygenation of one of the

seed bioreactors. This led to decreased

growth, viability and increased specific

lactate production. Investigation of

the incident led to the discovery that

oxygen was leaking into the process air

line. The DO probes had been calibratedper ticket instructions but the oxygen

leak led to false readings and improper

DO control of the bioreactor. The

only indicator, other than cell culture

performance, of these false readings

was the nano-Amp readings of the DO

probes, which were found to be much

higher than normal.

TEMPERATURE CONTROLTemperature is a key parameter requiring

monitoring and control throughout

bioprocesses to ensure an actively

growing and productive mammalian

cell population. In general, an accuracy

of 0.5C is considered adequate

for cell culture, although transient

excursions may exceed that range with

no impact to product quality or cell

culture performance. Typical bioreactor

temperature measurement devices are

resistance temperature devices (RTDs),which are highly accurate, reproducible

and only moderately expensive. The

response time of these devices is in

the order of several seconds. These

RTDs rely on the fact that the platinum

core wire conductance varies with

temperature to quantify temperature.

In the RTD temperature sensor control

scheme, the signal is amplified,

linearized and transmitted to a controllerwhereupon it is compared to a setpoint.

Based on this continuous comparison,

the bioreactors temperature is regulated

by adjusting the temperature of the

jacket surrounding the bioreactor. If and

when temperature deltas are recorded,

the temperature of the jacket is adjusted

appropriately through use of heat

exchangers.

DISSOLVED OXYGEN CONTROL

Mammalian cell cultures require oxygen

for the production of energy from

organic carbon sources e.g., glucose.

Given oxygens poor solubility in water-

based solutions, the control of oxygen

flow is carefully regulated to ensure

it does not become a rate-limiting

factor in the process. In contrast, a

hyperoxygenated bioreactor air supply

can irreversibly and adversely impact

culture performance.

Due to fluctuating cell concentrations

and the associated fluctuating oxygen

consumption rate, the quantity of

dissolved oxygen (DO) in culture

medium is in a state of dynamic

equilibrium. At a constant temperature,

the DO concentration in the culture

media (CL) is proportional to the

amount of oxygen in the vapor phasewithin the media (CG) in a manner that

is dependent on temperature and media

composition (represented by Henrys law

constant, H in the equation below).

CL = HCG

Amperometric DO probes are typically

used, which measure the reduction of

oxygen at a cathode and the formationof silver chloride at the anode with an

electrolyte solution bridging the gap

between the nodes. Given the nature of

amperometric DO probes, it is necessary

for these probes to be allowed to

polarize prior to their use. A calibration

is then performed, and in the event that

the probe falls outside of the acceptable


28/31


calibration range, the probe membrane

body and electrolyte are replaced.

PH CONTROLAlong with temperature and dissolved

oxygen control, effective pH control

is vital to ensure process success

given the sensitivity and potential

cellular damage that may occur if pH

control remains unchecked. Although

cell culture media typically provides

substantial buffering of pH, mammalian

cell metabolism routinely decreases

the culture pH due to the productionof lactate and carbon dioxide, both of

which are acidic in nature. Excessive

hydrogen ion concentration may

alter normal cell metabolism and

proliferation by impairing substrate

uptake and product release. In addition,

it is possible that the bioactivities of

some secreted monoclonal antibodies

or therapeutic peptides could be pH

sensitive.

Typically, the pH probes on the

bioreactors are calibrated while

connected to the transmitters on the

bioreactor that is destined for use

and prior to installation into the tanks.

Typical calibrations are conducted

using two buffers, with a calibration

check performed in an intermediate

buffer. Failed calibrations are typically

due to damaged pH probes, but may

also be attributed to faulty cables ortransmitters.

Once calibrated, pH probes have

occasionally been observed to generate

incorrect readings, due to probe

drifting, slowed response time or

impaired sensitivity. These erroneous

readings are typically attributed to

sensor membrane alterations due

to extreme temperature swingsand fouling from media and cellular

components. As a result, a policy for

re-standardization of the probes

may need to be developed using an

orthogonal pH measurement method

as the gold standard.

Effective pH control can be achieved

through use of two separate PID

loops, where one is the acid controllerand one is the caustic controller. In

a bicarbonate-buffered system, the

acid controller controls the carbon

dioxide flow and is configured such

that the carbon dioxide flow ramps up

very quickly when the process value is

above set point and instantly turns off

when the acid controller set point is

reached. The liquid caustic controller

utilizes a pulse width modulator (PWM)

to control the amount of time the

caustic peristaltic pump is on or off.The set point on the peristaltic pump

is set manually per manufacturing

ticket instructions and the controller

only turns the pump on and off. The

frequency of measurement and pulse

addition and duration can be altered

to effect varying levels of control by

tuning the control loop. Due to the

high pH of the caustic feed, it should

be fed into the bioreactor through asub-surface port to facilitate quick

dispersion into the culture.

DISSOLVED CARBON DIOXIDE

CONTROL

Dissolved and evolved (i.e., headspace)

carbon dioxide levels can be indicative of

cellular metabolism and are thus routinely

monitored as indicators of culture

performance. In general, the mammaliancell cultures display sensitivities to

extremes of dissolved carbon dioxide

(pCO2) be they low or high. High pCO

2

levels have been reported in the literature

as an inhibitor of growth and metabolism

and can impact product quality

characteristics such as glycosylation

of the protein product.


29/31


Several parameters can aect the

pCO2levels, including pH set point,

temperature, sodium bicarbonate

concentration, cellular metabolism,caustic addition to the medium and

gas flows. Each of these parameters

must be considered carefully to

enable successful pCO2control.

pH, temperature and bicarbonate

concentrations are typically not

adjusted during a process to control

pCO2, but rather gas flows and caustic

addition are controlled to maintain the

pCO2within the desired target range.The gas flows can be chosen to strip

out the desired amount of dissolved

carbon dioxide as experience has shown

the carbon dioxide levels are influenced

more by total gas flow through the

bioreactor rather than kLa.

If the culture pH has drifted to the

acidic side of the dead band, increasing

the airflow strips out carbon dioxidepotentially leading to an overall

reduction of caustic addition. The

reduced amount of caustic can lead to

a lower pCO2at the end of the culture

when lactate levels typically decrease.

However, if the pH is on the basic side

of the dead band such that the CO2is

being fed, increasing the airflow will only

lead to increased CO2flow and will not

affect the pCO2.

BACKPRESSURE CONTROLThe stainless steel bioreactors are

maintained under positive pressure

to create an environment that is more

conducive to axenic operation. A

backpressure setpoint is generated by

maintaining a constant overlay process

air flow into the headspace of the

bioreactor. The backpressure can then

be controlled via a PID control loop

that operates a flow control valve onthe vent line. To avoid safety concerns

associated with over-pressurization,

rupture discs may be incorporated into

all pressurized stainless steel vessels

to act as pressure relief devices. In

addition to the bioreactor headspace,

positive pressure should be maintained

on all transfer lines within the sterile

boundary and any associated auxiliary

stainless steel vessels used for additionsto the bioreactors.

Backpressure can also be used as the

driving force to govern bioreactor-to-

bioreactor transfers and bioreactor-

to-primary recovery transfers. Care

should be given to ensure the transfer is

fast enough to not allow cells to settle

during the transfer, but not so fast as

to subject the cells to excessive shear.

The transfer time can be dictated by the

pressure drop and pipe dimensions.

ALARM STRATEGY

The alarm strategy should be

configured to alert the operators

that the process is deviating from its

acceptable range, but should also

provide early warnings such that the

operator can respond in time to prevent

loss of the batch. To this end, multiple

levels of alarming may be implemented.The first level of alarms can be set to

include the normal variability present

within a control loop such that if the

alarm is activated, operations can

assume that an unexpected excursion

has occurred but will have time to take

pre-emptive action before the process

is negatively impacted. The final level

of alarms should be set to match the

acceptable ranges listed in the processflow chart specific to a process. While

determining the alarm strategy, care

should be taken to apply alarms only to

the appropriate parameters. If excessive

alarming occurs, operators may begin

to not respond effectively to alarms to

the extent that important alarms may

be missed.


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USE OF OFFLINE DATA

Additional information regarding

culture health and performance may be

obtained offline using analysis of asepticsamples of the culture. Typical offline

testing will yield information regarding

cell numbers and cell viability using

an automated cell counter system. In

addition, a blood gas analyzer can be

used to determine the levels of relevant

parameters including lactate, glucose,

pCO2and pH. The offline samples may be

used primarily for informational purposes

and not linked into automated responsesystems to drive bioreactor control

changes. However, offline measurements

may be used by technical services

to monitor the process and instruct

operators to, for example, restandardize

the pH probes should a drift from

setpoint be observed.

BIOREACTOR FEEDING STRATEGY

The basal medium may be added to thebioreactor from a disposable bag via

peristaltic pump, or for larger volumes

from stainless steel media make-up

tanks via pressure transfer. For the tank

transfers, the media is typically sterilized

in-line via two hydrophilic filters, a pre-

filter followed by a sterilizing grade filter.

The filters are steam sterilized in line

simultaneously with the transfer path

and are cooled prior to media transfer.

Nutrient feeds are typically preparedin disposable bags. The nutrient feed

typically consists of multiple stock

components that need to be well mixed

and may require pH adjustment. Upon

one nutrient feed into a bioreactor, a

high pH excursion was observed in the

bioreactor which was later attributed

to poor mixing of the nutrient feed

components. The nutrient feed bag was

subsequently re-designed to allow forbetter mixing within the bag to avoid the

pH change in the bioreactor.

Nutrient feeds are delivered to the

bioreactors at pre-determined times

during the cell culture process. These

feeds are manually added to the

bioreactors, with very little automation

associated with them. In fact, the

automation is configured such that thenear-to block valve on the nutrient feed

line is always open during culture phase

to maintain positive pressure on the line.

Therefore, the peristaltic pump head is

the only block on the line, such that the

operator can initiate the feed simply

by turning on the pump. The nutrient

feeds may be slightly acidic, so a typical

concern associated with addition of the

feed is a change in pH in the bioreactor.

To account for this, the addition flow

rate of the feed is dictated, as well asinstructions to the operators to stop the

feed if the pH exceeds the acceptable

range.

ABOUT THE AUTHORS

Cillian McCabe has a first-class honours degree

in Biotechnology from National University

of Ireland (NUI), Galway, and a PhD in Gene

Therapy Approaches to the Treatment of Type

1 Diabetes Melitus from the School of Medicine

at NUI. He joined Eli Lilly & Co. in 2007 and

has supported Bioprocess Development and

Manufacturing Operations both in the U.S. and

Ireland as part of Lillys Manufacturing Science

& Technology functional group.

Brian Stamper has a B.S. in Biochemistry from

Indiana University (Bloomington, Ind.) and an

M.S. in Biological Engineering from Purdue

University (West Lafayette, Ind.). He joinedEli Lilly in 2001 as a development scientist

in Bioprocess Development and transitioned

to Bioprocess Operations in the Clinical

Trial Material Supply pilot plant in 2005 as a

manufacturing associate.


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ABOUT HAMILTON COMPANY

For more than 30 years, the name Hamilton has been associated worldwide withuncompromised quality in precision fluid measuring and analytical products aswell as in fully automated analytical processes.The same competence has led the Sensor Technology Group to design a line ofhigh quality pH, ORP, optical and amperometric Dissolved Oxygen, Conductivity

and Cell Density electrodes for process and laboratory measurement.

Hamilton Company4970 Energy WayReno, NV 89502

Phone: 775-858-3000www.HamiltonCompany.com

ADDITIONAL RESOURCES CLICK ON LINK

Brochure: Biopharmaceutical Sensor Solutions

http://www.hamiltoncompany.com/downloads/Hamilton_Sensors_Solutions_for_BioPharm_06.2012.pdf

Biopharmaceutical Process Sensor Selection Tool

http://www.hamiltoncompany.com/products/sensors/c/1008/

Article: Advances in Sensor Technology Improve

Biopharmaceutical Development

http://www.hamiltoncompany.com/downloads/Advances_in_Sensor_Technology.pdf

Article: In-Line Sensor Systems

http://www.hamiltoncompany.com/downloads/In-Line_Sensor_System.pdf
http://www.pharmamanufacturing.com/http://www.hamiltoncompany.com/http://www.hamiltoncompany.com/http://www.hamiltoncompany.com/downloads/Hamilton_Sensors_Solutions_for_BioPharm_06.2012.pdfhttp://www.hamiltoncompany.com/products/sensors/c/1008http://www.hamiltoncompany.com/downloads/Advances_in_Sensor_Technology.pdfhttp://www.hamiltoncompany.com/downloads/In-Line_Sensor_System.pdfhttp://www.hamiltoncompany.com/downloads/In-Line_Sensor_System.pdfhttp://www.hamiltoncompany.com/downloads/Advances_in_Sensor_Technology.pdfhttp://www.hamiltoncompany.com/products/sensors/c/1008http://www.hamiltoncompany.com/downloads/Hamilton_Sensors_Solutions_for_BioPharm_06.2012.pdfhttp://www.hamiltoncompany.com/http://www.pharmamanufacturing.com/

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