1. B i o P r o c e s s Technical
An Industry Perspective
on Quality By Design
Rick Johnston, Jim Lambert, and Emily Stump
T
www.photos.com
he US FDA’s quality by design The FDA provides a basic definition
(QbD) initiative and associated in a current strategic guidance
ICH Q8, Q9, and Q10 document: “QbD is understanding the
guidance documents are manufacturing process and identifying
increasingly embraced by the the key steps for obtaining and
biopharmaceutical manufacturing assuring a pre-defined product
industry for ensuring consistent quality” (3). Here we consider QbD
product quality and lower costs of with ICH Q8, Q9, and Q10 together
development and manufacturing. One as they form part of a broader industry
critical problem the industry faces perspective on risk-based
involves understanding how to manufacturing. The aim is to increase
implement QbD and determine the process knowledge through a data collection and statistical analysis of
benefit of such projects — which systematic understanding of how manufacturing process parameters to
require the work of many groups quality attributes are derived. detect changes in a process or in product
across quality, manufacturing sciences, Understanding how quality relates to quality. Figure 1 summarizes the main
and engineering departments. Here the manufacturing process is more themes and data flows in each of these
we present the results from a survey of difficult in biotech than in many other two complementary approaches.
biopharmaceutical manufacturing industries because of the sensitivity of The two cycles of process
professionals undertaken to determine biologic processes to seemingly small development and manufacturing
current QbD understanding in the changes (both deliberate and improvement are related by way of
industry. We outline key gaps and unintentional) in process parameters. ICH Q8, Q9, and Q10 through
show how QbD has been implemented Although the effect on product quality process data. Although each cycle uses
in practice. We focus on simple, may be understood, the cause of that data for different purposes (process
practical implementation of QbD effect is not as clear. The combinatorial development to improve current
principles and show the perceived effect of process variables on product designs and establish better process
value of such investment by industry. quality traditionally has not been “platforms”; manufacturing
sufficiently evaluated. Historically, that improvement to deliver quality product
QbD and ICH: A Primer has encouraged a risk-adverse approach more reliably), process data are key to
Extensive definitions of QbD and the to manufacturing process development both. As such, biomanufacturers are
associated ICH Q8–Q10 framework and improvement due to significant currently attempting to assess how to
can be found in the references (1, 2). costs associated with process change. • better collect process data
Our belief, shared by many others in the (continuous time historians, better
industry, is that this has discouraged laboratory information management
Product Focus: All biologics innovation and kept manufacturing systems, batch context)
Process Focus: Manufacturing costs comparatively high. • achieve direct control over
The QbD approach has been processes (through means such as
Who Should Read: QA/QC, implemented in the biomanufacturing process analytical technologies)
process development, validation, and industry in two distinct but related • perform more sophisticated
manufacturing ways. First, QbD increases up-front analyses of collected data
experimentation as part of process • determine ways to allow more
Keywords: Risk management, data development to establish the operating groups in an organization to see and
management, statistics, PAT, ICH Q8,
boundaries between where quality is analyze data in real time.
ICH Q9, ICH Q10, process validation and isn’t affected. Second, QbD With traditional risk assessment
Level: Intermediate increases the quantity and fidelity of approaches, biomanufacturers are
26 BioProcess International 10(3) M arch 2012

2. Figure 1: Implementation of QbD for process development and manufacturing improvement
Science Base
Different unit operations
• Equipment data Process
• Operating characteristics
Development
Process Optimization ICH Q8 Tech Transfer
Know your process
• Design of Experiments • Optimized process,
• Quantification of CPPs and CQAs
operating ranges and • Quality target product
quality controls profile (QTPP)
• Alternatives evaluated
Process Design
Management
ICH Q9
Risk
Improvement Manufacturing
Cycle Improvement
Design Lessons Facility Process Data ICH Q8
Know your process Facility Model
• Design or actual • Statistical process control • Process performance
• Performance data on • Golden batch root or theoretical
areas of improvement cause analysis • Analysis of predictive
• Lessons learned ICH Q10 • Stochastic models
power of models
Risk Effectiveness
Monitoring
Management
ICH Q9
Manufacturing
Risk
Improvement
Cycle
Production Economics Process
• Lower failure rates Improvements
• Improved productivity • Risk-based focus
• Better monitoring • Better measurement
• Faster lot release
ICH Q10 and control systems
Risk Effectiveness
Monitoring
attempting to lower their failure rates is made of data collection, statistical The FDA on Data
to improve quality and productivity analysis, and the need to operationalize
“Managers should use ongoing
and justify implementation of analysis methods rather than perform
programs to collect and analyze
monitoring efforts based on economic them infrequently and offline. product and process data to evaluate
value. Increased data accessibility However, it is far from clear what such the state of control of the process.”
during process development provides guidance actually means in practice for
“We recommend that a statistician or
information that allows future process organizations wanting to implement person with adequate training in
designers to properly focus on a robust approaches that look more closely at statistical process control techniques
control strategy and those parts of process–quality relationships. develop the data collection plan.”
each process with the greatest risk “We recommend continued monitoring
density. Together, the process and Industry Perspective and sampling of process parameters
manufacturing improvement cycles We assessed the state of the industry and quality attributes … Process
provide concrete ways to design and through a recent survey conducted variability should be periodically
operate better biopharmaceutical with BioProcess International (survey assessed.”
production processes, which deliver on details available on request). The goals From CDER/CBER/CVM. Guidance for
the promise of higher quality and of our survey were to determine the Industry: Process Validation — General
lower compliance costs. level of implementation of QbD, key Principles and Practices. US Food and Drug
Administration: Rockville, MD, January 2011;
Recent FDA (and other) guidance advantages companies perceived in the
www.fda.gov/downloads/
has focused on quantitative aspects of approach, and challenges encountered drugsguidancecomplianceregulatory
QbD by way of data collection (see the in justifying implementation of QbD information/guidances/ucm070336.pdf.
“FDA on Data” box). Explicit mention and ICH Q8–Q10 guidelines.
28 BioProcess International 10(3) M arch 2012

3. Figure 2: Two ranged questions (participants asked to rank each on a Figure 3: Ranged answers (1–5) regarding implementation of ICH Q8,
scale of 1–5) compared the perceived importance of QbD and its actual Q9, and Q10 principles
implementation How extensive is your organization’s
implementation of ICH Q8, Q9, and Q10 principles?
How would you rate QbD’s perceived
importance to your organization? 0% 5% 10% 15% 20% 25% 30% 35%
0% 5% 10% 15% 20% 25% 30%
None or never heard of them 1
Not important or
no assigned budget 1
2
Average: We have
2 implemented Q8 standards 3
but have not achieved
Important: Assigned budget continuous quality systems.
3
and dedicated headcount 4
Excellent: We have been
4 using continuous quality
verification systems Q8−Q10 5
Critical: Mandated by for 3 years or more.
management that all 5
filings include QbD
turn, leads to increased flexibility in process design and an
How would you rate QbD implementation in your organization? ability to make changes after regulatory filing as well as an
0% 5% 10% 15% 20% 25% 30%
Low: We don't have any
increased understanding of key business risks.
kind of activities, but we 1 Although the potential benefits of QbD are well known,
have a team looking at it. biomanufacturers experience road blocks in its actual
Medium: We are trialing QbD
2
execution. Difficulty with implementing QbD relates to
and have made some two main themes, both based in a lack of information.
postapproval regulatory 3
filing changes as a result First, knowledge is limited regarding business drivers and
of these efforts. 4 return on investment (RoI) associated with implementing
High: We use QbD for all QbD (Figure 5). Evidence available suggests that once such
existing and new product 5 systems are in place, they do have significant benefits
filings and day-to-day
analysis at all of our sites. across manufacturing, quality, and process development.
Biomanufacturers implementing such systems can expect to
see productivity improve as resources are focused on a small
Figure 2 illustrates a disconnect between the current, number of key variables. Closer monitoring of that reduced
perceived importance of QbD and its implementation state set can justify faster batch release and dramatically decrease
in the industry. Most companies have some assigned the time required to perform root-cause investigations. But
budget and dedicated staff associated with QbD — with we need to report on success stories in the industry to help
>50% of survey respondents identifying it as “important.” justify the up-front costs associated with QbD.
However, despite this level of interest, few companies use The second issue slowing implementation is a lack of
QbD today in regular filings. Most current approaches are practical knowledge related to putting QbD into practice.
based on limited piloting of QbD. Figure 5 also shows that biomanufacturers are unaware of
Similar analysis applies to implementation of ICH what steps to take in applying QbD to their processes and
principles (Figure 3). ICH Q8 calls for increased organizations. For example, statistical process control
experimentation to determine the importance of variables and (SPC) techniques are currently used in other industries
potential interactions that affect product quality. Although (such as semiconductor manufacturing), but those cannot
Q8 has been implemented at many companies, few have be applied “ad hoc” to biotech because of the inherently
established a comprehensive model for continuous quality high levels of biologic variability, and sequentially
verification, which is a central goal of ICH guidelines. dependent results (autocorrelation) (4).
The nascent state of the industry in this regard presents
a number of questions: To what extent should QbD be A Roadmap to Continuous Quality Verification
embraced by an organization? What concrete steps should Whether biomanufacturers choose to increase
be taken to change manufacturing systems? And should experimentation and incorporate QbD-related concepts in
QbD be focused primarily around development or their regulatory filings, the operations community has come
manufacturing — or both? to broad consensus regarding the importance of increasing
process knowledge. Most biomanufacturers collect large data
Perceived Benefits and Roadblocks sets about their processes. However, the challenge is to ensure
The overwhelming majority of survey respondents that what’s collected is in the correct format and to adopt
identified the primary benefit of QbD as a better tools that use the information in the best way to gain insights
understanding of manufacturing processes leading to into processes. Reuters reported in December 2010 that Erich
higher product quality. Figure 4 shows top reasons for Hunziker (head of IT at Roche) called dealing with this ever-
implementing QbD. They are all tightly aligned with FDA growing steam of data one of his biggest concerns (5).
goals for QbD as well as the overall ICH Q8–Q10 goals: a Biopharmaceutical manufacturing organizations have (to a
better understanding of process fundamentals. This, in greater or lesser level of sophistication) adopted the following
30 BioProcess International 10(3) M arch 2012

4. Figure 4: Primary benefits of QbD and top reasons for investment in QbD
four steps in this regard: Collect the
right kind of data; perform biotech- In theory, what do you view as the primary benefit of QbD?
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
informed SPC and statistical quality
control (SQC); correlate process Better understanding of
the relationship between process
parameters to quality attributes; and parameters and quality outcomes
create a comprehensive stochastic model.
Improved product quality
Below are some successful
approaches to those steps that have Better understanding of business
been applied in the industry. risks (e.g. higher inventories,
low yields, and high discards)
SPC and SQC seem to be the first
significant QbD-related changes to Increased flexibility/ability to
tweak the process
biomanufacturing. SQC is
retrospective (e.g., testing quality Shortened quality release times
processes into a manufacturing
process), whereas SPC is prospective
Lower compliance costs
(e.g., real-time process variable
monitoring). These tool sets have the
ability to provide insight by focusing What is the primary reason you think would justify
further QbD investment in your organization?
on variation of a process from a “mean”
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
or reference batch. Variability is both
significant in biotech and typically the Process Understanding: to
drive better understanding of the core
cause of quality variations. So it makes processes and technologies supporting
sense to establish SPC controls that them to drive long-term change
can more accurately detect and control Lower Compliance Risk: to
those variations. understand variability
and prevent product withdrawal,
Figure 6 shows survey results contamination, or other adverse events
relating to adoption and sophistication
of SPC/SQC approaches within Business Drivers: to
biopharmaceutical manufacturing. increase yields, lower reported
deviations, decrease discard rates
Data indicate that SPC is in use by
~50% of manufacturing companies, but
its use is mostly offline and constrained
to performing analyses that support Figure 5: QbD implementation challenges
experimental design rather than What are the challenges to implementing QbD in your organization?
activities on the “shop floor.” 0% 10% 20% 30% 40% 50%
SPC has been very successful in
Additional upfront costs
other industries (e.g., automotive and
semiconductor manufacturing) for
Poor understanding of the benefits
driving costs lower and quality higher. of QbD and its return on investment
However one issue with its
implementation in biotech is that No in-house experience with QbD
biological processes are both highly
sensitive and inherently variable. That Unsure how to actually implement
QbD principles in practice
can lead to false positives — SPC
readings that appear to be trending No business drivers for implementing
out of control but are actually not — QbD for existing/legacy products
and those need to be carefully Perceived longer new product
accounted for. Poor application of introduction times
SPC theory to a process, low
Management hostile or don’t
understanding of the fundamental understand or support QbD
drivers of a process, and poor ICH Q8
effort to determine critical process
parameters can all cause problems.
Figure 7’s sample X-bar chart from statistical methods. Here, the red star distinct from a control limit, which is
Bio-G’s Crosswalk software shows identifies one. The colored regions are defined according to regulatory filing
deviations from a sample mean. calculated from sample data and used documents as a deviation from the
Certain trends (e.g., the upward trend to calculate whether a process is in or process important enough to require
to the right) can be detected using out of statistical control. (This is special investigation.)
32 BioProcess International 10(3) M arch 2012

5. Figure 6: Adoption and sophistication of SPC within biopharmaceutical Figure 7: X-bar chart for a process showing sigma bands and statistical
manufacturing (participants were asked to answer with 1–5 rankings) control limits
How extensive is your organization’s
use of statistical process controls?
0% 5% 10% 15% 20% 25% 30% 35%
190
Low: Limited to a few 1
process experts
Sample Mean
180
2
Medium: Used regularly 170
by manufacturing sciences
3
and quality for
root-cause analysis 160
4
High: Used hourly by 150
operators and 5
supervisors on the floor
January February March April May June
How sophisticated is your organization’s Date (grouped by all, 2007)
use of statistical process controls?
0% 5% 10% 15% 20% 25% 30% 35% 40%
Low: Use offline, Minitab or
other manual toolsets 1
Figure 8: “Golden batch” measurements for a chromatogram showing
deviation from a reference batch
2
Medium: Use enterprise 350
toolsets that do multifactor 3 300
DOE but are not real time
250
4
200
High: Real-time multifactor
tool sets with the ability to 150
incorporate stochastic 5
multivariate models 100
Values
50
0
Graphs such as those in Figures 7 and 8 are important for −50
two reasons. First, they provide a means to extract patterns −100
−150
from single data sources and identify points at which corrective
−200
action needs to be taken independent of a specification limit. −250
That provides a more sensitive manner of detecting issues than 0.0 0.2 0.4 0.6 0.8
Normalized Measurements
alarms that are at best lagging indicators that lack textured
information because they offer only a “binomial view” of the
process (within or outside specification). Second, these graphs those untrained in complex stochastic methods. Users can
are easy for operators to understand. So they can be used in exclude >90% of the possible set of interactions and focus
real time on the shop floor (rather than offline with on those remaining, for which interactions are important.
applications such as Minitab software). This moves adverse This offers multiple benefits in relation to the ICH guides.
event identification (and root-cause analysis) closer to where It reinforces process knowledge (ICH Q8) and supplements
problems occur — rather than being performed after the fact risk-based assessments performed as part of ICH Q9
by statistical process experts with less operational knowledge of activities (by evaluating probability of occurrence and
affected manufacturing systems. detection in near real time). And it can be continual to
Correlation of Process Parameters to Quality Attributes: promote risk-effectiveness monitoring (ICH Q10).
Another aspect of QbD that is currently under examination Stochastic Control Tool Sets: The biopharmaceutical
is how to relate process parameters to adverse quality events. industry’s approach to stochastic control tool sets is
This was identified in our survey results as one of the most evolving. In mapping current tool sets, it becomes clear that
important potential benefits for QbD (Figure 4), but the more advanced use of QbD will rely on our ability to
biotech community is debating how best to approach it. One analyze collections of data sets that are highly complex,
option is multivariate SPC; however, the sheer number of highly variable, and autocorrelated. Figure 10 shows some
possible interactions between process parameters and quality tool sets available today.
outcomes complicates this approach. The industry is currently leaning toward multivariate
Another strategy that has met with success is stochastic process tool sets. They go beyond simple correlation
performing large-scale correlations of process parameters and SPC analysis of raw data. These tool sets combine real-
with one another and with quality attributes (or measurable time data with a model that can be used to understand the
surrogates for them). Figure 9 shows one such correlation. relationship between a process parameter in one unit operation
Each cell in the “heat map” correlates that process and its effect on quality throughout the manufacturing
parameter with associated quality attributes. Hot-colored process. This is a necessary precursor to implementing true
cells indicate either high positive or negative correlation real-time release and “release by exception” methods by
and cool colors little or none. The advantage of such a allowing companies to look at how a batch evolves through
technique is that it is rapid. It provides visual references to many processing steps will map against the design space.
34 BioProcess International 10(3) M arch 2012

6. Figure 9: Massive-scale correlations of process parameters and quality outcomes
Time to Get Started
High correlation
Quality by design and associated ICH
guidelines are becoming increasingly Correlation Process Parameters
important in the biotherapeutics Scale
industry, which is currently focused 1.0
0.8
around the initial stages: data
Quality Outcomes
0.6
collection, SPC/SQC pilots, and 0.4
mostly offline analysis. Clearly there 0.2
are significant capital drivers for 0.0
implementing such projects. These −0.2
−0.4
benefits come in
−0.6
• improved productivity (focusing −0.8
resources on what is often a small −1.0
number of key variables) Low correlation
• fewer variables allowing real-time
monitoring and timely release of
product (a closer observation of key improvements to quality. Such an Corresponding author Dr. Rick Johnston is
process parameters and quality approach will also lead to development executive director of the University of
outcomes) of processes and technologies that are California at Berkeley’s CELDi Center for
• improved control strategies and increasingly flexible and adaptable to Research in Biopharmaceutical Operations,
an adjunct professor at Keck Graduate
robust risk assessments the changing needs of the market.
Institute (Claremont Colleges), and founder
• a valuable knowledge and CEO of Bioproduction Group, 1250
management system (people no longer References Addison Street, Suite 107 Berkeley, CA 94702;
spend substantial periods performing 1 McCormick K. Introduction to ICH:
1-510-704-1803, fax 1-510-704-0569; rick@
Essential Background to PQLI. Pharmaceut.
root-cause analysis) bio-g.com, www.bio-g.com. James
Eng. May–June 2008; 1–3.
• lower rates of production failure Lambert (lambert.james@gene.com) is
2 Berridge J. PQLI: What Is It?
and scrap. Pharmaceut. Eng. May–June 2009: 33–39.
director of quality engineering at
Figure 11 shows some of the next 3 US Food and Drug Administration.
Genentech’s Hillsboro, OR fill–finish project.
steps. Biotech companies will need to Advancing Regulatory Science at FDA. US Dept.
And Emily Stump (Emily.Stump@CAgents.
get the QbD basics right. Forming a of Health and Human Services: Rockville, com) is a validation scientist for
team to look more closely at QbD and MD, August 2011; www.fda.gov/downloads/ Commissioning Agents, Inc. This article
ScienceResearch/SpecialTopics/ references no real data from
data collection was cited most
RegulatoryScience/UCM268225.pdf. biomanufacturers other than 193 responses
frequently in our survey results (by obtained in the survey conducted through
4 O’Neill, J. Continued Process
61% of respondents). Verification for Biological Processes. 16th BioProcess International in August 2011.
It is clear from our survey that Symposium on the Interface of Regulatory and Details survey are available on request.
biomanufacturers believe in the Analytical Sciences for Biotechnology Health
importance of QbD and associated ICH Products, 23-25 January 2012.
To order reprints of this article, contact
guidelines. Although a significant 5 Hirschler B. Roche Fears Drug Industry
Rhonda Brown (rhondab@fosterprinting.com)
Drowning in “Spam” Data. Reuters 1 December
number of roadblocks to implementation 1-800-382-0808. Download a low-resolution
roche-data-idUSLDE6B01XF20101201. •
2010; www.reuters.com/article/2010/12/01/
remain, biomanufacturers are looking at PDF online at www.bioprocessintl.com.
QbD as a method for increasing process
understanding and driving fundamental
Figure 10: Tool set mapping against process complexity and variability Figure 11: Top “next steps” for QbD implementation in the
biopharmaceutical industry
High
Minitab What do you think your organization’s
Multivariate next step toward is likely to be?
SPC/SQC
Process Variability
Stochastic Process 0% 10% 20% 30% 40% 50% 60%
tools
Toolsets Form a team to look
Crystal Ball
Monte Carlo more closely at QbD
and collect more data.
JMP
Add sensors and
process control
Excel to manual systems (PAT).
ERP/MRP systems
DoE
toolsets Implement a real-time
Low statistical process control
Low Process Complexity High (SPC) package.
Implement a modeling
framework that uses SPC
data to perform multiunit
operation stochastic
modeling.
M arch 2012 10(3) BioProcess International 35