Transcript Pharmaceutical 6-Sigma Quality by Design: How can this be

Pharmaceutical 6-Sigma
Quality by Design
Ajaz S. Hussain, Ph.D.
Office of Pharmaceutical Science
CDER FDA
The 28th Annual Midwest Biopharmaceutical Statistical Workshop
May 23-25, 2005 * Ball State University, Muncie, IN
Outline
• Background & Terminology
• Pharmaceutical product and process
quality – what is the current “sigma”?
• Challenges in moving towards “6-sigma”
levels?
• What are the steps necessary for the
pharmaceutical continuous improvement
journey in the 21st Century?
Remembering a few Guru’s of
Quality
“Failure of management to plan for the future and
to foresee problems has brought about waste
of manpower, of materials, and of machine-time,
all of which raise the manufacturer's cost
and price that the purchaser must pay.” - Deming
Manufacturing Process
Performance
• A set of causes and conditions that
repeatedly come together to transform
inputs into outcomes
– Inputs: Information, Materials, ....
– Outcomes: Products, Information,…
• Quality characteristics of the outcomes are
indicators of performance
– Will vary over time and location, and analysis
of this variation is generally a basis for action
Nolan and Provost. Quality Progress, May 1990
Decisions: Interpretation of
variation
• Decisions are often based on interpretation of
patterns of variations
– Indicative of a trend or of random variation (that is
similar to what has been observed in the past)
– Misinterpretation leads to losses
• Blaming people for problems beyond their control
• Spending unnecessary resources investigating and/or taking
actions to address perceived trends nothing has changed
• “Crying wolf” too often may desensitize a quality system and
reduce its alert level to address a “real wolf” when one will
appear
Methods to Manage Variation: A
Historical Perspective
Specifications &
Fitness
for Use
Tolerances
5000 BC
1800 AD
Control
Chart
1924
Interchangeability of parts
Provost and Norman. Quality Progress December 1990
Shewhart’s Common & Special
Causes of Variation
• Common causes of variation are inherently part
of the process (or system) all the time and affect
every one working in the system
• Special causes are those that arise because of
specific circumstances, i.e., not present all of the
time and do not affect every one working in the
system
• A “Control Chart” is a tool to distinguish between
the two types
Nolan and Provost. Quality Progress, May 1990
Stable and Unstable Process
• A process (or a system) that has only common
cause affecting the outcomes is called a stable
process (in a state of statistical control)
– When such a process is demonstrated to meet its
intended function, variation in such a system are
acceptable
• When both common and special cause affect the
outcomes – Unstable process (magnitude of
variation from one time period to the next is
unpredictable)
Nolan and Provost. Quality Progress, May 1990
Stable and Unstable Process
Stable & Capable
Unstable
Benefits of a Stable Process
(Deming)
• The process has an identity; its performance is
predictable
– Rational basis for planning leading to the concept of
“just in time manufacturing”
• Cost of quality is predictable - Productivity is at a
maximum and costs at a minimum for a give
system
• The effect of changes in the process can be
measured with greater speed and reliability
– In an unstable system it is difficult to separate changes
to the process from special causes. Therefore, it is
difficult to know when a change results in improvement
Nolan and Provost. Quality Progress, May 1990
Cost of Quality (Taguchi)
“Six Sigma”
Before
3s
A 3s process - because 3 standard
deviations fit between target and
acceptance goalposts
Customer
Specification
Target
“Design for
Six Sigma”
1s
2s
3s
After
“Defects ~ 66807 ppm”
Target
Continuous improvement:
By reducing variability
we improve the process
6s
1s
3s
6s
“Defects ~ 3.4 ppm”
Customer
Specification
Process Capability: Cp and Cpk
• Cp does not take into account any noncentering of the process relative to the
specification limits
• Cp = S/P
• Cpk = (1-K)Cp
• K = [(D-X)/(X/2)] or [(X-D)/(S/2)]
– S = acceptance criteria width; P = process
width (+/- 3σ limits); D = design center; X =
process average
Process Capability & “Sigma”
Cp
Sigma*
Defect (OOS)
0.67
± 2σ
5%
1.0
± 3σ
0.13%
1.33
± 4σ
60 ppm
1.66
± 5σ
1 ppm
2.0
± 6σ
2 ppb
* Statistical σ; not the “Six Sigma” calculation (Bhote and Bohte, 2000)
What is Continuous
Improvement
• Two concepts that describe Continuous
Improvement are
– KAIZEN (Ky’ zen) a Japanese word is often
translated in the west as ongoing, continuous
improvement
– Evolutionary Operation (EVOP)
• It is distinguished from “innovation” and
“corrective actions”
Elements necessary for
Continuous Improvement
Slides from Norman Winskill and Steve Hammond
FDA Science Board Nov. 2001
Need to recognize the underlying
operating characteristics of our specifications
Probability of Meeting Criteria,
• Human resources are the most
important company asset
• Processes must evolve by gradual
improvement rather than radical
changes
• Improvement must be based on
statistical/quantitative evaluation of
process performance
120
100
80
60
40
20
0
0 1 2 3 4 5 6 7 8 9 10 11 12
Total RSD, %
Quality System Requirements QS-9000
Third Edition element 4.2.5—
Continuous Improvement (1998).
• For those product characteristics and process parameters that
can be evaluated using variable data, continuous
improvement means optimizing the characteristics and
parameters at a target value and reducing variation around
the value.
• For those product characteristics and process parameters that
can only be evaluated using attribute data, continuous
improvement is not possible until characteristics are
conforming.
– If attribute data results do not equal zero defects, it is by definition
nonconforming product. Improvements made in these situations are
definition corrective actions, not continuous improvement.
• Continuous improvement [shall be undertaken] in processes
that have demonstrated stability, acceptable capability and
performance.
What is the current
pharmaceutical “sigma” value?
• How should we define pharmaceutical “sigma”?
– Product quality
• % of units in a batch outside the regulatory or compendial
acceptance criteria
• % of batches recalled
– Process quality
• % of batches rejected
• % of batches “right 2nd or 3rd time”
• What is the minimum regulatory “sigma” value?
– One interpretation: “A process is no longer considered
validated when the recall rate exceeds 10%”?
What is the current
pharmaceutical “sigma” value?
For many products and processes:
Process Quality at about “2σ”?
Product Quality > “5σ”?
If so, are we not trapped in a “corrective action crisis”
and also wasting lot of resources?
Pharmaceutical “Customer”
Specifications
• Often combine attribute (no unit outside..)
and continuous variable (RSD) in quality
decision process
• For example: Dose Content Uniformity
– Upper Specification Limit = 125%
– Lower Specification Limit = 75%
– Standard Deviation not to exceed 7.8%
– Test sample size 30
– “No unit in 30 is outside 75-125%”
Process Capability and
Variability
• Without the “attribute” criterion
– Assuming a stable process; normal
distribution
– Mean = 100%, %RSD = 7.8%, n=30
• Cp=Cpk = 1.07 and
• ~ “3σ” process
– Standard Deviation = 2.0%
• Cp=Cpk = 4.17
• >”6σ” process
Combined Criteria
~ 10% can be rejected
“σ < 2”
> 40% can be rejected
PQRI Blend Uniformity Working Group Report
Other Challenges
Difficult questions faced by
Manufacturing Groups and Regulators…
• If we chose to use a calibrator tablet for a
Gauge R&R study....
• σ2(Total for Calib.)
σ
• = σ2(Calib.) + σ2C*Measurement
σ
σ
• What is the measurement for the Calibrator and what
is its variability? σ2(C*Measurement)
• Since σ2(Calib.) is not known; we have to use σ2(Total for
σ
σ
σ
Calib.)
• σ2Total for Product = σ2Product + σ2Total for Calib.
σ
σ
σ
Hussain, A.S. Biopharmaceutics and Drug Product Quality: Performance Tests
for Drug Products, A Look Into the Future. USP Annual Scientific Meeting
"The Science of Quality“. September 26–30, 2004
Other Challenges
Difficult questions faced by
Manufacturing Groups and Regulators…
• Assumption of independent variable?
• Another aspect – is the measurement capability for a
Calibrator tablet representative of the drug product?
What if there are differences such as disintegration
mechanism and buoyancy between the Calibrator and
the drug product?
Hussain, A.S. Biopharmaceutics and Drug Product Quality: Performance Tests
for Drug Products, A Look Into the Future. USP Annual Scientific Meeting
"The Science of Quality“. September 26–30, 2004
Other Challenges
• “Root cause unknown”
– Common cause Vs. Special Cause?
– The Common cause trap
• “Zero tolerance” (e.g., OOS during stability testing – when is
this simply a sample size issue?)
• Confounded metrics (e.g., dissolution Q values instead of a
“rate” metric - % label amount confounded with content
uniformity)
• Our decision system for mass production is
based on a “compounding pharmacy” model
– Mind set – we are not learning from other sectors
http://www.fda.gov/cder/gmp/gmp2004/manufSciWP.pdf
Pharmaceutical Challenges in
moving towards 6 Sigma?
• Are we measuring the “right” characteristics?
• Are our measurement systems capable?
• Are we establishing the “right” acceptance criteria for the
clinical trial product?
• The process is “approved” and “validated” – why bother?
• Zero defect mindset – better not to know the “sigma”?
• Reducing variability can result in a change in regulatory
acceptance criteria to keep the system at a low “sigma”
value – how else would you know if your quality system
is working?
For some products we may already be at Six Sigma,
but we may not be able to prove it?
The Pharmaceutical Quality:
Challenges and Opportunities
Quality – Clinical Gap!
CMC & CGMP Commitments*
CMC – CGMP Gap*
“Market Failure”!
“Corrective Actions” the only *
leverage for continuous improvement
Specification – Capability Gap*
http://www.ge.com/sixsigma/SixSigma.pdf
*Opportunity for continuous improvement*
Challenges to overcome!
What are the steps
necessary for the
pharmaceutical continuous
improvement journey in the
21st Century?
DFSS & Six Sigma
DFSS
Six Sigma
The Goal and Characteristics of
Pharmaceutical Quality Decision System
Goal
• “The quality of drug substances and
drug products is determined by their
design, development, in-process
controls, GMP controls, process
validation, and by specifications
applied to them throughout
development and manufacture.”
Characteristics
Life-cycle
ICH Q6A
What is the ICH Q8 Opportunity?
ICH Q6A
Decision Characteristics
Specifications
In process controls
Development
Design
Process validation
GMP Controls
“…where the provision of greater understanding of pharmaceutical and
manufacturing sciences can create a basis for flexible regulatory approaches.”
Steps Necessary
• Ask the “right questions”
– Begin with end in mind – Intended use
• System based (connecting the key disciplines
and regulatory submission sections)
• Facilitate structured product development
process, yet not dictate a specific process
• Leverage pre-approval changes & “bridging
studies”
• Cumulative – and support use prior knowledge
• Scientific hypothesis format
Constructing and Justifying
“Design Space”
• Build on “minimal” expectations such as
stability, bioavailability, and other
performance assessment to “test of
hypothesis”
• Scientific risk assessment
• Opportunity to demonstrate the level of process
understanding and reliability of proposed “design
space”
Steps Necessary
• Routine production
– Process control – stable process in a state of control
•
Control charts of variables (not attributes)
– Target value +/- Upper and Lower Limits
– Process capability analysis
– Not “hypothesis testing” on every lot
• Specification and Process Validation
–
–
–
–
Hypothesis testing
Parametric or non parametric tolerance interval
No penalty for higher sample size
Continuous quality verification
Specifications, Standards and
Control Limits
If, Specification = Standards
• Specification =
Standard
(no room for risk based decision)
– Non-conformance
rejection or recall
• Control limit
– Target value
– Common cause
variability
• Alert limit
– Potential “Special
cause” – investigate,
take necessary action
to prevent OOS
Control Limit
Alert Limit
Scope of the Proposed
Guideline (ICH Q10)
• Comprehensive quality system for product
life cycle that
– Complements existing GMP’s
– Focuses on those elements that facilitate
application of ICH Quality Guidelines (e.g.,
ICH Q8), and
– Facilitates continuous improvement in
pharmaceutical manufacturing
Proposed Guideline
• The starting point for a harmonized
pharmaceutical quality system (QS) will be ISO
9000 standards
• Key Considerations
– The pharmaceutical context of elements that define
the QS framework will be explained
– Elements of the QS that link to science will be
identified and enhanced
• for achieving the integrated systems approach to quality
emphasized in the ICH vision
• to facilitate continuous improvement over a product life cycle
Goals & Characteristics
• Product quality and
performance achieved and
assured by design of effective
and efficient manufacturing
processes
• Product specifications based
on mechanistic understanding
of how formulation and
process factors impact
product performance
• An ability to effect continuous
improvement and continuous
"real time" assurance of
quality
•
•
•
•
•
•
Develop effective CAPA –
eliminate “special cause”
variability
Utilize Process capability analysis
– reduce/control “common cause”
variability
Identify, understand and acquire
ability to predict critical to quality
attributes (CQA)
(product/process/measurement)
Focus on the “critical few”
Establish CQA target values and
acceptable variability around the
target value
Utilize a monitoring system that
demonstrates “state of control”
preferably based on critical
material attributes (not just end
product testing)
Summary
• Background & Terminology
• Pharmaceutical product and process
quality – what is the current “sigma”?
• Challenges in moving towards “6-sigma”
levels?
• What are the steps necessary for the
pharmaceutical continuous improvement
journey in the 21st Century?