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MBSW 2012Midwest Biopharmaceutical Statistics Workshop
May 21-23, 2012
Presenter: Krista WitkowskiCo-author: Julia O’Neill
Merck & Co., Inc.
Capability Assessments and Process Validation Stage 3 Implementation:
1.33 and Beyond
2
Abstract
This talk will discuss considerations for practitioners in pharmaceutical manufacturing as they implement the new FDA guidance for process validation. We will focus on Stage 3 - ongoing monitoring, or continued process verification - and how process capability is established, evaluated, and monitored. Examples on overcoming obstacles to implementation will be discussed, and the use of statistical thinking in our implementation strategy is highlighted.
3
Requirements of FDA Validation Guidance
• FDA Guidance for Industry: Process Validation: General Principles and Practices, published January 2011 distinguishes three stages of validation:
– Stage 1 – Process Design: The commercial manufacturing process is defined during this stage based on knowledge gained through development and scale-up activities.
– Stage 2 – Process Qualification: During this stage, the process design is evaluated to determine if the process is capable of reproducible commercial manufacturing.
– Stage 3 – Continued Process Verification: Ongoing assurance is gained during routine production that the process remains in a state of control.
• Further states that manufacturers should understand the sources of variation
– Detect the presence and degree of variation– Understand the impact of variation on the process and ultimately on product
attributes– Control the variation in a manner commensurate with the risk it represents to
the process and product
4
Stage 3: Continued Process Verification
Stage 1
Stage 2
Stage 3
Process Qualification
Continued Process
Verification
Process Design
Process Validation
5
Stage 3: Continued Process Verification
Develop Monitoring Reports Assessing the data on a frequent basis
(e.g., monthly, quarterly)
Make any adjustmentsto continually
assure the process remains in a state of control. Update
Control Strategy documentif needed
Develop Monitoring
Plan from ControlStrategy Document.Continually monitorcritical areas of the
process
Goal=To continually assure that the process remains in a state of control (the validated state) during commercial manufacture.
6
Understanding Variation for Pharmaceutical Processes
• Pharmaceutical Processes– Autocorrelation– Specifications based on process
history– Non-normal distributions common
(e.g., lognormal)
• SPC Assumptions– Independent results– Specifications based on customer
needs– Normally distributed results
Issue: Statistical Process Control (SPC) procedures are generally designed based on assumptions not typically met by pharmaceutical processes:
7
Issue 1: Autocorrelation
Independent results
Result vs. Previous Result – correlation not significant
Autocorrelated results
Result vs. Previous Result – significant correlation = .35
-4
-3
-2
-1
0
1
2
3
4
Res
ult
-4 -3 -2 -1 0 1 2 3 4
Previous result
-4
-3
-2
-1
0
1
2
3
4
Res
ult
-4 -3 -2 -1 0 1 2 3 4
Previous Result
-4
-3
-2
-1
0
1
2
3
4
Res
ult
8 16 24 32 40 48 56 64 72 80 88 96104
Sample
Avg=-0.19
-4
-3
-2
-1
0
1
2
3
4
Res
ult
8 16 24 32 40 48 56 64 72 80 88 96104
Sample
Avg=0.01
8
One Cause for Autocorrelation
Introduction ofNew raw material lot
Production lots
Growth Propagation Purification
Production (Weeks)
Introduction ofNew raw material lot
A new raw material lotintroduced late in the productioncycle has little opportunity to impact a product lot; however, a new raw material lotintroduced early in the productioncycle has a much greater opportunityto impact a product lot.This creates gradual trends (autocorrelation),rather than abrupt shifts, in product properties.
9
One Solution: Use long-term sigma
-2
-1
0
1
2
4
LT LCL = - 3.49
LT UCL = 3.11
Res
ult
8 16 24 32 40 48 56 64 72 80 88 96104
Sample
Avg=-0.19
LCL=-3.67
UCL=3.28
-4
-2
-1
0
1
2
4
LT UCL = 3.00
LT LCL = -2.99
Res
ult
8 16 24 32 40 48 56 64 72 80 88 96104
Sample
Avg=0.01
LCL=-2.24
UCL=2.25
Independent results:short-term and long-termlimits are nearly equal.
Autocorrelated results:short-term limits are narrowerthan long-term limits.
Long-term limits are morerepresentative of process capability.
Long-term
Long-term
Short-term
Short-term
2d
RST
n
i
iLT n
xx
1
2
1
10
Example 2: Inherent mean shifts
Mean shifts may be inherent – due tocampaign effects, raw material changes,slight changes in processing conditions(e.g., seasonal effects).
Results with mean shifts:short-term limits are narrowerthan long-term limits.
Long-term limits are morerepresentative of process capability.
2d
RST
n
i
iLT n
xx
1
2
1
10997857361493725131
1.25
1.00
0.75
0.50
0.25
0.00
Observation
Indiv
idual V
alu
e
_X=0.479
UCL=0.888
LCL=0.071
2
2
1
22
1
22222
1
2
1
2
22222
1
2
1
I Chart of Process dataShort-term limits based on MRbar/d2
10997857361493725131
1.25
1.00
0.75
0.50
0.25
0.00
Observation
Indiv
idual V
alu
e
_X=0.479
UCL=1.124
LCL=-0.165
2
2
22
2
222
22
2
2
2
22222
2
I Chart of Process dataLong term 3s limits
Short-term
Long-term
11
26242220181614
LCL UCL
Distribution of variable A reflecting initial sources of variability, µ=20, σ=2Distribution of Variable A, with additional source of variability, µ=21.5, σ=2Distribution of Variable A, with additional source of variability, µ=19, σ=2.5Distribution reflecting all sources of variability
26242220181614
LCL UCL
26242220181614
LCL UCL
26242220181614
LCL UCL
Observation
Indiv
idual V
alu
e
28252219161310741
25.0
22.5
20.0
17.5
15.0
_X=20.23
UCL=25.38
LCL=15.08
Observation
Indiv
idual V
alu
e
554943373125191371
25.0
22.5
20.0
17.5
15.0
_X=20.23
UCL=25.38
LCL=15.08
mu20 mu211
Observation
Indiv
idual V
alu
e
8273645546372819101
25.0
22.5
20.0
17.5
15.0
_X=20.23
UCL=25.38
LCL=15.08
mu20 mu21 mu19
6
1
5
1
1
Observation
Indiv
idual V
alu
e
8273645546372819101
28
26
24
22
20
18
16
14
12
_X=20.07
UCL=26.88
LCL=13.25
mu20 mu21 mu19
Understanding sources of variability
Do not set limits too early, before all sources of variability are captured.
Early limits (n=30)
Final limits (n=90)
12
Statistical Thinking Strategy: for Autocorrelation
• Standard Statistical Process Control (SPC) chart assumptions:– Observations are statistically independent – very important!– Observations are Normally distributed – much less important.– Limits are representative of expected performance.
• Autocorrelation can have profound effects on the performance of SPC charts.
• Considerations for control chart design:– Quickly signal real changes in results.– Reduce false alarms.– Make the chart easy to interpret –
• present results in original scale, and • limits with a physical meaning.
• Recommendation;– Set limits using the overall standard deviation based on a “long” stable
period.– Bisgaard and Kulahci provide an elegant justification.
13
Issue 2: Establishing Process Capability
• Two challenges:– Fundamental questions for pharmaceutical processes:
• Are long-term shifts (for example, from raw material trends) “extraneous” sources of instability?
• Or are they known and predictable special causes inherent to pharmaceutical process behavior?
– Specifications may be set based on process consistency, not customer requirements.
14
Three Approaches to Capability Strategy
higher is better
Business Requirements
6 * long-term Sigma
“Business” =
Specification Spread
6 * long-term Sigma
“Quality” =
Specification Spread
6 * short-term Sigma
Short-Term =Often underestimates total process variation
15
Basics of Capability Calculations
Well Off-target / Too Much Variation
Relatively Close to Target / Moderate Variation
Very Little Deviation From Target
LSL USL
Cpk < 1
LSL USL
Cpk = 1
LSL USL
Cpk > 1
2/,2/
min3
,3
min)(LCLUCL
LSLX
LCLUCL
XUSLLSLXXUSLPorC pkpk
The mean and standard deviation are estimated from the centerline and control limits of the control charts, where three sigma is half the width of (UCL-LCL).
16
Short term vs Long term
Grp 1
Grp 2
Grp 3
Grp 4
Grp 5
Long Term Study
Short Term Studies
17
Example 2: Short term variability < Long termC indices underestimate total process variation when autocorrelation is present (when “within subgroup” variation is low compared to overall).
Use the P-indices to provide a realistic assessment of long-term performance. For independent (not autocorrelated) processes, the P-indices and C-indices will be nearly equal.
14.02
d
RST
21.0
11
2
n
i
iLT n
xx
Short-term
Long-term
10997857361493725131
1.0
0.5
0.0
Indiv
idual V
alu
e
_X=0.479
UCL=0.888
LCL=0.071
10997857361493725131
0.50
0.25
0.00
Movin
g R
ange
__MR=0.1535
UCL=0.5015
LCL=0
120115110105100
1.0
0.5
0.0
Observation
Valu
es
0.900.750.600.450.300.15-0.00
USL
USL 1Specifications
1.00.50.0-0.5
Within
Overall
Specs
StDev 0.136082Cp *Cpk 1.28
WithinStDev 0.214831Pp *Ppk 0.81Cpm *
Overall
1
1111
1
1
1
Process Capability Sixpack of Process dataI Chart
Moving Range Chart
Last 25 Observations
Capability Histogram
Normal Prob PlotAD: 0.802, P: 0.037
Capability Plot
0.900.750.600.450.300.15-0.00
USL
LSL *Target *USL 1Sample Mean 0.479301Sample N 120StDev(Within) 0.136082StDev(Overall) 0.214831
Process Data
Cp *CPL *CPU 1.28Cpk 1.28
Pp *PPL *PPU 0.81Ppk 0.81Cpm *
Overall Capability
Potential (Within) Capability
PPM < LSL *PPM > USL 0.00PPM Total 0.00
Observed PerformancePPM < LSL *PPM > USL 65.02PPM Total 65.02
Exp. Within PerformancePPM < LSL *PPM > USL 7680.36PPM Total 7680.36
Exp. Overall Performance
WithinOverall
Process Capability of Process data
Cpk = 1.28Ppk = 0.81
18
Example 2: Short term variability < Long termC indices underestimate total process variation when autocorrelation is present (when “within subgroup” variation is low compared to overall).
Use the P-indices to provide a realistic assessment of long-term performance. For independent (not autocorrelated) processes, the P-indices and C-indices will be nearly equal.
2d
RST
n
i
iLT n
xx
1
2
1
Short-term
Long-term
Cpk = 1.28 Ppk = 0.81
Long-termShort-term
10997857361493725131
1.25
1.00
0.75
0.50
0.25
0.00
Observation
Indiv
idual V
alu
e
_X=0.479
UCL=0.888
1.124=Long term
-0.165=Long term
1 = USL
LCL=0.071
2
2
1
22
1
22222
1
2
1
2
22222
1
2
1
I Chart of Process dataShort-term limits based on MRbar/d2
One-sided: USL = 1
19
Risk Strategy: Ppk Comparison of CQA’s
-2
-1
0
1
2
3
4
5
CpK
-1.214
-0.423-0.284-0.105-0.0790.043
0.265 0.34 0.509 0.61 0.774 0.949 1.03 1.152 1.286 1.411 1.5411.948 2.019
2.284 2.31 2.323 2.3812.717 2.821 2.946
3.77
1X
Pro
Qua
d R
efr
ige
rate
d
1X
Zosta
vax R
efige
rate
d
Rub
ella H
arv
est
1X
Zosta
vax F
roze
n
Rub
ella P
ool
1X
Pro
Qua
d F
roze
n
1X
Va
rivax R
efr
ige
rate
d
2X
Pro
Qua
d R
efr
ige
rate
d
1X
Va
rivax F
roze
n
Measle
s H
arv
est
2X
Zosta
vax R
efr
ige
rate
d
2X
Zosta
vax F
roze
n
2X
Pro
Qua
d F
roze
n
2X
Va
rivax R
efr
ige
rate
d
2X
Va
rivax F
roze
n
1X
Pro
Qua
d F
roze
n -
Ru
be
lla H
arv
est
Rota
vir
us G
2
1X
Pro
Qua
d F
roze
n -
Mea
sle
s H
arv
est
Rota
vir
us P
1
Mum
ps H
arv
est
Rota
vir
us G
4
Rota
vir
us G
3
VA
QTA
1X
Pro
Qua
d F
roze
n -
Mum
ps H
arv
est
Rota
vir
us P
1 w
itho
ut lo
w lots
Rota
vir
us G
1
1X
Pro
Qua
d F
roze
n -
Ru
be
lla P
oo
l
Product
Chart
Process Robustness & SimplificationOpportunities (<1.33)
Capable & Stable Process (≥1.33)
Ppk for 27 Critical Quality Attributes of a family of pharmaceutical products.Each bar represents the estimated Ppk for a single CQA.The bars are ordered from lowest Ppk (greatest risk) to highest.
Note: Ppk is long-term capability, but takes into account centering of the process within specifications.
In cases when there is a very large range of values for Ppk, a log scale can make this more read-able, while still maintaining the “red, yellow, green” risk categories
Frequency of monitoring report guided by risk strategy
20
Other Choices in Capability Indicators
Gather Data
Process Characterization
Summary Statistics
Xbar (Mean) (Std. Dev.)
IndicatorsCp Cpk
Pp Ppk
Z Score
ZUPPER
ZLOWER
Convert to DPM
DPM(Upper) +
DPM(Lower) =
DPM (Total)
Calculate using specifications and
process data
Calculate
Cal
cula
te
ProportionDefects OR
Cal
cula
te
ProcessZ
Score Use Z Table or Minitab
Use Z Table or Minitab
21
Translating Pass/Fail to Ppk - type Index
• Non-normal or pass/fail data: Use a "z-score" approach• Calculate the z-score using normal distribution theory
– Proportion good z-score
– Translate z-score to a “Ppk-type" scale: divide by 3.
• Does not account for sample size, so results should be viewed in light of the amount of information you have
• Example:– If 99% is "good“ (“within spec”):
• z-score is 2.33, • Ppk = 2.33/3 = 0.78
3*Ppk = z-scorePpk = z-score / 3
USL- LSL 6Cp for a “6 sigma process”:
126 = 2Cp = =
22
Statistical Background on Capability
• Capability index assesses whether a process is capable of meeting customer requirements.
• Capability: “the natural or undisturbed performance after extraneous influences are eliminated”
– from the Western Electric Company Statistical Quality Control Handbook (1956)
• “Cpk can be calculated when the process is stable. Otherwise, for processes with known and predictable special causes and output meeting specifications Ppk should be used.”
– from the AIAG PPAP Manual (2006)
• Most important: PLOT THE DATA ON A CONTROL CHART.– Exact value of capability index is secondary.
23
Issue 3: LogNormally Distributed Results
Normal Results LogNormal Results
Error is proportional to measurement.Characterized by constant Relative Standard Deviation (RSD)Results are not symmetric within limits.
Error does not depend on measurement.Characterized by constant Standard Deviation.Results are symmetric within limits.
Has little impact if range of results is less than 10X.Easily corrected by analyzing results on the log scale.
-4
-3
-2
-1
0
1
2
3
4
Nor
ma
l
8 16 24 32 40 48 56 64 72 80 88 96104
Sample
Avg=0.24
LCL=-3.06
UCL=3.55
-10
-5
0
5
10
15
20
25
30
35
Log
nor
ma
l
8 16 24 32 40 48 56 64 72 80 88 96104
Sample
Avg=4.21
LCL=-7.06
UCL=15.47
24
Solution: Log Transform Results
LogNormal Results Log (LogNormal Results)
Log transform makes error constant and results symmetric within limits.
-10
-5
0
5
10
15
20
25
30
35
Log
nor
ma
l
8 16 24 32 40 48 56 64 72 80 88 96104
Sample
Avg=4.21
LCL=-7.06
UCL=15.47
-3
-2
-1
0
1
2
3
4
5
Log
(Log
nor
ma
l)
8 16 24 32 40 48 56 64 72 80 88 96104
Sample
Avg=1.00
LCL=-1.79
UCL=3.78
Same data on different scale
25
References
• Bisgaard, S., Kulahci, M.. (2005) Quality Quandaries: The Effect of Autocorrelation on Statistical Process Control Procedures. Quality Engineering 17: 481-489.
• AIAG. “Definition of Process Measures.” Statistical Process Control. AIAG, 1995. pp 80-81. 2nd Printing.
• The Black Belt Memory JoggerTM. (2002) GOAL/QPC Six Sigma Academy. First edition. p. 96.