Control Charts for CPV – A Pharma
Perspective
Maneesha Altekar, Principal Statistician, AstraZeneca
MBSW Conference, May 22-24, 2017, Muncie, IN
Overview
•
Why use Control Charts?
•
Type of Data vs Type of Control Chart
•
Calculating Limits
•
Responding to Signals - Western Electric Rules
•
Challenges
2
Overview
•
Why use Control Charts?
•
Type of Data vs Type of Control Chart
•
Calculating Limits
•
Responding to Signals - Western Electric Rules
•
Challenges
3
Background
•
2011 FDA Guidance on Process Validation, EU Annex 15
•
Demonstrate that the validated process continues to remain in a
validated state – Continued Process verification (CPV)
•
Emphasis on the use of statistical methods
4
Control Charts
•
Statistical analysis methodology, used for CPV
•
Visually monitor process over time against established limits
•
Ensure that it is stable and in statistical control
•
•
React to real changes in the process
•
5
Exhibits only common cause variability
Not over-react to minor changes that are part of routine
variation
Control Charts
Control Chart displaying only Common
Cause Variation
Control Chart displaying Special
Cause Variation
Avg Dissolution
UCL=104.04
104
Avg Dissolution
102
UCL=104.04
104
100
102
_
X=98.47
98
100
_
X=98.47
98
96
96
94
LCL=92.90
92
1
3
5
7
9
11
13
Batch
15
17
19
21
94
55
5
92
LCL=92.90
5
5
1
1
1
90
1
6
5
9
13
17
21
25
Batch
29
33
37
41
CPV Implementation
•
Put control of monitoring process in the hands of process
owners, not statisticians – this is key!
•
Make it simple to execute and interpret
•
Facilitate decision making
Requires adjustments to how we implement control charts
7
Control Charts – assumptions
•
Process stable
•
Data are normally distributed (for X, X-bar, etc)
•
Data are identically and independently distributed
•
Monitored in real time
Not often met in pharmaceutical data!
8
Overview
•
Why use Control Charts?
•
Type of Data vs Type of Control Chart
•
Calculating Limits
•
Responding to Signals - Western Electric Rules
•
Challenges
9
Data Type vs Control Charts
•
Data can be continuous or discrete
•
•
•
Not all control charts apply to all types of data
•
10
Continuous – assay, dissolution, tablet weight
Discrete – number of defects, proportion defective
But sometimes, we may be able to get away with an
“incorrect” chart
Control Charts – Continuous Data
•
X-Bar and R charts (Normal dist)
• Multiple measurements (reported values) per batch
• Batch means, range,
• Example, tablet weights, dissolution, CU
•
X and MR chart (Normal dist)
• Single measurement (reported value) per batch
• Example, water content, pH, assay
11
Control Charts – Discrete Data
•
P chart
• Proportion of defective units (Binomial dist)
•
NP chart
• Number of defective units (Binomial dist)
•
C chart
• Number of defects (Poisson dist)
•
U chart
• Number of defects per unit (Poisson dist)
12
Control Charts
Month
Jan 12
Feb 12
Mar 12
Apr 12
May 12
Jun 12
Jul 12
Aug 12
Sep 12
Oct 12
Nov 12
Dec 12
Jan 13
Feb 13
Mar 13
Apr 13
13
Defects
1
1
2
0
1
1
0
1
0
0
0
0
0
0
0
1
Month
May 13
June 13
Jul 13
Aug 13
Sep 13
Oct 13
Nov 13
Dec 13
Jan 14
Feb 14
Mar 14
Apr 14
May 14
Jun 14
Jul 14
Aug 14
Defects
2
0
1
0
0
1
0
0
0
2
0
0
1
0
0
0
Control Charts
I Chart of Number of Defects per Month
2.5
UCL=2.270
2.0
1.5
1.0
Correct chart
_
X=0.469
0.5
0.0
C Chart of Number of Defects per Month
-0.5
UCL=2.523
2.5
-1.0
LCL=-1.333
-1.5
2.0
Jan 12 Apr 12 Jul 12 Oct 12 Jan 13 Apr 13 Jul 13 Oct 13 Jan 14 Apr 14 Jul 14
Month
Common mistake
1.5
1.0
_
C=0.469
0.5
LCL=0
0.0
Jan 12 Apr 12 Jul 12 Oct 12 Jan 13 Apr 13 Jul 13 Oct 13 Jan 14 Apr 14 Jul 14
Month
14
Normality can sometimes be approximated
Number of Broken Tablets
Normal
25
20
I Chart
15
Number of Broken Tablets
10
30
29
5
25
0
12
15
18
21
24
27
20
20
15
Good Enough
11
10
1
15
5
10
15
20
25
Batch
30
35
40
45
Overview
•
Why use Control Charts?
•
Type of Data vs Type of Control Chart
•
Calculating Limits
•
Responding to Signals - Western Electric Rules
•
Challenges
16
Creating Control Charts
Calculating Limits
•
17
Legacy products
• Process history, historical data
•
Use most recent data – 25-30 batches
• Capture short term and long term variability
•
Trend data
• Is it stable? If not, is there a root cause?
• Should any data be excluded?
• Some special cause variation is expected
•
Are there outliers? Should we exclude them?
Creating Control Charts
Calculating Limits
18
•
Look at histogram
• Are data normally distributed?
• Are data approximately normally distributed?
•
Calculate limits based on historical mean and SD
Control Charts
Trend Plot of Historical Assay
105
104
103
102
101
100
99
98
97
96
1
19
7
14
21
28
35
42
Batches
49
56
63
70
Control Charts
I Chart of Assay
105
UCL=104.45
104
103
102
101
_
X=100.19
100
I Chart of Assay
99
103
98
97
UCL=102.7
102
96
LCL=95.93
1
8
15
22
29
36
Batch
43
50
57
64
101
_
X=100.3
100
99
98
LCL=97.9
1
20
5
9
13
17
21
Batches
25
29
33
37
Control Charts
Trend Plot of CQA1 by Batch
85
80
I Chart of CQA1
CQA1
85
11
1
1
1 11
75
11
1
1
1
UCL=80.67
80
CQA1
70
1
19
38
57
76
95
114
133
152
171
_
X=75.70
75
190
Batch #
LCL=70.74
70
11
1
1 11
1 1
1
1
1
20
39
58
77
96
Batch #
21
115
1 34
1 53
1 72
1 91
Control Charts
I Chart of CQA1
90
UCL=87.04
85
CQA1
80
_
X=75.80
75
70
65
LCL=64.56
1
20
39
58
77
96
Batch #
22
115
1 34
1 53
1 72
1 91
Control Chart – Assumption of Normality
•
X-bar, Individual charts – assume normality
•
Can test for normality but . . .
•
Test is sensitive to number of samples
• Too small => everything will pass normality test
• Too large => even known normal data will fail normality if
there is an outlier or two
•
Practical approach – assume normality for tests that are known
to be normal, e.g., assay, CU, tablet weight
• Look at histograms to check for extreme/unusual values
23
Control Chart – Assumption of Normality
•
If data truly non-normal
•
•
•
24
Consider transformation
• Interpretation can be difficult
Use chart appropriate for underlying distribution, if known
Simply trend and track visually
• For example, degradant products
• Keep specification in mind
Creating Control Charts
Calculating Limits
•
25
New products
•
No process history, limited data
•
Trend and track only – no limits
• Monitor data visually
• Calculate limits once 30 batches are available
•
Preliminary limits
• Similar considerations as before but less rigorous
• Update when additional data available
Control Charts – Calculating Limits
•
New products
•
26
Common mistake - monitoring current data with limits
calculated based on current data!
Overview
•
Why use Control Charts?
•
Type of Data vs Type of Control Chart
•
Calculating Limits
•
Responding to Signals - Western Electric Rules
•
Challenges
27
Interpreting Control Charts
Responding to Signals
•
Western Electric rules - Decision rules for detecting an out-ofcontrol process
• Look for patterns in data
•
A few key ones
•
•
•
•
28
A single result outside the +/- 3σ limits
2 out of 3 consecutive results outside the 2σ limits, on the
same side of the mean
8 consecutive points on the same side of the mean
6 consecutive results increasing (or decreasing)
•
Processes are rarely truly stable
•
Batch results are rarely independently and identically distributed
•
Many charts assume underlying normal distribution; data are
not always normal
29
•
•
Batches are often not tested for days after manufacture
•
Many more batches produced in the interim
•
Batches tested in order different from manufacturing
Signals often observed only during periodic review
Limited ability to react in real time!
30
I Chart of Assay
0.53
0.525
0.52
1
React to this signal?
0.51
UCL=0.508
_
X=0.499
0.50
2
2 2222
22 2
0.49
22
22
2 22
LCL=0.49
React to this signal?
0.48
0.475
0.47
1
31
Is this rule useful?
8
15
22
29
36
43
Batch
50
57
64
71
Control Chart – Assumption of Independence
•
Batches often manufactured in campaigns
• Example, based on lots of raw material
•
Testing often done in groups
• Example, multiple batches may be simultaneously tested
for assay in the HPLC
•
May see patterns in data related to above rather than true lackof control
32
Control Chart – Assumption of Independence
I Chart of Results
115
UCL=113.64
110
2
105
2
_
X=98.04
100
By manufacturing date
95
Tested on 14/07/15
90
2
I Chart of Result
115
85
UCL=113.64
LCL=82.44
80
110
5
5
5
5
5
5
5
5
5
5
01
01
01
01
01
01
01
01
01
01
/2
/2
/2
/2
/2
/2
/2
/2
/2
/2
1
2
4
5
5
5
6
6
7
8
/0
/0
/0
/0
/0
/0
/0
/0
/0
/0
01
05
28
11
11
29
17
17
14
16
Date Tested
By testing date
105
100
_
X=98.04
95
90
85
LCL=82.44
80
1
33
6
11
16
21
26
Batch
31
36
41
46
Control Chart – Assumption of Normality
•
X-bar, Individual charts – assume normality
•
Can test for normality but . . .
•
Test is sensitive to number of samples
• Too small => everything will pass normality test
• Too large => even known normal data will fail normality if
there is an outlier or two
•
Practical approach – assume normality for tests that are known
to be normal, e.g., assay, CU, tablet weight
• Look at histograms to check for extreme/unusual values
34
Control Charts – Response to Signals
•
35
Risk based approach
•
Signals should trigger a response
•
But, response should be commensurate with risk – to
patient, process
Control Charts
I Chart of Assay
105.0
105
1
UCL=102.7
102.5
_
X=100.3
100.0
I Chart of Assay
110
110
LCL=97.9
97.5
105
95.0
1
95
1
5
9
13
17
21
Batch
25
29
33
37
UCL=102.7
_
X=100.3
100
LCL=97.9
95
90
90
1
36
5
9
13
17
21
Batch
25
29
33
37
Control Charts – Response to Signals
• All signals need not be classified as “deviations”
•
All signals should not lead to full scale investigations
•
Mindset change for QA organizations
37
Overview
•
Why use Control Charts?
•
Type of Data vs Type of Control Chart
•
Calculating Limits
•
Responding to Signals - Western Electric Rules
•
Challenges
38
Challenges
• Training provided to staff implementing CPV
• Difficult to proceduralize a trained statistician’s thought process
and insights
• “Number of batches needed” taken too literally
• Failure to appreciate limitation of reported (rounded) test results
for analysis
• Continued coaching / mentoring needed until proficient
39
Summary
• Control charts are an important tool for CPV and help us
• monitor processes
• understand variability
• demonstrate that process remains stable and in statistical
control
• Control chart may need to be looked at differently for
pharmaceutical manufacturing
• Risk based assessment
• Easing of assumptions
• Flexibility in implementation
40
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