Watch online at: https://hubs.ly/H0hswm60 Organizations in the pharmaceutical and health sectors are being asked by regulators to: - Apply more complete methods to validate analytical techniques and measurement systems, known as Data Integrity -Monitor and evaluate the performance of production processes, otherwise called Statistical Process Control (SPC) In this presentation you will learn how to: -Improve the precision and accuracy of analytical techniques, using Minitab's tools for Gage R & R, Gage Linearity and Bias studies and Design of Experiments -Select the relevant control charts and capability analyses for data that does and does not follow the normal distribution The presentation will explain how data integrity and process monitoring are critical to each other for regulatory compliance. If the data is not healthy, the evaluation of the process could also be incorrect. You will finish with the confidence to use more sophisticated statistical techniques, in particular for data integrity.

1. Data Integrity and Continuous
Process Verification
In compliance with
Pharmaceutical regulations
Robert Collis
Trainer and Technical Specialist

2. © 2019 Minitab, Inc.
2
Presentation Objectives
• General statistical approaches for ensuring data
quality
• Defining precision and accuracy
• Long term method validation
• Process Monitoring
• Checking process stability
• Estimating process performance

3. © 2019 Minitab, Inc.

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Trainer and Technical Specialist

6. Data Integrity and Continuous
Process Verification
In compliance with
Pharmaceutical regulations
Robert Collis
Trainer and Technical Specialist
Watch Now Online >

7. © 2019 Minitab, Inc.
7
Presentation Objectives
• General statistical approaches for ensuring data
quality
• Defining precision and accuracy
• Long term method validation
• Process Monitoring
• Checking process stability
• Estimating process performance

8. © 2019 Minitab, Inc.
8
Criticalness of Data Quality - Breaking Trust
Consequences:
• Validation and control of processes
incorrect
• Trust is lost between the authorities and
health care organizations
• Patient safety comprised

9. © 2019 Minitab, Inc.
9
Criticalness of Data Quality

10. © 2019 Minitab, Inc.
10
Criticalness of Data Quality
σTotal
2= σPart to Part
2 + σMethod
2 + σTechnician
2
Number of parts in study 10
Number of operators in study 3
Number of replicates 2
Study Information
study.
variation. The process variation is estimated from the parts in the
The measurement system variation equals 33,1% of the process
Yes No
0% 10% 30% 100%
33,1%
The measurement system variation equals 47,7% of the tolerance.
Yes No
0% 10% 30% 100%
47,7%
ReprodRepeatTotal Gage
45
30
15
0
30
10
%Study Var
%Tolerance
total variation in the process.
This equals 84,9% of the measurement variation and is 28,1% of the
variation that occurs when different people measure the same item.
• Operator and Operator by Part components (Reproducibility): The
variation in the process.
equals 52,8% of the measurement variation and is 17,5% of the total
when the same person measures the same item multiple times. This
• Test-Retest component (Repeatability): The variation that occurs
reproducibility to guide improvements:
gage variation is unacceptable, look at repeatability and
Examine the bar chart showing the sources of variation. If the total
>30%: unacceptable
10% - 30%: marginal
<10%: acceptable
General rules used to determine the capability of the system:
Variation by Source
(Replicates: Number of times each operator measured each part)
Comments
Gage R&R Study for Quantity of Active Ingredient
Summary Report
Can you adequately assess process performance?
Can you sort good parts from bad?

11. © 2019 Minitab, Inc.
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Criticalness of Data Quality
► This study consists of at least 50 trials where five reference samples are measured at least 10 times
Constant 0,36734 0,03461 0,000
Slope -0,004958 0,004743 0,300
Predictor Coef SE Coef P
Gage Linearity
S 0,114299 R-Sq 1,8%
Average 0,334617 0,000
2,2 0,362667 0,000
4,4 0,326000 0,000
6,6 0,345250 0,000
8,8 0,336083 0,000
11 0,303083 0,000
Reference Bias P
Gage Bias
Gage name:
Date of study:
Reported by:
Tolerance:
Misc:
12108642
0,8
0,6
0,4
0,2
0,0
Reference Value
Bias
0
Regression
95% CI
Data
Avg Bias
Gage Linearity and Bias Report

12. © 2019 Minitab, Inc.
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Criticalness of Data Quality
• Often difficult to identify what is
causing imprecision and bias.
• Robustness is a requirement
for method validation and some
organizations run 2 level
factorial experiments to
determine which factors affect
the bias and precision
• Here there are 5 factors where
a half fractional 2 level factorial
design was run – that is 16 runs
out of 32 possible treatment
combinations
40
20
0,1-0,1
10
-10
20
-20
10-10
Column Tempearture
Flow rate
NH3
MeOH
Acetic Acid
Centerpoint
Factorial Point
Cube Plot
Worksheet: Worksheet 5

13. © 2019 Minitab, Inc.
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Criticalness of Data Quality
Term
D
BD
BE
E
A
CE
DE
AE
BC
C
AB
AC
CD
AD
B
0,80,70,60,50,40,30,20,10,0
A Acetic Acid
B MeOH
C NH3
D Flow rate
E Column Tempearture
Factor Name
Effect
0,6340
Pareto Chart of the Effects
(response is stddev; α = 0,05)
Worksheet: Worksheet 5
Lenth’s PSE = 0,246639
20-20
0,020
0,018
0,016
0,014
0,012
0,010
MeOH
Meanofstddev
Main Effects Plot for stddev
Fitted Means
Worksheet: Worksheet 5
Term
E
AB
AE
AC
BC
B
DE
BD
C
AD
BE
CD
D
CE
A
0,100,080,060,040,020,00
A Acetic Acid
B MeOH
C NH3
D Flow rate
E Column Tempearture
Factor Name
Effect
0,0161
Pareto Chart of the Effects
(response is means; α = 0,05)
Worksheet: Worksheet 5
Lenth’s PSE = 0,00625
10-10
0,850
0,825
0,800
0,775
0,750
Acetic Acid
Meanofmeans
Main Effects Plot for means
Fitted Means
Worksheet: Worksheet 5
Precision Bias

14. © 2019 Minitab, Inc.
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Criticalness of Data Quality
Cur
High
Low
D: 0,3555
Optimal
Predict
d = 1,0000
Targ: 0,80
y = 0,80
means
d = 0,12638
Minimum
y = 0,0087
stddev
D: 0,3555
Desirability
Composite
-20,0
20,0
-10,0
10,0
MeOHAcetic A
[-0,2660] [20,0]
• Method Optimization

15. © 2019 Minitab, Inc.
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Criticalness of Data Quality
28252219161310741
9,3
9,2
9,1
9,0
Sample
SampleMean
__
X=9
UCL=9,0530
LCL=8,9470
28252219161310741
0,4
0,2
0,0
Sample
SampleRange
_
R=0,0518
UCL=0,1333
LCL=0
1
11
1
1
1
Worksheet: DensitéFilm.MTW
At least one estimated historical parameter is used in the calculations.
Xbar-R Chart
28252219161310741
9,2
9,1
9,0
Sample
SampleMean
__
X=9,0209
UCL=9,0809
LCL=8,9609
28252219161310741
0,2
0,1
0,0
Sample
SampleRange
_
R=0,0586
UCL=0,1509
LCL=0
11
1
1
Xbar-R Chart of process
Worksheet: DensitéFilm.MTW
Method Process

16. © 2019 Minitab, Inc.
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Overall /LT
SD
151413121110987654321
9,10
9,05
9,00
8,95
subgroup
process
Individual Value Plot of process
Worksheet: DensitéFilm.MTWWithin/ST SD
Better understanding
of the two different
types of process
variation
Process Monitoring and Control

17. © 2019 Minitab, Inc.
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Process Monitoring and Control
151413121110987654321
9,050
9,025
9,000
SampleMean
__
X=9,02458
UCL=9,06228
LCL=8,98688
151413121110987654321
0,16
0,08
0,00
SampleRange
_
R=0,078
UCL=0,1563
LCL=0
15105
9,12
9,04
8,96
Sample
Values
9,189,129,069,008,948,888,82
LSL 8,8
USL 9,2
Specifications
LSL USL
Overall
Within
9,19,08,9
StDev 0,03173
Cp 2,10
Cpk 1,84
PPM 0,02
Within
StDev 0,03723
Pp 1,79
Ppk 1,57
Cpm *
PPM 1,23
OverallOverall
Within
Specs
Process Capability Sixpack Report for process
Worksheet: DensitéFilm.MTW
Xbar Chart
R Chart
Last 15 Subgroups
Capability Histogram
Normal Prob Plot
AD: 0,762, P: 0,046
Capability Plot
Need to demonstrate that process mean
and variation are stable
Data relatively normal
Moderately poor resolution

18. © 2019 Minitab, Inc.
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Fat Pencil Test
• Large sample size drawn from normal
population
• Can lead to low p-value for AD test
• Test is overly sensitive
• Fat pencil test
• Are able to assume the data is normal
5,02,50,0-2,5-5,0
99,99
99
95
80
50
20
5
1
0,01
Mean -0,02108
StDev 1,054
N 5000
AD 1,188
P-Value <0,005
T Distribution
Percent
Probability Plot of T Distribution
Worksheet: SampleSize.MTW
Normal

19. © 2019 Minitab, Inc.
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Capability Indices
Cp
• Potential Capability
• (Voice of the
Customer)/(Voice of
the Process)
LSL USL
Voice of the Customer
-3s +3s
Customer Tolerance
Voice of the Process

20. © 2019 Minitab, Inc.
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Capability Indices
Customer Tolerance
+3SD-3SD
• FDA requires a Ppk of 1.33 as a minimum (when
the process is not in control)
• Defect rate 0.0032%
USL LSL
Ppk
• Actual Capability
• (Voice of the Customer)/(Voice
of the Process)

21. © 2019 Minitab, Inc.
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Capability Indices
USL LSL
Customer Tolerance
+3SD-3SD
Cpk
• Capability if process not
recentered but variation
reduced to within or potential
standard deviation
• (Voice of the Customer)/(Voice
of the Process)

22. © 2019 Minitab, Inc.
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Non-normal data
• Use of normal capability incorrectly
• Capability sensitive to non-
normality
• Issues also with control charts
when subgroup size is 1
363024181260-6
300
250
200
150
100
50
0
Mean 4,133
StDev 4,227
N 1500
Process dataFrequency
Normal
Histogram of Process Data

23. © 2019 Minitab, Inc.
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Causes of Non-normality
• Data naturally non-normal
• Example: Time
• Outliers
• Example: Unexpected sudden
change
• Measurement Resolution
• Example: tape measure
• Cluster at one value
• Example: Impurities
• Data from different sources
• Example: different reactors
• Shift and Drift
• Example: process parameter
changes or increasing bias in
method used over time

24. © 2019 Minitab, Inc.
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Control Charts : Non-normal data

25. © 2019 Minitab, Inc.
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EWMA Charts
1361211069176614631161
25
20
15
10
5
0
Sample
EWMA
__
X=5,12
UCL=10,69
LCL=-0,45
EWMA Chart of Process Data
• Robust to Non-Normality
• Adapted to small shifts in the
mean (for example 1
standard deviation)
• Can be adjusted to modify
sensitivity

26. © 2019 Minitab, Inc.
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Non-Normal Capability Analysis
2,1002,0251,9501,8751,8001,7251,650
LSL *
Target *
USL 2,1
Sample Mean 1,94735
Sample N 150
Location 1,99433
Scale 0,0822776
Process Data
Pp *
PPL *
PPU 0,64
Ppk 0,64
Overall Capability
% < LSL *
% > USL 3,33
% Total 3,33
Observed Performance
% < LSL *
% > USL 2,70
% Total 2,70
Exp. Overall Performance
USL
Process Capability Report for Process Data
Calculations Based on Smallest Extreme Value Distribution Model
• The Ppk is based on the Z-score
method (default in Minitab)
• The expected defect rate is 2,7%

27. © 2019 Minitab, Inc.
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Non-Normal Capability Analysis - Zscore
0,4
0,3
0,2
0,1
0,0
X
Density
1,927
0,0269898
0
Distribution Plot
Normal; Mean=0; StDev=1
Worksheet: newexp2
Mean =0
Standard deviation = 1
• Z-score=(USL – Mean)/(overall standard deviation)
• Ppk=(USL – Mean)/(3 X overall standard deviation)
• Ppk = Z-score/ 3
• Ppk=Z-score/3=1.927/3=0.64

28. © 2019 Minitab, Inc.
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Non-Parametric Capability
28
0,900,750,600,450,300,150,00
USL
Empirical Process Capability of C10
Using the Empirical Percentile Method
Worksheet: newexp
Process Data
LSL
USL
Sample Median
Sample Mean
Sample StDev
Sample N
*
0,950000
0,491151
0,491744
0,292804
1200
Capability Indices
Cnp
Cnpl
Cnpu
Cnpk
*
*
0,910859
0,910859
Observed Performance
PPM < LSL
PPM > USL
PPM Total
*
48333,3
48333,3
• (Upper Spec Limit – 50th
Percentile)/(99.5th
Percentile– 50th Percentile)
• The Non-Parametric
Capability Statistic is 0.91

29. © 2019 Minitab, Inc.
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Conclusions
• Continuous control of methods required
• Ongoing stability studies
• More application of Design of experiments
• Better education required for Continuous Process Monitoring &
Method Validation
• Improve Statistical Knowledge
• Understand how to treat non-normal data
• Deal with data not following any particular underlying distribution

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