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

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w lots

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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.

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-3

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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.