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Using Process Capability to Enhance Product Quality

Daniel Y. Peng, Ph.D. Senior Product Quality Reviewer

Office of Process and Facility (OPF) OPQ/CDER/FDA

IFPAC 2015 Annual Meeting Arlington, Virginia January 27, 2015

Vision for 21st Century Manufacturing

“A maximally efficient, agile, flexible pharmaceutical manufacturing sector that reliably produces high quality drug products without extensive regulatory oversight.”

- J. Woodcock, M.D. CDER/FDA - 2005 and 2012

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3

Are We There Yet?

3

CM

C S

uppl

emen

ts

Tota

l Pro

duct

Rec

all

Lawrence Yu, NIPTE meeting, 2013 June 4

Lagging and Leading Indicator Lagging indicator

– An indicator that follows an event. – “Output” oriented, easy to measure, but

hard to influence – Reactive in nature

Leading indicator – An indicator that predicts future events

and tends to change ahead of that event. – Typically input oriented, measuring

“activity or behavior” – Proactive in nature, focusing on continual

improvement and failure prevention

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1. Calories taken in 2. Calories burned

It is recommended to use a combination of Lagging and Leading Indicators.

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Concept of Process Capability First introduced in Statistical Quality Control

Handbook by the Western Electric Company (1956). – “process capability” is defined as “the natural or

undisturbed performance after extraneous influences are eliminated. This is determined by plotting data on a control chart.”

ISO, AIAG, ASQ, ASTM ….. published their guideline or manual on process capability index calculation.

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Four indices: – Cp: process capability index – Cpk: minimum process capability index – Pp: process performance index – Ppk: minimum process performance index

Nomenclature

ASTM E2281: Standard Practice for Process and Measurement Capability Indices

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Calculation Formula

Cpk= min (Cpkl, Cpku) Ppk= min (Ppkl, Ppku)

∧

−=

σ6

)( LSLUSLCp

SDLSLUSLPp 6

)( −=

∧

−=

σ3

LSLMeanCpkl

∧

−=

σ3

MeanUSLCpku

SDLSLMeanPpkl

3−

=

SDMeanUSLPpku

3−

=

USL: upper specification limit; LSL: lower specification limit; Mean: grand average of all the data Sigma hat: estimated inherent variability (noise) of a stable process SD: overall variability

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A Perfectly Centered Process… USL

LSL

-5 -4 -3 -2 -1 0 1 2 3 4 5

LSLUSL

For this case: USL= +4σ LSL = -4σ USL-LSL= 8σ Cp= 1.333 Cpku=1.333 Cpkl=1.333 Cpk=1.333

Mean (μ ), Sigma (σ)

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Process Mean is not Centered…

When the process is not centered, or deliberately run off-center for economic reasons, or only a single specification limit is involved, Cpk should be used.

For this case: USL= +4σ LSL = -4σ USL-LSL= 8σ Cp= 1.333 Cpkl = 1.667 Cpku = 1.0 Cpk= 1.0

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Cpk, Sigma Value, and PPM

Cpk Value

Sigma Value

Area under normal

distribution curve (%)*

Non conforming parts per million (ppm) Capability Rating** Unilateral specification Bilateral specification*

0.333 1 68.27 158650 317300 Terrible

0.667 2 95.45 22750 45500 Poor

1.0 3 99.73 1350 2700 Marginally capable

1.333 4 99.993636 32 64 Capable

1.667 5 99.999942 0.29 0.58 Good

2.0 6 99.9999998 0.001 0.002 Excellent

**Bothe, D. R., Measuring Process Capability, Cedarburg, W.I., Landmark Publishing Inc., 2001

*Process mean is centered at middle of the specification limits and has normal distribution

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Denominator Difference between Cpk and Ppk

Inherent variability Overall variability

∑= −

−=

N

i

i

NXXSD

1

2

1)(

422 cSor

dMRor

dR

=∧

σ

SD: standard deviation of all individual (observed) values, which accounts for both common cause variability (noise) and special cause variability. It is often referred to as overall variability.

: the inherent variability (noise) due to common cause of a stable process. It is often estimated by using within subgroup variability which is linked to the use of control charts.

∧

σ

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Difference between Cpk and Ppk Cpk represents the potential process capability (i.e.

how well a given process could perform when all special causes have been eliminated).

Ppk addresses how the process has performed without the demonstration of the process to be stable.

Predict future batch failure rate – Cpk (Yes) – Ppk (No)

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Control Chart

To evaluate if a process is in a state of statistical control – Western Electric Rules

Two Types of Control Chart – Variable control chart: continuous numeric measurements (e.g. Xbar-

Range chart, Moving Range chart, Std. deviation chart) – Attribute control chart: discrete data (pass or fail, or counts of defects)

(e.g. p chart, np chart, c chart, u chart)

Central line (CL): the grand average Statistical process control limits

(UCL and LCL): • Typically: ±3 sigma from CL

Should not be confused with upper and lower specification limits (USL and LSL)

ASTM E2587- Standard Practice for Use of Control Charts in Statistical Process Control

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Product level: CQA Monitoring and Trending

252321191715131197531

102

100

98

Batch No.

Subg

roup

Mea

n

__X=100.287

UCL=102.108

LCL=98.466

252321191715131197531

4

2

0

Batch No.

Subg

roup

Ran

ge

_R=1.78

UCL=4.582

LCL=0

252015105

104

102

100

98

96

Batch No.

Assa

y (%

)

1041021009896

LSL USL

LSL 96USL 104

Specifications

1051029996

Within

Overall

Specs

StDev 1.051Cp 1.27Cpk 1.18PPM 229.14

WithinStDev 1.079Pp 1.24Ppk 1.15Cpm *PPM 323.15

Overall

Process Capability Analysis of Tablet Assay (first 25 batches, subgroup size =3)Xbar Chart

R Chart

Run Chart

Capability Histogram

Normal Prob PlotA D: 0.636, P: 0.094

Capability Plot

Data source: Chopra, V., Bairagi, M., Trivedi, P., et al., “A case study: application of statistical process control tool for determining process capability and sigma level,” PDA J Pharm Sci and Tech, 66 (2), 2012, pp. 98-115

Cpk: 1.18

USP: 90-110

Cpk: 2.95

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Understand the Science and Risk Product and process understanding

– Identification of the critical aspects of drug substance, excipients, formulation variables, process variables, container closure systems, in process control…

Establish appropriate control strategy & risk mitigation Product and process monitoring and trending

– Listen to the voice of the process – Learn what’s normal and observe if different

Continual improvement – Detect early and take actions

Transform from reactive trouble shooting to proactive failure reduction or prevention

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Site Level: Performance Monitoring

252321191715131197531

0.15

0.10

0.05

0.00

Month

Pro

po

rtio

n

_P=0.0437

UC L=0.1809

LC L=0

252015105

6

5

4

3

2

Month

Cu

mu

lati

ve

Un

succ

ess

Ra

te

Upper C I: 1.9123

%Defectiv e: 4.37Lower C I: 2.79Upper C I: 6.49Target: 0.00PPM Def: 43726Lower C I: 27917Upper C I: 64891Process Z: 1.7090Lower C I: 1.5150

(95.0% confidence)

Summary Stats

302520

20

10

0

T otal Batch Manufactured/Month

% U

nsu

cce

ss R

ate

129630

10.0

7.5

5.0

2.5

0.0

% Unsuccess Rate

Fre

qu

en

cy

Tar

Binomial Process Capability Analysis of Unsuccess BatchP Chart

Tests performed w ith unequal sample sizes

Cumulative Unsuccess Rate

Unsuccess Rate

Histogram

% rejected batch: 4.37%

Binomial process capability index:

0.569 (Cpk <<1)

% of “rejected batch”/month at Site A (# of lots rejected/# of lots attempted ×100%)

17 Katy George, McKinsey & Company, Brookings Institute, Washington DC (May 1, 2014)

Correlation coefficients based on data samples from 14 production sites

Paradigm Shift – “Culture of Quality” Pharmaceutical Quality System (PQS) obviously important,

measurement of PQS maturity/effectiveness not easy Manufacturers take full responsibility for quality of their

products – Focus on meeting patients’ expectations – Regulators’ expectations considered minimal approach

Strive for continual improvement Management and organizational commitment to prioritizing

quality Each person in organization understands and embraces their

role in quality

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Summary: Process Capability Indices Patient first: clinical relevant specification based on safety and

efficacy Consider not only process mean & variability but also in relation

to the specification Process capability index (Cpk): in control and capable Quantitative and action enabling Applicable for cross sectors (brand, generic, OTC and biotech) No additional testing is required since batch release data is

available per current regulation A valuable tool to transform from the reactive trouble shooting

paradigm to a proactive failure reduction or prevention paradigm

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Acknowledgements

Lawrence Yu Christine Moore Naiqi Ya Ubrani Venkataram