managing quality in pharmaceutical -...

Managing Quality in Pharmaceutical

Industry Using Six Sigma

Edited by

Mahmoud Farouk Moussa

TQM, CSSBB, MBA

Outlines

• Pharmaceutical Manufacturing Process and Drug Product Quality.

• Process Excellence Approach in Pharmaceutical Industry.

• Regulatory Integrated Model of Pharmaceutical Process Validation Using Six Sigma.

Pharmaceutical Manufacturing Process

The international pharmaceutical agencies which areresponsible for the safety and efficacy of the drug productssuch as WHO, FDA, and European Medicine Agencyintroduced many regulations to ensure that pharmaceuticalproducts are being produced according to the goodmanufacturing practices (GMP) of this industry, in orderprovide the consumers, healthcare professionals and patientswith safe and effective product, and eliminate the risk ofadulterated products.

Inputs/Outputs of Pharmaceutical Manufacturing Process

PROCESS

OUTPUT

A step or sequence of steps

that uses inputs and produces a

drug product as an output.

Machine

Man

Materials

Method

Measurements

Mother of Nature

INP

UTS

Quality of Pharmaceutical Product

Pharmaceutical Product Quality means the suitability of either a drugsubstance or drug product for its intended use. This term includes suchattributes as the identity, strength, and purity (ICH Q6 A) .

Correct to

SpecificationIncorrect to

Specification

Incorrect to

Specification

Lower specification

limit

Upper specification

limit

Qualified Drug should be produced that is fit for its intended use (FDA,2011).

Outlines




Which Quality Level Do You Like

For Your Drug Product

GOOD

BETTER

BEST

Process Design Tolerance and Process Variation Relationship

-3 σ +3 σ

Nominal Specification

Process Variation

Design Specification or Tolerance

Lower specification

limit

Upper specification

limit

Process Excellence

• Definition

Process excellence - an advanced and scientific workmethodology which is customer value driven, resultsoriented, and project focused.

• Objectives

To eliminate the process variations and waste, inaddition to quick delivery of the value to the customerwith reliable utilisation of the business resources.

• Methodology

It utilizes various schematic and statistical quality toolsto enhance the total quality management system withinthe company (South, 2005).

Excellence in Pharmaceutical Manufacturing Processes

Process excellence in pharmaceutical industry, could beachieved through the implementation of the regulatoryconcept of process validation (PV) with the aid of six sigmaelements to manage the product quality andmanufacturing process efficiency.

Process Validation

6σ

I am The BEST

Definition of Six Sigma

• A sigma - statistical indication of variation in terms of thestandard deviation of the characteristics under consideration.It indicates the spread of each unit around the target value,and therefore it is essentially and indication of how a productor service is.

• Sigma measures the process capability to produce defect-freework and is a means of calibrating process performance tomeet the requirements of customers.

• The higher the sigma value, the lower the number of defectsassociated with the process, the lower the costs of reworkand scrap and the lower the cycle time of the process.

Sigma Level vs. DPMO

Example:

A process that is a quality level of three sigma means 66,806defects per million opportunities (DPMO), while six sigma is 3.4DPMO.

Regulatory Concept of Pharmaceutical Manufacturing Process Validation

“Process validation - collection and evaluation of data, from the process design stage through commercial production, which establishes scientific

evidence that a process is capable of consistently delivering quality product. Process validation involves a series of activities taking place over

the lifecycle of the product and process” (FDA, 2011).

“Process development studies should provide the basis for process improvement, process validation and any process control requirements. All CPPs should usually be identified, monitored or controlled to ensure

that the product is of the desired quality” (WHO, 2012)

“Process Validation - scientific concept based on the risk management approach to ensure that the process is operating according a pre-definedparameters to produce product conforms all its CQAs and control strategy

requirements.” (ICH Q9, 2005, Q10, 2008).

Regulatory Objectives of Pharmaceutical PV

• Quality, safety, and efficacy are designed or built into the product.

• Quality cannot be adequately assured merely by in-process and finished-product inspection or testing.

• Each step of a manufacturing process is controlled to assure that the finished product meets all quality attributes including specifications.

Regulatory Stages of Pharmaceutical PV

Process Design

Process

Qualification

Continued Process

Verification

The commercial manufacturing process is defined during this stage based on knowledge gained through development and scale-up activities.

Process design is evaluated to determine if the process is capable of reproducible commercial manufacturing.

Ongoing assurance is gained during routine production that the process remains in a state of control.

Outlines




Approach

• Following the requirements of the FDA, (2011), WHO TRS. 970, (2012), ICH Q8 R2 (2009) and ICH Q10 (2008).

• Six sigma elements with the three stages of pharmaceutical PV for efficient understanding, control and improving solid dosage pharmaceutical product and its manufacturing procedure.

• Manage the risk on the product quality and maintain the process robustness to ensure that the manufacturing process provides the intended product quality, in addition to exploring the improvement opportunities.

Methodology

• Retrospective and prospective pharmaceutical PV studies - six production batches of a tablet drug product (WHO, 2012).

• Retrospective study – historical three manufactured batches

— To examine the (COAs) of the output product and its critical process parameters (CPPs) according to a pre-defined process parameter and product specification or design.

• Prospective study - Other optimised three manufacturing batches.

― To examine the potential quality improvement for the product’s quality standards and process performance.

Integrated Model: PV Stages and Six Sigma Elements

Process Design

Process

Qualification

Continued Process

Verification

Product Definition

Design of Experiment

Statistical process control

Process Definition

Statistical process control

Process Map

First Stage PV – Process Design

Objectives

• “building and capturing process knowledge and understanding” - data derived from the product development stages e.g. pharmaceutical dosage form, the product quality attributes, and a general manufacturing pathway (FDA, 2011).

• Establish strategy for process control for each operation unit and the process overall, in order to reduce the input variations or/and adjustment for input variation during manufacturing (FDA, 2011).

Product & Process Data Definition

Item

No.

Product Critical

Quality Attributes

(CQAs)

Quality Specifications

Limits

1 Blend Homogeneity 90 - 110 % or 0-3 RSD

2 Content Uniformity 90 - 110 % or 0-3 RSD

3 Tablet Average weight 100-110 mg

4 Tablet Disintegration 0 - 3 min

5 Tablet Hardiness 3 - 5 KP

6 Tablet Friability 0 – 0.3 %

7 Tablet Dissolution 80 – 100 %

8 Tablet Potency 90 – 110 %

StageCritical Process

Parameters (CPPs)

Machine

Settings

Blending Stage

Blending Speed

10 rpm

Blending Time

20 min

Compression Stage

Tablet Depth

5.8 mm

Compression Speed

20 rpm

Compression Force

1 mm

Drug Product CQAs Manufacturing CPPs

Normal process Flow

Re-processing Flow

Process Parameters

Packaging Material

Raw Material Sieving (2)Mesh Size

Blending Time

Blending Speed

In-process Quality

Control (A)

Compression Force

Compression SpeedTablet Thickness

In-process Quality

Control (B)

Coating suspension spray Rate

Atomization Pressure Time

Pan rotation speedPre-heat time

Product bed temperature

Dispensing of Raw Material (1)

Blending of Raw Material (3)

Tablet Compression (4)

Tablet Coating (5)

Finished Product Quality

Control (C)

Finished Product Packaging (6)

Pass

Fail

Fail

Pass

Fail

Pass

Manufacturing Process Pathway

Second Stage PV – Process Qualification

Objectives

• In the guidance of FDA, (2011), ICH Q10, (2009), Statistical metrics are highly recommended to (FDA, 2011, ICH Q10, 2009):

― To monitor and analyse objectively the process performance for adequate assurance that the process is performed according to the design and \to produce the expected product quality,

― To examine the process capability and robustness,

― To set the priority of the points to be improved in a further stage.

Second Stage PV – Process Qualification

Statistical Process Control - SPC

WHY SPC1. To maintain process stability.2. Process improvement guide through variation reduction and

keep it minimised. 3. To assess the process performance.4. To support the decision making over the process by providing

adequate information (Dale and Shaw, 2007).

Process Measurements(Process Responses)

Manufacturing Stage

Process Control Parameters(Variables)

Product CQAs (Responses)

Blending Stage1. Blending Speed

2. Blending Time

1. Blend Homogeneity

2. Content Uniformity

3. Potency of API

Compression Stage

1. Tablet Depth

2. Compression Speed

3. Compression Force

1. Content Uniformity

2. Average weight

3. Tablet Hardness

4. Tablet Disintegration

5. Tablet Friability

6. Tablet Dissolution

7. Potency of API

Process Performance(Blend Homogeneity Product CQA)

I Unusually small value 6-8 MR Unusually large moving range 6, 10

Chart Reason Out-of-Control Points

102

96

90

Ble

nd

Ho

mo

gen

ity

_X=97.81

UCL=105.05

LCL=90.57

151413121110987654321

10

5

0

Observation

Mo

vin

g R

an

ge

__MR=2.72

UCL=8.89

LCL=0

Is the process stable?

Investigate out-of-control points. Look for patterns and trends.

I-MR Chart of Blend Homogenity - Initial BatchesStability Report

110

105

100

Averag

e W

eig

ht

_X=104.68

UCL=108.77

LCL=100.60

10997857361493725131

4

2

0

Observation

Mo

vin

g R

an

ge

__MR=1.535

UCL=5.016

LCL=0

I Unusually small value 28, 36, 58

Unusually large value 106, 107, 110

Shift in mean 36-41, 109-116


I-MR Chart of Average Weight

Stability Report - Initial Process Performance



Process Performance(Average Weight Product CQA)

I Unusually small value 104, 105 Unusually large value 22, 31-33

Shift in mean 19-40, 49-62, 77-85 MR Unusually large moving range 22, 23, 41, 104, 106, 111, 116


105

100

95

Co

nten

t U

nifo

rm

ity B

efo

re

_X=99.92

UCL=103.53

LCL=96.32

10997857361493725131

8

4

0

Observation

Mo

vin

g R

an

ge

__MR=1.355

UCL=4.427

LCL=0



I-MR Chart of Content Uniformity Before - Initial BatchesStability Report

Process Performance(Content Uniformity Product CQA)

Third Stage PV – Continued Process Verification

Finding

• The initial process performance unfolded poor process performance and capability.

Objectives

1. DOE quality tool - to optimise the CPPs aiming to bring consistent production of drug product through a robust process

2. SPC - to ensure that the sources of variability have been detected and treated after CPPs optimization for more stable and robust manufacturing process of the drug product.

Steps of DOE

Definition of the Variability and responses Factors

Design the Experiment

Definition of the Process Design Space

Process Optimisation

Design of ExperimentI- Definition of the variability and response factors

No. CPPs (factors) Operating ranges CQAs (Response) Specification Limits

1Blending Speed 10 - 20 rpm Blend Homogeneity 0-2 RSD

2 Blending Time20 - 25 min Content Uniformity 0-3 RSD

Potency of API 90-110%

No. CPPs (factors) Operating ranges CQAs (Response) Specification Limits

1 Tablet Depth 5.8,6.2 mm Content Uniformity 0 - 4 RSD

2Compression Speed 20,30 rpm

Average weight 100 - 110 mg

3Compression Force 1,2 mm

Tablet Hardness 3 - 5 kp

Blending Stage: Critical Process Parameters (Variables) and Product CQAs (Responses)

Compression Stage: Critical Process Parameters (Variables) and Product CQAs (Responses)

(continued)

Design of ExperimentII-Design the experiment

Std. Order

RunOrder

CentrePt.

BlocksFactors (CPPs) Responses of Product CQAs

Blend Time (min)

Blend speed(RPM)

BlendHomogeneity (RSD)

Content Uniformity (RSD)

3 1 1 1 20 20 1.7 2.8

1 2 1 1 20 10 2.3 3

4 3 1 1 25 20 1.1 2.2

2 4 1 1 25 10 1.5 2.5

Std

Ord

er

Ru

n O

rder

Cen

tre

Pt

Blo

cks

Factors (CPPs) Responses (Product CQAs)

Compression Depth

Compress. Speed

Compress.Force

Content Uniformity (RSD)

Average Weight (mg)

Tablet Hardness(Kp)

1 1 1 1 5.8 20 1 2.8 104.3 5.5

4 2 1 1 6.2 30 1 3.1 107.5 6.2

6 3 1 1 6.2 20 2 2.6 109 4.2

7 4 1 1 5.8 30 2 3.25 103 3.4

3 5 1 1 5.8 30 1 3.2 103.4 5.4

2 6 1 1 6.2 20 1 2.1 109.5 6.3

8 7 1 1 6.2 30 2 2.7 107.2 4.1

5 8 1 1 5.8 20 2 2.3 104 3.5

Blending Stage: Design the experiment – Variables vs. Responses

Compression Stage: Design the experiment – Variables vs. Responses

(continued)

Design of ExperimentIII-Definition of the process design space

Blend time

ble

nd

sp

ee

d

252423222120

20

18

16

14

12

10

0

2

Homogenity

Blend

0

3

ContentUniformity

90

110

API

Potencyof

Contour Plot of Blend Homogenity, ContentUniformity, Potencyof API

Blending Stage: Definition of Design Space.

(continued)

Compression depth

Co

mp

ressio

n F

orc

e

6.26.16.05.95.8

2.0

1.8

1.6

1.4

1.2

1.0

0

3

C ontentUniformity

100

110

weight

A v erage

1.5

3.5

Disintegration

Tablet

3

5

Hardness

Tablet

0

0.3

F riability

Tablet

80

110

Dissolution

Tablet

90

110

A PI

Potency of

Contour Plot of Product CQAs and Stage CPPs

Compression speed

Co

mp

ressio

n F

orc

e

302826242220

2.0

1.8

1.6

1.4

1.2

1.0

0

3

C ontentUniformity

100

110

weight

A v erage

1.5

3.5

Disintegration

Tablet

3

5

Hardness

Tablet

0

0.3

F riability

Tablet

80

110

Dissolution

Tablet

90

110

A PI

Potency of


Compression depth

Co

mp

ressio

n s

pe

ed

6.26.16.05.95.8

30

28

26

24

22

20

0

3

C ontentUniformity

100

110

weight

A v erage

1.5

3.5

Disintegration

Tablet

3

5

Hardness

Tablet

0

0.3

F riability

Tablet

80

110

Dissolution

Tablet

90

110

A PI

Potency of


Blending Stage: Definition of Design Space.

(continued)

Design of Experiment IV- Process optimisation

No.

Product CQAsLower Specification Limit

(LSL)

Target

Values of CQAs

Upper Specification

Limit

(USL)

1 Blend Homogeneity (RSD) 0 1 2

2 Content Uniformity (RSD) 0 2 3

3 Average weight (mg) 100 105 110

5 Tablet Hardness (kp) 3 4 5

Process Optimization Using Target Quality Values

(continued)

Figure 4.23: Response optimisation of blending stage

CurHigh

Low0.71138D

Optimal

d = 0.90000

Targ: 1.0

Blend Ho

y = 1.10

d = 0.80000

Targ: 2.0

ContentU

y = 2.20

d = 0.50000

Targ: 100.0

Potencyo

y = 105.0

0.71138

Desirability

Composite

10.0

20.0

20.0

25.0blend spBlend ti

[25.0] [20.0] C ur

High

Low0.53500

D

O ptimal

d = 0.66387

Targ: 2.0

C ontentU

y = 2.6723

d = 0.41410

Targ: 105.0

A v erage

y = 107.9295

d = 0.77940

Targ: 2.0

Tablet D

y = 2.2206

d = 0.54444

Targ: 4.0

Tablet H

y = 4.4556

d = 0.13968

Targ: 0.10

Tablet F

y = 0.2721

d = 0.99920

Targ: 100.0

Tablet D

y = 100.0080

Potency o

0.53500 1.0

2.0

20.0

30.0

5.80

6.20

C ompress C ompressC ompress

[6.20] [26.2626] [1.8485]

Figure 4.24: Response optimisation of compression stage

StageCPPs (factors)

Initial Operating

Parameters

Optimised Operating

Parameters

Blending StageBlending Speed 20-25 min 25 rpm

Blending Time 10-20 rpm 20min

Compression Stage

Tablet Depth 5.8-6.2 mm 6.20 mm

Compression Speed 20-30 rpm 26.2626 rpm

Compression Force 1-2 mm 1.8485 mm (continued)

Design of Experiment IV- Process optimisation

Optimized Process Performance(Blend Homogeneity Product CQA)

109.2106.6104.0101.498.896.293.691.0

LSL Target USL

Total N 15

Subgroup size 15

Mean 100.22

StDev (overall) 0.29093

StDev (within) 0.29617

Process Characterization

Cp 11.25

Cpk 11.01

Z.Bench *

% Out of spec (expected) 0.00

PPM (DPMO) (expected) 0

Actual (overall)

Pp 11.46

Ppk 11.21

Z.Bench *

% Out of spec (observed) 0.00


PPM (DPMO) (observed) 0


Potential (within)

Capability Statistics

Actual (overall) capability is what the customer experiences.

shifts and drifts were eliminated.

Potential (within) capability is what could be achieved if process

Capability Analysis for Blend Homogenity

Optimised Process Performance Report

Capability Histogram

Are the data inside the limits and close to the target?

109.5108.0106.5105.0103.5102.0100.5

LSL Target USL

Total N 120

Subgroup size 120

Mean 105.24




Cp 2.12

Cpk 2.02

Z.Bench 6.05



Actual (overall)

Pp 2.12

Ppk 2.02

Z.Bench 6.06





Potential (within)





Capability Analysis for Average Weight




Optimized Process Performance(Average Weight Product CQA)

108.0105.3102.699.997.294.591.8

LSL Target USL

Total N 120

Subgroup size 120

Mean 100.19




Cp 5.56

Cpk 5.45

Z.Bench *



Actual (overall)

Pp 5.57

Ppk 5.46

Z.Bench *





Potential (within)





Capability Analysis for Content Uniformity




Optimized Process Performance(Content Uniformity Product CQA)

1. Percentage of out of controls (OCCs) before and / process optimisation.

2. Percentage of DPMO before and / process optimisation.

3. Percentage of Out Of Specifications (OOS) before and / process optimisation.

Process Improvement Measurements

CQAs

Status

Tablet

Poten

cy

Tablet

diss

olutio

n

Tablet

Friab

ility

Tablet

Har

dness

Tablet

disin

tegra

tion

Averag

e Weig

ht

C onten

t Unif

ormity

Blend

Homogen

ity

BABABABABABABABA

90.00%

80.00%

70.00%

60.00%

50.00%

40.00%

30.00%

20.00%

10.00%

0.00%

Perc

enta

ge o

f O

OCs

A= Batches Produced Before CPPs optimisation

B= Batches Produced After CCPs optimisation

Status

0.00%

19.40%

2.80%

20.80%

11.70%

46.70%

4.20%

15.80%

3.30%

80.00%

0.80%

15.00%

0.80%

39.20%

0.00%

20.00%

Chart of Percentage of Out of Control Points (OOCs)

Percentage of out of controls (OCCs) before and /

process optimization

CQAs

Table

t Pot

ency

Table

t diss

olutio

n

Table

t Fria

bility

Table

t Hard

ness

Tablet

disin

tegr

ation

Avera

ge Weig

ht

Conte

nt U

niform

ity

Blend

Hom

ogen

ity

70000

60000

50000

40000

30000

20000

10000

0

Tablet

Pote

ncy

Tablet

diss

olutio

n

Tablet F

riabilit

y

Tablet

Har

dnes

s

Table

t disi

nteg

ratio

n

Avera

ge W

eight

Conte

nt U

nifor

mity

Blend H

omog

enity

A

PPM

(DP

MO)

(ex

p) B

B= Batches After Process Optimisation

A= Batches Before Process Optimsation

Status

Panel variable: Status

Chart of Expected Defects Per Milion Opportunities (DPMO)

Percentage of DPMO before and / process

optimization

CQAs

Table

t Pot

ency

Table

t diss

olutio

n

Tablet

Friabil

ity

Table

t Hard

ness

Table

t disi

nteg

ratio

n

Avera

ge W

eight

Conte

nt U

niform

ity

Blend

Homog

enity

7

6

5

4

3

2

1

0

Table

t Pot

ency

Tablet

diss

olutio

n

Table

t Fria

bility

Tablet

Har

dnes

s

Table

t disin

tegra

tion

Avera

ge W

eight

Conte

nt U

nifor

mity

Blend H

omog

enity

A

% O

OS

(exp

)

B

B= Baches After the process optimisation

A= Batches Before Process Optimisation

Status

Panel variable: Status

Chart of Expected Percentage of Out of Specification Results (% OOS)

Percentage of Out Of Specification OOS before and /

process optimisation

References

• Dale, B and Shaw, P. (2007). Statistical process control. In: Dale, B. G., Wiele, A. V. D. and Iwaarden, J. V. (2007). Malden, MA: Blackwell Pub. 2007. Managing Quality. 5th ed. Malden, MA.

• Food and Drug Administration (FDA), Guidance for Industry, Process Validation: General Principles and Practices, U.S. Department of Health and Human Services, Food and Drug Administration, Centre for Drug Evaluation and Research (CDER), Centre for Biologics Evaluation and Research (CBER), Centre for Veterinary Medicine (CVM), Current Good Manufacturing Practices (CGMP) Revision 1, Rockville, MD, January 2011.

• International conference on harmonisation (ICH) of technical requirements for registration of pharmaceuticals for human use, 2008. Pharmaceutical quality system – Q10.

• International conference on harmonisation (ICH) of technical requirements for registration of pharmaceuticals for human use, 2005. Quality risk management - Q9.

Continued

• International conference on harmonization (ICH) of technical requirements for registration of pharmaceuticals for human use or human use, 1999. specifications: test procedures and acceptance criteria for new drug substances and new drug products: chemical substances – Q6 A.

• International conference on harmonization (ICH) of technical requirements for registration of pharmaceuticals for human use, 2000. Good manufacturing practice guide for active pharmaceutical ingredients – Q7.

• Kubiak, T. and Benbow, D. (2009). The certified six sigma black belt handbook. 2nd ed. Milwaukee, Wis.: ASQ Quality Press.

• South, S. (2005). Achieving breakthrough improvements with the application of lean six sigma tools and principles within process excellence. Lab Medicine, 36(4): 240-242.

• World Health Organization (WHO) good manufacturing practices for active pharmaceutical ingredients. In: WHO Expert Committee on Specifications for Pharmaceutical Preparations. Forty-fourth Report. Geneva, World Health Organization, (2012), Annex 3 (WHO Technical Report Series, No. 970).

THANK YOU

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