managing quality in pharmaceutical -...
TRANSCRIPT
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
• Pharmaceutical Manufacturing Process and Drug Product Quality.
• Process Excellence Approach in Pharmaceutical Industry.
• Regulatory Integrated Model of Pharmaceutical Process Validation Using Six Sigma.
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
• Pharmaceutical Manufacturing Process and Drug Product Quality.
• Process Excellence Approach in Pharmaceutical Industry.
• Regulatory Integrated Model of Pharmaceutical Process Validation Using Six Sigma.
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
Chart Reason Out-of-Control Points
I-MR Chart of Average Weight
Stability Report - Initial Process Performance
Is the process stable?
Investigate out-of-control points. Look for patterns and trends.
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
Chart Reason Out-of-Control Points
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
Is the process stable?
Investigate out-of-control points. Look for patterns and trends.
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
Contour Plot of Product CQAs and Stage CPPs
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
Contour Plot of Product CQAs and Stage CPPs
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
% Out of spec (expected) 0.00
PPM (DPMO) (observed) 0
PPM (DPMO) (expected) 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
StDev (overall) 0.78556
StDev (within) 0.78721
Process Characterization
Cp 2.12
Cpk 2.02
Z.Bench 6.05
% Out of spec (expected) 0.00
PPM (DPMO) (expected) 0
Actual (overall)
Pp 2.12
Ppk 2.02
Z.Bench 6.06
% Out of spec (observed) 0.00
% Out of spec (expected) 0.00
PPM (DPMO) (observed) 0
PPM (DPMO) (expected) 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 Average Weight
Optimised Process Performance Report
Capability Histogram
Are the data inside the limits and close to the target?
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
StDev (overall) 0.59860
StDev (within) 0.59986
Process Characterization
Cp 5.56
Cpk 5.45
Z.Bench *
% Out of spec (expected) 0.00
PPM (DPMO) (expected) 0
Actual (overall)
Pp 5.57
Ppk 5.46
Z.Bench *
% Out of spec (observed) 0.00
% Out of spec (expected) 0.00
PPM (DPMO) (observed) 0
PPM (DPMO) (expected) 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 Content Uniformity
Optimised Process Performance Report
Capability Histogram
Are the data inside the limits and close to the target?
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).
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