Transcript Presentation Title

The Role of
Process Analytical Technologies in
the Quality by Design Framework
Carl A. Anderson, Ph.D.
James K. Drennen, III, Ph. D.
Benoît Igne, Ph. D.
Interfex, 23 April 2013
New York, NY
THE WALL STREET JOURNAL
“The pharmaceutical industry has a little secret: Even as it
invents futuristic new drugs, its manufacturing techniques lag far
behind those of potato-chip and laundry-soap makers.”
“In other industries, manufacturers constantly fiddle with their
production lines to find improvements. But FDA regulations
leave drug-manufacturing processes virtually frozen in time.”
Abboud, L; Hensley, S. Factory shift: New prescription for drug makers: Update the plants; After years of neglect,
Industry focuses on manufacturing; FDA acts as a catalyst; The three-story blender. Wall Street Journal (Eastern
Edition). September 3, 2003, pg. A.1.
Cogdill, Knight, Anderson, Drennen; Journal of
Pharmaceutical Innovation, Oct., 2007.
Inventory Turnover- major branded,
generic, mid-sized, and non-pharma.
The Desired State of
Pharmaceutical Manufacturing
• Mechanistically and scientifically driven
development with multivariate experimental
designs
• Flexible, science-driven operation
• Validation based on continuous process
verification via in- or on-line analyses
• Risk-based control strategies for assurance of
product quality
• Use of feed forward and feedback controls
• Proactive management approach focused on
continuous improvement
• Real-time release
Q8 (R1): Pharmaceutical Development, Revision 1. ICH Harmonized Tripartite Guidelines. International Conference on
Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use; 2007.
Advantages of the Desired
State
•
•
•
•
•
Demonstration of process understanding
Additional regulatory flexibility
Enhanced product quality and process efficiency
Foundation for continuous improvement
Potential reductions in the time-to-market for
finished products
PAT
Product Attributes
QbD
Design Space
PBQS
Patient Characteristics
From the PAT Guidance
“…(PAT) is intended to support innovation and efficiency in
pharmaceutical development, manufacturing, and quality
assurance.”
“…(efficient pharmaceutical manufacturing) is a critical part
of an effective U.S. health care system. The health of our
citizens depends on the availability of safe, effective, and
affordable medicines.”
“Guidance for Industry: PAT -- A Framework for Innovative
Pharmaceutical Development, Manufacturing, and Quality Assurance”
(U.S. Department of Health and Human Services, Food and Drug
Administration, 2004).
Quality by Design (QbD)
• ICH Q8R1 describes QbD as a:
“systematic approach to development that begins with
predefined objectives and emphasizes product and
process understanding and process control, based on
sound science and quality risk management”
QbD facilitates PAT
system development
PAT verifies QbD
Quality
Adapted from: R.C. Lyon, Process monitoring of pilot-scale pharmaceutical blends by near-infrared chemical imaging
and spectroscopy, Eastern Analytical Symposium (EAS), Somerset, NJ, 2006.
Manufacturing Systems designed using
QbD and Implemented via PAT
SPCTech QbD/PAT Philosophy © 2006
Cycle Time Improvement with PAT
Cogdill, Knight, Anderson, Drennen; Journal of Pharmaceutical
Innovation, Oct., 2007.
Quality + Efficiency =
Profitability
“…there is ample evidence that process analytics can be
implemented with an expressed goal of improving
efficiency and profitability so long as the new
technology’s impact on process quality assurance is
positive (as detailed in advance, e.g. by a project
comparability protocol).”
The Financial Returns on Investments in Process analytical
technology and Lean Manufacturing: Benchmarks and Case Study.
Cogdill, Knight, Anderson, Drennen; Journal of harmaceutical
Innovation, Oct., 2007.
Where does QRM fit within
Development and Manufacturing?
• Elements of Pharmaceutical Development
Quality Target Product Profile
Critical Quality Attributes
Select manufacturing process
Risk Assessment: Linking Material Attributes and Process
Parameters to Drug Product CQAs
• Design Space
• Control Strategy
• Product Lifecycle Management and Continual Improvement
•
•
•
•
ICH Q8(R2), Part II: Pharmaceutical Development- Annex
QbD Approach Includes:
• Systematic evaluation, understanding and refining of the
formulation and process
– Identify through prior knowledge, experimentation, and risk
assessment, the material attributes and process parameters that
can have an effect on product CQAs
– Determine the functional relationships that link material
attributes and process parameters to product CQAs
• Using product and process understanding in combination
with quality risk management to establish an appropriate
control strategy which can include a proposal for a
design space and/or real-time release testing.
– This facilitates continual improvement and innovation
throughout the product lifecycle
12
Risk Assessment
• Risk Assessment: Linking Material
Attributes and Process Parameters to Drug
Product CQAs
– A science-based process used in quality risk
management, to aid in identifying which
material attributes and process parameters
have an effect on product CQAs
• Performed early in product development, and
revisited as more information becomes available
• Identify and rank parameters that might have an
impact on product quality
13
Risk Assessment
• List of potential parameters is refined
through experimentation to determine the
significance of individual variables and
potential interactions
• Study of significant parameters leads to
process understanding
14
Risk Assessment
• An important component of product
lifecycle management and continual
improvement
– identify functional relationships linking
material attributes and process parameters to
product CQAs
– link the design of the manufacturing process
to product quality
15
Histogram of all Failure Modes
Assessed (RPN values)
70
100%
Medium
90%
Frequency
Cumulative %
60
80%
Frequency
50
70%
60%
40
50%
30
40%
High
30%
20
20%
10
10%
0
0%
0
10
20
30
40
50
Bin
60
70
80
90
More
High: > 60
Medium: = 60
Histograms of RPN Values for Current
and Initial Risk Assessments
52
Granulation,
Fluid bed drying,
and
Shipping
Granulation,
83
50
61
Compression and Granulation:
- Formation of lactam
- Reduced chemical stability
Compression
Fluid bed drying
and Shipping
45
Current
Frequency
40
Initial
35
26
30
25
20
26
24
12
15
Formation of unknown
4 forms in
physical
4
Granulation, FBD, Shipping
15
15
15
10
9
4
5
7
5
0
10
20
10
0
30
40
50
60
RPN
70
Granulation
6
0
80
90
0
4
100
0
Quality Risk Management and
Continuous Improvement
Initiate
Quality Risk Management Process
Risk Assessment
Risk Identification
Risk Analysis
Risk Communication
Risk Control
Risk
Risk Reduction
Reduction
Risk Acceptance
Output / Result of the
Quality Risk Management Process
Risk Review
Review Events
Adapted from: ICHQ9
Risk Management Tools
unacceptable
Continuous
improvement
cycle
Risk Evaluation
Validation Pathways for PAT
Methods
Validation
Pathway
1
2
3
4
Intended Routine
PAT Measurement
Mode
Off-line/
At-line
On-line/
In-line
On-line/
In-line
On-line/
In-line
On-line/In-line
Commercial
Scale
Validation PAT
Measurement Mode
Off-line/At-line
Off-line/At-line
On-line/In-line
Pilot Scale
Validation Sampling
Static
Static
Dynamic
Dynamic
Validation Reference
Measurement Mode
(if necessary)
Off-line/At-line
Off-line/At-line
On/In-line or
Off/At-line
On/In-line or
Off/At-line
Additional Validation
on Transfer to
On-line/In-line
N/A
Yes
Yes
N/A
Adapted from ASTM E55 standard.
Connecting Quality Specifications with Patient
Needs:
Performance Based Quality Specifications
(PBQS)
Inefficacy and Toxicity Risk Contour Plots
Toxicity
Inefficacy
32
8
15
31
7
16
8
7
14
30
13
6
6
12
4
27
T
T
11
5
10
63.2
28
(Hours)
5
63.2
(Hours)
29
9
4
8
26
3
3
7
25
2
6
2
5
24
94
96
98
100
102
Theophylline (% Nominal)
104
Adapted from:
Short, Robert P. et.al., J. Pharm. Sci., 2010, 99(12), 5046-5059
Short, Robert P. et.al., J. Pharm. Sci., 2011, 100(4), 1566-1575
106
94
96
98
100
102
Theophylline (% Nominal)
104
106
Dissolution
Blending
PAT
Content
Uniformity
Tableting
Feedforward Control
Risk = f(CU,T63.2,…)
T63.2 = f(RTS,...)
Feedback
Control
8
PAT
7
5
T
63.2
(Hours)
6
4
Design Space
3
2
94
96
98
100
102
Theophylline (% Nominal)
104
106
RTS =
f(Pressure,
Concentrations,…)
Introduction
• Acceptable CQA ranges defines the design
space: “the multidimensional combination
and interaction of input variables (e.g.,
material attributes) and process
parameters that have been demonstrated
to provide assurance of quality” (ICHQ8)
MacGregor et al.,2008. JPI, 3,
15-22
Introduction
• Factors not typically studied in initial DoE:
–
–
–
–
–
Full extent of raw material variability
Supply chain disruption
Manufacturing chain relocation
Storage condition variability
Equipment wear
• When variability is detected in the
underlying factors of the design space, it is
necessary to adapt the relevant models
(the design space) while maintaining product
efficacy and safety
Objectives
• Evaluate the possibility to adapt critical
process parameters and consequently
establish a dynamic design space based
on raw material characteristics while
maintaining product quality
Strategy
1. Create knowledge space
2. Determine CQAs and the design space
3. Test robustness of design space with
respect to raw material variability
4. Evaluate the possibilities of a dynamic
design space to compensate for
variability (from raw material properties)
– Key goal: maintain product quality
Results:
Knowledge and design spaces
• Knowledge space
– CQAs: RTS and disintegration time
– CPPs: Excipient ratio and tablet force to
failure
Disintegration time (s)
Radial Tensile Strength (MPa)
12
12
200
180
11
1.8
1800
11
10
140
120
9
100
80
8
Tablet force to failure (kP)
Tablet force to failure (kP)
160
1.6
1600
10
1.4
1400
9
1.2
1200
8
60
7
1.0
1000
7
40
0.8
800
20
6
1
1.5
2
2.5
3
Excipient ratio (MCC:Lactose)
( > 80s)
3.5
4
6
1
1.5
2
2.5
3
Excipient ratio (MCC:Lactose)
3.5
(1.25 - 1.60 MPa)
4
Results:
Knowledge and design spaces
• Design space
Design space
12
Tablet force to failure (kP)
11
10
9
8
7
6
1
1.5
2
2.5
3
Excipient ratio (MCC:Lactose)
3.5
4
The
multidimensional
combination and
interaction of input
variables and
process
parameters
Results: Effect of raw material properties on
the robustness of the design space
• An optimal set of critical process
parameters was chosen and its robustness
tested regarding raw material variability
– Excipient ratio of 2
(41.3% of MCC and 20.7% of
lactose)
– 2% of Croscarmellose Sodium
– Target force to failure at the press of 11 kp
– RMSNV weights were 1-1-1 (for APIs, Excipients and
Croscarmellose Sodium respectively)
Results: Effect of raw material properties on
the robustness of the design space
Design space
12
Tablet force to failure (kP)
11
10
9
8
7
6
1
1.5
2
2.5
3
Excipient ratio (MCC:Lactose)
3.5
4
• Given these CPPs, the corresponding CQAs
were 1.53 MPa and 104 s for RTS and
disintegration time respectively.
Results: Effect of raw material properties on
the robustness of the design space
• When considering the variability in raw
materials, 2 of the 3 runs were outside of
the design space
Run #
1
2
3
RM
Disintegration Radial Tensile
Strength (MPa)
Characteristic
Time (s)
Larger
APAP
50:50 Lac
Both
1452
63
68
98
1396
1395
Results: Process adjustments to compensate
for raw material characteristics
Adjustments to tablet force
to failure setting
Design space
14
*
Tablet force to failure (kp)
13
12
11
10
9
8
7
1A 1B 1C 1D 1E 1F 2A 2B 2C 2D 2E 2F 3A 3B 3C 3D 3E 3F
Design points
APAP
Excip
Excip +
APAP
*
Run #
Sub-run #
1
1
1
1
1
1
2
2
2
2
2
2
3
3
3
3
3
3
A
B
C
D
E
F
A
B
C
D
E
F
A
B
C
D
E
F
APAP
Particle size
600 μm
600 μm
600 μm
600 μm
600 μm
600 μm
100 μm
100 μm
100 μm
100 μm
100 μm
100 μm
600 μm
600 μm
600 μm
600 μm
600 μm
600 μm
Lactose
forma
100:0
100:0
100:0
100:0
100:0
100:0
50:50
50:50
50:50
50:50
50:50
50:50
50:50
50:50
50:50
50:50
50:50
50:50
Compression Compressi
speed (rpm) on force (p)
30
9,000
30
11,000
30
13,000
45
9,000
45
11,000
45
13,000
30
9,000
30
11,000
30
13,000
45
9,000
45
11,000
45
13,000
30
9,000
30
11,000
30
13,000
45
9,000
45
11,000
45
13,000
*Compression force outside of original design space required to meet specifica
Changing CPPs can allow specifications to be met!
Conclusions
• Adapting CPPs based on raw material
characterization allows the creation of
drug products with repeatable acceptable
characteristics
• Adjustments to design space are critical to
ensure process robustness
Conclusions
• Process analytical technology plays a
critical role in monitoring the state of the
process and enables control to achieve
desired product attributes by adjusting
process parameters
– Improved raw material characterization can
mitigate some, but not all of the potential
variations
– Such approach currently exist for granulation
and drying control based on Environment
Equivalency Factors
Acknowledgements
Steve Short, Ph.D.
Zhenqi (Pete) Shi, Ph.D.
Ma Hua, Ph. D.
Robert Bondi, Ph.D.
NIPTE