1. 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

2. 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.

3. Inventory Turnover- major branded, generic, mid-sized, and non-pharma.
Cogdill, Knight, Anderson, Drennen; Journal of Pharmaceutical Innovation, Oct., 2007.

4. 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.

5. 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
QbD
Design Space
PBQS
Product Attributes
Patient Characteristics

6. 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).

7. 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”
Quality
QbD facilitates PAT system development
PAT verifies QbD
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.

8. Manufacturing Systems designed using QbD and Implemented via PAT
SPCTech QbD/PAT Philosophy © 2006

9. Cycle Time Improvement with PAT
Cogdill, Knight, Anderson, Drennen; Journal of Pharmaceutical Innovation, Oct., 2007.

10. 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.

11. 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
At the end of the section on ___ ___, you should be able to: (3-5 objectives, 14 pt. Calibri):
For consistency throughout the modules, use verbs like Identify…, Name…, Select…, etc., which objectively demonstrate mastery of main ideas (related to review questions).
ICH Q8(R2), Part II: Pharmaceutical Development- Annex

12. 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

13. 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

14. 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

15. 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

16. Histogram of all Failure Modes Assessed (RPN values)
Medium
High: > 60
Medium: = 60
High

17. Histograms of RPN Values for Current and Initial Risk Assessments
Compression and Granulation:
- Formation of lactam
- Reduced chemical stability
Compression 4
Granulation,
Fluid bed drying,
and Shipping 6
Granulation
Granulation,
Fluid bed drying
and Shipping
Formation of unknown physical forms in
Granulation, FBD, Shipping
Decrease from 80 in granulation due to lack of observation of hydrate formation.

18. Quality Risk Management and Continuous Improvement
Initiate
Quality Risk Management Process
Risk Assessment
Risk Control
Risk Review
Risk Identification
Risk Analysis
Risk Evaluation
Risk Reduction
Risk Acceptance
Review Events
Output / Result of the
Risk Communication
Risk Management Tools
unacceptable
Risk Reduction
Continuous improvement cycle
Adapted from: ICHQ9

19. Validation Pathways for PAT Methods1 2 3 4
Intended Routine PAT Measurement Mode
Off-line/
At-line
On-line/
In-line
Validation PAT
Measurement Mode
Off-line/At-line
On-line/In-line Pilot Scale
On-line/In-line Commercial Scale
Validation Sampling
Static
Dynamic
Validation Reference
(if necessary)
On/In-line or
Off/At-line
Additional Validation
on Transfer to
On-line/In-line
N/A
Yes
Adapted from ASTM E55 standard.

20. Inefficacy and Toxicity Risk Contour Plots
Connecting Quality Specifications with Patient Needs: Performance Based Quality Specifications (PBQS)
Inefficacy and Toxicity Risk Contour Plots
Inefficacy
Toxicity
Adapted from:
Short, Robert P. et.al., J. Pharm. Sci., 2010, 99(12),
Short, Robert P. et.al., J. Pharm. Sci., 2011, 100(4),

21. RTS = f(Pressure, Concentrations,…) Risk = f(CU,T63.2,…)
Dissolution
Blending
PAT
Tableting
RTS =
f(Pressure,
Concentrations,…)
Feedforward Control
Risk = f(CU,T63.2,…)
T63.2 = f(RTS,...)
Feedback Control
PAT

22. 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

23. 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
Variability is detected in the underl
What happens when the inputs or process transformations change in the design space model previously reviewed.

24. 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
General:
A design space is a model
Changes not in initial DoE have the potential to modify the underlying effects that will cause innaccuracies in the model
Therrefore, the model coefficients should be periodically checked and if necessary modified.
Specific to this work, as new conditions are encountered, new “design spaces” (or sub-spaces) are used to account for the changes. The outcome is the modification of the process via PCCP.
CQAs remain the same, but the model to achieve them is modified.

25. Strategy Create knowledge space Determine CQAs and the design space
Test robustness of design space with respect to raw material variability
Evaluate the possibilities of a dynamic design space to compensate for variability (from raw material properties)
Key goal: maintain product quality

26. Results: Knowledge and design spaces
Knowledge space
CQAs: RTS and disintegration time
CPPs: Excipient ratio and tablet force to failure
1.8
1.6
Note the ‘passing’ regions for each excipient ratio/force to failure setting
Friability and dissolution passed for all conditions
1.4
1.2
1.0
0.8
( > 80s)
( MPa)

27. Results: Knowledge and design spaces
The multidimensional combination and interaction of input variables and process parameters

28. 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 (for APIs, Excipients and Croscarmellose Sodium respectively)

29. Results: Effect of raw material properties on the robustness of the design space
Given these CPPs, the corresponding CQAs were 1.53 MPa and 104 s for RTS and disintegration time respectively.

30. Radial Tensile Strength (MPa)
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 # RM
Characteristic
Disintegration
Time (s)
Radial Tensile Strength (MPa) 1
Larger APAP 63
1452 2
50:50 Lac 68
1396 3
Both 98
1395
Note that the single change caused failure.

31. Compression speed (rpm)
Results: Process adjustments to compensate for raw material characteristics
Changing CPPs can allow specifications to be met!
Adjustments to tablet force to failure setting
Run #
Sub-run #
APAP
Particle size
Lactose
forma
Compression speed (rpm)
Compression force (p) 1 A
600 μm
100:0 30
9,000 B
11,000 C
13,000 D 45 E F 2
100 μm
50:50 3 *
Green, spample met specs (highlighted in blue on table), red – sample failed specs
Colors represent the original force to failure at a fixed exp ratio of 2:1 (ca 40%:20%) *
APAP
Excip
Excip +
APAP
*Compression force outside of original design space required to meet specifications

32. 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

33. 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

34. Acknowledgements
Steve Short, Ph.D. Zhenqi (Pete) Shi, Ph.D. Ma Hua, Ph. D. Robert Bondi, Ph.D.
NIPTE

35. 35