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.

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

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 QbD Design Space PBQS Product Attributes 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” 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.

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 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

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

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

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

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

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

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.

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

Validation Pathways for PAT Methods 1 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.

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), 5046-5059 Short, Robert P. et.al., J. Pharm. Sci., 2011, 100(4), 1566-1575

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

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

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.

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

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) (1.25 - 1.60 MPa)

Results: Knowledge and design spaces 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 Given these CPPs, the corresponding CQAs were 1.53 MPa and 104 s for RTS and disintegration time respectively.

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.

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

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

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