Quality by Design : Control strategy
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The document provides an overview of control strategies in pharmaceutical manufacturing, emphasizing the importance of a well-defined approach to ensure consistent product quality through risk management and process understanding. It outlines components of a control strategy, including in-process controls, real-time release testing, and adjustments based on data trends to improve product quality throughout the product lifecycle. Additionally, it highlights the need for regulatory compliance and the consideration of variability in materials and processes during strategy development.
![ICH Quality Implementation Working Group - Integrated Implementation Training Workshop
Case Study
Developing Product and Process
Understanding: DOE Investigation of factors affecting Dissolution
Multifactorial DOE study of Exp No
1
Run Order
1
API
0.5
MgSt
3000
LubT
1
Hard
60
Diss
101.24
2 14 1.5 3000 1 60 87.99
variables affecting dissolution 3 22 0.5 12000 1 60 99.13
4 8 1.5 3000 10 60 86.03
• Factors: 5 18 0.5 12000 10 60 94.73
- API particle size [API] 6
7
9
15
1.5
0.5
12000
3000
10
1
60
110
83.04
98.07
unit: log D90, microns 8 2 0.5 12000 1 110 97.68
- Mg-Stearate Specific Surface Area
9
10
6
16
1.5
0.5
12000
3000
1
10
110
110
85.47
95.81
11 20 1.5 3000 10 110 84.38
[MgSt] 12 3 1.5 12000 10 110 81
unit: cm2/g 13 10 0.5 7500 5.5 85 96.85
- Lubrication time [LubT] unit: min 14 17 1.5 7500 5.5 85 85.13
-
15 19 1 3000 5.5 85 91.87
Tablet hardness [Hard] unit: N 16 21 1 12000 5.5 85 90.72
17 7 1 7500 1 85 91.95
• Response: 18 4 1 7500 10 85 88.9
- % API dissolved at 20 min [Diss]
19
20
5
11
1
1
7500
7500
5.5
5.5
60
110
92.37
90.95
21 12 1 7500 5.5 85 91.95
• DOE design: 22 13 1 7500 5.5 85 90.86
- RSM design
23 23 1 7500 5.5 85 89
- Reduced CCF (quadratic model)
Note: A screening DoE may be used first to identify
- 20+3 center point runs
which of the many variables have the greatest effect
www.drugragulations.org slide 49](https://image.slidesharecdn.com/qbdforbeginnerscontrolstrategy-120817110947-phpapp02/85/Quality-by-Design-Control-strategy-49-320.jpg)
Quality by Design : Control strategy
- 1. Control strategy Presentation prepared by Drug Regulations – a not for profit organization. Visit www.drugregulations.org for the latest in Pharmaceuticals. www.drugragulations.org 1
- 2. Product Profile Quality Target Product Profile (QTPP) CQA’s Determine “potential” critical quality attributes (CQAs) Risk Assessments Link raw material attributes and process parameters to CQAs and perform risk assessment Design Space Develop a design space (optional and not required) Control Strategy Design and implement a control strategy Continual Manage product lifecycle, including continual Improvement improvement www.drugragulations.org 2
- 3. Product Profile CQA’s This presentation Part III of the Risk Assessments series “QbD for Beginners” covers basic aspects of Design Space ◦ Control Strategy Control Strategy Continual Improvement www.drugragulations.org 3
- 4. A control strategy is designed to ensure that a product of required quality will be produced consistently. The elements of the control which contribute to the final product quality include ◦ In-process controls, ◦ The controls of input materials (drug substance and excipients), ◦ Intermediates (in-process materials), ◦ Container closure system, and ◦ Drug products www.drugragulations.org 4
- 5. Control Strategy ◦ Planned set of controls ◦ Derived from current product and process understanding that assures process performance and product quality ◦ The controls can include parameters and attributes related to Drug substance , Drug product materials and components, Facility and equipment operating conditions, In-process controls, Finished product specifications, and The associated methods and Frequency of monitoring and control.‟ (ICH Q10) www.drugragulations.org 5
- 6. In-Process Control (or Process Control): Checks performed during production in order to monitor and, if appropriate, to adjust the process and/or to ensure that the intermediate or API conforms to its specifications (Q7) Applies similarly to the drug product In-Process Tests: Tests which may be performed during the manufacture of either the drug substance or drug product, rather than as part of the formal battery of tests which are conducted prior to release (Q6A) www.drugragulations.org 6
- 7. „Real time release testing (RTRT) is the ability to evaluate and ensure the quality of in- process and/or final product based on process data, which typically include a valid combination of measured material attributes and process controls‟ (Q8(R2)) Process Analytical Technology (PAT): A system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring final product quality (Q8(R2)) www.drugragulations.org 7
- 8. Control strategy derives from management of risk and should lead to assurance of consistent quality of product in alignment with the Quality Target Product Profile (QTPP) Control strategy is: ◦ Not a new concept ◦ Not just specifications ◦ Based on product and process understanding and risk management ◦ While design space is optional, control strategy is not. www.drugragulations.org 8
- 9. Every process and product has an associated control strategy. ◦ There is one overall control strategy for a given product. ◦ There are control strategies for unit operations ◦ It could include some site specific aspects For a given product, different approaches for the control strategy are possible (e.g. in-process testing, RTRT, end product testing) Specifications for API and drug product are still needed for stability testing, regional regulatory testing requirements, etc. www.drugragulations.org 9
- 10. Control strategy and batch release should not be confused. Control strategy is a key component, but not the only element needed for the batch release decision. Scale-up, technology transfer and manufacturing experience can lead to refinements of the control strategy under the PQS considering regulatory requirements www.drugragulations.org 10
- 11. Process for defining the control strategy ◦ What are the quality criteria (QTPP) ◦ Initial design of specific product & process ◦ Assess prior knowledge to understand materials, process and product with their impact Experience with different approaches to control ◦ Risk assessment for process steps and variables Assure all CPPs are identified during QRA ◦ Development to further determine what type of controls are appropriate for each variable ◦ Consider design space, if submitted ◦ Specifications Scale-up considerations Quality system requirements of control strategy ◦ Implementation, maintenance and updating www.drugragulations.org 11
- 12. Industry selects control approach based on multiple factors ◦ Factors may include analytical testing sensitivity, equipment limitations, etc. Regulators evaluate the control strategy and whether the risk has been adequately controlled Inspector reviews the implementation of the control strategy at site, including adaptation at scale up, and the adequacy of the site quality system to support it www.drugragulations.org 12
- 13. These controls should be based on ◦ Product, ◦ Formulation and ◦ Process understanding They should include, at a minimum, control of the critical process parameters and material attributes. www.drugragulations.org 13
- 14. A comprehensive pharmaceutical development approach will generate process and product understanding and identify sources of variability. Sources of variability that can impact product quality should be identified, appropriately understood, and subsequently controlled. www.drugragulations.org 14
- 15. Understanding sources of variability and their impact on downstream processes or processing, in-process materials, and drug product quality can provide an opportunity to shift controls upstream and minimise the need for end product testing. www.drugragulations.org 15
- 16. Product and process understanding, in combination with quality risk management, will support the control of the process such that the variability (e.g., of raw materials) can be compensated for in an adaptable manner to deliver consistent product quality. www.drugragulations.org 16
- 17. This process understanding can enable an alternative manufacturing paradigm where the variability of input materials could be less tightly constrained. Instead it can be possible to design an adaptive process step (a step that is responsive to the input materials) with appropriate process control to ensure consistent product quality. www.drugragulations.org 17
- 18. Enhanced understanding of product performance can justify the use of alternative approaches to determine that the material is meeting its quality attributes. The use of such alternatives could support real time release testing. For example, disintegration could serve as a surrogate for dissolution for fast-disintegrating solid forms with highly soluble drug substances. Unit dose uniformity performed in-process (e.g., using weight variation coupled with near infrared (NIR) assay) can enable real time release testing. www.drugragulations.org 18
- 19. This can provide an increased level of quality assurance compared to the traditional end- product testing using compendial content uniformity standards. Real time release testing can replace end product testing, but does not replace the review and quality control steps called for under GMP to release the batch. www.drugragulations.org 19
- 20. A control strategy can include, but is not limited to, the following: Control of input material attributes (e.g., drug substance, excipients, primary packaging materials) based on an understanding of their impact on processability or product quality; Product specification(s); Controls for unit operations that have an impact on downstream processing or product quality (e.g., the impact of drying on degradation, particle size distribution of the granulate on dissolution); www.drugragulations.org 20
- 21. Controls for unit operations that have an impact on downstream processing or product quality (e.g., the impact of drying on degradation, particle size distribution of the granulate on dissolution); In-process or real-time release testing in lieu of end-product testing (e.g. measurement and control of CQAs during processing); A monitoring program (e.g., full product testing at regular intervals) for verifying multivariate prediction models. www.drugragulations.org 21
- 22. A control strategy can include different elements. For example, one element of the control strategy could rely on end-product testing, whereas another could depend on real-time release testing. The rationale for using these alternative approaches should be described in the submission www.drugragulations.org 22
- 23. The lifecycle of the control strategy is supported by ◦ pharmaceutical development, ◦ quality risk management (QRM),and ◦ the pharmaceutical quality system (PQS), As described in ICH Q8(R2), Q9, and Q10. www.drugragulations.org 23
- 24. The control strategy is generally developed and initially implemented for production of clinical trial materials. It can be refined for use in commercial manufacture as new knowledge is gained. Changes could include acceptance criteria, analytical methodology, or the points of control (e.g., introduction of real-time release testing). www.drugragulations.org 24
- 25. Additional emphasis on process controls should be considered in cases where products cannot be well-characterized and/or quality attributes might not be readily measurable due to limitations of testing or detectability (e.g., microbial load/sterility). www.drugragulations.org 25
- 26. Consideration should be given to improving the control strategy over the lifecycle. In response to assessment of data trends over time and other knowledge gained Continuous process verification is one approach that enables a company to monitor the process and make adjustments to the process and/or the control strategy, as appropriate. www.drugragulations.org 26
- 27. When multivariate prediction models are used, systems that maintain and update the models help to assure the continued suitability of the model within the control strategy. www.drugragulations.org 27
- 28. Attention should be given to outsourced activities to ensure all changes are communicated and managed. The regulatory action appropriate for different types of changes should be handled in accordance with the regional regulatory requirements. www.drugragulations.org 28
- 29. Different control strategies could be applied at different sites or when using different technologies for the same product at the same site. Differences might be due to equipment, facilities, systems, business requirements (e.g., confidentiality issues, vendor capabilities at outsourced manufacturers) or as a result of regulatory assessment/inspection outcomes. www.drugragulations.org 29
- 30. The applicant should consider the impact of the control strategy implemented on the residual risk and the batch release process. www.drugragulations.org 30
- 31. Risk associated with scale-up should be considered in control strategy development to maximize the probability of effectiveness at scale. The design and need for scale-up studies can depend on the development approach used and knowledge available. www.drugragulations.org 31
- 32. A risk-based approach can be applied to the assessment of suitability of a control strategy across different scales. QRM tools can be used to guide these activities. This assessment might include risks from ◦ processing equipment, ◦ facility environmental controls, ◦ personnel capability, ◦ experiences with technologies, and ◦ historical experience (prior knowledge). www.drugragulations.org 32
- 33. Complexity of product and process Differences in manufacturing equipment, facilities and/or sites Raw materials: ◦ Differences in raw material quality due to source or ◦ batch-to-batch variability Impact of such differences on process controls and quality attributes www.drugragulations.org 33
- 34. Process parameters: ◦ Confirmation or optimization ◦ Confirmation of the design space(s), if used In-process controls: ◦ Point of control ◦ Optimization of control methods ◦ Optimization and/or updating of models, if used www.drugragulations.org 34
- 35. Product specification: ◦ Verification of the link to QTPP ◦ Confirmation of specifications (i.e., methods and acceptance criteria) ◦ Confirmation of real-time release testing (RTRT), if used www.drugragulations.org 35
- 36. To base the release of a product on product and process understanding rather than on end product testing alone and/or on the results of batch analysis. This implies ◦ Understanding the science around the product and process Identifying the parameters (critical) of active, excipients, process influencing the quality ◦ Establishment of a control strategy –risk based- which monitors the important parameters influencing the CQAs; gives the basis for RTR or reduced end product testing. www.drugragulations.org 36
- 37. Example: Sterilisation ◦ Injectables: compliance with the specification “sterile”: via parametric release rather than with the conventional Ph.Eu. “Sterility test”; monitoring of critical parameters (time, pressure, temperature, ….) Example: dissolution ◦ Release parameters e.g. Particle size active substance and/or excipients Hardness of the tablet …….. www.drugragulations.org 37
- 38. Sample & Sample Sample Test & & Test Test API Pass Excipient Blend Screen Blend Tablet or Fail Excipient Fixed processes Quality Criteria met if: • Meets specification(s) (off-line QC tests) • GMP Procedures followed John Berridge, Pfizer www.drugragulations.org 38
- 39. Characterise Adaptive processes API 100% Excipient Blend Screen Blend Tablet Pass Excipient Real PAT PAT time release Standards and acceptance criteria for a PAT/QbD approach are not the same as a “Test to Document Quality” approach www.drugragulations.org 39
- 40. Minimal Approaches ◦ Drug product quality controlled primarily by intermediates (in-process materials) and end product testing Enhanced, Quality by Design Approaches ◦ Drug product quality ensured by risk-based control strategy for well understood product and process ◦ Quality controls shifted upstream, with the possibility of real-time release testing or reduced end-product testing www.drugragulations.org 40
- 41. Example Control Strategy for Real Time Release Testing NIR Spectroscopy NIR Monitoring Laser Diffraction (At-Line) Blend Uniformity Particle Size • Identity • Assay Raw materials & • API to Excipient API dispensing ratio • Specifications based on product Roller Tablet Pan Dispensing Blending Sifting compaction Compression Coating www.drugragulations.org 41
- 42. Liquid product, used to determine mix time CQA related to mix uniformity CPP‟s (Critical Process Parameters) included agitator speed, time after addition of one ingredient until the addition of another, solution temperature, and recirculation flow rate. Process analyzer used was a refractometer Resulted in cost savings and quality enhancement SCADA, User Mix Interface RI Tank Sensor Control Data System Historian Pump www.drugragulations.org 42
- 43. Example from ICH case study Blending Process Control Options Decision on conventional vs. RTR testing Key message: Both approaches to assure blend uniformity are valid in combination with other GMP requirements ICH-GCG ASEAN, Kuala Lumpur, 26-28 July 2010 slide 43 www.drugragulations.org
- 44. Example from ICH case study ICH-GCG ASEAN, Kuala Lumpur, 26-28 July 2010 slide 44 www.drugragulations.org
- 45. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Breakout B: Control Strategy www.drugragulations.org slide 45
- 46. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Breakout B: Control Strategy RTRT of Assay and Content Uniformity • Finished Product Specification – use for stability, regulatory testing, site change, whenever RTR testing is not possible - Assay acceptance criteria: 95-105% of nominal amount (30mg) - Uniformity of Dosage Unit acceptance criteria - Test method: HPLC • Real Time Release Testing Controls - Blend uniformity assured in blending step (online NIR spectrometer for blending end-point) - API assay is analyzed in blend by HPLC - Tablet weight control in compression step • No end product testing for Assay and Content Uniformity (CU) - Would pass finished product specification for Assay and Uniformity of Dosage Units if tested because assay assured by combination of blend uniformity assurance, API assay in blend and tablet weight control (if blend is homogeneous then tablet weight will determine content of API) www.drugragulations.org slide 46
- 47. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Example: Real Time Release Testing (RTRT) for Dissolution www.drugragulations.org slide 47
- 48. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Developing Product and Process Understanding Investigation of the effect of API particle size on Bioavailability and Dissolution Drug Substance with particle size D90 of 100 microns has slower dissolution and lower Cmax and AUC In Vivo In Vitro correlation (IVIVC) established at 20 minute timepoint Early time points in the dissolution profile are not as critical due to PK results www.drugragulations.org slide 48
- 49. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Developing Product and Process Understanding: DOE Investigation of factors affecting Dissolution Multifactorial DOE study of Exp No 1 Run Order 1 API 0.5 MgSt 3000 LubT 1 Hard 60 Diss 101.24 2 14 1.5 3000 1 60 87.99 variables affecting dissolution 3 22 0.5 12000 1 60 99.13 4 8 1.5 3000 10 60 86.03 • Factors: 5 18 0.5 12000 10 60 94.73 - API particle size [API] 6 7 9 15 1.5 0.5 12000 3000 10 1 60 110 83.04 98.07 unit: log D90, microns 8 2 0.5 12000 1 110 97.68 - Mg-Stearate Specific Surface Area 9 10 6 16 1.5 0.5 12000 3000 1 10 110 110 85.47 95.81 11 20 1.5 3000 10 110 84.38 [MgSt] 12 3 1.5 12000 10 110 81 unit: cm2/g 13 10 0.5 7500 5.5 85 96.85 - Lubrication time [LubT] unit: min 14 17 1.5 7500 5.5 85 85.13 - 15 19 1 3000 5.5 85 91.87 Tablet hardness [Hard] unit: N 16 21 1 12000 5.5 85 90.72 17 7 1 7500 1 85 91.95 • Response: 18 4 1 7500 10 85 88.9 - % API dissolved at 20 min [Diss] 19 20 5 11 1 1 7500 7500 5.5 5.5 60 110 92.37 90.95 21 12 1 7500 5.5 85 91.95 • DOE design: 22 13 1 7500 5.5 85 90.86 - RSM design 23 23 1 7500 5.5 85 89 - Reduced CCF (quadratic model) Note: A screening DoE may be used first to identify - 20+3 center point runs which of the many variables have the greatest effect www.drugragulations.org slide 49
- 50. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Factors affecting Dissolution Scaled & Centered Coefficients for Diss at 60min • Key factors influencing 0 -1 in-vitro dissolution: - API particle size is the -2 dominating factor -3 (= CQA of API) % -4 -5 - Lubrication time has a -6 small influence MgSt*LubT Hard API MgSt LubT API Mg Lubrication Tablet Mg St*LubT (= low risk parameter) Stearate Blending Hardness Particle Size N=23 SSA R2=0.986 R2time Adj.=0.982 DF=17 Q2=0.981 RSD=0.725 Conf. lev.=0.95 MODDE 8 - 2008-01-23 10:58:52 Acknowledgement: adapted from Paul Stott (AZ) – ISPE PQLI Team www.drugragulations.org slide 50
- 51. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Predictive Model for Dissolution • Prediction algorithm - A mathematical representation of the design space for dissolution - Factors include: API PSD D90, magnesium stearate specific surface area, lubrication time and tablet hardness (linked to compression pressure) Prediction algorithm: Diss = 108.9 – 11.96 × API – 7.556×10-5 × MgSt – 0.1849 × LubT – 3.783×10-2 × Hard – 2.557×10-5 × MgSt × LubT www.drugragulations.org slide 51
- 52. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Predictive Model for Dissolution • Account for uncertainty - Sources of variability (predictability, measurements) • Confirmation of model - compare model results vs. actual dissolution results for batches - continue model verification with dissolution testing of production material, as needed Batch 1 Batch 2 Batch 3 Model prediction 89.8 87.3 88.5 Dissolution testing 92.8 90.3 91.5 result (88.4–94.2) (89.0-102.5) (90.5-93.5) www.drugragulations.org slide 52
- 53. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Dissolution: Design Space • Response surface plot for effect of API particle size and magnesium stearate specific surface area (SSA) on dissolution Diss (% at 20 min) Area of potential risk Design for dissolution failure Space Graph shows interaction between two of the variables: API particle size and magnesium stearate specific surface area API particle size (Log D90) Acknowledgement: adapted from Paul Stott (AZ) www.drugragulations.org slide 53
- 54. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Dissolution: Control Strategy • Controls of input material CQAs - API particle size distribution - Control of crystallisation step - Magnesium stearate specific surface area - Specification for incoming material • Controls of process parameter CPPs - Lubrication step blending time - Compression pressure (set for target tablet hardness) - Tablet press force-feedback control system • Prediction mathematical model - Use in place of dissolution testing of finished drug product - Potentially allows process to be adjusted for variation in API particle size, for example, and assure dissolution performance www.drugragulations.org slide 54
- 55. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Example 2: Real Time Release Testing (RTRT) for Assay and Content Uniformity www.drugragulations.org slide 55
- 56. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Quality Risk Assessment Impact on Assay and Content Uniformity CQAs • QRA shows API particle size, moisture control, blending and lubrication steps have potential to affect Assay and Content Uniformity CQAs - Moisture is controlled during manufacturing by facility HVAC control of humidity (GMP control) Drug Moisture substance content in Blending Lubrication Compression Coating Packaging particle size manufacture in vivo performance Dissolution Assay Degradation Content uniformity Appearance Friability Stability-chemical Stability-physical - Low risk - Medium risk - High risk www.drugragulations.org slide 56
- 57. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Blending Process Control Options Decision on conventional vs. RTR testing www.drugragulations.org slide 57
- 58. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Process Control Option 1 DOE for the Blending Process Parameter Assessment to develop a Design Space - Factors Investigated: Blender type, Rotation speed, Blending time, API Particle size Blending time Rotation speed Particle size D90 Experiment Run Condition Blender (minutes) (rpm) ( m) No. 1 2 varied 2 10 V type 5 2 7 varied 16 10 V type 40 DOE design 3 10 varied 2 30 V type 40 4 5 varied 16 30 V type 5 5 6 varied 2 10 Drum type 40 6 1 varied 16 10 Drum type 5 7 8 varied 2 30 Drum type 5 8 11 varied 16 30 Drum type 40 9 3 standard 9 20 V type 20 10 12 standard 9 20 Drum type 20 11 9 standard 9 20 V type 20 12 4 standard 9 20 Drum type 20 www.drugragulations.org slide 58
- 59. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Process Control Option 2 Blend uniformity monitored using a process analyser • Control Strategy to assure homogeneity of the blend - Control of blending end-point by NIR and feedback control of blender - API particle size In this case study, the company chooses to use online NIR to monitor blend uniformity to provide efficiency and more flexibility www.drugragulations.org slide 59
- 60. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Process Control Option 2 Blend uniformity monitored using a process analyser • On-line NIR spectrometer used 0.045 to confirm scale up of blending mean spectral standard deviation 0.04 • Blending operation complete 0.035 when mean spectral std. dev. 0.03 Pilot Scale Full Scale reaches plateau region 0.025 - Plateau may be detected 0.02 using statistical test or rules 0.015 • Feedback control to turn off 0.01 Plateau region blender 0.005 • Company verifies blend does 0 not segregate downstream 0 32 64 96 128 - Assays tablets to confirm Revolution Revolutions of Blender Number of (block number) uniformity Data analysis model will be provided - Conducts studies to try to Plan for updating of model available segregate API Acknowledgement: adapted from ISPE PQLI Team www.drugragulations.org slide 60
- 61. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study Tablet Weight Control in Compression Operation Conventional automated control of Tablet Weight using feedback loop: Sample weights fed into weight control equipment which sends signal to filling mechanism on tablet machine to adjust fill volume and therefore tablet weight. www.drugragulations.org slide 61
- 62. ICH Quality Implementation Working Group - Integrated Implementation Training Workshop Case Study RTRT of Assay and Content Uniformity • Real Time Release Testing Controls - Blend uniformity assured in blending step (on-line NIR spectrometer for blending end-point) - API assay is analyzed in blend by HPLC - API content could be determined by on-line NIR, if stated in filing - Tablet weight control with feedback loop in compression step • No end product testing for Assay and Content Uniformity (CU) - Would pass finished product specification for Assay and Uniformity of Dosage Units if tested because assay assured by combination of blend uniformity assurance, API assay in blend and tablet weight control (if blend is homogeneous then tablet weight will determine content of API) www.drugragulations.org slide 62
- 63. Process C ontrol Philosophy - Paradigm Shift Conventional approach - lab based End of phase testing of quality, to reduce the risk in m oving to the next stage O btain raw Mix active and Press tablets Package m aterials excipeints P.A.T approach - process based, at-line or on-line O btain raw Mix active and Press tablets Package m aterials excipeints Continuously or m ore frequently test quality during each phase, to rem ove the risk in m oving to the next stage www.drugragulations.org 63
- 64. Granulation Fluidized Bed Dispensation Dryer Scale Water Content – NIR Identity-NIR Extent of Wet Air Particle size – FBRM Massing - Power Consumption Raw Materials Blending Blend Homogeneity - NIR Multivariate Model (predicts Disintegration) Tableting Content Uniformity NIR Unit Operations Attributes Packaging Controls www.drugragulations.org 64
- 65. Product Profile Quality Target Product Profile (QTPP) CQA’s Determine “potential” critical quality attributes (CQAs) Risk Assessments Link raw material attributes and process parameters to CQAs and perform risk assessment Design Space Develop a design space (optional and not required) Control Strategy Design and implement a control strategy Continual Manage product lifecycle, including continual Improvement improvement www.drugragulations.org 65