Real time release testing


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Medicinal products must comply with their approved specifications before they are released into the market. Compliance with release specifications can be demonstrated by performing a complete set of tests on the active substance and/or finished product, according to the approved specifications. Under certain conditions, an alternative strategy to systematic end product testing is possible. So far this concept has been mainly applied to sterility testing of terminally sterilised products and has become associated with parametric release applications. Recent guidelines adopted in the ICH context (ICH Q8, Q9 and Q10) have made it possible to apply a similar release decision process to tests other than sterility, this approach has been called Real Time Release Testing (RTRT).

RTRT is a system of release that gives assurance that the product is of intended quality, based on the information collected during the manufacturing process, through product knowledge and on process understanding and control. RTRT recognises that under specific circumstances an appropriate combination of process controls (critical process parameters) together with pre-defined material attributes may provide greater assurance of product quality than end-product testing and the context as such be an integral part of the control strategy. The RTRT principle is already authorised for use as an optional alternative to routine sterility testing of products terminally sterilised in their final container i.e. parametric release. Enhanced product knowledge and process understanding, the use of quality risk management principles and the application of an appropriate pharmaceutical quality system, as defined within ICH Q8,Q9 and Q10 provide the platform for establishing RTRT mechanisms for other applications, for new products as well as established marketed products. Release of a product can be a combination of a RTR approach for certain critical quality attributes (CQAs) and a more conventional evaluation for other CQAs (partial RTR).

This presentation deals with the concepts of Real Time Release Testing. This presentation was compiled from material freely available from FDA , ICH , EMEA and other free resources on the world wide web.

Published in: Health & Medicine, Business

Real time release testing

  1. 1. Presentation prepared by Drug Regulations – a not forprofit organization. Visit for the latest in Pharmaceuticals. 1
  2. 2.  This presentation will cover ◦ What is Real Time Release Testing ◦ Batch Release & RTRT ◦ Organizational approach ◦ Examples ◦ End product testing Vs RTRT ◦ Process control : paradigm shift ◦ Benefits & challenges ◦ Relationship between QbD, PAT, Control Strategy & RTRT ◦ Control Strategy – Conventional Vs RTRT ◦ ICH and other published examples of RTRT 2
  3. 3.  Medicinal products must comply with their approved specifications before they are released into the market. Compliance with release specifications can be demonstrated by performing a complete set of tests on the active substance and/or finished product, according to the approved specifications. Under certain conditions, an alternative strategy to systematic end product testing is possible. 3
  4. 4.  So far this concept has been mainly applied to sterility testing of terminally sterilized products and has become associated with parametric release applications. Recent guidelines adopted in the ICH context (ICH Q8, Q9 and Q10) have made it possible to apply a similar release decision process to tests other than sterility, this approach has been called Real Time Release Testing (RTRT). 4
  5. 5.  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. ICH Q8(R2) Typically include a valid combination of measured ◦ Material attributes and ◦ Process controls 5
  6. 6.  The exact approach to RTRT will vary depending on the process requirements. The RTRT strategy may be based on control of process parameters, monitoring of product attributes or on a combination of both at appropriate steps throughout the process. Critically, the RTRT strategy should be based on a firm understanding of the process and of the relationship between process parameters, in- process material attributes and product attributes. 6
  7. 7.  Process monitoring may be applied to various manufacturing steps or unit operations, such as tabletting, on the basis of appropriate testing at various stages in the process. Some parameters/attributes are usually checked routinely at defined intervals regardless of the design of the manufacturing process of a tablet. Uniformity of mass, crushing strength and disintegration are such examples. 7
  8. 8.  The results of a comprehensive set of in-process tests and controls in these cases may constitute sufficient grounds for replacing the corresponding end product testing. This may also offer greater assurance of the finished tablet meeting certain criteria in the specification, without the tests being repeated on a sample of the finished product, as the amount of data will in general be substantially larger. 8
  9. 9.  If testing of units is part of the RTRT a sampling strategy should be defined that provides the number of locations sampled throughout the batch as well as the number of dosage units tested at each location. 9
  10. 10.  RTRT will, in general, comprise a combination of process controls which may utilise process analytical technology (PAT) tools e.g. ◦ Near infrared spectroscopy (NIR) and ◦ Raman spectroscopy (usually in combination with multivariate analysis), ◦ Together with the control of relevant material attributes. 10
  11. 11.  Spectral data monitored on-line ◦ Controlling content of active substance, ◦ Polymorphism, ◦ water content, ◦ Blending homogeneity, ◦ Particle/powder properties or ◦ Film thickness could thereby replace end-product testing e.g. ◦ Uniformity of content, ◦ Tablet strength and ◦ Drug dissolution. 11
  12. 12.  In active substance manufacturing, RTRT can apply to ◦ Continuous manufacturing processes, and ◦ Also to discrete unit operations such as  Distillations,  Hydrogenations,  Crystallisations and  All sorts of other chemical reactions or separations (e.g. diastereoisomers). 12
  13. 13. Real time release testing is “moving the QC lab into the process” and “measure the CQAs where they are generated” 13
  14. 14. Parametric Release: One type of RTRT. Parametric release is basedon process data (e.g. temperature, pressure, time for terminal sterilization) rather than the testing of a sample for a specific attribute (ICH Q8 Q&A). 14
  15. 15. Real time release testing can replace endproduct testing, but does not replace thereview and quality control steps called for under GMP to release the batch. 15
  16. 16.  Batch release: Approved RTRT may form a basis but More aspects needs to be taken into account in the decision of a Qualified Person to release a batch. These aspects could include batch results of testing for an attribute not subject to RTR as well as specific GMP requirements. 16
  17. 17. FormulationOperations Quality Development AnalyticalRegulatory RTRT Decision DevelopmentTechnology Development Chemometrics Multi-disciplinary / cross-functional teams are key to RTRt New skill sets may be needed 17
  18. 18.  On-line or in-line measurements and/or controls, ◦ Tablet weight after compression ◦ Particle size measurement after granulation or milling ◦ Moisture measurement during drying ◦ Blend uniformity Fast at-line measurements, ◦ NIR for tablet assay ◦ Disintegration in lieu of dissolution Models as surrogate for traditional release tests, ◦ Multivariate model as a surrogate for dissolution Process signatures ◦ An evolving approach 18
  19. 19. Fixed OutputInput Process Disturbance: Variation due to materials or process Several days latter QC End Product Testing 19
  20. 20. Process analyzers used to NIR measure processInterface parameters and adjust the process Adjustable Output Input Process Disturbance: Variation due to Immediate Feed back/ materials or forward loop process 20
  21. 21.  Reaction developed and understood during development – typical tools are IR, NIR and Raman. At commercial scale NIR is used to control the reaction. Stop the reaction at Maximal API Concentration Stopping time differs from Batch to Batch Real time release measurement of the API assay and bi-product (impurity) No sampling for in-process control or end-product testing for this CQA 21
  22. 22. Holistic Control Strategy e.g.:Content Uniformity = Blend uniformity + Drugconcentration + Weight controlRTRT1 = Blend Uniformity2 = Granule particle size3 = Weight, Hardness, Potency, Drugconcentration, Identity, Rate–controllingpolymer concentration 22
  23. 23. Process C ontrol Philosophy - Paradigm ShiftConventional 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 excipeintsP.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 23
  24. 24. Granulation Fluidized BedDispensation 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 24
  25. 25.  The outcome of a high level of process understanding1. Controlling the process2. Adjust for variability in raw and in-process materials3. Increase yield, reduce waste, scrap4. Reduce the risk of losing a batch5. Reduced QC test6. Increased control activity on the manufacturing shop floor7. Reduced cycle time8. Real time monitoring of CPPs and CQAs for free (must also be included in continuous process verification and Annual Product Review)9. Quality of the finished product can be measured during manufacturing – no surprises!10. Regulators might be more interested in the beginning but this will fade as process understanding has been demonstrated – reduced inspection frequency 25
  26. 26. 26
  27. 27.  New – not familiar to many PAT tools in place (in-line analysers, PAT data management, multivariate data analysis, process control) Require new skills and reorganisation of work Risk associated with implementing PAT Installation of probes, representative sampling, failure of instrument, failure of multivariate models, failure in feed forward & backward controls, etc Backup strategy must be in place Models needs frequent update If RTRT fails it cannot be replaced by end-product testing Regulators might be very interested in the beginning... 27
  28. 28. QbD Control Design Strategy Space RTRTCMA CPP CQA RTRT PAT 28
  29. 29. QbD isreallyabout QbD and PAT linksthe the patient,patient product and process Patient1. Understanding what the patient needs2. Designing and developing a product meeting these needs Process3. Designing and developing a Understanding manufacturing process capable of delivering the product that meets these needs Product Process 29
  30. 30. PAT RTRTA 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 30
  31. 31. CQA’s & CPP’s In Line On Line Process Analytical Technology is: Analyzers 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-processPredictive materials and processes with the goal of Models ensuring final product quality Real Time Real Testing 31
  32. 32. Quality What is Product Profile Target Identify critical to Product CQA the Profile CQA’s Patient QRM PATRisk Assessments Identify CMA & Design Space CPP Design space Control StrategyControl Strategy Continual Improvement PAT , PAT RTRT SOP PAT RTRT 32
  33. 33.  RTRT, when used, is part of the Control Strategy ◦ Can include some or all of the final product CQAs QbD is not directly correlated to RTRT ◦ You can have QbD approaches without RTRT ◦ However, it would be difficult to justify RTRT without a science and risk based approach 33
  34. 34.  Not all Process Analytical Technology (PAT) leads to RTRT ◦ PAT systems can be designed to control CQAs of raw materials or in-process materials and not contribute to RTRT A design space is not required for RTRT ◦ Having a design space can increase operational flexibility, without additional regulatory approval 34
  35. 35.  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) 35
  36. 36. Manual Automated & Advanced Simple 36
  37. 37. Every product MUST have a control strategy Minimal Enhanced approachDrug product quality • Drug product qualitycontrolled primarily ensured by risk-basedby intermediates (in control strategy forprocess materials) well understoodand end product and processproduct testing • Quality controls shifted upstream, with the possibility of real- time release testing or reduced end-product testing 37
  38. 38.  Identify CQAs Identify related CPPs and Material Attributes (MAs) Develop the design space for the CPPs and MAs Develop the control strategy ensuring the CPPs and MAS are always within the design space Based on risk-assessment plan how the control strategy can be implemented ◦ This process starts in development ◦ It is a lifecycle activity and ◦ The Control Strategy can be updated as new knowledge has been gained 38
  39. 39. NIR, at-line (idraw materials) IR, on-line (purity, assay ) NIR, on-line (Moisture, purity Assay (HPLC) Purity, related Conventional Testing impurities, ((HPLC) Residual solvent (GC) Moisture content (KF) Heavy Metals Etc… ID, Assay, CU (HPLC) Purity, ((HPLC) NIR, at-line (id raw Dissolution,NIR, on-line materials) Appearance(reaction id) Moisture content (KF) Etc FBRM, on- line (PSD) NIR, on-line, blend homogeneity NIR, on-line, blend NIR, on-line homogeneity (assay, CU, ID) 39
  40. 40. 40
  41. 41.  NIR can be used for RTRT of water determination Conventional lab-based NIR system ◦ Validated over range 1 – 6% Tablets dried and “spiked” to encompass historical range and regulatory specification Prepare calibration curve In line NIR for water content determination 41
  42. 42.  CQA: CU, dissolution, Crystal size during formation - PSD 1. Focuses beam reflectance measurements can be used to measure PSD 2. Measure crystal diameter 3. Probe inserted into reactor 42
  43. 43.  FBRM used to define the best cooling ramp FBRM used to measure PSD inline RTRT of PSD No sampling and QC test 43
  44. 44. Mock P 2 example Design Space 44
  45. 45. In line Monitoring of drying Process 45
  46. 46. 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 followedJohn Berridge, Pfizer 46
  47. 47. Characterise Adaptive processes API 100% Excipient Blend Screen Blend Tablet Pass Excipient Real PAT PAT time releaseStandards and acceptance criteria for a PAT/QbD approach are not the same as a “Test to Document Quality” approach 47
  48. 48.  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 48
  49. 49. Example from ICH case studyBlending Process Control OptionsDecision on conventional vs. RTR testing Key message: Both approaches to assure blend uniformity are valid in combination with other GMP requirements
  50. 50. Example from ICH case study
  51. 51.
  52. 52. 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 analysed in blend by HPLC - Tablet weight control in compression step
  53. 53. RTRT of Assay and Content Uniformity• 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)
  54. 54.
  55. 55. 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 timepointEarly time points in the dissolutionprofile are not as critical due to PKresults
  56. 56. Multifactorial DOE study of Exp No 1 Run Order 1 API 0.5 MgSt 3000 LubT 1 Hard 60 Diss 101.24variables affecting dissolution 2 3 14 22 1.5 0.5 3000 12000 1 1 60 60 87.99 99.13 Factors: 4 8 1.5 3000 10 60 86.03 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 6 1.5 12000 1 110 85.47 10 16 0.5 3000 10 110 95.81 [MgSt] 11 20 1.5 3000 10 110 84.38 unit: cm2/g 12 3 1.5 12000 10 110 81 13 10 0.5 7500 5.5 85 96.85 ◦ Lubrication time [LubT] unit: min 14 15 17 19 1.5 1 7500 3000 5.5 5.5 85 85 85.13 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 5 1 7500 5.5 60 92.37 20 11 1 7500 5.5 110 90.95 DOE design: 21 12 1 7500 5.5 85 91.95 22 13 1 7500 5.5 85 90.86 ◦ RSM design 23 23 1 7500 5.5 85 89 Note: A screening DoE may be used first to ◦ Reduced CCF (quadratic model) identify which of the many variables have the ◦ 20+3 center point runs greatest effect
  57. 57. Scaled & Centered Coefficients for Diss at 60min• Key factors 0 influencing in-vitro -1 dissolution: -2 - API particle size is -3 the dominating % -4 factor (= CQA of API) -5 -6 - Lubrication time has API Mg Lubricatio Tablet Mg St*LubT MgSt*LubT Hard API MgSt LubT n Particle Stearate Hardness a small influence Size N=23 SSA R2=0.986 Blending R2 Adj.=0.982 (= low risk DF=17 Q2=0.981 time RSD=0.725 Conf. lev.=0.95 parameter) MODDE 8 - 2008-01-23 10:58:52 Acknowledgement: adapted from Paul Stott (AZ) – ISPE PQLI Team
  58. 58.  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
  59. 59.  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)
  60. 60.  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 Design risk for dissolution Space failure 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)
  61. 61.  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
  62. 62.
  63. 63. 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 manufacturein vivo performanceDissolutionAssayDegradationContent uniformityAppearanceFriabilityStability-chemicalStability-physical - Low risk - Medium risk - High risk
  64. 64. Decision on conventional vs. RTR testing
  65. 65. 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 40DOE 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
  66. 66. Blend uniformity monitored using a process analyzer 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
  67. 67.  On-line NIR spectrometer 0.045 used to confirm scale up of mean spectral standard deviation 0.04 blending 0.035 Blending operation complete 0.03 Pilot Scale when mean spectral std. dev. Full Scale 0.025 reaches plateau region 0.02 ◦ Plateau may be detected 0.015 using statistical test or rules Plateau region 0.01 Feedback control to turn off blender 0.005 Company verifies blend does 0 0 32 64 96 128 not segregate downstream Revolution Revolutions of Blender Number of (block number) ◦ Assays tablets to confirm uniformity ◦ Conducts studies to try to Data analysis model will be provided segregate API Plan for updating of model available Acknowledgement: adapted from ISPE PQLI Team
  68. 68. Conventional automated control of Tablet Weight usingfeedback 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.
  69. 69. NIR Spectroscopy NIR Monitoring Laser Diffraction (At-Line) Blend Uniformity Particle Size • Identity • AssayRaw materials & • API to ExcipientAPI dispensing ratio• Specifications based on product Roller Tablet PanDispensing Blending Sifting compaction Compression Coating 69
  70. 70.  Real Time Release Testing Controls ◦ Blend uniformity assured in blending step (on-line NIR spectrometer for blending end-point) ◦ API assay is analysed 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)
  71. 71.  Before a medicinal product is released for sale, the Qualified Person responsible for its release should take into account, among other aspects, the conformity of the product to its specification. In the case of approved RTRT, this conformity would not routinely be supported by results of end product testing. Nevertheless a specification has to be established and each batch of a product should comply with it if tested. 71
  72. 72.  The application for RTRT should be supported by adequate validation of the RTR test method. The relationship between the RTR test, including acceptance criteria, and the end product test and associated specification should be well understood and, where applicable, supported by substantial comparative data at commercial scale (parallel testing). 72
  73. 73.  When RTRT has been approved this should be routinely used for batch release. In the event that the test results of RTRT fail or are trending toward failure, RTRT may not be substituted by end-product testing. Any failure should be investigated and trending should be followed up appropriately. Batch release decisions will need to be made based on the results of these investigations, and must comply with the content of the marketing authorization and current GMP requirements. 73
  74. 74.  Attributes (e.g. uniformity of content) that is indirectly controlled by approved RTRT should still appear in the Certificate of Analysis for batches. The approved method for end-product testing should be mentioned and the results given as ”Complies if tested” with a footnote: ”Controlled by approved Real Time Release testing”. 74
  75. 75.  In case of equipment failure the control strategy provided in the application should include a contingency plan specifying the use of alternative testing or monitoring approaches on a temporary basis. In this situation, the alternative approach could involve use of end-product testing or other options, while maintaining an acceptable level of quality. Testing or monitoring equipment breakdown needs to be managed in the context of a deviation under the Quality Management System and can be covered by GMP. 75
  76. 76. In principle, end product testing should not be substitutedfor failure of an RTRT release method. The failure shouldbe investigated and followed up appropriately. 76
  77. 77.  When RTRT is applied, the attribute that is indirectly controlled (e.g. sterility, uniformity of content) together with a reference to the associated test procedure, should still be included in the specification as “Conforms if tested”. The relationship between end-product testing, material attributes, process monitoring and acceptance criteria, should be fully explained and justified. In addition, the use of any prediction models should be fully explained, justified and verified at the commercial site. 77
  78. 78.  Batch release is the final decision to release the product to the market regardless of whether RTR testing or end-product testing is employed. End-product testing involves performance of specific analytical procedures on a defined sample size of the final product after completion of all processing for a given batch of that product. 78
  79. 79.  Results of real-time release testing are handled in the same manner as end-product testing results in the batch release decision. Batch release involves an independent review of batch conformance to predefined criteria through review of testing results and manufacturing records together with appropriate good manufacturing practice (GMP) compliance and quality system, regardless of which approach is used. 79
  80. 80.  Real-time release testing does not necessarily eliminate all end-product testing. For example, an applicant can propose RTR testing for some attributes only or not all. If all critical quality attributes (CQAs) (relevant for real-time release testing) are assured by in- process monitoring of parameters and/or testing of materials, then end-product testing might not be needed for batch release. Some product testing will be expected for certain regulatory processes such as stability studies or regional requirements. 80
  81. 81.  Product specifications (see ICH Q6A and Q6B) still need to be established and met, when tested. 81
  82. 82.  Even where RTR testing is applied, a stability monitoring protocol that uses stability indicating methods is required for all products regardless of the means of release testing (see ICH Q1A and ICH Q5C). 82
  83. 83.  RTR testing, if utilized, is an element of the control strategy in which tests and/or monitoring can be performed as in-process testing (in-line, on-line, at-line) rather than tested on the end product. 83
  84. 84.  Traditional sampling plans for in-process and end-product testing involve a discrete sample size that represents the minimal sampling expectations. Generally, the use of RTR testing will include more extensive on-line/in-line measurement. A scientifically sound sampling approach should be developed, justified, and implemented. 84
  85. 85.  In principle the RTR testing results should be routinely used for the batch release decisions and not be substituted by end-product testing. Any failure should be investigated and trending should be followed up appropriately. However, batch release decisions should be made based on the results of the investigations. The batch release decision should comply with the content of the marketing authorization and GMP compliance. 85
  86. 86.  In-process testing includes any testing that occurs during the manufacturing process of drug substance and/or finished product. Real-time release testing includes those in- process tests that have a direct impact on the decision for batch release through evaluation of critical quality attributes. 86
  87. 87.  RTR testing can be based on measurement of a surrogate (e.g., process parameter, material attribute) that has been demonstrated to correlate with an in-process or end-product specification (see ICH Q8(R2); Annex, section II.E (2.5)). 87
  88. 88.  Is there a potential for the measured CQA to change downstream from the measurement point? For example, ◦ Blend desegregation ◦ Loss of weight (e.g., chipping) after weighing step ◦ Hydrolytic degradation during aqueous film coating Is identity determined at a point that is visually unique? ◦ Mitigation of potential human and/or system error ◦ Unique identifiers on the intermediate when measured (e.g., embossing, size, shape) Risk assessment is valuable to exploring potential failure modes 88
  89. 89.  Probe/sample location representative of entire vessel Sample frequency representative of entire batch Sample acquisition time ◦ Suitable for system dynamics/mixing Sample volume/mass ◦ Determine amount of sample measured ◦ Representative of unit dose Sample interface ◦ Remains constant over the process (e.g., no fouling) ◦ Environmental factors (e.g., temperature, humidity) 89
  90. 90.  Specification still required in an RTRT approach ◦ (CFR §314.50(d) and CFR § 211.165(a)) Should be representative of actual measurement ◦ Can include in-process measurements (e.g., NIR measurements for assay of uncoated tablets) ◦ Can include surrogate measurements (e.g., models for dissolution) ◦ Methods should be appropriately validated (including models used as surrogate measurements) Alternatives can be included for stability monitoring Appropriate statistical criteria for large sample sizes 90
  91. 91.  Calibration models for spectroscopic analysis ◦ NIR, Raman, FTIR ◦ Typically use chemometric models Surrogate models for time consuming measurements ◦ Dissolution models relating process parameters and/or material attributes to dissolution Design space models ◦ Surface response plots ◦ Mechanistic models Process control models ◦ Tunable controllers for individual unit operations ◦ Statistical process control & multivariate statistical process control Other models 91
  92. 92.  Calibration data ◦ Include potential sources of variance (e.g., operating conditions, raw materials, scale) ◦ Cover intended areas of operation/design space ◦ Appropriate distribution of spectra over the analysis range Model development ◦ Appropriate data pre-treatment ◦ Appropriate spectral ranges ◦ Number of model factors justified (avoid overfitting) Model validation ◦ Internal validation using subsets of calibration data ◦ External validation using an independent data set Robust and representative reference method 92
  93. 93.  NIR model results may change with time as new sources of variability are introduced. ◦ Changes in raw material suppliers, process or analyzer changes Evaluation of outliers as part of maintenance. ◦ Can detect bad spectra or interface problems ◦ Usually implemented through examination of residuals Procedures in place to monitor and update the model ◦ Done under the manufacturer’s quality system ◦ Include frequency and methods of periodical model evaluation Depth of validation done on updated model, depending on level of change 93
  94. 94.  Robust calibration model ◦ Use an appropriate reference method ◦ Include variations in raw materials ◦ Cover the entire design space Include an independent dataset for validation Demonstrate model performance at commercial scale ◦ Understand and work within the model limitations and model assumptions ◦ Compare model results to a reference method for a statistically acceptable number of batches 94
  95. 95.  Develop and document procedures on how to evaluate and update the calibration model ◦ How to deal with OOS results ◦ Develop criteria for model re-calibration Verify or recalibrate the model for process changes: ◦ Revising the operating ranges ◦ Change in raw materials ◦ Change in manufacturing equipment or measuring instrument Include plans for model maintenance/update in the firm’s Quality System ◦ Tracking/trending (for process monitoring) included within the Quality System 95
  96. 96.  Level of detail in submission should depend on the importance of the model to the overall control strategy Low Impact Model (e.g., Models for development) ◦ General discussion of how model was used to make decisions during process development Medium Impact Model (e.g., Design space models) ◦ More detailed information about model building, summary of results and statistical analysis ◦ Discussion of how the model fits into the control strategy High Impact Model (e.g., RTRT models) ◦ Full description of data collection, pretreatment and analysis ◦ Justification of model building approach ◦ Statistical summary of results ◦ Verification using data external to calibration set ◦ Discussion of approaches for model maintenance and update 96
  97. 97. Quality What is Product Profile Target Identify critical to Product CQA the Profile CQA’s Patient QRM PATRisk Assessments Identify CMA & Design Space CPP Design space Control StrategyControl Strategy Continual Improvement PAT , PAT RTRT SOP PAT RTRT 97