Practical Considerations for Implementation of Real Time Release
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Practical Considerations for Implementation of Real Time Release testing (RTRt) Graham D. Cook Ph.D., Senior Director, Global Quality Operations Havant, UK graham.cook@pfizer.com CPAC Satellite Meeting Rome, March 25, 2010 Overview What needs to be considered when implementing Real Time Release Testing? Case Study 1 – Product X Case Study 2 – Product Y Comparison of some aspects of Product X and Y Conclusions 1 Real Time Release Testing ‘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.’ (ICH Q8(R2)) 2 Real Time Release Testing - Essential Elements Science People Systems REAL TIME RELEASE TESTING ICH Q8 Pharmaceutical Development ICH Q9 Quality Risk Management ICH Q10 Pharmaceutical Quality System 3 Considerations for Implementing Real Time Release Testing Topic discussed in Case Study Product X Product Y Science 9 9 Process data and Control strategy? Analytical methods and specifications? 9 9 Sampling and Statistics Acceptance criteria People Organization and Training? Pharmaceutical Quality System Quality risk management? Disaster recovery? Model maintenance? Handling of outliers? Data management? Batch disposition? Regulatory Interactions 9 9 9 9 9 9 9 9 9 9 Note: All above factors considered for Product X and Product Y 4 Overview What needs to be considered when implementing Real Time Release Testing? Case Study 1 – Product X Case Study 2 – Product Y Comparison of some aspects of Product X and Y Conclusions 5 Product X - Product Description Dosage Form BCS Class 3 compound (High Water Solubility, Moderate Permeability) Monolithic Extended Release Tablets (multiple strengths) Relatively high dose compound (high drug load) Level A IVIVC has been established for all dose strengths Polymer is the primary driver of clinical performance Variation in process parameters did not impact the dissolution performance Potential to apply Real Time Release Testing Process understanding at pilot-scale translated well to commercial manufacturing scale Robust control strategy 6 Product X - Manufacturing Process Robust control strategy → Increased assurance of quality → RTRt Holistic Control Strategy e.g.: Content Uniformity = Blend uniformity + Drug concentration + Weight control PAT: 1 = Blend Uniformity 2 = Granule particle size 3 = Weight, Hardness, Potency, Drug concentration, Identity, Rate–controlling polymer concentration PAT 1 PAT PAT 2 1 PAT 3 7 Development of Sampling Plans PAT Analyzers Measure condition of the process material in real time Collect more information about the batch When and where do we sample? Use of prior knowledge of product and risk assessment in design of plan e.g. consider potential for segregation (failure mode) in a compression mix Statistical rationale for sampling plan Performance of plan relative to assurance of quality Relationship between Real Time Release Testing and Pharmacopoeial Testing Placement of PAT device in manufacturing equipment Ensure analyzer measurements are representative of the batch 8 8 Operating Characteristic Curves Plot the probability of accepting a batch against the quality level What is the probability of rejecting a good batch? (Producer risk) What is the probability of releasing a bad batch? (Consumer risk) Operating Characteristic curves enable Different batch acceptance sampling plans to be investigated Design of a batch sampling plan with the desired performance N = batch size n = sample size c = critical acceptance number AQL = Acceptable Quality Level LQL = Limiting Quality Level Steepness of the curve indicates discriminating ability of the plan 9 Operating Characteristic Curves for UDU Test The USP OC curve shows 90% probability of passing a batch with 2% product outside 85-115% of Label Claim The Alternative Plan OC curve shows 0% probability of passing a batch with 2% outside 85-115% of Label Claim The Alternative Plan uses a larger sample size and provides a better assurance of quality than USP Probability to pass Alternative Plan 90% probability of passing bx by USP Probability to pass USP If the Alternative Plan is chosen for sampling using a PAT analyzer what is the fall-back if the analyzer fails? - Must maintain same level of quality by using the same sampling plan with the replacement analytical technology 0 1 2 3 4 5 6 7 % Product outside 85‐115% of Label Claim Note: UDU = Uniformity of Dosage Units (Content Uniformity) ‘Coverage’ = % Product meeting 85-115% of Label Claim ‘Coverage’ term used in paper by Sandell, D. et al. (see reference slide 24 ) 10 Product X - Sampling Location of PAT Analyzer in Blender Rationale based on prior knowledge (including publications) Blender design: no ‘hot’ spots therefore any location suitable Analyser placed in bottom of blender Sampling plan for Tablets Tablets samples taken uniformly across batch X tablets taken every Y minutes during compression run Drug Content of Tablets Drug concentration by NIR and Weight Uniformity determined on different tablet samples Low risk because high drug loading Check using Monte Carlo simulation 11 Monte Carlo Simulations Monte Carlo Simulation* A technique that converts uncertainties in input variables of a model into probability distributions. By combining the distributions and randomly selecting values from them, it recalculates the simulated model many times and brings out the probability of the output Allows several inputs to be used at the same time to create the probability distribution of one or more outputs The output is generated as a range instead of a fixed value and shows how likely the output value is to occur in the range Assessment of Drug Content of Tablets Drug Concentration (NIR) and Tablet Weights are independent probability distributions What is probability of releasing a batch of marginal quality? Use Monte Carlo simulation to explore the variation in: Drug Concentration (NIR mean; NIR std.dev.) Tablet Weight (Weight mean; Weight std. dev.; difference in weights from doublesided tablet press) *Sanford Bolton, Charles Bon: Pharmaceutical Statistics - Practical and Clinical Applications, Fourth Ed., Marcel Dekker 12 Using Monte Carlo Simulations to Assess Risk Assessment of risk of releasing a marginal quality batch for chosen Coverage level: Combination of Drug Concentration (NIR mean) and Tablet Weight (WT mean) parameters Monte Carlo simulations: • 1400 cases, 5000 simulations run • Worst-case scenarios run Very low number of high probability cases found – equates to very low probability of releasing marginal quality batch Simulations help provide an assessment of the risk for the chosen coverage level and sampling plan Indicates potential scenarios where there is a high probability of releasing a batch of marginal quality: ■ cases with probability < 6% + cases with probability 6 - 8% cases with probability 8 - 10% cases with probability > 10% 13 RTRt - Organization Multi-disciplinary / cross-functional teams are key to RTRt New skill sets may be needed Real Time Release testing (RTRt) Operations Regulatory Affairs Technology Quality Operations Formulation Development Analytical Development/PAT Chemometrics Statistics 14 Impact on Quality Systems Some considerations What happens if parameters are outside Normal Operating Ranges (NORs)? Design space? What do we do if a PAT measurement system stops functioning? How do we handle atypical results/outliers? Cannot ‘test into compliance’ using alternative methods (ICH IWG Q&A) What do we do if the chemometric model is no longer appropriate? What is the impact on the batch release process? What are alternative sampling plans and measurement systems to enable disposition decisions? 15 15 Quality Risk Management Quality Risk Management Enabler for implementation of Real Time Release testing Incorporate QRM into procedures Facilitates uniform implementation of QRM across the entire organization Consistent use of the same language (terminology) and process Establish criteria for re-evaluation of risks and mitigation plans Triggered by time, event or new knowledge Training program Different levels Awareness training User training - participant, facilitator, team leader Facilitates selection of correct QRA approach and tool Culture - proactive approach to quality 16 Decision Tree for Equipment Failure Extract of Decision Tree for PAT equipment failure Generate Event Report Form, Fix/replace instrument Does PAT pass System Suitability? YES YES Is PAT functional during the Process? NO Define response in advance = ‘proactive quality’, rather than after problem has occurred = ‘reactive quality’ Are there alternative controls to ensure/ control process variability? Are there measurements downstream which could be used to correlate data? Do we need further sampling? NO Stop Process*: Evaluate Instrument YES Proceed with unit operation/manufacturing process Can the instrument be repaired in a suitable time frame? Can the instrument be replaced with a spare instrument? YES Generate Event Report Form, Capture Process/ Action Items Revert to Testing using Regulatory Analytical Procedure NO Generate Investigation/ERF to identify root cause Capture Process/ Action Items * Note that it may be impractical to stop some unit operations (e.g. wet granulation) during processing 17 Quality Systems and ProcessesHandling of Outliers Atypical Results/Outliers Bad data or valuable information? ‘Statistical’ processes subject to random variability Pre-defined mechanisms or systems should be developed = proactive approach to quality Process to define outliers or invalid data Definition of deviations that require investigation Specifications must be carefully defined Consideration of Impact on Product Quality Use science- and risk-based approaches Use an holistic assessment of all product and process measurements to assess quality Frequency of outliers may be important Extent of process knowledge and process/product history (e.g. changes) Potential impact to patient safety and efficacy 18 Product X – Regulatory Interactions FDA FDA CMC Pilot February 2008 NDA Approved Comparability Protocol for RTRt approved as part of the original application July 2007 Pre-Operational Visit to manufacturing facility October 2009 Prior Approval Supplement for RTRt submitted November 2009 PAI for RTRt Pre-Operational Visit FDA personnel Center (Reviewers (2), Compliance officer (1)) Field (District (2), Patriot team (1)) Focus on PAT systems and Quality System including Procedures for chemometric model maintenance Chemometric models Equipment failure recovery systems Sampling Plans Risk Assessments (PAI similar to POV) 19 Overview What needs to be considered when implementing Real Time Release Testing? Case Study 1 – Product X Case Study 2 – Product Y Comparison of some aspects of Product X and Y Conclusions 20 Product Y - Product Background Dosage Form BCS Class 1 compound (High Water Solubility, High Permeability) Immediate Release Tablets Potent, low dose compound, low drug load Launched from small-scale containment manufacturing facility Potential for Real Time Release Testing Increased demand – need to increase efficiency More information in real-time Robust control strategy 21 Product Y - Manufacturing Process API & Excipients 1 PAT Mag Stearate PAT Dispensing 2 Blend PAT Sieving Granulation Blend PAT: 1 = Identification 2 = Blend Uniformity 3 = Weight, Hardness, Disintegration, Potency, Content Uniformity 4 = Water Determination Coating PAT 4 2 Tabletting PAT 3 Mag Stearate Blend PAT 2 22 Control Strategy Methods for conventional QC Release Testing Methods for Real Time Release Testing Identity (TLC / HPLC) Identity (NIR) Impurities (HPLC) Not Tested routinely - due to process capability and understanding Assay / CU (HPLC) Assay / CU (NIR + Weight) Dissolution (Dissolution Tester) Disintegration (Disintegration Tester) Water Determination (Karl Fischer) Water Determination (NIR) Appearance (Visual) Appearance (Visual) Microbial Quality (Microbiology) Not Tested routinely - due to process capability and understanding Note: Real Time Release Testing does not mean less testing! 23 Specifications for ‘Large n’ UDU testing Specifications based on zero tolerance* problematic when testing large samples (i.e. >30) ‘Large n Counting Test’** Failing Tablets Analyze X samples Assess number of samples (n) outside of limits Compare n to defined criteria (c) for allowed number of samples outside of limits Batch is in control when n < c 75% 100% 125% *Zero tolerance specifications (e.g. no tablet outside of 75 – 125% of target) discourage intensive sampling to gather process information because the larger the sample size, the greater the chance of an OOS result **Sandell, D., Vukovinsky, K., Diener, M., Hofer, J., Pazdan, J. and Timmermans, J (2006), ‘Development of a content uniformity test suitable for large sample sizes’, Drug Information Journal 40 (3), 337 - 344. 24 Product Y - Sampling Sampling plan for Tablets OC curves used to assess performance of sampling plans ‘Large n Counting Test’ adopted Tablets samples taken using stratified sampling plan across batch Tablets samples taken at minimum of P points during compression run Minimum of Q tablet samples taken Drug Content of Tablets Tablet is assayed NIR for drug concentration and weight measured on the same tablet 25 Product Y – Sampling for Content Uniformity 125% XXXXXXXXX Boxplot of CU in batch 810467400 6,5 6,25 115% 5,75 ‘Large n counting test’: CU Single Values 6,0 5,5 -- accounts for possibility of tablets outside 85% to 115% when collecting large samples 5,0 4,5 4,25 4,0 3,75 3,5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Location Number ‘Large n Counting Test’ Criteria n 100 150 183 200 250 500 750 1000 c 4 6 8 8 11 23 35 47 n = number of tablets sampled c = acceptable number of tablets outside 85% to 115% 26 Chemometric Model Maintenance and Update Periodic Evaluation of Applicability of NIR Model used for UDU NIR model evaluated with every batch (system suitability) Annual evaluation of model Updating the Model Mechanism - Internal change control procedures used to identify material or process changes with potential to impact the NIR model NIR measurements may not be valid Operation outside NIR model limits New variability in materials and/or process Fall-back: reference HPLC method used for batch release Assessment for model update: No impact: – New variability is unrelated to active (i.e. not significant in the regression coefficients) or is compensated for by model parameters. – Update model (without changes) to include robustness to this new variability Impact: – Update the model e.g. include new variability within the model or optimize calibration model parameters 27 RTRt - Training Quality by Design Training General concepts Site-wide training Culture change? Quality System Quality System Elements revised Deviations Change Management Validation Knowledge Management Training on revised SOPs 28 Integrating PAT into Facility Data Management 10X Unit Report NIR Control Charts MES-Systems 29 Batch Disposition Batch disposition Batch disposition process and decision to accept or reject the batch is still needed when RTRt is used (Ref. ICH IWG Q&A) Batch disposition decision may be facilitated by Use of electronic batch records and exception reporting systems (flag deviations) to provide information in real time Product/process monitoring systems (e.g. Statistical Process Control) can provide real time assessment of process performance Certificate of Analysis Values from online testing reported Tests not routinely performed: ‘Would comply if tested’ Impact on Qualified Person / Quality Release person Release process essentially unchanged with RTRt Need to understand systems supporting the disposition decision 30 Product Y – Regulatory Interactions EMEA July 2006 - MAA Approved via Centralised procedure July 2009 - Meeting with EMEA PAT Team July 2009 - Type II Variation for RTRt submitted September 2009 - Questions and Responses October 2009 - Final Assessment report November 2009 - Approval FDA November 2005 - Submission May 2006 - FDA CMC Pilot NDA Approved September 2009 - Prior Approval Supplement for RTRt submitted October 2009 - Information Request January 2010 - User Fee goal date 31 Overview What needs to be considered when implementing Real Time Release Testing? Case Study 1 – Product X Case Study 2 – Product Y Comparison of some aspects of Product X and Y Conclusions 32 Comparison of projects – Control Strategy Examples Product X Product Y Identification NIR testing of Tablets NIR test of API input into a closed manufacturing system Related Substances / Impurities Product/process knowledge shows low risk of generation during manufacture – ‘Would pass if tested’ Product/process knowledge shows low risk of generation during manufacture – ‘Would pass if tested’ Bioavailability / Dissolution Level A IVIVC – specification and content of rate-controlling polymer Disintegration used as surrogate for dissolution 33 Comparison of Projects – Common Features Science Extensive use of PAT systems for online monitoring and control Operational Characteristic curves used to assess relationship between online testing and conventional end-product testing Sampling plans different Quality Systems Evolution, not revolution, of existing quality systems Use of Quality Risk Management Regulatory Products approved with more conventional end-product testing focussed control strategies Real Time Release Testing introduced via Variation to Marketing Authorisation 34 Challenges and Opportunities -1 Resources Cross-functional / multi-disciplinary team necessary for successful implementation of Real Time Release testing Increased need for statistics, control engineering etc. skills for successful PAT implementation Culture/mindset challenges (proactive versus reactive quality) Initial capital commitment is needed for PAT equipment Quality Systems Development Robust change management systems necessary Quality risk management e.g. Need systems in place for PAT equipment failure Impact to QP/ Q release person Understand control strategy, quality systems etc. 35 35 Challenges and Opportunities -2 Regulatory challenges Acceptance of alternative sampling plans to pharmacopoeial plans Global acceptance of RTRt and harmonization? Benefits Lower manufacturing costs Improved yields Fewer deviations and/or rejects Reduced QC resources Faster cycle times Increased assurance of quality for our patients 36 Overview What needs to be considered when implementing Real Time Release Testing? Case Study 1 – Product X Case Study 2 – Product Y Comparison of some aspects of Product X and Y Conclusions 37 Conclusions - 1 Multiple approaches to achieve RTRt are possible RTRt does not mean less testing RTRt may be applied to new or existing products RTRt and Quality by Design (QbD) RTRt is a possible outcome of QbD development QbD may not be necessary for RTRt RTRt implementation is supported by Product and process understanding Quality Risk Management A robust control strategy (including PAT and appropriate sampling plan) Science and risk-based quality systems aligned with ICH Q10 Discussions with the Regulatory Agencies prior to submissions 38 Conclusions - 2 RTRt and Quality Systems Evolution, not revolution, of quality systems to enable RTRt Pre-define responses to deviations and failures RTRt benefits may include Lower manufacturing costs and cycle times Improved yields through less waste Increased assurance of quality for our patients 39 Acknowledgements John Groskoph Thomas Katzschner Roger Nosal Karl Redl Mark Smith John O’Sullivan Ferdinando Aspesi Chunsheng Cai Carlos Conde-Reyes Plinio Delos-Santos Parimal Desai Nirdosh Jagota Carl Longfellow Steve Simmons Shailesh Singh Merlin Utter T.G. Venkateshwaran Dom Ventura 40 40
