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  • Practical Considerations for Implementation of Real Time Release testing (RTRt)

    Graham D. Cook Ph.D.,Senior Director, Global Quality OperationsHavant,

    CPAC Satellite MeetingRome, March 25, 2010

  • 1


    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


  • 2

    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))

  • 33




    ICH Q10 Pharmaceutical Quality System

    ICH Q8 Pharmaceutical Development

    ICH Q9 Quality Risk Management

    Real Time Release Testing - Essential Elements

  • 44

    ScienceProcess data and Control strategy?Analytical methods and specifications?

    Sampling and StatisticsAcceptance criteria

    PeopleOrganization and Training?

    Pharmaceutical Quality SystemQuality risk management?Disaster recovery?Model maintenance?Handling of outliers?Data management? Batch disposition?

    Regulatory Interactions

    Considerations for Implementing Real Time Release Testing

    Topic discussed in Case StudyProduct X Product Y

    Note: All above factors considered for Product X and Product Y

  • 5


    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


  • 666

    Product X - Product Description

    Dosage FormBCS 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 performanceVariation in process parameters did not impact the dissolution performance

    Potential to apply Real Time Release TestingProcess understanding at pilot-scale translated well to commercial manufacturing scaleRobust control strategy

  • 7

    Product X - Manufacturing Process

    Robust control strategy Increased assurance of quality RTRt

    PAT1 PAT


    PAT:1 = Blend Uniformity2 = Granule particle size3 = Weight, Hardness, Potency, Drug concentration, Identity,

    Ratecontrolling polymer concentration

    PAT2 PAT


    Holistic Control Strategy e.g.:Content Uniformity = Blend uniformity + Drug concentration

    + Weight control

  • 88


    Development of Sampling Plans

    PAT AnalyzersMeasure condition of the process material in real timeCollect 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 mixStatistical rationale for sampling plan

    Performance of plan relative to assurance of qualityRelationship between Real Time Release Testing and Pharmacopoeial TestingPlacement of PAT device in manufacturing equipment

    Ensure analyzer measurements are representative of the batch

  • 9

    Operating Characteristic CurvesPlot 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 enableDifferent batch acceptance sampling plans to be investigatedDesign 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

  • 1010

    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 ClaimThe 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



    0 1 2 3 4 5 6 7

    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 )

    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

  • 11

    Product X - Sampling

    Location of PAT Analyzer in BlenderRationale based on prior knowledge (including publications)Blender design: no hot spots therefore any location suitableAnalyser placed in bottom of blender

    Sampling plan for TabletsTablets samples taken uniformly across batchX tablets taken every Y minutes during compression run

    Drug Content of TabletsDrug concentration by NIR and Weight Uniformity determined on different tablet samples

    Low risk because high drug loadingCheck using Monte Carlo simulation

  • 12

    Monte Carlo SimulationsMonte 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 outputAllows several inputs to be used at the same time to create the probability distribution of one or more outputsThe 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 TabletsDrug Concentration (NIR) and Tablet Weights are independent probability distributionsWhat is probability of releasing a batch of marginal quality?Use Monte Carlo simulation to explore the variation in:

    Drug Concentration (NIR mean; NIR Weight (Weight mean; Weight std. dev.; difference in weights from double-sided tablet press)

    *Sanford Bolton, Charles Bon: Pharmaceutical Statistics - Practical and Clinical Applications, Fourth Ed., Marcel Dekker

  • 13

    Using Monte Carlo Simulations to Assess RiskAssessment of risk of releasing a marginal quality batch for chosen Coverage level:Combination of Drug Concentration (NIR mean) and Tablet Weight (WT mean) parameters

    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%


    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

  • 1414

    RTRt - OrganizationMulti-disciplinary / cross-functional teams are key to RTRt

    New skill sets may be needed

    Real Time Release testing (RTRt)


    Regulatory Affairs







  • 1515


    Impact on Quality Systems

    Some considerationsWhat 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?

  • 16

    Quality Risk Management

    Quality Risk ManagementEnabler for implementation of Real Time Release testing

    Incorporate QRM into proceduresFacilitates uniform implementation of QRM across the entire organizationConsistent use of the same language (terminology) and processEstablish criteria for re-evaluation of risks and mitigation plans

    Triggered by time, event or new knowledge

    Training program Different levels

    Awareness trainingUser training - participant, facilitator, team leader

    Facilitates selection of correct QRA approach and toolCulture - proactive approach to quality

  • 1717

    Decision Tree for Equipment Failure

    * Note that it may be impractical to stop some unit operations (e.g. wet granulation) during processing

    Does PAT pass System Suitability?


    Proceed with unit operation/manufacturing





    Is PAT functional during

    the Process?


    Can the instrument be repaired in a suitable time frame?

    Can the instrument be replaced with a spare instrument?

    Generate Event Report Form, Fix/replace



    Are there alternative controls to ensure/control process variability?

    Are there measurements downstreamwhich could be used to correlate data?

    Do we need further sampling?

    Generate Event Report Form, Capture Process/


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