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Quality by Design : Design Space


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FDA’s emphasis on quality by design began with the recognition that increased testing does not improve product quality (this has long been recognized in other industries).In order for quality to …

FDA’s emphasis on quality by design began with the recognition that increased testing does not improve product quality (this has long been recognized in other industries).In order for quality to increase, it must be built into the product. To do this requires understanding how formulation and manufacturing process variables influence product quality.Quality by Design (QbD) is a systematic approach to pharmaceutical development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management.

This presentation - Part IV in the series- deals with the concepts of Design Space, Design of experiments and Models. This presentation was compiled from material freely available from FDA , ICH , EMEA and other free resources on the world wide web.

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  • 1. Design Space Presentation prepared by Drug Regulations – a not forprofit organization. Visit for the latest in Pharmaceuticals. 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 2
  • 3. Product Profile CQA’s  This presentation Part IV of the series “QbD for Beginners” coversRisk Assessments basic aspects of Design Space ◦ Design Space ◦ Design of experiments ◦ ModelsControl Strategy Continual Improvement 3
  • 4.  The relationship between the process inputs (material attributes and process parameters) and the critical quality attributes can be described as the design space. 4
  • 5.  The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality. Working within the design space is not considered as a change. Movement out of the design space is considered to be a change and would normally initiate regulatory post approval change process. Design space is proposed by the applicant and is subject to regulatory assessment and approval (ICH Q8). 5
  • 6.  The Multi-dimensional region which encompasses the various combinations of product design, manufacturing process design, manufacturing process operating parameters and raw material quality which produce material of suitable ( defined) quality. 6
  • 7. Critical Quality AttributesInput Materials Output Materials Process Step (Product or Intermediate)DesignSpace Input Measured Process Parameters Parameters or Attributes Process Control Model Measurements and Controls 7
  • 8. 100.0 2 95.0 90.0 Surface Plot Contour Plot 1.8Dissolution (%) 1.6 85.0 Dissolution (%) 80.0 1.4 90.0-95.0 75.0 Parameter 2 1.2 70.0 85.0-90.0 65.0 1 80.0-85.0 60.0 75.0-80.0 0.8 55.0 70.0-75.0 0.6 50.0 Design Space 65.0-70.0 2 0.4 60.0-65.0 40 (non-linear) Pa 2 0.2 ram 50 1 er ete et Design Space r1 am 0 60 0 P ar 40 42 44 46 (linear ranges) 48 50 52 54 56 58 60 Parameter 1 • Design space proposed by the applicant • Design space can be described as a mathematical function or simple parameter range • Operation within design space will result in a product meeting the defined quality attributes 8
  • 9. Knowledge Space Design Space: “Multidimensional Design Space combination and interaction of input variables (e.g., material attributes) and process parameters that have been NOR demonstrated to provide assurance of quality.” CQA Knowledge Space: “A summary of all process knowledge obtained during product development.” 9
  • 10. There are no regulatoryrequirements to have a Design Space 10
  • 11.  Design space can illustrate understanding of parameter interactions and provides manufacturing flexibility Proven acceptable range alone is not a design space Design space should be verified and operational at full scale No requirement to develop a design space at the full manufacturing scale 11
  • 12.  A design space can be described in terms of ranges of material attributes and process parameters. It can also be described through more complex mathematical relationships. It is possible to describe a design space as a time dependent function (e.g., temperature and pressure cycle of a lyophilisation cycle), or As a combination of variables such as components of a multivariate model. 12
  • 13. Design Space 13
  • 14.  Scaling factors can also be included if the design space is intended to span multiple operational scales. Analysis of historical data can contribute to the establishment of a design space. Regardless of how a design space is developed, it is expected that operation within the design space will result in a product meeting the defined quality. 14
  • 15.  Independent design spaces can be established for one or more unit operations, or Single design space that spans multiple operations can also be established. A separate design space for each unit operation is often simpler to develop. However a design space that spans the entire process can provide more operational flexibility. For example, in the case of a drug product that undergoes degradation in solution before lyophilisation, the design space to control the extent of degradation (e.g., concentration, time, temperature) could be expressed for each unit operation or as a sum over all unit operations. 15
  • 16. Identify Q T P P Identify C Q A Risk AssessmentDefine product design spaceDefine process design space Risk AssessmentRefine process design space Process Characterization Define Control strategy Risk Assessment Process Validation Process Monitoring 16
  • 17.  Consider QTPP in establishing the Design Space Initial determination of CQAs Assess prior knowledge to understand variables and their impact Scientific principles & historical experience Perform initial risk assessment of manufacturing process relative to CQAs to identify the high risk manufacturing steps (->CPPs) Conduct Design of Experiments (DoE) Evaluate experimental data Conduct additional experiments/analyses as needed 17
  • 18.  First-principles approach ◦ Combination of experimental data and mechanistic knowledge of chemistry, physics, and engineering to model and predict performance Statistically designed experiments (DOEs) ◦ Efficient method for determining impact of multiple parameters and their interactions Scale-up correlation ◦ A semi-empirical approach to translate operating conditions between different scales or pieces of equipment 18
  • 19.  The risk assessment and process development experiments can lead to an understanding of the linkage and effect of process parameters and material attributes on product CQAs and Also help identify the variables and their ranges within which consistent quality can be achieved. These process parameters and material attributes can thus be selected for inclusion in the design space. 19
  • 20.  Prior knowledge may include : Internal knowledge from development and manufacturing External knowledge: scientific and technical publications (including literature and peer-reviewed publications) Citation in filing: regulatory filings, internal company report or notebook, literature reference No citation necessary if well known and accepted by scientific community 20
  • 21.  Risk assessment is based on prior knowledge and relevant experience for the product and manufacturing process Gaps in knowledge could be addressed by further experimentation Assignments of risk level must be appropriately justified Risk assessments/control will iterate as relevant new information becomes available Final iteration shows control of risks to an acceptable level 21
  • 22. ◦ Design space could include critical and non-critical parameters  Critical parameter ranges/model are considered a regulatory commitment and non-critical parameter ranges support the review of the filing  Critical parameter changes within design space are handled by the Quality System and changes outside the design space need appropriate regulatory notification◦ Non-critical parameters would be managed by Quality System 22
  • 23.  DOE useful tool in development of a DS but not the only one ◦ 1st Principles models DS may cover one, or multiple unit operation(needs to be clear in the dossier) Not all unit operations must have a DS Unit operations without a DS will obviously not achieve the regulatory benefits (ie, ability to move within DS) 23
  • 24.  DS is usually developed at lab scale There is no need to perform full DOEs at full scale to confirm the DS at full scale. Good understanding of scale up phenomena is needed, some parameters may be scale independent (needs to be justified) Scale up factors could be used to reduce concern about moving within DS at scale Experiments within the DS at full scale could also be used to reduce the same concern. Another option is have additional monitoring controls applied when there is a change within the DS to ensure that the DS is still valid and then relaxation to a less stringent control strategy. 24
  • 25.  DS needs to be complemented by an appropriate control strategy Critical process parameters remain critical even if controlled, CQAs: appropriate specs need to be set, even if not tested routinely Release based on CQAs and control of process parameters is possible if satisfactorily demonstrated ( e.g. dissolution release based on particle size control, and disintegration test) 25
  • 26.  One-factor-at-a-time (the classical approach) Designed experiments (DOE) 26
  • 27.  One-factor-at-a-time ◦ Procedure (2 level example)  Run all factors at one condition  Repeat, changing condition of one factor  Continuing to hold that factor at that condition, rerun with another factor at its second condition  Repeat until all factors at their optimum conditions ◦ Slow, expensive: many tests ◦ Can miss interactions! 27
  • 28. Process: Yield = f(temperature, pressure) 50% yield 30% yield Max yield: 50% at 78 C, 130 psi? 28
  • 29. A better view of the maximum yield! Optimized yield is over 85% Process: Yield = f(temperature, pressure) 29
  • 30. 30
  • 31.  Multiple-Factors-at-a-Time, DOE ◦ Full Factorials ◦ Fractional Factorials ◦ Plackett – Burman designs ◦ Central Composite designs 31
  • 32.  DOE is defined as “a structured analysis wherein inputs are changed and differences or variations in outputs are measured to determine the magnitude of the effect of each of the inputs or combination of inputs.” Full factorial example: Dependent Independent Variable Variable (Controlling Factors) (Response) Run Factor X1 Factor X2 Factor Y1 1 High High Output1 2 Low High Output2 3 High Low Output3 4 Low Low Output4 32
  • 33. (1) Choose experimental design (e.g., full factorial, d-optimal) (2) Conduct randomized experiments Experiment Factor A Factor B Factor C 1 + - - A 2 - + - 3 + + + B C 4 + - +(3) Analyze data (4) Create multidimensional surface model (for optimization or control) 33
  • 34.  Several families n = number of Factor tested and L : level/factor Semi Factorial Design : the lowest number of experiments required : 2n-k Used for a first screening of mains factors and at least single interactions Used for demonstration of a Proven Acceptable Range (PAR) or Design Space ◦ Don‟t be afraid by the number of factor. Factorial Design : higher number of experiments : 2n For both Design, only two levels (L = 2) + eventual central point(s), Models will always be linear. Response Surface Model : higher number of experiments : Ln. Non linear models. The number of experiments can be decreased by historical methods or by computer optimisation (D Optimal). ◦ Used for optimisation/ modeling of a process ◦ Used for searching the „‟ edge of failure‟‟ Mixture : RSM + constraint (sum of component = fixed value)  Used in chemistry, formulation,… Combined : Mixture + (semi) Factorial or RSM  Used for combines mixture/process such as formulation (excipents) and freeze drying conditions. 34
  • 35.  The kind of question to answer must be understood : ◦ Critical parameters ◦ Interactions ◦ Optimisation ◦ Demonstration of Proven Acceptable Range ◦ Modeling The experiments are planned before starting Apparently a high number of experiments, more work, more time, more money. In reality, far less experiments (semi factorial or reduction for RSM) to obtain far less valuables results. Allow a better planning of experiments including Analytical. 35
  • 36.  Randomization, blocking and replication are the three basic principles of statistical experimental design. By properly randomizing the experiment, the effects of uncontrollable factors that may be present can be “averaged out”. Blocking is the arrangement of experimental units into groups (blocks) that are similar to one another. Blocking reduces known but irrelevant sources of variation between groups and thus allows greater precision in the estimation of the source of variation under study. Replication allows the estimation of the pure experimental error for determining whether observed differences in the data are really statistically different 36
  • 37.  ANOVA results should accompany all DOE data analysis, especially if conclusions concerning the significance of the model terms are discussed. For all DOE data analysis, the commonly used alpha of 0.05 is chosen to differentiate between significant and non significant factors. It is important that any experimental design has sufficient power to ensure that the conclusions drawn are meaningful. Power can be estimated by calculating the signal to noise ratio. If the power is lower than the desired level, some remedies can be employed to increase the power. For example, by adding more runs, increasing the signal or decreasing the system noise. ICH Points to Consider document for guidance on the level of DOE documentation recommended for regulatory submissions. 37
  • 38.  A design space can be updated over the lifecycle as additional knowledge is gained. Risk assessments, as part of the risk management process, help steer the focus of development studies and define the design space. Operating within the design space is part of the control strategy. The design space associated with the control strategy ensures that the manufacturing process produces a product that meets ◦ The Quality Target Product Profile (QTPP) and ◦ Critical Quality Attributes (CQAs). 38
  • 39.  Since design spaces are typically developed at small scale, an effective control strategy helps manage potential residual risk after development and implementation. When developing a design space for a single-unit operation, the context of the overall manufacturing process can be considered, particularly immediate upstream and downstream steps that could interact with that unit operation. Potential linkages to CQAs should be evaluated in design space development. 39
  • 40.  In developing design spaces for existing products, multivariate models can be used for retrospective evaluation of historical production data. The level of variability present in the historical data will influence the ability to develop a design space, and additional studies might be appropriate. 40
  • 41.  Design spaces can be based on ◦ scientific first principles and/or ◦ empirical models. An appropriate statistical design of experiments incorporates a level of confidence that applies to the entire design space, including the edges of an approved design space. 41
  • 42.  However, when operating the process near the edges of the design space, the risk of excursions from the design space could be higher because of normal process variation (common cause variation). 42
  • 43.  The control strategy helps manage residual risk associated with the chosen point of operation within the design space. When changes are made (e.g., process, equipment, raw material suppliers), results of risk review can provide information regarding additional studies and/or testing that might verify the continued applicability of the design space and associated manufacturing steps after the change. 43
  • 44.  Capturing development knowledge and understanding contributes to design space implementation and continual improvement. Different approaches can be considered when implementing a design space (e.g., process ranges, mathematical expressions, or feedback controls to adjust parameters during processing (see also Figure 1d in ICH Q8(R2)). The chosen approach would be reflected in the control strategy to assure the inputs and process stay within the design space. 44
  • 45.  Although the entire design space does not have to be reestablished (e.g., DoE) at commercial scale, design spaces should be initially verified as suitable prior to commercial manufacturing. Design space verification should not be confused with process validation. However, it might be possible to conduct verification studies of the performance of the design space scale-dependent parameters as part of process validation. 45
  • 46.  Design space verification includes monitoring or testing of CQAs that are influenced by scale- dependent parameters. Additional verification of a design space might be triggered by changes (e.g., site, scale, or equipment). Additional verification is typically guided by the results of risk assessments of the potential impacts of the change(s) on design space. 46
  • 47.  A risk-based approach can be applied to determine the design of any appropriate studies for assessment of the suitability of a design space across different scales. Prior knowledge and first principles, including simulation models and equipment scale-up factors, can be used to predict scale-independent parameters. Experimental studies could help verify these predictions. 47
  • 48.  Some aspects of the design space that could be considered for inclusion in the regulatory submission: The design space description, including critical and other relevant parameters. The design space can be presented as ranges of material inputs and process parameters, graphical representations, or through more complex mathematical relationships. The relationship between the inputs (e.g., material attributes and/or process parameters) and the CQAs, including an understanding of the interactions among the variables. 48
  • 49.  Data supporting the design space, such as prior knowledge, conclusions from risk assessments as part of QRM, and experimental studies with supporting data, design assumptions, data analysis, and models. The relationship between the proposed design space and other unit operations or process steps. Results and conclusions of the studies, if any, of a design space across different scales. Justification that the control strategy ensures that the manufacturing process is maintained within the boundaries defined by the design space. 49
  • 50.  The control strategy used for implementation of a design space in production depends on the capabilities of the manufacturing site. The batch records reflect the control strategy used. For example, if a mathematical expression is used for determining a process parameter or a CQA, the batch record would include the input values for variables and the calculated result. 50
  • 51.  As part of the technology transfer of a design space to a site and throughout the lifecycle, it is important to share the knowledge gained during development and implementation that is relevant for using that design space both on the manufacturing floor and under the PQS of the company or site. This knowledge can include results of risk assessments, assumptions based on prior knowledge, and statistical design considerations. Linkages among the design space, control strategy, CQA, and QTPP are an important part of this shared knowledge. 51
  • 52.  Each company can decide on the approach used to capture design space information and movements within the design space under the applicable PQS, including additional data gained through manufacturing experience with the design space. In the case of changes to an approved design space, appropriate filings should be made to meet regional regulatory requirements. 52
  • 53.  Movement within the approved design space, as defined in the ICH Q8(R2) glossary, does not call for a regulatory filing. For movement outside the design space, the use of risk assessment could be helpful in determining the impact of the change on quality, safety, and efficacy and the appropriate regulatory filing strategy, in accordance with regional requirements. 53
  • 54.  A model is a simplified representation of a system using mathematical terms. Models can enhance scientific understanding and Possibly predict the behavior of a system under a set of conditions. Mathematical models can be used at every stage of development and manufacturing. 54
  • 55.  They can be derived from ◦ first principles reflecting physical laws (such as mass balance, energy balance, and heat transfer relations), or ◦ From data, or ◦ From a combination of the two. 55
  • 56.  There are many types of models. The selected one will depend on ◦ The existing knowledge about the system, ◦ The data available, and ◦ The objective of the study. 56
  • 57.  Models can be categorized in multiple ways. The categorization approaches are intended to facilitate the use of models across the lifecycle, including ◦ Development, ◦ Manufacturing, ◦ Control, and ◦ Regulatory processes. 57
  • 58.  For the purposes of regulatory submissions, an important factor to consider is the model‟s contribution in assuring the quality of the product. The level of oversight should be commensurate with the level of risk associated with the use of the specific model. 58
  • 59.  Low-Impact Models: These models are typically used to support ◦ Product and/or ◦ Process development ◦ (e.g., formulation optimization). 59
  • 60.  Medium-Impact Models: Such models can be useful in assuring quality of the product. However these models are not the sole indicators of product quality (e.g., most design space models, many in- process controls). 60
  • 61.  High-Impact Models: A model can be considered high impact if prediction from the model is a significant indicator of quality of the product. (e.g., a chemometric model for product assay, a surrogate model for dissolution). 61
  • 62.  For the purpose of implementation, models can also be categorized on the basis of the intended outcome of the model. Within each of these categories, models can be further classified as ◦ Low, ◦ Medium or ◦ High, Classification based on their impact in assuring product quality. 62
  • 63.  Models for supporting process design: This category of models includes (but is not limited to) models for ◦ Formulation optimization, ◦ Process optimization  (e.g., reaction kinetics model), ◦ Design space determination, and ◦ Scale-up. 63
  • 64.  Models for supporting process design: Models within this category can have different levels of impact. For example, a model for design space determination would generally be considered a medium-impact model, While a model for formulation optimization would be considered a low-impact model. 64
  • 65.  Models for supporting analytical procedures: In general, this category includes empirical (i.e., chemometric) models based on data generated by various Process Analytical Technology (PAT)-based methods. 65
  • 66.  Models for supporting analytical procedures: A calibration model associated with a near infrared (NIR)-based method. Models for supporting analytical procedures can have various impacts depending on the use of the analytical method. For example, if the method is used for release testing, then the model should be high- impact. 66
  • 67.  Models for process monitoring and control: ◦ Univariate Statistical Process Control (SPC) or ◦ Multivariate Statistical Process Control (MSPC)- based models: These models are used to detect special cause variability; The model is usually derived and the limits are determined using batches manufactured within the target conditions. 67
  • 68.  Models for process monitoring and control: If an MSPC model is used for continuous process verification along with a traditional method for release testing, then the MSPC model would likely be classified as a medium-impact model. 68
  • 69.  Models for process monitoring and control: However, if an MSPC model is used to support a surrogate for a traditional release testing method in an RTRT approach, then the model would likely be classified as a high-impact model. 69
  • 70.  Models used for process control (e.g., feed forward or feedback). Data-driven models should be developed through appropriately designed experiments. These models are typically medium-impact or high-impact. For example, a feed forward model to adjust compression parameters on the basis of incoming material attributes could be classified as a medium-impact model. 70
  • 71.  Sequential steps Steps can be repeated to impart an iterative nature to this process. Overall steps are given in following slides: 71
  • 72. 1. Defining the purpose of the model.2. Deciding on the type of modeling approach. ◦ (e.g. mechanistic or empirical) and ◦ Possible experimental/sampling methodology to be used to support the model development. 72
  • 73. 3. Selecting variables for the model; this is typically based on ◦ Risk assessment, ◦ Underlying physicochemical phenomena, ◦ Inherent process knowledge, and ◦ Prior experience. 73
  • 74. 4. Understanding the limitations of the model assumptions to: ◦ Correctly design any appropriate experiments; ◦ Interpret the model results; and ◦ Include appropriate risk-reduction strategies. 74
  • 75. 5. Collecting experimental data to support model development. ◦ These data can be collected at  Laboratory,  Pilot, or  Commercial scale, (depending on the nature of the model. ) ◦ It is important to ensure that variable ranges evaluated during model development are representative of conditions that would be expected during operation. 75
  • 76. 6. Developing model equations estimating parameters, based on a scientific understanding of the process and collected experimental data. 76
  • 77. 7. Validating the model, as appropriate.8. In certain cases, evaluating the impact of uncertainty in model prediction on product quality. ◦ If appropriate, defining an approach to reduce associated residual risk  (e.g., by incorporating appropriate control strategies (this can apply to high-impact and medium-impact models)). 77
  • 78. 9. Documenting the outcome of model. ◦ Development ◦ Assumptions Developing plans for verification and update of the model throughout the lifecycle of the product. The level of documentation would be dependent on the impact of the model 78
  • 79.  Model validation is an essential part of model development and implementation. Once a model is developed and implemented, verification continues throughout the lifecycle of the product. 79
  • 80.  In the case of well-established first principles- driven models, prior knowledge can be leveraged to support model validation and verification, if applicable. The following elements can be considered for model validation and verification and generally are appropriate for high-impact models The applicability of the elements listed below for medium-impact or low-impact models can be considered on a case-by-case basis. 80
  • 81.  Acceptance criteria relevant to the purpose and to its expected performance. In setting the acceptance criteria, variability in sampling procedure (e.g., for blending) could also be considered. In situations where the model is to be used to support a surrogate for a traditional release testing method, the accuracy of the model performance versus the reference method could be considered. 81
  • 82.  For example, a multivariate model (e.g. a partial least squares (PLS) model), when appropriate, can be used as a surrogate for traditional dissolution testing. In this case, the PLS model should be developed in terms of in-process parameters and material attributes and can be used to predict dissolution. 82
  • 83.  One of the ways to validate and verify model performance in this case would be to compare accuracy of prediction of the PLS model with the reference method (e.g., a traditional dissolution method). 83
  • 84.  Comparison of the accuracy of calibration versus the accuracy of prediction. This can often be approached through internal cross-validation techniques using the same data as the calibration data set. 84
  • 85.  It can be beneficial to verify the prediction accuracy of the model by parallel testing with the reference method during the initial stage of model implementation. This testing can be repeated throughout the lifecycle, as appropriate. If models are used to support a design space at commercial scale or are part of the control strategy, it is important to verify the model at commercial scale. ◦ If a calibration model associated with an NIR-based method is developed at the laboratory scale and the method is then transferred to and used in commercial scale. 85
  • 86.  In addition, the data sets used for calibration, internal validation, and external validation should take into account the variability anticipated in future routine production ◦ (e.g., a change in the source of raw material that might impact NIR prediction). Low-impact models typically do not call for verification. 86
  • 87.  Approaches for model verification can be documented according to the PQS of the company and can include the following: ◦ A risk-based frequency of comparing the model‟s prediction with that of the reference method, ◦ Triggers for model updates (e.g., because of changes in raw materials or equipment), ◦ Procedures for handling model-predicted Out of Specification (OOS) results, ◦ Periodic evaluations, and approaches to model recalibration 87
  • 88.  The level of detail for describing a model in a regulatory submission is dependent on the impact of its implementation in assuring the quality of the product. For the various types of models, the applicant can consider including: 88
  • 89.  Low-Impact Models: A discussion of how the models were used to make decisions during process development. 89
  • 90.  Medium-Impact Models: ◦ Model assumptions, ◦ A tabular or graphical summary of model inputs and outputs, ◦ Relevant model equations (e.g., for mechanistic models), ◦ Statistical analysis where appropriate, ◦ a comparison of model prediction with measured data, and ◦ A discussion of how the other elements in the control strategy help to mitigate uncertainty in the model, if appropriate. 90
  • 91.  High-Impact Models: Data and/or prior knowledge (e.g., for established first principles-driven models) such as ◦ Model assumptions, ◦ Appropriateness of the sample size, number and distribution of samples, ◦ Data pretreatment, ◦ Justification for variable selection, ◦ Model inputs and outputs, ◦ Model equations, ◦ Statistical analysis of data showing fit and prediction ability, ◦ Rationale for setting of model acceptance criteria, ◦ Model validation (internal and external), and ◦ A general discussion of approaches for model verification during the lifecycle. 91
  • 92. Rittinger’s law: The work required in crushing isproportional to the new surface created.Where: P=power required, dm/dt=feed rate to crusher, Dsb =ave diameter before crushing, DSQ=ave after crushing,Kr=Rittinger’s coef.Kick’s law: the work required for crushing a given mass ofmaterial is constant for the same reduction ratio, that is theratio of the initial particle size to the finial particle sizeKk=Kick’s coef.
  • 93. For fine grains, the Characteristic regionboundary betweenthe characteristic Blender head spaceregion and theremaining powderbed is parabolic inshape n m o mThe powder bed Vr rV V r 1below the boundary r 1rotates with themixer as a solid as fraction mixedbody. n f rm rf o f rm1 r 1
  • 94. 0.40 Avicel® PH-200 compacts VFS Speed: 200 rpm 0.35 HFS Speed: 30 rpm Roll Pressure: 6560 lb/inSlope of NIR Spectrum 0.30 Roll Speed (RPM) 0.25 y = 0.3672x + 0.1754 4 5 6 R2 = 0.9899 7 8 9 0.20 10 11 12 0.15 0.0 0.1 0.2 0.3 0.4 0.5 0.6 20 Force at break/Thickness/Width (N/mm2) 18 Avicel® PH-200 compacts  The strength is a 16 VFS Speed: 194 - 197 rpm HFS Speed: 29 - 30 rpm Roll Gap: 0.031 - 0.038" linear function of the 14 Roll Pressure: 6551 lb/in Force at break (N) 12 density which is 10 monitored by NIR 8 y = 21.54e -0.4493x  Semi Empirically 6 R2 = 0.9884 4 F=(SNIR-0.17)/0.37 2 0 4 5 6 7 8 9 10 11 12 Roll Speed (RPM)
  • 95. Avicel® PH-200 Milled Compacts 1000 Increaing Roll Speed Day1 Day2 800Particle Size ( m) d90 600 400 d50 200 d10 0 3 4 5 6 7 8 9 10 11 12 13 Roll Speed (rpm) Avicel® PH-200 Milled Compacts 1200 Increaing Roll Speed  The particle sizes d90 Day1 of the milled 1000 Day2 material is also 800 Particle Size ( m) manifest in the 600 d50 slope of the NIR signal (as 400 d10 predicted) 200 0 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 1 / Slope NIR Spectrum
  • 96. Optimum Conditions (u,v)= *(u,v) 0Equilibrium Moisture Content SIZE M=M0-Kt M=M0’exp(-K’t) 18 0.600 GRANULATION TIME 16 0.550 14 Moisture Content (% w/w) Mean Particle Size (mm) 12 0.500 Moisture Content Particle Size 10 0.450 8 6 0.400 4 0.350 2 0 0.300 0 20 40 60 80 100 Elapsed Time (min)
  • 97. Funicular Modeling Wet GranulationPendular Over WettingDroplet Capillary Drying
  • 98. 0.0010 X1=110 g H13 (1 min) (=X3) H15 (3.5 min) X2=255 rpmNIR Treated Response H14 (6 min) 0.0008 610 m 410 m Slope 320 m 0.0006 0.0004 MIXING SPRAYING WET MASSING 0.0002 0 100 200 300 400 500 600 Process time (s)
  • 99. [Kunii and Levenspiel, Fluidization Engineeri [Kunii and Levenspiel, Fluidization Engineering, Pub. Krieger, pg. 424-428,1977] 180 65 Evaporative Moisture Content Temperature 63 160 Q Qo Kt 61 Critical 140 moisture Temperature (°C) 59 MM55 Reading T 57 120 55 100 53 80 Diffusive 51 MM55 Q Q Qok EXP( k t) 49 60 47 40 45 0 5 10 15 20 25 30 Drying Time (min)K.R. Morris, S.L. Nail, G.E. Peck, S.R. Byrn, U.J. Griesser, J.G. Stowell, S.-J. Hwang, K. Park Pharm Sci Tech Today 1 6 235–245 (1998).
  • 100. 235NIR Monitor (Arbitrary Values) 215 195 175 155 135 115 Conventional Drying Fast Drying 95 75 0.00 5.00 10.00 15.00 20.00 25.00 Time (min) Morris, Drug Dev. Ind. Pharm., 26 (9):985-
  • 101. 60.00 240.0 Average Exhaust Temp 55.00 220.0MM55 Gauge Reading 200.0 50.00 180.0 45.00 160.0 40.00 140.0 Active Melting Temp 35.00 120.0 30.00 100.0 80.0 25.00 60.0 20.00 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 Elapsed Drying Time (min) 120 Batch 018 sub1 Batch 018 sub2 Batch 019 sub1 Batch 019 sub2 Batch 020 sub1 Batch 021 sub1 Batch 021 sub2 Average % Release 100 80 60 40 Baseline Data 20 Temp Excursion 0 0 20 40 60 80 100 120 Time (min)
  • 102. WHOLE TABS HALF TABS QUARTER TABS Active 1 Active 2 Active 1 Active 2 Active 1 Active 2MEAN 101.9 100.9 101.8 99.6 102.1 100.5SD 0.7 1.6 1.4 2.8 2.4 5.1CV (%) 0.7 1.6 1.3 2.8 2.3 5.1 CU for constant size portions of tablets must be larger than for the whole, so in spec using real time monitoring of “part” of the tablets means in spec for the whole tablet CVP CVT T. Li, et. al., in press Pharm. Res. BioMed Anal.
  • 103. HPMC and Sulfanilamide Calculations (Peak Height) 0.4 0.35Absorbance (log(1/R)) 0.3 HPMC Sulfanilamide 0.25 0.2 0.15 0 30 60 90 120 150 Elapsed Time (min) 200 700 100 500 Sulfanilamide Gauge 0 M oisture Gauge Sulfanilam ide 300 Moisture -100 100 -200 -100 -300 -400 -300 0 20 40 60 80 100 120 140 160 Elapsed Time (min)
  • 104.  These principles and techniques are applicable to batch and continuous processing and may be linked by multi-variate (chemometric) methods after univariate conformation. Ultimately this give us the ability to understand how development variables interact to influence the final product and to design in the quality 10 4
  • 105. Quality by Design for ANDAs:An Example for Immediate-Release Dosage Forms Published by FDA 105
  • 106. Aqueous 0.1 N HCL 0.015 mg/mlsolubility as a pH 4.5 buffer 0.015 mg/mlfunction ofpH: pH 6.8 buffer 0.015 mg/mlHyroscopicity Acetriptan Form III is non-hygroscopic and requires no special protection from humidity during handling, shipping or storageDensity (Bulk, • Bulk density: 0.27 g/ccTapped, and • Tapped density: 0.39 g/ccTrue) and • True density: 0.55 g/ccFlowability: • The flow function coefficient (ffc) was 2.95 and the Hausner ratio was 1.44 which both indicate poor flow properties.Chemical • pKa: Acetriptan is a weak base with a pKa of • Overall, acetriptan is susceptible to dry heat, UV light and oxidative degradation.Biological • Partition coefficient: Log P 3.55 (25 °C, pH 6.8)properties • Caco-2 permeability: 34 × 10-6 cm/s. Therefore, acetriptan is highly permeable. • BCS Class II compound (low solubility and high permeability) 106
  • 107. Drug Substance AttributesDrug Solid PSD Hygrosc Solubil Mois Residual Process Chemi FlowProduct State opicity ity ture Solvent Impurit cal prop ContCQA Form ies stabili ent tyAssay Low Med Low Low Low Low Low High MedCU Low High Low Low Low Low Low Low HighDissolution High High Low High Low Low Low Low LowDegradation Med Low Low Low Low Low Low High Lowproducts 107
  • 108. Component Function Unit Unit ( mg/tablet) ( % W/W)Acetriptan, USP Active 20 10Lactose Monohydrate, NF Filler 64-86 32-43Microcrystalline Cellulose Filler 72-92 36-46(MCC), NFCroscarmellose Sodium Disintegrant 2-10 1-5(CCS), NFMagnesium Stearate, NF* Lubricant 2-6 1-3Talc, NF Glidant/Lubricant 1-10 0.5-5Total tablet weight 200 100 108
  • 109. Formulation VariablesDrug product DS PSD MCC/ CCS Level Talc Level Mag StearateCQA Lactose Level ratiosAssay Medium Medium Low Low LowContent High High Low Low LowUniformityDissolution High Medium High Low HighDegradation Low Low Low Low MediumProducts 109
  • 110.  Formulation development focused on evaluation of the high risk formulation variables as identified in the initial risk assessment shown earlier. The development was conducted in two stages. The first formulation study evaluated the impact of the drug substance particle size distribution, the MCC/Lactose ratio and the disintegrant level on the drug product CQAs. The second formulation study was conducted to understand the impact of extragranular magnesium stearate and talc level in the formulation on product quality and manufacturability. Formulation development studies were conducted at laboratory scale (1.0 kg, 5,000 units). 110
  • 111.  Goal of Formulation Development Study #1 Select the MCC/Lactose ratio and Disintegrant level and To understand if there was any interaction of these variables with drug substance particle size distribution. This study also sought to establish the robustness of the proposed formulation. A 2³ full factorial Design of Experiments (DOE) with three center points was used to study the impact of these three formulation factors on the response variables. 111
  • 112. Process step EquipmentPre-Roller Compaction 4 qt V-blenderBlending and Lubrication o 250 revolutions for blending (10 min at 25 rpm) Alexanderwerk10 WP120 with 25 mm roller width and 120 mm roller diameter o Roller surface: KnurledRoller Compaction and o Roller pressure: 50 barIntegrated Milling o Roller gap: 2 mm o Roller speed: 8 rpm o Mill speed: 60 rpm o Coarse screen orifice size: 2.0 mm o Mill screen orifice size: 1.0 mmFinal Blending and 4 qt V-blenderLubrication o 100 revolutions for granule and talc blending (4 min at 25 rpm) o 75 revolutions for lubrication (3 min at 25 rpm) 16-station rotary press (2 stations used) o 8 mm standard round concave toolsTablet Compression o Press speed: 20 rpm o Compression force: 5-15 kN o Pre-compression force: 1 kN 112
  • 113. Factors : Formulation Variables Levels -1 0 +1A Drug substance PSD (d90, μm) 10 20 30B Disintegrant (%) 1 3 5C % MCC in MCC/Lactose combination 33.3 50 66.7 113
  • 114. Responses Goal Acceptable RangeY1 Dissolution at 30 min (%) (with hardness of 12.0 kP) Maximize ≥ 80%Y2 Disintegration time (min) (with hardness of 12.0 kP) Minimize < 5 minY3 Tablet content uniformity (% RSD) Minimize % RSD < 5%Y4 Assay (% w/w) Target at 100 % 95.0 to 105.0 w/wY5 Powder blend flow function coefficient ( ffc) Maximize >6Y6 Tablet Hardness @ 5 kN ( kP ) Maximize > 5 kPY7 Tablet Hardness @ 10 kN ( kP ) Maximize > 9 kPY8 Tablet Hardness @ 15 kN ( kp ) Maximize > 12 kPY9 Friability@ 5 kN ( kp ) Maximize <1%Y10 Friability@ 10 kN ( kp ) Maximize <1%Y11 friability@ 15 kN ( kp ) Maximize <1%Y12 Degradation products (%) (observed at 3 months, 40 Minimize ACE12345: NMT 0.5% °C/75% RH) Any unknown impurity: NMT 0.2% Total impurities: NMT 1.0% 114
  • 115. A B C Y1 Y3 Y5 Y7 Batch DS PSD Disintegra % MCC Dissolution Content Ffc Tablet No nt level in MCC/ in Uniformity Value Hardness Lactose 30 min @ 10 kN Mix (d90, μm) ( %) (%) (%) ( % RSD ) -- (kP)1 30 1 66.7 76.0 3.8 7.56 12.52 30 5 66.7 84.0 4.0 7.25 13.23 20 3 50.0 91.0 4.0 6.62 10.64 20 3 50.0 89.4 3.9 6.66 10.95 30 1 33.3 77.0 2.9 8.46 8.36 10 5 66.7 99.0 5.1 4.77 12.97 10 1 66.7 99.0 5.0 4.97 13.58 20 3 50.0 92.0 4.1 6.46 11.39 30 5 33.3 86.0 3.2 8.46 8.610 10 1 33.3 99.5 4.1 6.16 9.111 10 5 33.3 98.7 4.0 6.09 9.1 115
  • 116.  Initially, dissolution was tested using the FDA- recommended method. All batches exhibited rapid and comparable dissolution (> 90% dissolved in 30 min) to the RLD. All batches were then retested using the in-house dissolution method . Results are presented in earlier table. Since center points were included in the DOE, the significance of the curvature effect was tested using an adjusted model. The Analysis of Variance (ANOVA) results are presented in next table 116
  • 117. Source Sum of df Mean F value P value Comme squares square ntsModel 742.19 3 247.40 242.94 < 0.0001 SignificantA- Drug Substance PSD (d90, μm) 699.8 1 699.78 657.72 < 0.0001 SignificantB- Disintegrant ( % ) 32.81 1 32.81 32.21 0.0013 SignificantAB – Interaction 39.61 1 39.61 38.89 0.0008 SignificantCurvature 1.77 1 1.77 1.74 0.2358 Not SignificantResidual 6.11 6 1.02 --- ----- ----Lack of fit 2.67 4 0.67 0.39 0.8090 Not SignificantPure error 3.44 2 1.72 ---- ---- ------Total 750.07 10 --- ---- ----- ----- 117
  • 118.  The curvature effect was not significant for dissolution; Therefore, the factorial model coefficients were fit using all of the data (including center points). As shown in ANOVA results of the unadjusted model (next slide), the significant factors affecting tablet dissolution were A (drug substance PSD), B (disintegrant level) and AB (an interaction between drug substance PSD and the intragranular disintegrant level). 118
  • 119. Source Sum of df Mean F value P value Comme squares square ntsModel 742.19 3 247.40 219.84 < 0.0001 SignificantA- Drug Substance PSD (d90, μm) 699.8 1 699.78 595.19 < 0.0001 SignificantB- Disintegrant ( % ) 32.81 1 32.81 29.15 0.0010 SignificantAB – Interaction 39.61 1 39.61 35.19 0.0006 SignificantResidual 7.88 7 1.13 --- ----- ----Lack of fit 4.44 5 0.89 0.52 0.7618 Not SignificantPure error 3.44 2 1.72 ---- ---- ------Total 750.07 10 --- ---- ----- ----- 119
  • 120. Under Quality by Design, establishing a designspace or using real-time release testing is not necessarily expected (ICH Q8(R2)). 12 0
  • 121.  It is not necessary to study multivariate interactions of all parameters to develop a design space. The applicant should justify the choice of material attributes and parameters for multivariate experimentation based on risk assessment and desired operational flexibility. 12 1
  • 122.  When appropriately justified design space can be applicable to scale-up. Design space can be applicable to a site change. It is possible to justify a site change using a site independent design space based on a demonstrated understanding of the robustness of the process and an in depth consideration of site specific factors (e.g., equipment, personnel, utilities, manufacturing environment, and equipment). 12 2
  • 123.  There are region specific regulatory requirements associated with site changes that need to be followed. Design space can be developed for a single unit operations or across a series of unit operations. 12 3
  • 124.  It is possible to develop a design space for existing products. Manufacturing data and process knowledge can be used to support a design space for existing products. Relevant information should be utilized from ◦ Commercial scale manufacturing, ◦ Process improvement, ◦ Corrective and preventive action (CAPA), and ◦ Development data 12 4
  • 125.  For manufacturing operations run under narrow operational ranges in fixed equipment, an expanded region of operation and an understanding of multi parameter interactions may not be achievable from existing manufacturing data alone. Additional studies may provide the information to develop a design space. Sufficient knowledge should be demonstrated, and the design space should be supported experimentally to investigate interactions and establish parameter/attribute ranges. 12 5
  • 126.  There is no regulatory expectation to develop a design space for an existing product. Development of design space for existing products is not necessary unless the applicant has a specific need and Desires to use a design space as a means to achieve a higher degree of product and process understanding. This may increase manufacturing flexibility and/or robustness. 12 6
  • 127.  Design space can be applicable to formulations. It may be possible to develop formulation (not component but rather composition) design space consisting of the ◦ ranges of excipient amount and ◦ its physicochemical properties (e.g., particle size distribution, substitution degree of polymer) Based on an enhanced knowledge over a wider range of material attributes. 12 7
  • 128.  The applicant should justify the rationale for establishing the design space with respect to quality attributes such as ◦ bioequivalence, ◦ stability, ◦ Manufacturing ◦ robustness etc. Formulation adjustment within the design space depending on material attributes does not need a submission in a regulatory postapproval change. 12 8
  • 129.  A set of proven acceptable ranges alone does not constitute a design space. A combination of proven acceptable ranges (PARs) developed from univariate experimentation does not constitute a design space Proven acceptable ranges from only univariate experimentation may lack an understanding of interactions between the process parameters and/or material attributes. 12 9
  • 130.  However proven acceptable ranges continue to be acceptable from the regulatory perspective but are not considered a design space. The applicant may elect to use proven acceptable ranges or design space for different aspects of the manufacturing process 13 0
  • 131.  Outer limits of the design space need not be evaluated during process validation studies at the commercial scale. There is no need to run the qualification batches at the outer limits of the design space during process validation studies at commercial scale. The design space should be sufficiently explored earlier during development studies. 13 1
  • 132.  “If the experimental design is poorly chosen, so that the resultant data do not contain much information, not much can be extracted, no matter how thorough or sophisticated the analysis. On the other hand, if the experimental design is wisely chosen, a great deal of information in readily extractable form is usually available, and no elaborate analysis may be necessary. In fact, in many happy situations all the important conclusions are evident from visual examination of the data.” 132
  • 133. 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 133