Qbd continued
- 1. QUALITY- BY- DESIGN Presented by, Pratiksha C Chandragirivar M pharma 2nd sem Dept. of pharmaceutics HSK COP Bagalkot Facilitated to, Dr. Laxman Vijapur Assistant professor Dept. of pharmaceutics HSK COP Bagalkot caad 1
- 2. CONTENTS: 1. ICH GUIDELINE – 8 2. ICH GUIDELINE – 9 3. ICH GUIDELINE– 10 4. METHODS OF DEVELOPMENT OF DOE 5. REFERENCES caad 2
- 3. ICH Q8(R) Annex to ICH Q8 Describes principles of QbD vs. minimal approach Provides further clarification of key concepts of Q8 caad 3
- 4. ICH Q8(R) UPDATE Reached Step 4 in Brussels, November 11, 2008 Only a few minor step 4 revisions: Quality Target product Profile QTPP forms the basis of design for development Design space versus proven acceptable ranges Combination of PARs doesn’t constitute design space Real Time Release Testing (RTRT) To distinguish between RTRT and batch release caad 4
- 5. ICH Q9: QUALITY RISK MANAGEMENT A systematic process for the assessment, control, communication and review of risks to the quality of the drug product. Guidance includes principles and examples of tools for quality risk management Evaluation of risk to quality should: be based on scientific knowledge link to the protection of the patient Applies over product lifecycle: development, manufacturing and distribution caad 5
- 7. ROLE OF QUALITY RISK MANAGEMENT IN DEVELOPMENT & MANUFACTURING caad 7
- 8. ICH Q 10: WHY FOCUS ON PQS? The regulatory flexibility provided with a design space approach requires effective change management at the manufacturing site. Track and trend product quality. Respond to process trends before they become problems. Maintain and update models as needed. Internally verify that process changes are successful. caad 8
- 9. EXAMPLE QBD APPROACH (ICH Q8R) Target the product profile Determine critical quality attributes (CQAs) Link raw material attributes and process parameters to CQAs and perform risk assessment Develop a design space Design and implement a control strategy Manage product lifecycle, including continual improvement caad 9
- 10. QUALITY TARGET PRODUCT PROFILE “Begin with the end in mind” Summary of the quality characteristics of a drug product to ensure safety and efficacy. Includes, but not limited to: 1.Dosage form 2.Route of administration 3.Pharmacokinetic characteristics e.g., dissolution, aerodynamic performance 4.Quality characteristics for intended use e.g., sterility, purity caad 10
- 11. CRITICAL QUALITYATTRIBUTES (CQAS) Physical, chemical, biological or microbiological property or characteristic. Drug product, drug substance, intermediates, and excipients can possess CQAs 1. Directly affect product quality 2. Affect downstream processability Drug product CQAs affect product quality, safety, and/or efficacy 1. Attributes describing product purity, potency, stability and release 2.Additional product specific aspects (e.g., adhesive force for transdermal patches) caad 11
- 12. RISK ASSESSMENT (ICH Q9) Tools for parameter screening Examples: Ishikawa diagrams, What-if analysis, HAZOP analysis Tools for risk ranking Examples: FMEA/FMECA, Pareto analysis, Relative ranking Experimental tools for process understanding Examples: Statistically designed experiments (DOE), mechanistic models caad 12
- 13. DESIGN SPACE (ICH Q8) Definition: The multidimensional combination and interaction off input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality Regulatory flexibility 1. Working within the design space is not considered a change 2. Important to note caad 13
- 14. DESIGN SPACE DETERMINATION First-principles approach combination of experimental data and mechanistic knowledge of chemistry, physics, and engineering to model and predict performance Non-mechanistic/empirical approach statistically designed experiments (DOEs) linear and multiple-linear regression Scale-up correlations translate operating conditions between different scales or pieces of equipment Risk Analysis determine significance of effects Any combination of the above caad 14
- 15. Describing Design Spaces Linear Ranges of Parameters Mathematical Relationships Time-dependent functions Combinations of variables e.g., Principle components of multivariate model Scaling Factors Single or multiple unit operations “The applicant decides how to describe and present the design space” caad 15
- 16. DESIGN SPACES EXAMPLE #1 caad 16
- 17. •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 caad 17
- 18. DESIGN SPACES EXAMPLE #2 caad 18
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- 20. CONTINUAL IMPROVEMENT Flexibility for movement within design space 1. Wider range of material attributes or process parameters. 2. No reporting if moving operating range within design space. 3. Potential scale or equipment change without supplement (subject to regional regulatory requirements). Post-Approval Management Plan (CMC-PMP) 1. A mechanism for applicant to propose a regulatory strategy specific to a product and/or process. 2. Currently under development by FDA. caad 20
- 21. ELEMENTS OF QBD caad 21 QTPP CMA CQA CPP Quality Target Product Profile Quantitative surrogate for aspects of clinical safety and efficacy that can be used to design and optimize a formulation and manufacturing process Ex: Route of administration, strength, identity, Target patient population… Critical Quality Attributes Critical Material Attributes Physical, chemical, biological or microbiological properties of an input Material Ex: particle size, solubility, polymorphic form, stability…. Critical Process Parameters Parameters which influence the CQA of the drug product are called as CPPs. Ex: Homogenization speed, duration of agitation, machine parameters…… Physical, chemical, biological or microbiological properties of an output Material Ex: Appearance, particle size, zeta potential, encapsulation efficiency….
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- 23. DESIGN OF EXPERIMENTS (DOE) A structured, organized, method for determining the relationship between factors affecting a process and the output of that process is known as DoE. It helps in identification of optimal conditions, CMA’s, CPP’s and design space. It is widely employed for formula optimization and process optimization technique for designing of dosage form and unit operations. caad 23
- 24. Statistical Tools for Design of Experiments caad 24
- 25. Factorial Response Surface Mixture Simplex Lattice Simplex Lattice Centroid Augmented Simplex centroid Mixture Box Behnken Central composite Face centered CCD Response Surface Plackett Burman 2 Level Factorial 3 Level Factorial Full and Fractional factorial Factorial ROADMAP FOR DESIGN OF EXPERIMENTS caad 25
- 26. Goal of Design of Experiments How to select a design ? To screen out insignificant factors and identify significant factors and to get some idea about the existence of interaction effects. Use a Factorial Design. To characterize how the significant factors affect your responses. Use a Response surface Design. Use Central composite or Box-Behnken to study factors as various levels. If your product is actually a mixture, or a formulation, then you should use a Mixture Design. These designs allow you to set a total amount for the mixture. caad 26
- 27. Goal of Design of Experiments • Inlet Temp • Outlet Temp • Product Temp • Spray rate • Atomization air • Pattern air • Gun to bed distance • Pan speed • Spray viscosity • Differential pressure • % weight gain Screening • Inlet Temp • Pan Speed • Spray rate Design Space Control Strategy Optimization Design-Expert® Software Factor Coding: Actual Overlay Plot Drug release at 30 min RSD for UOD Flow Function Hardness at 10KN Design Points X1 = A: Particle size D90 X2 = B: Disintegrant concentration Actual Factor C: Ratio of MCC & Lactose = 50.00 10.00 15.00 20.00 25.00 30.00 1.00 2.00 3.00 4.00 5.00 Overlay Plot A: Particle size D90 B:Disintegrantconcentration 3 • Inlet Temp (40 to 60 degrees C) • Pan Speed (2 to 10 RPM) • Spray rate ( 2 – 6 gm/min/Kg) Factorial Designs Factorial Designs Response Surface caad 27
- 28. MODELS USED IN DOE: caad 28
- 29. 1. SCREENING DESIGN Screening designs are used to identify the most influential factors from those that potentially have an effect on studied responses. Huge number of factors (f) can be screened by varying them on 2 levels in a relatively small number of experiments N ≥ f + 1. If number of factors are less, then full factorial designs can also be used for screening purposes. caad 29
- 30. The number of experiments, N, in this design is Lf = 2f. They are usually denoted as ef, meaning that in a 23 design, 3 factors (f) are varied on 2 levels (e). Coded values - lower factor level is denoted as −1, and 1 stands for the upper factor level. Factorial design types. - 2-level factorial designs (2-21 factors) - Min Run Res V designs (6-50 factors) - Min Run Res IV designs (5-50 factors) - General Factorial designs (1-12 factors) caad 30
- 31. SCREENING DESIGN caad 31 2 Level Fractional Factorial design 4 Runs 2 Level Full Factorial design 8 Runs 2 Level Full Factorial design with Center Point 9 Runs 3 Level Full Factorial design 27 Runs
- 32. SCREENING DESIGN caad 32 23 Full Factorial Design 1 6 2 5 8 4 7 3 8 Factorial Points + 1 Center Point = 9 Points / Runs
- 33. SCREENING DESIGN caad 33 After experimentation, responses are added and factor effects are used to build the regression model: Y = b + b1X1 + b2X2 + b3X3 Y is the measured response, b is the intercept and b1 to b3 are regression coefficients computed from observed experimental values of Y In addition to main effects, full factorial design also allows identification of factor interactions. Interaction factors
- 34. SCREENING DESIGN Plackett - Burman designs (up to 31 factors) – These are highly confounded designs that are useful if you can safely assume that interactions are not significant. Another useful application is ruggedness testing where you are testing factor levels that you hope will NOT effect the response. Factor effects f = N – 1 factors Experiments are usually in multiples of 4. Useful for preliminary investigations. caad 34 Plackett-Burman design for 7 factors
- 35. Taguchi Designs (up to 63 factors) – A set of classic designs from Taguchi teachings. These may be used as a base to build a particular design. Note that all analyses will be completed using standardized ANOVA reports and interaction graphs. Optimal designs (2-30 factors) – This is offered as an alternative to the General Factorial designs, which may produce a design with too many runs. caad 35
- 36. 2. Response Surface Methodology These are quadratic or cubic model designs and yield a curvature effects. Quadratic models are commonly used in Pharmaceuticals. Classic quadratic designs include: Box-Wilson central composite design (CCD’s) Box Behnken Design CCD has three groups of design points: a) Two-level factorial or fractional factorial design points b) Axial points / Star points c) Center point caad 36
- 37. Response Surface Methodology Inscribed CCD Circumscribed CCD No. of Factor: 3 No. of Levels: 3 Center Points: 5 Total Runs: 8FP + 6SP + 5CP = 19 Runs No. of Factor: 3 No. of Levels: 5 Center Points: 6 Total Runs: 8FP + 6SP + 6CP = 20 Runs Face centered CCD No. of Factor: 2 No. of Levels: 3 Center Points: 5 Total Runs: 4FP + 4SP + 5CP = 13 Runs caad 37
- 38. Response Surface Methodology Inscribed CCD No. of Factor: 3 No. of Levels: 3 Center Points: 5 Total Runs: 8IFP + 6ISP + 5ICP = 19 Runs In Response surface designs, the regression model are defined as: Y = b + b1X1 + b2X2 + b3X3 + b12X1X2 + b13X1X3+ b23X2X3 Y is the measured response, b is the intercept and b1 to b3 are regression coefficients computed from observed experimental values of Y. X1X2, X2X3 and X1X3 are interaction terms caad 38
- 39. Response Surface Methodology Box Behnken Design No. of Factor: 3 No. of Levels: 3 Center Points: 3 Total Runs: 12 MP+ 3 CP = 15 Runs • BBD is an independent quadratic design and does not contain an embedded factorial or fractional factorial points. • In BBD, design points are mid points of the edges of the process space and at the center. caad 39
- 40. 3.Mixture Designs Mixture designs are used to study mixture variables such as excipients in a formulation. The characteristic feature of a mixture is that the sum of all its components adds up to 100%, meaning that the mixture factors (components) cannot be manipulated completely independently of one another. caad 40
- 41. In comparison to other experimental designs, mixture designs cannot be viewed as squares, cubes, instead viewed as a triangle. Mixture designs can be Simplex lattice, or Simplex lattice- centroid design or augmented Simplex lattice – centroid mixture design caad 41
- 42. Mixture Designs Simplex lattice mixture designs can be defined with 3 (experiments 1 – 3) or six experiments (1 – 6). If experiment 7 is included, then it is a simplex lattice-centroid design and if all 10 experiments are considered, then it is an augmented simplex lattice – centroid mixture design. caad 42
- 43. Simplex Lattice Design Simplex Lattice Centroid Design Augmented Simplex Lattice Centroid Design caad 43
- 44. ISHIKAWA DIAGRAM: FISHBONE DIAGRAM caad 44
- 45. REFERENCES: QBD FOR ANDA IR FINAL SUBMISSION APRIL- 12-2012, SLIDES FROM CONFERENCES ON QUALITY BY DESIGN, CHINA, BEINJING. caad 45