Agenda. Applying Quality by Design to Generic Drug Manufacturing. Bikash Chatterjee President & CTO Pharmatech Associates

Transcription

1 Applying Quality by Design to Generic Drug Manufacturing Bikash Chatterjee President & CTO Pharmatech Associates 1 Agenda What is QbD? Why it has become important What companies need to know, overview How to set up a team to develop QbD Process understanding Knowledge space Design space Required statistical processes Practical application of the ideas-case Study Review of past records to determine CPP-Case Study Development of acceptable operation range Benefits Cost savings 2

2 QbD s Proposition QbD concerns the making of drug substances and drug products QbD is the new pharmaceutical quality system that: Replaces current GMP concepts Does not depend on the trial and error approach of drug substance and drug product development & production Is a systemic, knowledge and risk-based quality methodology Complies with the general purpose of product quality: the product is suitable for use Patient driven philosophy A quality system customized for pharmaceuticals QbD is GMP for the 21 st century 3 What is Quality by Design (QbD)? First introduced in 1985 by Dr. Juran Juran said most quality problems are designed into the process. A clear plan is needed to identify and eliminate these issues No single definition 4

3 ICH Q8 Definition of QbD a 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 5 Another Way of Thinking about QbD Once a system has been tested to the extent that the test results are predictable, further testing can be replaced by establishing that the system was operating within a defined design space. 6

4 My definition of QbD Understanding what factors have an impact on variation in your process and also on your product s performance; then establishing a control plan to monitor and maintain product quality 7 Elements of Quality by Design (QbD) Risk Critical Quality Attributes CQAs ICH Q8,Q9 Q10 Q11 GMPs EU USFDA PIC/S GMPs EU USFDA PIC/S GMPs Quality Target Product Profile CTPP Critical Process Parameters CPPs Control Strategy QbD Design Space 8

5 Quality by Design Stages Quality Planning Quality Planning Quality Target Product Profile (QTPP) Quality Critical Attributes (QCAs) Quality Improvement QbD Quality Control Quality Control Critical Process Parameters (CPPs) Control Strategy Quality Improvement Process Control Process Monitoring 9 What is Quality by Design (QbD)? Pharmas version of Jurans Model Inputs Process Outputs QTPP CQAs Product Development Process Control Features QbD Implementation 10

6 Sources of Variation Management Man Method Cause Cause Cause Cause Cause Cause Cause Cause Cause Cause Cause Cause Cause Cause Cause Cause Cause Cause Measurement Machine Material Effect (Y) 11 Agenda What is QbD? Why QbD has become important What companies need to know, overview How to set up a team to develop QbD Process understanding Knowledge space Design space Required statistical processes Practical application of the ideas-case Study Review of past records to determine CPP- Case Study Development of acceptable operation range Benefits Cost savings 12

7 Business Dynamics 13 Why Has QbD Become Important? Business Drivers o New market opportunities o Improved market competitiveness o Improved profitability o Reduced product risk exposure 14

8 Three Areas of Improvement... Better QbD Quality Faster Time/Flow Cheaper Waste/Costs 15 Drive Financial Performance Increase Revenue:Grow the Business Improve customer satisfaction, sales, throughput, and competitive position Decrease the Cost of Goods Sold Reduce process variation and defects, improve yield Identify and eliminate root causes of problems Develop systems robust to problems Reduce unnecessary costs and excessive cycle time 16

9 QbD is a Better Business Model R&D drives new innovative products Do we really need QbD? The conservative criticism New FDA commissioner Margaret Hamburg s keynote address at Regulatory Affairs Professionals Society annual conference in Philadelphia, September 2009 All the billions of dollars poured into research and development in the U.S. won t mean a thing. We must streamline and strengthen the regulatory science Areas cited where this is being accomplished include FDA s partnership with ICH around Quality by Design (QbD) Conclusion: QbD is a way to innovate within the pharmaceutical industry 17 Regulatory Drivers for QbD Escalating and non-uniform compliance expectations: - ASEAN Harmonization Activities - ICH, PIC/S, EU, CFDA (China), MHLW (Japan), CDSCO (India), MOH (Malaysia), FDA Thailand, NA-DFC (Indonesia) 18

10 US/EU/PIC/S QbD Regulatory Timeline 19 ASEAN Harmonization Milestones PPWG IWG GMP MRA TF BA/BE TF A-CTD Implementation A-CTD Implemented A-CTR & technical guidelines established (maintenance and enhancement of common interpretation ongoing) Post-Market Alert System established GMP Inspection MRA finalized Training identified Pan-ASEAN registration 20

11 Regulatory Drivers-ICH Q8, 9, 10, 11 ICH Q8, Q9, Q10 & Q11are designed as separate but linked in a series of documents exploring pharmaceutical products lifecycle ( ICH Q8 - Pharmaceutical Development ICH Q9 - Quality Risk Management ICH Q10 - Pharmaceutical Quality System ICH Q11 - Development and Manufacture of Drug Substances 21 Agenda What is QbD? Why it has become important What companies need to know, overview How to set up a team to develop QbD Process understanding Knowledge space Design space Practical application of the ideas-case Study Review of past records to determine CPP-Case Study Required statistical processes Development of acceptable operation range Benefits Cost savings 22

12 Is QbD a Shift in Quality Philosophy? You can t test quality into drug products has been heard for decades so whats new? Quality is based on process and product understanding, not just test results It s a shift in culture: incorporates quality principles and strong compliance function Incorporates risk assessment and management Refocuses attention and resources on what s important to the customer, i.e. the patients, health professionals, payors and distribution chain 23 QbD is a Commitment to Improve Continuous improvement is a key element of QbD - G. Taguchi on Robust Design: Design changes during manufacture can result in the last product produced being different from the first product However, in pharmaceutical manufacturing, we want improvement that improves consistency patients and physicians must count on each batch of drug working just like the batches that came before 24

13 QbD for Generic Drugs In generic pharmaceutical manufacturing, there are additional constraints: Fixed bioequivalence targets Regulatory requirements to duplicate formulation of innovator drug Lack of access to innovator development data 25 The Changing Regulatory Compliance Environment Current Regulatory Situation: US/EU Little guidance on adequate resources or qualifications Self-assessments not trusted Annual product reviews instead of continuous analysis Formidable barriers to change, including intimidating enforcement emphasis Seldom admit that anything is not important; test everything Quality by Design Adequate resources for quality: number, qualifications, etc. Self-assessments play key role Continuous analysis & improvement Change management based on good science Focus on what s important (risk management) 26

14 Quality by Design (QbD) Characteristics Basics: Uses systemic (multivariate statistics) development and manufacturing by use of prior knowledge Risk assessment guided design and process control Applies to the total life cycle of a product (continuous improvement) Implications: Quality back to the roots: product suited for its purpose Quality is dynamic: continuous improvement Quality must be built in Quality means first time right 27 The QbD Development Model is Different Traditional Idea Development Preclinical & Licensing Manufacturing Marketing/ Clinical Testing Sales QdB In the QbD Development Concept The Chain is Reversed Patient Idea Design Space Control Strategy Risk Assessment Product Life Cycle 28

15 QbD Will Require Enhanced Supplier Management Why? You will need to measure and control the important characteristics of your raw materials and API Clearly defined supplier quality and supply agreements are necessary 29 Agenda What is QbD? Why it has become important What companies need to know, overview How to set up a team to develop QbD Process understanding Knowledge space Design space Required statistical processes Practical application of the ideas Review of past records to determine CPP Development of acceptable operation range Benefits Cost savings 30

16 Building a QbD Organization Starts in product development Multidisciplinary team representing the product development lifecycle Presents opportunities to build in existing commercial experience into the product and process design phase Presents the opportunity to not repeat mistakes in formulation and product design 31 Team Structure R&D & Marketing Validation Corporate and Mfg. Engineering QbD Core Team Oversight Committee Facilities/GC Technical Services Regulatory and QA Compliance 32

17 QbD Core Team QbD Core Team Program Manager Decision makers from all six areas Clear mandate to deliver product. In the US FDA market measured by being the First to File 33 Team Chartering Process Define and Identify: Success metrics for the project Timeline Budgetary and cost tracking assumptions Key stakeholders Project champion and project milestones Extended Chartering to discussion of communication, review and issue resolution mechanism Also established initial team rules: what behaviors would be encouraged and what would not be encouraged 34

18 Managing Team Dynamics Structure Strategy Systems Team Charter Skills Style Staff 35 A Generic Drug QbD Framework Phase1 Phase2 Phase3 Phase4 Phase5 Product Selection Key Activity QTPP Strategic Analysis Site Capability Analysis Project Timeline Risk Analysis Process Understanding Process Predictability Measurement Go/NoGo Development And Characterization Key Activity Platform Knowledge Identify CPP MSA CMA Risk Analysis Process Risk Analysis Commercial Factors Go/No Go Site Selection/Process Design/Tech Transfer Key Activity Site Suitability Mapping CPP MSA Process Risk Analysis Confirmation Process DOE Process Validation Go/NoGo Reg. Filing Key Activity Filing Prep. Go/NoGo Continuous Monitoring Key Activity Metrics Review Knowledge Management 36

19 Agenda What is QbD? Why it has become important What companies need to know, overview How to set up a team to develop QbD Process understanding Knowledge space Design space Required statistical processes Practical application of the ideas-case Study Review of past records to determine CPP-Case Study Development of acceptable operation range Benefits Cost savings 37 Reducing Variation by Robust Design (QbD) By tighter controls of the inputs... Transfer Relationship By Robust Process Design... Process "Y" $ $ $ Input 38 Traditional Method of Reducing Variation Alternate Method of Reducing Variation

20 Effect of Design on the Product Development Life Cycle Cost &Time vs. Impact Potential is Positive Impact >Cost and Time Impact< Cost & Time Design Produce/Build Deliver Service Support 39 Scope of Recent Guidances Product Design Process Design Manufacturing Process Monitoring / Continuous Verification ICH Q8/Q8(R) - Pharmaceutical Development ICH Q11 Development and Mfg. of Drug Substances PAT Guidance ICH Q9 Quality Risk Management FDA Guidance on Quality Systems (9/06) FDA Process Validation Guidance ICH Q10 Pharmaceutical Quality Systems 40

21 ICH Q8- Pharmaceutical Development Introduces the concept of pharmaceutical Quality by Design Defines QbD as: A 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. 41 ICH Q8 Concept of QbD Process Understanding Process Parameters Process Controls 42

22 Designing a Robust Process PROCESS CONTROL Low High PROCESS UNDERSTANDING Low High potential for failures Reproducible process within narrow operating ranges High Problems detected after they occur, through product testing and inspection Robust & reproducible process 43 Role of Quality Risk Management in Development & Manufacturing Product Development Process Development Process Scale-up & Tech Transfer Manufacturing Implementation Product/prior Knowledge Process Understanding Process History Risk Assessment Risk Assessment Risk Control Risk Review Excipient & drug substance design space Process design space Product quality control strategy Continual improvement Risk Management 44 44

23 The FDA QbD Model Product & process design and development Quality by Design Define desired product performance upfront; identify product CQAs Design formulation and process to meet product CQAs Continually monitor and update process to assure consistent quality Identify and control sources of variability in material and process Understand impact of material attributes and process parameters on product CQAs Risk assessment and risk control 45 Process Step Analysis For product and process: - Risk assessment - Design of experiments - Design space definition - Control strategy - Batch release CRM DOEs Design Space Control Strategy Batch Release 46

24 Adding the QbD Framework QTPP/ CQAs CPPs Quality Risk Management CMA DOEs Design Space Control Strategy Batch Release 47 Quality Target Product Profile (QTPP) Defines the product development requirements. Used to be called the product Requirement Specification (PRS) ICH Q8 Definition is: A prospective summary of the quality characteristics of a drug product that ideally will be achieved to ensure the desired quality, taking into account safety and efficacy of the drug product 48

25 QTTP- PRS Example Dosage form and strength Specifications to assure safety and efficacy during shelf-life Description and hardness Appearance Immediate release tablet taken orally containing 30 mg of active ingredient Assay, Uniformity of Dosage Unit (content uniformity) and dissolution Robust tablet able to withstand transport and handling. Film-coated tablet with a suitable size to aid patient acceptability and compliance Total tablet weight containing 30 mg of active ingredient is 100 mg with a diameter of 6 mm 49 QTTP- Safety and Efficacy Example Tablet Product Requirements Critical to Quality Attributes (CQA) Dose 30 mg Identity, assay and CU Marketing Safety- Purity Efficacy-API PSD* Shelf Life Taste masking, coated tablet, suitable for global market Impurities and degradation products meet ICH guideline Drug bioavailable with PSD that meet mfg needs 2 years and meets ICH guidelines *PSD: Particle Size Distribution Size, Appearance, Potency API impurities and degradation products <1%, residual Solvents Dissolution >60% 1 hour per USP 711 Primary packaging oxygen barrier required for shelf life 50

26 Risk Analysis It doesn t need to be complex High, medium and low risk ratings are acceptable Anything with a high rating should be justified Apply the risk analysis to the product design (formulation) and the process design activity at the outset 51 Example Product Risk Analysis CQA Microcrystalline Povidone Mg. Stearate API cellulose Appearance Low Low Low Low Assay Low Low Low High Content Uniformity Low Low Medium High Dissolution Low Medium Medium High Hardness Medium Low Low Low Justification PSD critical to solubility of drug. Low loaded dose can affect CU 52

27 Process Unit Operation Risk Assessment CQA Process Steps Granulation Drying Milling Blending Compression Coating Appearance Low Low Low Low Medium High Assay Low Low Low Medium Low Low Impurity Low Low Low Low Low Low Blend Uniformity Low Low Medium High High Low Drug Release Low Low Low Medium Medium High Particle Size Medium Low High Low Low Low Distribution Justifications for High Rating N/A N/A Milling screen size and speed can affect the PSD and therefore the powder flow and tablet fill weight control Blending can affect blend uniformity, assay, and drug release profile Compression can affect drug uniformity in the tablet based upon particle size variability and flow The final appearance and drug release rate are affected by the coating quality and reproducibility 53 Risk Analysis Important to go back to the risk assessments at the end of the process development activity and finalize the risk assessment based upon real data 54

28 Agenda What is QbD? Why it has become important? What companies need to know, overview How to set up a team to develop QbD Process understanding Knowledge space Design space Required Statistical processes Development of acceptable operation range Practical application of the ideas-case Study Review of past records to determine CPP-Case Study Benefits Cost savings 55 The QbD Framework for Process Space 56

29 Knowledge Space The potential range of limits for all parameters controlled or measured during the process characterization process 57 Knowledge Space Determination Determine which parameters have an impact on the products performance Requires establishing a range for each parameter to evaluate for each unit operation Pharma has historically used One- Factor-At-A-Time (OFAT) to do this but this is not adequate today 58

30 Design of Experiments (DOEs) An approach which allows us to understand the contribution to variation of a parameter(s) upon a known response variable Establish a mathematical model which describes the impact of each variable controlled on the dependent variable of interest OFAT studies cannot do this 59 DOE vs. OFAT DOEs allow you to understand the process behavior in a very few studies DOEs allows the experimenter to apply statistics to back-up their conclusion The only way to have confidence your conclusion is correct 60

31 Experimental Variability Any experiment is likely to involve three kinds of variability: Planned, systematic variability This type of variability we want since it includes the differences due to the treatments Chance-like variability This type of variability our probability models allow us to live with. We can estimate the size of this variability if we plan our experiment correctly Unplanned, systematic variability This type of variability threatens disaster! We deal with this variability in two ways, by randomization and by blocking. Randomization turns unplanned, systematic variation into planned, chance-like variation, while blocking turns unplanned, systematic variation into planned, systematic variation The management of these three sources of variation is the essence of experimental design. Taken from In Introduction to the Design and Analysis of Experiments, George Cobb (1998) 61 Things to Consider Sample size Sampling Plan Additional characterization tests, e.g, powder flowability, bulk/tapped density, PSD- d10, d50 an d90, intermediate dissolution time points Acceptance criteria. Specifications are not always fully descriptive 62

32 High Level Map of Experiments Screening Designs One Factor at a time Fractional Factorials Characterization Studies Full Factorials Optimization Studies Response Surface Methods 63 Knowledge Space Output Will reduce the number of variables that matter in terms of product performance Will define the broad limits of the knowledge space which will be used to drive the Design Space 64

33 Example DOE - Compression Examine the impact of Turret Speed (rpm) and Compression Force (N) on Tablet Hardness and Tablet Dissolution Turret Speed: rom Compression Force : kn So we have 2 factors each with 2 levels 65 Example DOE - Compression Turret Speed Compression Force Tablet Hardness 4 Hr Dissolution (rpm) (kn) (kp) (%) Does Turret Speed matter? Does Compression Forces matter? 66

34 Example DOE- Compression ANOVA Do these variables have an impact on tablet hardness? = 0.05 Estimated Effects and Coefficients for Tablet Hardness (coded units) Term Effect Coef SE Coef T P Constant Turret Speed Compression Force Turret Speed*Compression Force Yes! S = PRESS = 18 R-Sq = 82.61% R-Sq(pred) = 30.43% R-Sq(adj) = 69.57% 67 Example DOE- Compression ANOVA Do these variables have an impact on tablet dissolution? = 0.05 Estimated Effects and Coefficients for 4 Hr Dissolution (coded units) Term Effect Coef SE Coef T P Constant Turret Speed Compression Force Turret Speed*Compression Force S = PRESS = 146 R-Sq = 34.68% R-Sq(pred) = 0.00% R-Sq(adj) = 0.00% 68

35 What if I am Not Strong In Statistics? The Pareto Chart provides the same answer 69 Agenda What is QbD? Why it has become important? What companies need to know, overview How to set up a team to develop QbD Process understanding Knowledge space Design space Required Statistical processes Development of acceptable operation range Practical application of the ideas- Case Study Review of past records to determine CPP-Case Study Benefits Cost savings 70

36 Design Space 71 Look Only at the Parameters that Affect the Drugs Performance In our example Compression force affected tablet hardness which was a drug release criteria Narrow the range to be evaluated and this becomes your new Design Space limits for this variable, e.g. conform the contribution from kn 72

37 Agenda What is QbD? Why it has become important? What companies need to know, overview How to set up a team to develop QbD Process understanding Knowledge space Design space Required Statistical Processes Development of acceptable operation range Practical application of the ideas- Case Study Review of past records to determine CPP-Case Study Benefits Cost savings 73 Statistical Testing The purpose of applying statistical tests is to compensate for the fact that we cannot test every unit we make So we make a guess,i.e. a hypothesis of whether, within a predefined level of error, our decisions are correct 74

38 What is a Test of Hypothesis? A statistical test designed to answer a question, or allow one to choose between two or more alternatives: Is material A better than material B? Does the new process have a larger yield over the our older process? Does this lot meet our specifications? Tests of hypothesis provide a structure for learning 75 Hypothesis testing concepts allow us to...? Properly handle uncertainty Minimize subjectivity Question assumptions Prevent the omission of important information Manage the risk of decision errors 76

39 Hypothesis Test Configuration H o : Parameter or Measure = a value, or is true H A : Parameter or Measure {< or > } a value, false = a low probability typically of 1%, 5%, or 10% The hypothesis of equality,or that condition that is considered true is typically called the Null Hypothesis The hypothesis of non-equality is called the Alternate Hypothesis All hypothesis' include a level of significance,, which is the risk of incorrectly rejecting a true Null Hypothesis 77 Fundamentals of Hypothesis Testing Based on existing knowledge, we form a hypothesis to explain something about the unknown observation Frequently, the hypothesis is the opposite of what we hope to show Collect data to evaluate the null hypothesis Assume the null hypothesis is true (favored hypothesis) Seek compelling evidence in the data to support or contradict that hypothesis If the null hypothesis is contradicted we reject the null hypothesis and accept the alternative hypothesis 78

40 Hypothesis and Decision Risk When testing a hypothesis, we do so with a known degree of risk and confidence We must specify in advance: Magnitude of acceptable decision risk Test sensitivity These provide the necessary information to determine an appropriate sample size Consider practical limitations of cost, time, and available resources to arrive at a rational sampling plan We can never acheive absolute certainty 79 The Decision Errors Keller and Warrack, Statistics for Management and Economics Your Decision Do not reject H o Reject H o The Truth H o is True Correct decision Type I Error Risk) H o is False Type II Error Risk) Correct decision 80

41 Example: Airport Security Alarm s Decision Nothing In Bag Weapon In Bag Nothing In Bag The Truth Weapon In Bag Correct Type II Error Risk) Type I Error Risk) Correct Consequences: Consequences: 81 Hypothesis Testing Assumptions Sampling from a distribution must be representative or independent Random sampling is the key assumption Often Normality is the key assumption The random sampling assumption is also known as the statistical independence assumption A plot of the data in time order should not show any trends Check by finding out how the samples were chosen and tested 82

42 Hypothesis Testing Common Tests 1 sample t-test (compares sample mean to a value) 2 sample t-test (compares one sample mean to another) 1 way analysis of variance (ANOVA) (compares more than two sample means) Correlation and Regression Analysis (compares paired data to a linear model) Design of Experiments (compares the effects of factors on the process output) Chi square test for independence (compares multiple proportions) 83 Hypothesis Testing Procedure Steps 1. Write the null hypothesis 2. Write the alternate hypothesis 3. Decide on the p value 4. Choose hypothesis test Example (2 Samples) H 0 : x Sample A = x Sample B (e.g. new way is the same as the old way) H A : There is a difference between Samples A and B p =.05 (typical for characterization projects) Choose the correct test, given the type of X and Y data (in this example, a t-test) 5. Gather evidence and test/conduct analysis 6. Reject H 0 /not reject H 0 and draw conclusion Collect data, run analysis, get p value If p >.05 conclude that your data does not show a significant difference between samples If p<.05 conclude the samples are different The Test is on Populations, NOT Samples 84

43 Making Decisions with Hypothesis Tests Key Question: Do you have sufficient evidence to reject the H o? The p-value is the most common way to evaluate the results of your test Common ways to remember what the p-value means: If p is low, H o must go! or If the p is high keep the guy! 85 How Low Must the p-value Be? P-value is required to reject H o We would like there to be less than a 10% chance of falsely rejecting H o ( =.10) 5% is much more comfortable ( =.05) 1% feels very good ( =.01) This alpha level is based on our assumption of no difference and a reference distribution of some sort But, it depends on interests and consequences For most cases we will use.05 86

44 Data Types Discrete Counts of discrete events (1, 2, 3, defects) Qualitative descriptions Good / Bad Supplier 1, Supplier 2, Method A, Method B, Continuous Decimal sub-divisions are meaningful Time, money, etc. 87 Hypothesis Testing Roadmap Different statistical tools apply to different types of input and output data combinations Minitab supports all these combinations Structured approach to choosing the right analysis method If the only tool you have is a hammer... every problem looks like a nail - Abraham Maslow 88

45 Testing Rubric Single X X Data Multiple Xs X Data Discrete Continuous X Data Discrete Continuous Single Y Y Data Continuous Discrete Chi-Square ANOVA T-test Logistic Regression Regression Y Data Discrete Continuous Multiple Logistic Regression 2, 3, 4+ way ANOVA Medians Tests Multiple Logistic Regression Multiple Regression 89 Sampling Strategy 90

46 Sampling Strategy Commonly Overlooked in terms of its importance in establishing process understanding If you do not have confidence your sample is representative of your true process population you cannot be sure your conclusions are correct 91 Terms: Additional Definitions Sample: Population: A subset of a population. For us this is the data we collect Not the same as sample, rather this is the data we would have collected if you had repeated the experiment an infinite number of times 92

47 Terms: Additional Definitions Acceptance Sampling: Form of inspection applied to lots or batches of items before or after a process, to judge conformance with predetermined standards Sampling Plans: Plans that specify lot size, sample size, number of samples, and acceptance/rejection criteria Single-sampling Double-sampling Multiple-sampling 93 Sampling Myths Sampling plans can make a bad process better Sampling plans have no impact on process capability and are not a surrogate for process improvement A stringent sampling plan ensures only good product goes out the door No. The only way to ensure 100% good product goes out the door is to make 100% good product My sampling plan justifies my quality decision for my production lots No. Your sampling plan can only extrapolate the behavior of the population from which it was sampled. You must use scientific inference to apply this to other lots 94

48 When to Use Sampling Product Development: Demonstrating product performance Process Development: Understanding process behavior Process Optimization: Improving process behavior Quality Control: Verifying incoming raw materials, API, components and product release testing Stability: Product Expiration testing In-Process Testing: Establishing an effective control strategy 95 Operating Characteristic Curves 96

49 Acceptance Sampling Modern acceptance sampling involves a system of principles and methods. Their purpose is to develop decision rules to accept or reject product based on sample data. Factors are: The quality requirements of the product in the marketplace The capability of the process The cost and logistics of sample taking 97 Probability of Acceptance (Pa) The primary characteristics when evaluating a sampling plan is to understand what the probability of accepting a lot is as the percentage defects in the population changes The behavior the sampling plan is defined by its Operating Characteristic (OC) Curve All OC Curves have certain properties in common: At 0% defective the probability of acceptance is 1 At 100% defective, the probability of acceptance is 0 As the percent defective is increased the OC curve decreases 98

50 Operating Characteristic Curve Probability of accepting lot % Lot quality (fraction defective) 99 Decision Criteria Probability of accepting lot Good Bad Ideal Not very discriminating Lot quality (fraction defective) 100

51 Sampling Terms Acceptance Quality Level (AQL) the percentage of defects at which consumers are willing to accept lots as good Lot Tolerance Percent Defective (LTPD) the upper limit on the percentage of defects that a consumer is willing to accept Consumer s Risk the probability that a lot contained defectives exceeding the LTPD will be accepted Producer s Risk the probability that a lot containing the acceptable quality level will be rejected 101 OC Definitions on the Curve Probability of Accepting Lot 100% 90% 75% 50% 25% = 0.10 Good AQL = 0.05 Indifferent Producer Risk LTPD Bad OC Curves can be summarized by two points: AQL: Percent defective with a 95% chance of acceptance LTPD: Percent defective with a 10% chance of acceptance Consumer Risk Lot Quality (Fraction Defective) 102

52 Probability of Accepting Lot 100% 75% 50% 25% OC Curves OC Curves come in various shapes depending on the sample size and risk of and errors This curve is more discriminating This curve is less discriminating Lot Quality (Fraction Defective) 103 The Perfect OC Curve 100% Probability of Accepting Lot 75% 50% 25% This curve distinguishes perfectly between good and bad lots. What would allow you to achieve a curve like this? Lot Quality (Fraction Defective) 104

53 Agenda What is QbD? Why it has become important? What companies need to know, overview How to set up a team to develop QbD Process understanding Knowledge space Design space Required Statistical processes Development of acceptable operation range Practical application of the ideas- Case Study Review of past records to determine CPP-Case Study Benefits Cost savings 105 Acceptance Criteria At a minimum all process testing must meet specifications The specifications should be derived from the product requirements and the process capability Ideally you can steer and predict the process performance 106

54 Establishing Acceptance Criteria Confidence Intervals: Determines the probability that the confidence interval produced will contain the true parameter value. Common choices for the confidence level C are 0.90, 0.95, and These levels correspond to percentages of the area of the normal density curve Because the normal curve is symmetric, half of the area is in the left tail of the curve, and the other half of the area is in the right tail of the curve. For a 95% confidence interval, the area in each tail is equal to 0.05/2 = Measures our degree of uncertainty in the population mean 107 Establishing Acceptance Criteria Prediction Intervals: Determines the probability interval that a single value will fall. Tends to be larger than confidence intervals. Measures our degree of uncertainty and the variability in the distribution of the population mean is affected by sampling error. 108

55 Agenda What is QbD? Why it has become important? What companies need to know, overview How to set up a team to develop QbD Process understanding Knowledge space Design space Required Statistical processes Development of acceptable operation range Practical application of the ideas- Case Study Review of past records to determine CPP-Case Study Benefits Cost savings 109 CASE STUDY 110

56 Case Study Framework This case study is a real process that has been qualified to US, EU and PIC/S standards Applies the principles of QbD to demonstrate process understanding and process control 111 Pharmatech s Technology Transfer Roadmap Product Design Establish PAR/NOR Process Understanding CPPs/Risk Assessment Historical Performance Equipment Design Process Reproducibility PPQ Point... Prerequisites Point... Point... Point... PPQ Point Point Risk Point Point Assessment Verification Process Monitoring Continuous Improvement Risk Assessment Verification Characterization Studies Change Control and Stage 3 Recommendation 112

57 Case Study Application 113 Lexicon Critical Process Parameter (CPP): A process parameter whose variability, within defined limits, has an impact on a critical quality attribute and therefore should be monitored or controlled to ensure the process produces the final drug product quality Critical Quality Attribute (CQA): A physical, chemical or microbiological property or characteristic that should be within a predetermined range, range or distribution to ensure the desired final product drug quality Critical To Quality Attribute (CTQ): An in-process output parameter that is measured and/or controlled that should be within a predetermined range, range or distribution to ensure the desired final product drug quality 114

58 Stage 1 Process Understanding Product Design Process Risk Assessment Equipment/Process Characterization Studies Sampling Plans Sampling Techniques Method Robustness Design Space Establishment Validation Master Plan 115 Product Design Why go back to product design? Understand what is important: Product Requirement Specification (PRS) Have solid grasp of formulation and product design rationale Formulation may provide insight as to which processing steps are critical downstream Rationale for product design helps define how the formulation, raw materials and process steps are related to achieving desired product performance 116

59 Key QTPP PRS Specifications Key criteria from the PRS include: Greater than 50 percent Active Pharmaceutical Ingredient (API) Round 200 mg tablet Coated to mask taste 12-hour drug release with the following specifications: 4 hour dissolution percent 8 hour dissolution percent 117 Raw Material and API Considerations Consider existing qualified Suppliers when choosing excipients Includes a review of products with similar PRS requirements Foundation for Knowledge Management effort API characterization includes Supply Chain and Quality Engineering feedback from current products 118

60 Tablet Formulation Raw Material %w/w Function API 60 Active ingredient Microcrystalline cellulose 22 Excipient filler Povidone K Granulation binder Lactose 12 Excipient filler Mg Stearate 1 Lubricant Purified water QS Solvent Coating Solution Raw Material %w/w Function Eudragit Coating Solution 12 Controlled release polymer Triethyl Citrate 1 Plasticiser Talc 1.5 Glidant Water QS Solvent 119 Process Risk Assessment Helps identify which processing steps could affect process stability in Stage 2 Process map to capture inputs, outputs, and control variables Process FMEA s to prioritize key process steps and KPIV s Critical to Quality Attributes(CTQs) identified Helps focus characterization studies 120

61 Risk Assessment Process Map Identifyformulation drivenprsrequirements Establishboundariesfor theprocesssteprisk assessment Identifycontrolled/ uncontrolledvariables Establishbasic measurementapproach Separatebetweenscale independentand dependentvariables Conductriskmap Reviewdevelopmentdata Analyzehistorical performancetoset acceptancecriteria DevelopProcessMap Identify CPP/CTQ/CQAs Development/ Historical DataGapAnalysis 121 Process Unit Operation Risk Assessment CQA Process Steps Granulation Drying Milling Blending Compression Coating Appearance Low Low Low Low Medium High Assay Low Low Low Medium Low Low Impurity Low Low Low Low Low Low Blend Uniformity Low Low Medium High High Low Drug Release Low Low Low Medium Medium High Particle Size Medium Low High Low Low Low Distribution Justifications for High Rating N/A N/A Milling screen size and speed can affect the PSD and therefore the powder flow and tablet fill weight control Blending can affect blend uniformity, assay, and drug release profile Compression can affect drug uniformity in the tablet based upon particle size variability and flow The final appearance and drug release rate are affected by the coating quality and reproducibility 122

62 Relationship Between Proven Acceptable Range and Normal Operating Range Max Set Point Run(s) Target Set Point Variability of actual data around set point PAR NOR Min Set Point Run(s) Limit of individual excursions Duration of process 123 Historical Analysis The absence of development data establishing the PAR and NOR for the CPP can be ascertained to some extent by evaluating the historical behavior of each parameter along with the corresponding behavior of the CQAs for the unit operation Data should be extracted from multiple batch records to determine whether the process is stable within lot and between lots The team went back into the batch records of approximately 30 lots across a period of one year to extract the necessary data. This exercise also gave some indication as to whether the parameter was truly a CPP, based upon whether it had an impact on the corresponding CQA for the unit operation Evaluate scale independent and scale dependent parameters 124

63 Control Charts 125 Process Capability Analysis 126

64 I Chart of PSD 127 Correlation Plot 128

65 Equipment Design Considerations Compare underlying equipment design and configuration differences Focus on impact of equipment design on scale dependent parameters Objective during transfer and scale-up is to understand where equipment can affect the PAR And NOR for the transferred process Also consider final PV considerations such as sampling plans, sampling technique, and method robustness 129 Historical data Review Conclusion Dissolution testing of uncoated tablets across the process range were 100% dissolved in 3 hours Storage studies determined bulk granulation and uncoated tablets were sensitive to humidity Operating characteristic (OC) curves developed for each unit operation to understand the relationship between sampling size and sampling risk (AQL vs. LTPD) Highlight sampling challenges prior to design space activity 130

66 Tech Transfer Equipment Comparison 131 Unit Operation CPP CTQ CQA Compounding Mixing speed Fully Dissolved- Visual Water temperature Addition rate Fluid Bed Granulation/Drying Spray Rate Granulation PSDd 10, d 50, d 90 Content Uniformity Inlet Air Humidity Moisture content Potency Atomization pressure LOD Bulk/Tapped Bulk Density Milling Screen size PSD Blending Mixing Speed Content Uniformity Mixing Time Potency-Assay Compression Pre-compression force Tablet Thickness Dissolution profile Compression force Tablet Weight Content Uniformity Tablet Hardness Potency-Assay Friability Coating Spray Rate Percent Weight Gain Atomization Air Pressure Inlet Air Temperature Appearance Dissolution Percentage at 4 and 8 hours Potency-Assay Summary of CPP/CTQ and CQA Assumption for Tech Transfer 132

67 Tech Transfer-Sampling Qualification Sampling Methodology Qualification Gage R&R conducted with sampling equipment for each unit operation. GRR< 20%, Distinct Categories > 5 Sampling Plan Development Could use ANSI Z or Zero-Acceptance Plan. Used Power calculation, e.g. Powered at 80% with 5% as significant difference for a known SD 133 Tech Transfer Characterization Study Historical review concluded final product CQA for dissolution is not affected by upstream process before coating Confirmation DOEs are required to establish PAR and NOR upstream with a focus on process predictability Coating process DOE s designed to demonstrate comparability, confirm CPP s, and provide supportive data for PAR and NOR Also included commercial studies, e.g. solution hold time, pan load studies, etc. 134

68 Drug Dissolution Dependence on Coating Weight 135 Validation Master Plan Summarizes the rationale for Process performance Qualification CPPs, CTQs and CQAs Summarizes the impact of controlled variables Introduces approach for understanding impact of uncontrollable parameters Justifies sampling plan based upon process risk Defines acceptance criteria based upon product CQA s 136

69 Stage 2- Process Qualification Demonstration phase of the PV cycle Precursors to this stage Facility and utilities that support the process must be in state of control Process equipment must be qualified (i.e. IQ, OQ, PQs are complete) In-process and release methods used for testing must be validated and their accuracy and precision well understood Cleaning validation is complete Essential to have precursors completed to ensure unknown variability is due to process alone 137 Stage 2 Process Qualification (cont.) New term: Process Performance Qualification (PPQ) Intended to include all known variables from the manufacturing process Focused on demonstrating reproducibility. This drives the acceptance criteria Cumulative understanding of Stage 1 and Stage 2 No more three lots and we re done Performed as many lots needed to demonstrate a clear understanding of variables and process is in control Data derived from studies will be used to measure manufacturing process in Stage 3 138

70 Establishing Acceptance Criteria Based upon reproducibility criteria For example if the Stage 1 performance for the 4 hr. dissolution was 32% 2% against a specification of 20-40%: Acceptance criteria could be: 95% confidence interval applied to a spec of 32 6% Used a 2 sided t-test with an = 0.05 (0.025 on the H A for < comparison) We used the 6% because it is 3 x std. dev. In a normal distribution this covers 99.7 of the data variability for a controlled process 139 Why Can t I Just Compare My Result Against the Acceptance Limits? We did not know the true mean and standard deviation of the population That is the premise behind the t-test. If we knew it we would use the z- test We only knew the behavior of our sample population and we must infer that the process population behaves the same. That is why we apply the confidence interval to the assessment and apply the alternative hypothesis to test if the variability and mean is within what has historically seen 140

71 Agenda What is QbD? Why it has become important? What companies need to know, overview How to set up a team to develop QbD Process understanding Knowledge space Design space Required Statistical processes Development of acceptable operation range Practical application of the ideas- Case Study Review of past records to determine CPP-Case Study Benefits Cost savings 141 Benefits Improved new product development capability and flexibility Reduced quality overhead and reduced quality issues Greater productivity and predictability of the process and overall business operations Ability to correct for process drift without impacting quality or yield Access to larger profitable pharmaceutical markets 142

72 Cost of Poor Quality (COPQ) COPQ is derived from the non-value adding activities of waste in a process and is made up of costs associated with one of the following five categories: 1. Internal failure 2. External failure 3. Appraisal 4. Prevention 5. Lost Opportunity Reference; Basu and Wright, Quality Beyond Six Sigma COPQ Components 144 Reference: Wild 2002

73 Cost Savings Examples Generic drug could not be made consistently. Off market for 1 year, Applying QbD principles over 6 weeks restored $200 million revenue stream Applying QbD to a platform drug product reduced the number of non-conformance reports by 75%saving nearly $1million/annually 145 Conclusion The principles of Quality by Design have been proven in multiple industries including pharmaceutical Pursuing Quality by Design does not require additional capital or overhead. Just good science The business benefits of improved control and greater productivity provide for amore stable and predictable business operation 146

74 Questions? 147 Thank You for Your Attention! Bikash Chatterjee, President & CTO Pharmatech Associates, Inc Foothill Blvd. #330 Hayward CA Or visit our website at: 148