Use Statistical Process Control (SPC) as a Tool of Understanding and Managing Variability. Jane Weitzel Independent Consultant

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1 Use Statistical Process Control (SPC) as a Tool of Understanding and Managing Variability Jane Weitzel mljweitzel@msn.com Independent Consultant

2 PHARMAQUAL 360º February 22 24, 2017 Orlando, FL Jane Weitzel

3 Jane Weitzel Biosketch Jane Weitzel has been working in analytical chemistry for over 40 years for pharmaceutical and other companies with the last 5 years at the director/associate director level. She is currently a consultant, auditor, and educator. Jane has applied Quality Systems and statistical techniques, including the estimation and use of measurement uncertainty, in a wide variety of technical and scientific businesses. She has obtained the American Society for Quality Certification for both Quality Engineer and Quality Manager. Jane has assisted laboratories with implementing the Lifecycle Approach to Analytical Procedures For the cycle, Jane is a member of the USP Statistics Expert Committee, Expert Panel on Method Validation and Verification, and Expert Panel on General Chapter <11>. mljweitzel@msn.com 3

4 Disclaimer This presentation reflects the speaker s perspective on this topic and does not necessarily represent the views of USP or any other organization. mljweitzel@msn.com 4

5 Topics Introduction to Lifecycle Analytical Target Profile (ATP) & Decision Rules (DR) &Target Measurement Uncertainty (TMU) Continued Process Verification Risk What risks are How to Identify and Assess Their Impact Analytical Control Strategy (ACS) Control Charts When are they appropriate? Different types of Control Charts & their use 5

6 Interactive Exercise Participants use risk tools to perform a risk analysis and design a risk management for a procedure. mljweitzel@msn.com 6

7 Lifcycle is Fantastic The lifecycle approach to analytical procedures addresses problems industry we have all struggled with. That is why it is fantastic. Do we release the lot? Is the variability acceptable? We know the lot is OK, but how do we show that? 7

8 Example - Do you release the lot? A lot of drug substance is ready to be released. Specification is 90.0 to 110.0% Value is 95.7%. Do you release the lot? mljweitzel@msn.com 8

9 Example Release of a Lot Today? The lot can be released because the chance of it being Out Of Specification, OOS, is low. Potency is 95.7% mljweitzel@msn.com 9

10 Now we can answer the following questions with numbers What is significant? What is critical? When is a control needed? When is a control not needed? What is good enough? mljweitzel@msn.com 10

11 USP Stimuli Articles Proposed New USP General Chapter: The Analytical Procedure Lifecycle <1220>;USP PF 42(6) Fitness for Use: Decision Rules and Target Measurement Uncertainty; USP PF 42(2) Analytical Target Profile: Structure and Application Throughout the Analytical Lifecycle; USP PF 42(5) Analytical Control Strategy; USP 42(5) Proposed new USP General Chapter <1210> Statistical Tools for Method Validation; USP PF 42(5) Proposed New USP General Chapter: The Analytical Procedure Lifecycle 1220 USP PF 43(1) USP.ORG Register once at no cost 11

12 References Analytical Methods and control Strategies; The Forgotten Interface?, Phil Borman, Matt Popkin, Nicola Oxby, Marion Chatfield, David Elder, Phar Outsourcing, January/February 2015;16(1) Using the Guard Band to Determine a Risk-Based Specification, Christopher Burgess, Pharmaceutical Technology, October 1, 2014 M. Schweitzer, M. Pohl et al.: QbD Analytics. Implications and Opportunities of Applying QbD Principles to Analytical Measurements, Pharmaceutical Technology, Feb. 2010, Number of articles in IVT & GXP publications mljweitzel@msn.com 12

13 More References Setting and Using Target Measurement Uncertainty; References regarding misclassification: Confidence intervals for misclassification rates in a gauge R&R study; Burdick RK, Park Y-J, Montgomery DC, Borror CM..J Qual Tech. 2005;37(4): Design and Analysis of Gauge R&R Studies; Making Decisions with Confidence Intervals in Random and Mixed ANOVA Models.; Burdick RK, Borror CM, Montgomery DC.; ASA-SIAM Series on Statistics and Applied Probability, SIAM, Philadelphia, ASA, Alexandria, VA, mljweitzel@msn.com 13

14 Coming Q12 Technical and Regulatory Considerations for Pharmaceutical Product Lifecycle Management Include analytical procedures 14

15 Scientifically Sound and Appropriate Where do these concepts fit in? CFR has always required use of sound science Decision rules, measurement uncertainty, risk and probability have been used in many scientific areas for many years. We can leverage this experience to more effectively meet cgmp requirements. 15

16 Sound Science Metrological approach to measurements Measurement uncertainty Target measurement uncertainty Completely characterises the variability 16

17 Measurement Uncertainty publications/guides/vim. html Euarachem.org QUAM2012:P1 I will call QUAM non-negative parameter characterizing the dispersion of the quantity values being attributed to a measurand, based on the information used (VIM III 2.2.6) EURACHEM CITAC Guide - Quantifying Uncertainty In Analytical Measurements mljweitzel@msn.com 17

18 Process to Estimate MU 1 Identify the measurand (set up the final concentration calculation equation) 2 List the steps in the analytical process 3 Identify potential sources of random variability in each step uncertainty components 4 Design a process that permits an estimate of each source of random variability or of a group of sources or look for the data 5 Combine the different estimates of random variability to get the overall uncertainty estimate A well designed robustness DOE is a good start mljweitzel@msn.com 18

19 Statistics and Validation For example, book by Lynn Torbeck The agency does not provide specific prescriptions on how to meet requirements. We need to use: Good science Metrology Statistical Tools 19

20 INTRODUCTION TO LIFECYCLE 20

21 Lifecycle Uses Quality by Design ICH Q8 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. (Process understanding- The recollection and comprehension of process knowledge such that process performance can be explained logically and/or scientifically as a function of process parameters/inputs.) mljweitzel@msn.com 21

22 What is Quality by Design Understand your desired output Understand your inputs Understand the relationship between inputs and outputs Control your inputs to the degree required to assure you achieve your designed outputs Person Reportable Value 22

23 Why apply QbD to Analytical Procedures? FDA Manufacturing Science White Paper - Innovation and Continuous Improvement in Pharmaceutical Manufacturing Variability and/or uncertainty in a measurement system can pose significant challenges when OOS results are observed. Measurement system variability can be a significant part of total variability. Similar and repeating OOS observations for different products across the industry and a less than optimal understanding of variability Continuous improvement is difficult, if not impossible. mljweitzel@msn.com 23

24 Why apply QbD to Analytical Procedures? Extensive deployment of lean and six sigma methodologies Increasing adoption of Quality by Design approaches to process development (focus on science and risk based strategies) 24

25 Why apply QbD to Analytical Procedures? Focus has been on compliance rather than science ICHQ2 often applied in the laboratory in a checkbox manner without the effect of the validation parameter on the fitness for purpose of the procedure being thoroughly understood mljweitzel@msn.com 25

26 QbD for methods 1. Define desired method performance 2. Ensure chosen method is designed to meet this requirement and is aligned with first intents where possible 3. Systematically identify all potential method input variables 4. Based on risk determine what experimentation is needed to understand how variation in inputs could effect outputs 5. Ensure controls defined in the method are based on this understanding 6. Maintain and use this understanding through the lifecycle. 26

27 Three Stage Approach to Analytical Lifecycle Stage 1 Procedure Design and Development Stage 2 Procedure Performance Qualification Stage 3 Continued Procedure Performance Verification Risk assessment Knowledge management Analytical Control Strategy Changes mljweitzel@msn.com 27

28 Stage 3 Continued Procedure Performance Verification To provide ongoing assurance that the analytical procedure remains in a state of control throughout its lifecycle Routine Monitoring: an ongoing program to collect and process data that relate to method performance, e.g. from analysis / replication of samples or standards during batch analysis by trending system suitability data by assessing precision from stability studies [J. Ermer et al.: J. Pharm. Biomed. Anal. 38/4 (2005) ] mljweitzel@msn.com 28

29 Continual Improvements (Changes) Risk assessment to evaluate Impact of the respective change Required actions to demonstrate (continued) appropriate performance Feedback loop As needed, return to Stage 2 or Stage 1 ATP establishes criteria for acceptability mljweitzel@msn.com 29

30 Risk Assessment Design of experiments (DOE) is a fundamental methodology for the QRM process. It is a systematic method to determine the relationships between variables affecting a process, and it is used to find cause-and-effect relationships Understand the procedure variables and their impact on the reportable value Detect presence and degree of variation Understand the impact of variation on the analytical procedure performance and ultimately on data attributes mljweitzel@msn.com 30

31 Report and Manage Post Marketing Changes to an Approved NDA, ANDA and BLA Lifecycle approach provides a structure, a language and techniques to evaluate, manage and report changes These will be understood by industry and regulatory bodies All understand the probability, the risk, the evaluations Understand how they are identified, evaluated and managed mljweitzel@msn.com 31

32 ANALYTICAL TARGET PROFILE 32

33 Wording for ATP Assay The procedure must be able to quantify the analyte in presence of (X, Y, Z) over a range of A% to B% of the nominal concentration with an accuracy and uncertainty so that the reportable result falls within ±C% of the true value with at least P% probability. Target The variables in orange are Measurement specific for each reportable result. Uncertainty (TMU) mljweitzel@msn.com 33

34 TMU and ATP The target measurement uncertainty becomes part of the analytical target profile. The TMU defines the acceptance criteria for the method. Remember, the uncertainty includes all random effects (including the uncertainty of the bias). 34

35 Analytical Target Profile (ATP) A predefined objective that states the performance requirements for the analytical procedure The output of the procedure is a reportable result that must be fit for its purpose. Applies throughout the life of the analytical procedure, including stage 3 Report and manage post marketing changes to an approved NDA, ANDA and BLA mljweitzel@msn.com 35

36 DECISION RULES AND TARGET MEASUREMENT UNCERTAINTY (TMU) 36

37 Intended Use can be Linked to Clinical Requirement 37

38 Intended Use can be Linked to Clinical Requirement - MU MU mljweitzel@msn.com 38

39 Used to explain fitness for intended purpose as part of change control Decision rules and their relevance to analytical procedure qualification will be presented DECISION RULES 39

40 What is the role manufacturing and clinical play in defining that use? Fitness for intended use needs to be known Decision rules proved that link Through probability 40

41 Types of Decisions What is the drug concentration in the blood (during a clinical study)? Does this batch of drug product meet specification for potency? Does this lot of drug substance meet specification for impurity A? Does this in-process solution have correct concentration, e.g. for excipient concentration? Is this environmental monitoring sample in specification? mljweitzel@msn.com 41

42 During change control and reporting to regulatory bodies, it is clear the user of the data is involved. Ensuring the reportable result is fit for use Purpose of a decision Rule HOW THE USER OF THE DATA IS KEY TO DEFINING THE DECISION RULE mljweitzel@msn.com 42

43 The USER Develops the Decision Rule For an example, consider the case for a brand new measurement The best source for the prescription of the decision rule is the person/organization that will use the output of the analytical procedure Can be one person (the expert) or a group of people (clinical studies, production, stability) The group can include management (financial risks) Called Decision Makers in ICH Q8 mljweitzel@msn.com 43

44 Analytical may develop decision rule If the end user of the data is not available E.g. often the case for a commercial contract laboratory The laboratory can create a decision rule to assist with ensuring its test results are suitable. This is especially useful for testing according to USP monographs. The approach provides a language for communication. mljweitzel@msn.com 44

45 Decision Rule A documented rule... that describes how measurement uncertainty will be allocated with regard to accepting or rejecting a product according to its specification and the result of a measurement. ASME B (reaffirmed 2006) mljweitzel@msn.com 45

46 Decision Rules References 46

47 Decision Rule Acceptance or Rejection Decision rules give a prescription for the acceptance or rejection of a product based on the measurement result, its uncertainty and the specification limit or limits, taking into account the acceptable level of the probability of making a wrong decision. E X C E L mljweitzel@msn.com 47

48 Meaning of Product The term product in the decision rule definition refers to the whatever is tested. Does not mean drug product lot only. Could be: In-process sample (buffer solution) Lot of drug substance Batch of drug product Lot of excipient Environmental monitoring sample 48

49 Why Use Decision Rules Decision rules clearly state the intended use of the procedure Risk and probability are used to develop the decision rule This means there is a defined process to define the intended use of the procedure Risk and Probability are consistent with QbD A guard band can be created using the uncertainty mljweitzel@msn.com 49

50 Decision Rule To decide whether a result indicates compliance or non-compliance with a specification, it is necessary to take into account the measurement uncertainty. Upper Limit 1 Result is above the limit. Limit is below expanded uncertainty. 2 Result above the limit. Limit is within the expanded uncertainty. 3 Result is below the limit. Limit is within the expanded uncertainty. 4 Result is below the limit. Limit is above expanded uncertainty. mljweitzel@msn.com 50

51 Decision Rule To decide whether a result indicates compliance or non-compliance with a specification, it is necessary to take into account the measurement uncertainty. Upper Limit How much overlap is acceptable? That is the acceptable probability of making a wrong decision. 1 Result is above the limit. Limit is below expanded uncertainty. 2 Result above the limit. Limit is within the expanded uncertainty. 3 Result is below the limit. Limit is within the expanded uncertainty. 4 Result is below the limit. Limit is above expanded uncertainty. mljweitzel@msn.com 51

52 Decision Rules Require 4 Components Decision rules give a prescription for the acceptance or rejection of a product based on 1. the measurement result, 2. its uncertainty and 3. the specification limit or limits, 4. taking into account the acceptable level of the probability of making a wrong decision. mljweitzel@msn.com 52

53 Example Decision Rule The lot of drug substance will be considered compliant with the specification of 95.0% to 105.0% if the probability of being above the upper limit is less than 2.5% and below the lower limit is less than 2.5%. Lower Limit 95 Nominal Concentration (Central Value) 100 Upper Limit 105 Measurement Uncertainty 2.5 % Below Lower Limit Total % Outside Limits % Above Upper Limit 2.28% 4.55% 2.28% Concentration LL UL mljweitzel@msn.com 53

54 Setting TMU This document discusses how to set a maximum admissible uncertainty, defined in the third edition of the International Vocabulary of Metrology as the target uncertainty, to check whether measurement quality quantified by the measurement uncertainty is fit for the intended purpose. Eurachem.org mljweitzel@msn.com 54

55 Stage 1 Procedure Design and Development Stage 2 Procedure Performance Qualification Stage 3 Continued Procedure Performance Verification Changes Risk assessment Knowledge management Analytical Control Strategy CONTINUED PROCESS VERIFICATION mljweitzel@msn.com 55

56 Analytical Control Strategy (ACS) the ACS is a planned set of controls, derived from understanding the requirements for fitness for purpose of the reportable value, the understanding of the analytical procedure as a process, and the management of risk, that assures the performance of the procedure and the quality of the reportable value, in alignment with the ATP, on ongoing basis mljweitzel@msn.com 56

57 TMU The role of the Analytical Control Strategy is to ensure that the TMU is met on consistent basis over the entire lifecycle of the analytical procedure therefore the reportable value conforms to the ATP. 57

58 Development of ACS The development of the Analytical Control Strategy requires consideration of all aspects of an analytical procedure that might impact the reportable value. A unit operation is any part of potentially multiple-step process which can be considered to have a single function with clearly defined boundary. For an analytical procedure three distinct unit operations can be identified. mljweitzel@msn.com 58

59 Replication Strategy USP General Notices states (7. TEST RESULTS Interpretation of Requirements) The reportable value, which often is a summary value for several individual determinations, is compared with the acceptance criteria. The reportable value is the end result of a completed measurement procedure, as documented. See Appendix in USP stim article on ACS PF 42(5) sem =s/ n Sem = standard error of the mean s = standard deviation for a single value n = number of values averaged mljweitzel@msn.com 59

60 Examples of Control Strategy (Operational Control) Specific instructions in procedure Strict control of time or temperature Training Specifying grades of materials System suitability Control charts 60

61 What they are How to Identify and Assess Their Impact RISK 61

62 Harm Hazard Risk Hazard: The potential source of harm (ISO/IEC Guide 51). Harm: Damage to health, including the damage that can occur from loss of product quality or availability. Risk: The combination of the probability of occurrence of harm and the severity of that harm (ISO/IEC Guide 51). Definitions from ICH Q9 62

63 Hazard Harm Risk Hazard Harm Risk 63

64 Risk Hazard - aspect of an analytical procedure that might impact the reportable value Harm how can it impact the Critical Quality Attributes (CQA) of the reportable value Eg. bias and uncertainty (accuracy & precision) Risk is a variable that has significant impact on bias and uncertainty mljweitzel@msn.com 64

65 Quality Risk Management The QRM for an analytical procedure is a systematic process for the assessment, control, communication and review of risk to the quality of the reportable value across the analytical procedure lifecycle. the risk refers the quality of the reportable value, which is the product of the analytical procedure Concentration LL UL mljweitzel@msn.com 65

66 Risk Risk is a combination of ACS Probability Severity Detectability Steps to eliminate risk or control risk Severity cannot change Reduce probability Increase detectability mljweitzel@msn.com 66

67 Temperature of column Concentration Time of extraction U, k=2 Analyst s Knowledge B i a s mljweitzel@msn.com 67

68 Temperature of column Concentration Time of extraction U, k= Analyst s Knowledge B i a s mljweitzel@msn.com 68

69 Temperature of column Concentration Time of extraction U, k= Analyst s Knowledge B i a s mljweitzel@msn.com 69

70 Temperature of column Concentration Time of extraction Has impact & needs control Restrict temp to narrow range U, k= Analyst s Knowledge B i a s mljweitzel@msn.com 70

71 Temperature of column No Risk No control Concentration Time of extraction Has impact & needs control Restrict temp to narrow range U, k=2 Analyst s Knowledge B i a s mljweitzel@msn.com 71

72 Risk Assessment 1. Risk identification 1. What might go wrong? (hazard & harm) 2. Risk analysis 1. Estimate of the risk 2. What is the likelihood (probability) it will go wrong? 3. Risk evaluation 1. What are the consequences (severity)? 72

73 USP ACS PF42(5) article Provides a useful example for HPLC mljweitzel@msn.com 73

74 Use of Uncertainty in Risk Analysis The Eurachem Guide Quantifying Uncertainty (QUAM) is valuable for providing a QRM process directly for analytical procedures. mljweitzel@msn.com 74

75 Measurand Quantity intended to be measured (VIM 2.3) Unambiguous and detailed description Includes analyte Eurachem Guide Terminology in Analytical Measurement (1.11) The measurand definition helps identify hazards in the risk analysis 75

76 Estimate uncertainty 1. Specify Measurand 2. Identify u sources 3. Group sources 4. Quantify groups 5. Quantify ungrouped 6. Convert to standard deviation 7. Combine u mljweitzel@msn.com 76

77 Compare u to TMU By comparing the estimated uncertainty to the TMU, you can demonstrate if the analytical procedure is fit for intended purpose AND You can know which uncertainty components will have an impact on the reportable value and which will not These are the risks and they are quantified mljweitzel@msn.com 77

78 Potential u sources (risks) QUAM, Section 6, deals with Identifying Uncertainty Sources Start with the formula for calculating the reportable value Uses Cause and Effect diagram mljweitzel@msn.com 78

79 Lists potential u sources Sampling Storage Instrument effects Reagent Purity Assumed stochiometry Measurement conditions Sample effects Computational effects Blank correction Operator effects Random effects Note how some of these are unique to analytical procedures mljweitzel@msn.com 79

80 The u is used to determine impact Risk includes assessment of impact When is impact significant? u and decision rules can determine that mljweitzel@msn.com 80

81 Use u to evaluate the risk 1. Risk evaluation 1. What are the consequences (severity)? QUAM section 7, Quantifying the uncertainty Evaluate the uncertainty for each individual source (examples A1 to A3 in QUAM) Determine directly the combined contribution to uncertainty using method performance data This is your analytical procedure development and qualification experimental values Eg. Repeatability, robustness, intermediate precision, LOD study mljweitzel@msn.com 81

82 Bias Study Risk to the reportable value includes bias QUAM section deals with bias studies Use of Reference Materials Comparison to another method Spikes, etc. Picture when there is no bias When is bias significant? Lower Limit 90 Nominal Concentration (Central Value) 100 Upper Limit 110 Measurement Uncertainty 2 % Below Lower Limit Total % Outside Limits % Above Upper Limit 0.00% 0.00% 0.00% Concentration LL UL mljweitzel@msn.com 82

83 Bias is significant Bias is significant because % Below the limit, as set by decision rule, is not acceptable (Return to method development & eliminate bias is best) If bias cannot be eliminated, some form of control strategy is required Eg/ bias is caused by interferent; control that interferent Lower Limit 90 Nominal Concentration (Central Value) 93 Upper Limit 110 Measurement Uncertainty 2 % Below Lower Limit Total % Outside Limits % Above Upper Limit 6.68% 6.68% 0.00% Concentration LL UL mljweitzel@msn.com 83

84 When is u significant If : the TMU is not met requirements of decision rule are not met (%outside the limits > than DR) Reduce u Use ACS Lower Limit 90 Nominal Concentration (Central Value) 100 Upper Limit 110 Measurement Uncertainty 5 % Below Lower Limit Total % Outside Limits % Above Upper Limit 2.28% 4.55% 2.28% Concentration LL UL mljweitzel@msn.com 84

85 Eg/ Use replication u = 5 Use Duplicates u = 5/ 2 = 3.5 Lower Limit 90 Nominal Concentration (Central Value) 100 Upper Limit 110 Measurement Uncertainty 3.5 % Below Lower Limit Total % Outside Limits % Above Upper Limit 0.21% 0.43% 0.21% Duplicates can be control charted! Concentration LL UL mljweitzel@msn.com 85

86 Control Charts When are they appropriate? Different types of Control Charts & their use ANALYTICAL CONTROL STRATEGY (ACS) 86

87 ACS Many types of controls Focus on control charts 87

88 CONTROL CHARTS 88

89 ICH Q9 list of control charts I.9 Supporting Statistical Tools A listing of some of the principal statistical tools commonly used in the pharmaceutical industry is provided: Control Charts, for example: Acceptance Control Charts (see ISO 7966); Control Charts with Arithmetic Average and Warning Limits (see ISO 7873); Cumulative Sum Charts (see ISO 7871); Shewhart Control Charts (see ISO 8258); Weighted Moving Average. mljweitzel@msn.com 89

90 ASQ.ORG is a great resource mljweitzel@msn.com 90

91 Control Charts and Statistical Process Control - References FDA Field Science and Laboratories describe control charts in their SOP online at: nceresearch/fieldscie nce/laboratorymanual/ ucm htm mljweitzel@msn.com 91

92 Control Charts Used to determine and demonstrate the measurement system is still in control The chart allows you to distinguish patterns The chart graphically displays the data and compares it to an average or expected value and an expected range. Common practice uses warning limits at ± 2 standard deviations and control limits at ± 3 standard deviations. A normal distribution allows us to calculate the probability of getting a result above the warning or control limits. mljweitzel@msn.com 92

93 Control Charts Control charts also allow you to detect trends, such as more random variability or a gradual downward trend. Some types of charts are Mean Chart, Shewhart Chart, Range Chart Based on the probabilities, rules for assessing and reacting to trends can be used. There are several types of rules, select yours and follow them. mljweitzel@msn.com 93

94 Construction Of Shewhart Control Chart (1) Ensure measurement process is in statistical control Include reference material in measurement runs (early in method) Collect reference sample data from a minimum of 20 results from routine runs mljweitzel@msn.com 94

95 Construction Of Shewhart Control Chart (2) Calculate mean and standard deviation (s) of at least 20 reference sample results collected Construct graph with lines at mean, +/- 2s (UWL & LWL) and at +/- 3s (UCL & LCL) Plot subsequent reference material results as they are obtained 95

96 Normal Distribution Curve Basis for Chart Percentage of Results Between z Factors for a Normal Distribution 99.7% 95% 68% z mljweitzel@msn.com 96

97 Normal Distribution Curve Basis for Chart Percentage of Results Between z Factors for a Normal Distribution 99.7% 95% 68% z UCL UWL LWL LCL mljweitzel@msn.com 97

98 Control Charts Are For The Analyst The analyst uses the control chart as part of the checks to confirm the method performed as expected and there are no trends to investigate. 98

99 Shewhart Control Chart Rules (1) No more than 5% (1 in 20) of the values should be outside the UWL and LWL. No points should fall outside the UCL and LCL. 2 successive points outside the UWL and/or the LWL signifies a possible loss of control. mljweitzel@msn.com 99

100 Value Shewhart Control Chart Rules (2) More than 4 successive points on one side or the other of the mean signifies a drift or a bias. Control Chart In-House Quality Conrol Standard Run Average LCL In House Value UCL mljweitzel@msn.com 100

101 Value Shewhart Control Chart Rules (3) A sudden increase in variation of values about the mean signifies a loss of precision. Control Chart In-House Quality Conrol Standard Run Average LCL In House Value UCL mljweitzel@msn.com 101

102 Control Chart and Bias If the control chart uses the data from measuring a Certified Reference Material: You can add the Certified Reference Value (CRV) to the control chart to demonstrate the accuracy. Is there a bias? Does the central value in the control chart agree with the CRV? mljweitzel@msn.com 102

103 Value Control Chart for a Reference Material Control Chart In-House Quality Conrol Standard Run Average LCL In House Value UCL mljweitzel@msn.com 103

104 Do You Have A Bias! mljweitzel@msn.com 104

105 Does Bias Exist? Remember that the Certified Reference Value for a Certified Reference Material has an uncertainty associated with it. Let s look at a chart to see how that uncertainty can be used. mljweitzel@msn.com 105

106 Does a Bias Exist? Reference Value The lab used EXCEL. They plotted the values they obtained for the reference material. mljweitzel@msn.com 106

107 Do You Have A Bias! mljweitzel@msn.com 107

108 Does a Bias Exist? Reference Value Uncertainty of reference value The lab used EXCEL. They plotted the values they obtained for the reference material. mljweitzel@msn.com 108

109 Example - Do you release the lot? A lot of drug substance is ready to be released. Specification is 90.0 to 110.0% Value is 93.7%. Do you release the lot? mljweitzel@msn.com 109

110 Example Release of a Lot Common Usage The lot can be released because the chance of it being Out Of Specification, OOS, is low. Potency is 95.7% Statistical Usage The lot can be released because the probability of the potency being OOS is < 0.3%. Potency is 95.7% ± 4.0 % with a coverage factor of 3 for a 99.7% level of confidence mljweitzel@msn.com 110

111 Picture of Release the Lot Lower Limit Upper Limit Probability Normal Distribution Concentration 111

112 More Details - Probability Nominal Concentration (Central Value) 95.7 Target Measurement Uncertainty (Standard Deviation) 2.00 Enter the largest standard deviation which results in the acceptable Lower Limit 90.0 "Total outside limits". Upper Limit Calculates Probability % below Lower Limit Standard Deviation 2.00 % above upper limit 0.22% Total outside limits 0.22% 0.00% Lower Limit Upper Limit Uses EXCEL Concentration mljweitzel@msn.com 112

113 Participants use risk tools to perform a risk analysis and design a risk management for a stability procedure. INTERACTIVE EXERCISE mljweitzel@msn.com 113

114 Prep of a Cd Calibration Standard QUAM Example A1 A calibration standard is prepared from a high purity metal (cadmium) with a concentration of Ca.1000 mg L-1. mljweitzel@msn.com 114

115 115

116 Identify uncertainty sources 116

117 Quantification of u components mljweitzel@msn.com 117

118 Combined standard uncertainty u c Discuss what is significant What needs control strategy mljweitzel@msn.com 118

119 Conlusion Lifecycle approach covers analytical procedure completely Risk analysis ACS Control Charts 119

120 120