Understanding the Characteristics and Establishing Acceptance Criteria for Analytical Methods Validation

Transcription

1 Understanding the Characteristics and Establishing Acceptance Criteria for Analytical Methods Validation Ying Verdi IVT LAB WEEK EUROPE June 2017 Partners in Health Since 1919

2 Regulatory View of Method Characteristics Statistics Behind Method Validation Statistical Tools Establishing Acceptance Criteria Based on Method Characteristics 2

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4 Guidance for Industry: Analytical Procedures and Methods Validation for Drugs and Biologics (July 2015) ICH Q2(R1): VALIDATION OF ANALYTICAL PROCEDURES: TEXT AND METHODOLOGY ICH Q6A: Specifications (Dec 2000, Aug 1999) ICH Q3A, Q3B, ICH M7 ICH Q8 (R2): Pharmaceutical Development (Nov 2009) ICH Q9: Quality Risk Management (Jun 2006) ICH Q10: Pharmaceutical Quality System (Apr 2009) 21CFR Part (e) EMEA Guidance on Validation of Analytical Procedures: Text and Methodology 4

5 The ATP is based on the understanding of the target measurement uncertainty, which is the maximum uncertainty that the data should have in order to maintain acceptable levels of confidence in data quality - USP Stimuli articles: Lifecycle Management of Analytical Procedures: Method Development, Procedure Performance Qualification, and Procedure Performance Verification 5

6 Original Results (Analyst A) Level Prep 1 Prep 2 Prep 3 Mean (n=3) 80% % % Mean (n=9) %RSD (n=3) %RSD (n=9) Acceptance Criteria: % Route Cause: Acceptance Criteria is set too tight. Really? Repeat Results (Analyst B) Level Prep 1 Prep 2 Prep 3 Mean (n=3) %RSD (n=3) 80% % % Mean (n=9) %RSD (n=9)

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9 Validation Parameter Accuracy & Specificity run RSD (n=5) NMT 2.0% RSD (overall) NMT 2.0% Tailing NMT 2.0 Check Standard %

10 Normalized Peak %Bias from 100% Level Conc. Peak Area Area Level

11 Bivariate Fit Residual Normal Quantile Plot R 2 =

12 Sample Prep Place 20 tablets into a 100-mL vol. flask Add 50 ml diluent, sonicate 10 min Cool to room temp QS with diluent, mix Dilute 10:50 12

13 Unlikely causes of OOS results Standard prep Spiking solution Instrument precision Analyst training Acceptance criteria Likely root cause of OOS results Placebo displacement Placebo displacement study confirmed the root cause Sample preparation modification 13

14 Level Prep 1 Prep 2 Prep 3 Mean (n=3) %RSD (n=3) 80% % % Mean (n=9) %RSD (n=9)

15 Cliffs of Moher 15

16 Assay: The procedure must be able to quantify [analyst] 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 a 90% probability determined with 95% confidence USP Stimuli articles: Lifecycle Management of Analytical Procedures: Method Development, Procedure Performance Qualification, and Procedure Performance Verification 16

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18 Assay: The procedure must be able to quantify [analyst] 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 a 90% probability determined with 95% confidence Specificity Range Accuracy Precision (Repeatability, Intermediate Precision, Reproducibility) Acceptable Risk Acceptable Uncertainty 18

19 USP <1225> ICH Q2(R1) FDA Guidance Accuracy Precision Repeatability Intermediate Precision Reproducibility Specificity DL QL Linearity Range Robustness * * * Can be done during method development 19

20 There are two types of method performance characteristics o o systematic variability (bias) inherent random variability (noise) Method Performance Characteristics Accuracy, Specificity, Linearity Precision, DL, QL Range, Robustness Defined by ICH and USP System Variability Inherent Random Variability N/A 20

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22 Manufacturing Process Laboratory Sample Representative Dispensing & Weighing Homogeneity Sample Preparation Test Portion Drying & Weighing Standard Preparation Instrument Calibration Analytical Measurement Selectivity Data Output (Acquisition and Processing) Integrity Results Presentation 22

23 Error of Measurement Difference between an individual result and the true value of the measurand Types of Error Random Error Systematic Error Gross Error EURACHEM/CITAC Guide Quantifying Uncertainty in Analytical Measurement 3 rd Edition,

24 Random Error (Noise) In replicate measurements varies in an unpredictable manner Systematic Error (Bias) In replicate measurements remains constant or varies in a predictable manner Gross errors Only abandonment of the experiment and a fresh start is an adequate cure 24

25 A parameter associated with the result of a measurement, that characterizes the dispersion of the values that could reasonably be attributed to the measurand EURACHEM/CITAC Guide Quantifying Uncertainty in Analytical Measurement 3 rd Edition,

26 Error A single value Uncertainty A range or interval The value of a known error can be applied as a correction to the result The value of the uncertainty cannot be used to correct a measurement result 26

27 S.D. Phillips PQI / IIGD&T 8/11/

28 There are known knowns; there are things we know that we know. There are known unknowns; that is to say, there are things that we now know we don't know. But there are also unknown unknowns there are things we do not know we don't know. United States Secretary of Defense, Donald Rumsfeld 28

29 Measured Result Reality Profit Conforms to Specification Passed Inspection Loss Does not Conform to Specification Passes Inspection Type II Error Loss Conforms to Specification Fails Inspection Type I Error Loss Does not Conform to Specification Fails Inspection 29

30 Type A Method of evaluation of uncertainty by the statistical analysis of series of observations Normal distribution Type B Method of evaluation of uncertainty by means other than the statistical analysis of series of observations Rectangular distribution Triangular distribution 30

31 Distribution Rectangular (Uniform) Triangular Normal Shape Most Conservative 31

32 Define Identify Estimate Combine Process Elements Error Sources Individual Contributions Overall Uncertainty 32

33 Accurately weigh approximately 100mg of reference standard into a 250-mL volumetric flask Reference standard; Purity (99.46 ±0.25) Dissolve in water at a laboratory temperature of 20 ± 4 C Dilute to Volume and mix well 33

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35 A tolerance interval is a statistical interval within which, with some confidence level, a specified proportion of a sampled population falls. The endpoints of a tolerance interval are called tolerance limits. An application of tolerance intervals is to compare specification limits with tolerance limits. For method validation, we can also compare accuracy study acceptance criteria with tolerance limits. 35

36 Confidence limits are limits within which we expect a given population parameter, such as the mean, to lie. Statistical tolerance limits are limits within which we expect a stated proportion of the population to lie. 36

37 Solid line represents population distribution Dotted line distributions result from the uncertainty of the sample mean The difference in location of the mean due to this uncertainty is defined by the confidence interval. 37

38 ANOVA gauge R&R measures the amount of variability induced in measurements by the measurement system itself, and compares it to the total variability observed to determine the viability of the measurement system. (Wikipedia) The Gauge R&R method analyzes how much of the variability in your measurement system is due to operator variation (reproducibility) and measurement variation (repeatability). Gauge is the measurement device 38

39 Collect a random sample of parts over the entire range of part sizes from your process. Select several operators at random to measure each part several times. Method validation: Precision 39

40 Assay Result Process Variability Method Variability Accuracy (Bias) Precision Gauge R&R Repeatability Reproducibility 40

41 Analyst Spiking Level Prep 1 Prep 2 Prep 3 Prep % % % % % % % % %

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45 Which one contributed more variability to the results, analyst or method itself? 45

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47 Based on combination of the following Intended Use Customer requirements Historical data Risk tolerance Statistical analysis Specified in protocol 47

48 Raw Material Attributes Manufacturing Process Variability Sampling Variability Test Method Variability Test Result Storage Conditions & Duration Before method validation, ask yourself this question: How much variability can I have for my method? 48

49 Product Release Specification Method Valuation Method Precision Instrument Precision Standard and Sample Preparation 49

50 USP <621> SYSTEM SUITABILITY 50

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52 Analyst Spiking Level Prep 1 Prep 2 Prep 3 Prep % % % % % % % % %

53 Analyst 1 Analyst 3 What will be the MV Acceptance Criteria? Analyst 2 All Analysts 53

54 Response Standard Concentration 54

55 The quantitation limit of an individual analytical procedure is the lowest amount of analyte in a sample which can be quantitatively determined with suitable precision and accuracy. The quantitation limit is used particularly for the determination of impurities and/or degradation products. - ICHQ2(R1) QL Reporting Threshold 55

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57 Method Development Stage ATP Initial MD Risk Assessment DoE Method Validation Stage Pre-validation Robustness Study Method Validation Lifecycle Stage Tech. Transfer Method verification Method Improvement Design Knowledge Control 57

58 Starting with end goal in mind Identify critical method parameters, understand them and control them Set appropriate acceptance criteria for method validation to demonstrate quality performance characteristics Achieve cost-effective method and generate quality data 58

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