Usp chemical medicines & excipients - evolution of validation practices


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12th USP Science & Standards Symposium - New Delhi

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Usp chemical medicines & excipients - evolution of validation practices

  1. 1. Track I, Session I: Chemical Medicines and Excipients-Evolution of Validation Practices Wednesday, April 17, 2013 (9:00 a.m. to 11:00 a.m.) IPC–USP Science & Standards Symposium Partnering Globally for 21st Century Medicines
  2. 2. Moderator: Milind Joshi, Ph.D. Chair, USP South Asia Stakeholder Forum
  3. 3. Acceptable Analytical Method Variation Setting System Suitability Requirements Todd L. Cecil, Ph.D. Vice President, Chemical Medicines USP
  4. 4. Method Variation  Sources – Instrument Characteristics – Sample Characteristics – Method Parameters – Environmental Affects – Analyst – Instrument Settings – Many others
  5. 5. Measuring Variation  Random Error  Systematic Error Indeterminate Error Experimental error – Determinate Error – Discoverable source (in theory) Estimated using Precision Estimated using Accuracy – –
  6. 6. Validation to Measure Variability   Developed in late 1980’s for the Pharmaceutical Industry – PhRMA -> USP <1225> -> ICH Q2A -> USP <1225> Defined “Analytical Performance Characteristics” Intermediate precision – Accuracy Repeatability – Precision Reproducibility – Specificity Ruggedness Robustness – Detection Limit – Quantification Limit Trueness Bias – Linearity – Range
  7. 7. Acceptable Variability Depends upon two factors Application Expectation  Test • Analyst Experience  Procedure • Instrument knowledge  Acceptance criteria • Matrix complexity
  8. 8. Acceptable Validation  ICH and USP do not describe acceptable limits  Therefore, Acceptable Validation/ Variation is open to interpretation by: – Bench Chemist – Supervisory Chemist – Regulatory Affairs Professional – The Regulator – The Pharmacopeial Professional – And fights ensue…
  9. 9. There is Another Way!  Recent publications – Pharma’s Analytical Target Profile (ATP) – USP’s Performance-Based Procedures  Upcoming publications – USP Validation and Verification Expert Panel – USP Statistics Expert Committee – USP “Requirements for Compendial Validation <1200>” (working title)
  10. 10. Defining Another Way Forward  Critical Validation Parameters – What are the critical features (parameters) of an acceptable procedure?  Procedure Performance Measures – How do we measure the critical parameters?  Procedure Performance Acceptance Criteria – What defines “good enough” for each performance measure?
  11. 11. Measuring the Parameters  Precision – % RSD with sufficient degrees of freedom  Accuracy – Spike Recovery or Comparison to Primary Standard  Specificity  Linearity  Range – Resolution or Spike Recovery – Slope, Intercept, R2 – Precision and accuracy  Limit of Detection – Precision  Limit of Quantification – Precision
  12. 12. Collapsing the Parameters  Precision – Measure of Random Error  Accuracy – Measure of Systematic Error  Specificity  Linearity  Range – Measure of Systematic Error – Measure of Systematic Error –?  Limit of Detection – Measure of Random Error  Limit of Quantification – Measure of Random Error  Why are we measuring things so many different ways?  Does agreement mean quality? or are we hiding behind tradition?  IF we combine critical components can we gain efficiency?
  13. 13. Extract from <1200> Category I* Analytical Performance Characteristics Accuracy Precision Specificity Detection Limit Quantification Limit Linearity Range 1 Category II* (Quantitative) Category II* (Semiquantitative) <1225> <1200> <1225> <1200> <1225> <1200> Y Y Y 1 1 2 Y Y Y 4 5 2 ? N Y N 5 2 N N N N Y 6 N N Y 4 N N Y Y 3 3 Y Y 4 4 N ? N N Covered in the Precision-Accuracy Study 2 Covered in the Specificity Study 3 Covered in the Range Study 4 Covered in Accuracy Study 5 Covered in Precision Study 6 Covered in the Detectability Study
  14. 14. Precision and Accuracy Study  When properly combined Precision and Accuracy yield a probability of passing. Bias-%CV Tradefoff, 98%-102% limits, True Value = 100, Prob'y Passing 0.95 1.2 1 %CV 0.8 0.6 0.4 0.2 0 0.00 0.20 0.40 0.60 0.80 1.00 Bias 1.20 1.40 1.60 1.80 2.00
  15. 15. Study Detail  Precision – % RSD of 6 independent samples at 100%  Accuracy – Δ from RS label at 100% – The data obtained for Precision can be used for Accuracy Combine with Acceptance Criteria to calculate probability Result = NORMDIST(Upper, Mean, SD, TRUE) -NORMDIST (Lower, Mean, SD, TRUE)  Limit: NLT 0.95
  16. 16. Specificity Study  Specificity is a special case of Accuracy.  Interferences considered  Separation Sciences – Resolution of NLT 1.5
  17. 17. Specificity Study  Non-Chromatographic Procedures are harder  Spiked samples with interferences  Measure the error caused by the addition of an interference  Limit is linked to the acceptance criteria of the analyte – The error caused by all interferences cannot exceed the allowable bias from the PrecisionAccuracy Study
  18. 18. Range Study      Retasked Range Precision-Accuracy evaluation at 80%, 90%, 100%, 110%, and 120% Instead of Mean in the calculation, use recovery value Recovery Value = [Mean]/[Known]*100% Limit: Each concentration is NLT 0.95
  19. 19. Linearity    Response vs Concentration  Calibration curve  Technique dependent application Calculated vs Known Concentration  Slope =1  Intercept =0  Accuracy evaluation How do you measure linearity?  Slope: not correlated to error  Intercept: not correlated to error  R2: limited correlation to error
  20. 20. Linearity  Slope and Intercept – Overwhelmed by random noise – Not correlatable to systematic noise – Adds no additional information  Basis for Linearity is not supported  Range adequate
  21. 21. Limit of Detection  Only  S/N applies to “Limit” procedures of 3 – independent samples at LOD – “adequate precision and accuracy”  What is adequate?  What is the purpose of the test?
  22. 22. Limit Test  Measure a Standard solution of the impurity at the limit  Measure a Sample solution – Is the response of the impurity in the Sample < Standard – Pass – Is the response of the impurity in the Sample ≥ Standard – Fail  Is the Δ between pass/fail adequate?  If the limit is 0.1%, then acceptable values are – 0.14% to 0.05% – LOD does not assure the measurement –Detectability does.
  23. 23. Detectability  A new term included in <1200>  Replaces 3 LOD steps – 1: Standard of impurity at limit – 2: Sample spiked with impurity at limit – 3: Standard spiked with impurity at 100%-%RSD* for the impurity – *can be estimated with Horwitz  If 1=2 and 3<2, then the difference is detectable  Otherwise, procedure is not adequate
  24. 24. What is Horwitz?
  25. 25. Limit of Quantitation  Why do we make this measurement? –10x S/N . . .  A meaningful quantity in development? –Yes  Necessary to validate? –No  Validation presumes –A known procedure –A typical value for the analyte –Known acceptance criteria  You already know the typical range of the analyte…  Use the Range Study –80%-120% for Assay; 50%-150% for impurities
  26. 26. Summary  USP is challenging validation concepts  Including  Include  Focus  Use DOE and QbD through the ATP measurable parameters and clear criteria on Precision and Accuracy results Specificity to aid understanding of Accuracy  Retask Range  Introduce detectabiltiy  Eliminate LOD, LOQ and Linearity
  27. 27. But Wait, There’s More…  Setting System Suitability Requirements  Validation is measured only once  System suitability is measured on a daily basis – Traditionally uses instrument dependent measurements – Resolution – Tailing – %RSD  System suitability rarely linked to variance  Use Validation protocol to evaluate Precision and Accuracy across the days run.  System suitability can then be linked to validation  Specificity should represent necessary minimums, but should exceed criteria of validation
  28. 28. Precision, Accuracy & Linearity Harry Yang, Ph.D. Member, USP Statistics Expert Committee
  29. 29. Method Validation  Validation is a snapshot (at any given time) of the assay’s performance.  It is confirmation that the assay is fit for its intended use  Required by regulatory guidelines
  30. 30. History of ICH Guidelines on Method Validation
  31. 31. Other Related Guidelines
  32. 32. Common Validation Characteristics  Accuracy  Precision        Repeatability Intermediate precision Specificity Limit of detection Limit of quantitation Linearity Range
  33. 33. Common Validation Characteristics  Accuracy  Precision        Repeatability Intermediate precision Specificity Limit of detection Limit of quantitation Linearity Range
  34. 34. Precision  Closeness between a series of measurements obtained from multiple sampling of the same homogeneous sample
  35. 35. Precision  Repeatability: intra-assay precision. Usually same day, operator, equipment  Intermediate Precision: same laboratory but different operators, equipment, etc.  Reproducibility: precision between laboratories  Expressed as standard deviation (SD) or relative standard deviation (RSD) n X  Xn i 1 n n , SD  (X i 1 i  X )2 n 1 , RSD  SD X
  36. 36. Accuracy  The closeness of agreement between the value which is accepted either as a conventional true value or an accepted reference value and the value found (ICH Q2(R1)) Bias Precision E[( X  T ) 2 ]  (  X  T ) 2  var[ X ] True value Mean measurement
  37. 37. Bias  Closeness of agreement between the average value obtained from a large series of test results and an accepted reference value Bias   X  T µT µX
  38. 38. Accuracy = bias + precision
  39. 39. Validation of Accuracy and Precision  Model or or
  40. 40. An Example Test result X  T Bias  X  T Burdick, LeBlond, Sandell, Yang, 2013 Intermediate precision Repeatability
  41. 41. An Example Test result X  T Bias  X  T Burdick, LeBlond, Sandell, Yang, 2013 Intermediate precision Repeatability
  42. 42. Assessment of Bias: Traditional Approach X s/n  t n 1, 0.025  Test the hypothesis that bias = 0 H0: µ = 0 vs. H1: µ ≠ 0 Y Reject H0 if s2 / n n where Y  Yn i 1 n  t n 1,0.025 (which is the same as p-value < 0.05) n , s2   (Y  Y ) i 1 2 i n 1 , t n 1,0.025 - cutpoint of t-distribution
  43. 43. Assessment of Bias: Traditional Approach  P-value < 0.05 is equivalent to that the 95% confidence interval contains zero, i.e.   0   Y  t n 1, 0.025s / n , Y  t n 1, 0.025s / n      p-value < 0.05 p-value ≥ 0.05
  44. 44. Issue with the Traditional Approach  Penalize more precise assay  Award small sample size   0   Y  t n 1, 0.025s / n , Y  t n 1, 0.025s / n      With of the 90% CI is proportional to assay precision (s) and reciprocal of the squared root of sample size n. Huberta et al, 2004
  45. 45. Equivalence Method  Bias is deemed acceptable if the 90% confidence interval of bias is bounded by prespecified acceptance limits (e.g., ±15%) Huberta et al, 2004    Y  t n 1, 0.025s / n , Y  t n 1, 0.025s / n     
  46. 46. Comparison Between Significance and Equivalence Is bias acceptable? Significance Equivalence Yes No Yes No Yes Yes No Yes
  47. 47. Equivalence Method  Bias is deemed acceptable if the 90% confidence interval at each concentration level is contained with in pre-specified range (LAL, UAL) Plot of Bias vs. True Value UAL 0 LAL a True value b c
  48. 48. Accessing Conformance to Acceptance Criteria: Precision  Intermediate precision is considered acceptable if the 95% confidence interval is bounded by a pre-selected number UAL < UAL Burdick, LeBlond, Sandell, Yang, 2013
  49. 49. Total Error Approach  Bias cannot be assessed independent of precision Huberta et al, 2004; Hoffman & Kringle, 2007
  50. 50. Total Error Approach  Measured value = True value + Method Bias + Method error Y = Test result - True value
  51. 51. Total Error Approach  Accuracy of a method is acceptable if it is very likely that the difference between every measurement of a sample and the true value is inside pre-chosen acceptance limits Huberta et al, 2004
  52. 52. Total Error Approach  Risk = 1 - Probability of meeting acceptance criterion Huberta et al, 2004
  53. 53. Methods for Testing H0:  Beta-expectation tolerance interval (Huberta et al, 2004)    With 100β% confidence that bias of a future measurement is bounded by λ Average (expected) probability for bias of a future observation is no smaller than 100β% Beta-content tolerance interval (Hoffman & Kringle, 2007)   With 100γ% confidence that bias of 100β% future measurements is bounded by λ Bayesian analysis (Burdick, LeBlond, Sandell, Yang, 2013)  Conditional on validation data, probability for bias of a future observation is no smaller than 100β% P(  Y  X     | data)   . T
  54. 54. Accuracy Profile Huberta et al, 2004
  55. 55. References 1 Graybill FA, Wang CM. Confidence intervals on nonnegative linear combinations of variances. J Am Stat Assoc. 1980;75:869– 873. 2. Nijhuis MB, Van den Heuvel ER. Closed-form confidence intervals on measures of precision for an interlaboratory study. J Biopharmaceutical Stat. 2007;17:123–142. 3. Satterthwaite FE. An approximate distribution of estimates of variance components. Biometric Bull. 1946;2:110–114. 4. Huberta P, Nguyen-Huub JJ, Boulangerc B, et al. Harmonization of strategies for the validation of quantitative analytical procedures: a SFSTP proposal—part I. J Pharm Biomed Anal. 2004;36:579–586. 5. Huberta P, Nguyen-Huub JJ, Boulangerc B, et al. Harmonization of strategies for the validation of quantitative analytical procedures: a SFSTP proposal—part II. J Pharm Biomed Anal. 2007;45:70–81. 6. Huberta P, Nguyen-Huub JJ, Boulangerc B, et al. Harmonization of strategies for the validation of quantitative analytical procedures: a SFSTP proposal—part III. J Pharm Biomed Anal. 2007;45:82–96. 7. Mee RW. b-expectation and b-content tolerance limits for balanced one-way ANOVA random model. Technometrics. 1984;26:251–254. 8. Hahn GJ, Meeker WQ. Statistical Intervals: A Guide for Practitioners. New York:Wiley; 1991:204. 9. Hoffman D, Kringle R. Two-sided tolerance intervals for balanced and unbalanced random effects models. J Biopharm Stat. 2005;15:283–293. 10. Montgomery D. Introduction to Statistical Quality Control. 3rd ed. New York: Wiley; 1996:441. 11. Kushler RH, Hurley P. Confidence bounds for capability indices. J Quality Technol. 1992:24(4):188–195. 12. Wolfinger RD. Tolerance intervals for variance component models using Bayesian simulation. J Quality Technol. 1998;30:18–32. 13. Ntzoufras I. Bayesian Modeling in WinBUGS. New York: Wiley; 2009:308–312. 14. Spiegelhalter D, Thomas A, Best A, and Gilks, W (1996) BUGS 0.5 Examples Volume 1(version i), Example 7, Dyes, pp 2426. Available from (accessed November 20, 2012). 15. Burdick R, LeBlond D, Sandell D, Yang H. Statistical methods for validation of method accuracy and precision. Pharmacopeia Forum, May –June Issue, 39 (3) .16. USP. USP 36–NF 31, Validation of Compendial Procedures <1225>. Rockville, MD: USP; 2013:983–988. 17. ICH. Validation of analytical procedures: text and methodology Q2(R1). 2005. Accessed 27 November 2012.
  56. 56. Linearity
  57. 57. Two Types of Linearity  Response vs concentration linear curve    This is a calibration curve It provides a means to convert a signal to a desired measured value Predicted concentration vs known concentration  This is a surrogate for Accuracy  Slope should be 1 and intercept should be 0 Todd L. Cecil, Personal communication, 2013
  58. 58. Calibration Curve  We wish to measure the concentration of an analyte in a test sample.  Standards = known concentrations of an analyte  To estimate the concentration, we create a standard curve
  59. 59. Standard Curve Novick and Yang, 2013
  60. 60. Standard Curve Sample Novick and Yang, 2013
  61. 61. Test of Linearity - ICH Q2(R1) guideline  Evaluate linearity by visual inspection Novick and Yang, 2013
  62. 62. Test of Linearity – Pearson Correlation r=1 r = -1 r=0
  63. 63. Test of Linearity – Lack of Fit (LOF)  Determine how close the predicted values to the mean values at each concentration level Evidence of lack of fit
  64. 64. The EP6-A Guidelines  Clinical and Laboratory Standards Institute   Compare straight-line to higher-order polynomial curve fits  Recommendation: Test higher-order coefficients. Novick and Yang, 2013
  65. 65. The EP6-A Guidelines Novick and Yang, 2013
  66. 66. Literature Novick and Yang, 2013
  67. 67. Drawbacks of Significance Test  Conduct hypothesis testing with linearity claim as the null hypothesis  Rely on failing to reject the null hypothesis to conclude linearity  Penalize precise assay  Award small sample size
  68. 68. More Literature Novick and Yang, 2013
  69. 69. Two Practical Approaches  Two one-sided tests (TOST) for calibration error    Estimate bias in concentration due to approximating either quadratic curve or proportional model using linear line Bias is expressed as a function of a ratio of two model parameters. Thus the Fieller’s Theorem can be applied to obtained 90% confidence interval of the bias Akaike information criterion (AIC)  Based on the principle of parsimony – the smallest possible number of parameters for adequate representation of the data where N – total number of data points, and K – the total number of estimated regression model parameters LeBlond, Tan and Yang, (2013a, 2013b)
  70. 70. Estimating Calibration Bias: Linear vs Quadratic Models: Bias: Assumption: Concentration levels used in the experiment are symmetrically spaced. LeBlond, Tan and Yang, (2013a, 2013b)
  71. 71. 90% Confidence Interval (CI) of Bias Fieller’s exact 90% confidence Interval Linearity is accepted if the above 90% CI is contained Within pre-specified limits. LeBlond, Tan and Yang, (2013a, 2013b)
  72. 72. Linear Model vs Proportional Model Models: Bias: LeBlond, Tan and Yang, (2013a, 2013b)
  73. 73. 90% Confidence Interval of Bias 90% CI of ratio of two model parameters: 90% CI of bias in concentration: Linearity is accepted if the above 90% CI is contained Within pre-specified limits. LeBlond, Tan and Yang, (2013a, 2013b)
  74. 74. Test Linearity for More General Experiment Design Conditions  An equally-spaced experimental design is not a necessary condition  Linearity can be tested under general conditions Yang, Novick and LeBlond, 2013; Novick and Yang, 2013
  75. 75. References 1. USP. USP 36–NF 31, Validation of Compendial Procedures <1225>. Rockville, MD: USP; 2013:983–988. 2. ICH. Validation of analytical procedures: text and methodology Q2(R1). 2005. Accessed 27 November 2012. 3. Clinical and Laboratory Standards Institute. EP06-A02 Evaluation of the linearity of quantitative measurement procedures: a statistical approach. 2003. Accessed 27 November 2012. 4. Anscombe FJ. Graphs in statistical analysis. Am Stat. 1973;27(1):17–21. 5. Van Loco J, Elskens M, Croux C, Beernaert H. Linearity of calibration curves: use and misuse of the correlation coefficient. Accred Qual Assur. 2002;7:281–285. 6. Bruggemann L, Quapp W, Wennrich R. Test for nonlinearity concerning linear calibrated chemical measurements. Accred Qual Assur. 2006;11:625–631. 7. Mandel J. (1964) The Statistical Analysis of Experimental Data. New York: Wiley; 1964. 8. Mark H, Workman J., Chemometrics in Spectroscopy, Linearity in Calibration How to Test for Non-linearity, Spectroscopy 2005;20(9):26–35 9. Liu J, Hsieh E. Evaluation of linearity in assay validation. In: Encyclopedia of Biopharmaceutical Statistics. 2nd ed. London: Informa Healthcare; 2010:467–474 10. Finney DJ. Statistical Method in Biological Assay. 2nd ed. London: Charles Griffin; 1964:27–29. 11. Berger RL, Hsu JC. Bioequivalence trials, intersection-union tests and equivalence confidence sets. Stat Sci. 1996:11(4):283–319. 12. Burnham KP, Anderson DR. Model Selection and Multimodel Inference: A Practical Information–Theoretic Approach. 2nd ed. New York: Springer; 1998:31. 13. Burnham KP, Anderson DR. Multimodel inference: understanding AIC and BIC in model selection. Sociol Meth Res. 2004;33(2):261–304. 14. David LeBlond, Charles Y Tan, Harry Yang (2013), Confirmation of Analytical Method Calibration Linearity, Pharmacopeial Forum 39(5), pp XX – XX. 15. David LeBlond, Charles Y Tan, Harry Yang (2013), Confirmation of Analytical Method Calibration Linearity: Practical Application, Pharmacopeial Forum ??(??), pp ?? – ??. 16. Steve Novick and Harry Yang (2013), Directly Testing the Linearity Assumption for Assay Validation, Accepted for publication in Journal of Chemometrics. 17. Steve Novick and Harry Yang (2013), Directly Testing the Linearity Assumption for Assay Validation, Accepted for publication in Journal of Chemometrics, The 36th Mid-west Biopharmaceutical Statistics Workshop, Muncie, Indiana, May, 2013i 18. Harry Yang, Steve Novick and David LeBlond (2013). Testing linearity under general experimental conditions. In preparation.
  76. 76. Lifecycle Management of Analytical Procedures Joachim Ermer, Ph.D. Member, USP Validation and Verification Expert Panel
  77. 77. Objectives of Expert Panel Validation & Verification  Adaptation of the lifecycle concept [ ICH Q8] and of modern concepts for process validation to analytical procedures  to holistically align analytical procedure variability with the requirements of the product to be tested  to demonstrate that the analytical procedure meets the predefined criteria over the whole lifecycle  to facilitate continual improvement  Proposal to revision and compile USP General Chapters <1225>, <1226> and <1224> into a single General Information Chapter on Lifecycle Management of Analytical Procedures  Stimuli article to be published in PF 39(5), Sep - Oct 2013
  78. 78. Quality by Design – Also Relevant for Analytics  “systematic approach that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management” [ICH Q8]  systematic approach that begins with predefined objectives and emphasizes analytical procedure understanding and analytical control, based on sound science and quality risk management”
  79. 79. Alignement of Process and Analytical Procedure PROCESS Quality Target Product Profile Prospective summary of the quality characteristics of a drug product to ensure quality, safety, efficacy ANALYTICAL PROCEDURE Analytical Target Profile Defines the objective of the test and quality requirements for the reportable result
  80. 80. Analytical Target Profile (ATP)  Developed starting 2008 by EFPIA / PhRMA Working Group “Analytical Design Space”   M. Schweitzer, M. Pohl et al.: QbD Analytics. Implications and Opportunities of Applying QbD Principles to Analytical Measurements, Pharmaceutical Technology, Feb. 2010, 2-8 jsp?id=654746 Quality (data) attributes of the reportable result  performance requirements for use  accuracy and measurement uncertainty including precision
  81. 81. Analytical Target Profile (ATP)  Based on the understanding of the target measurement uncertainty   Maximum allowed uncertainty to maintain acceptable levels of confidence Reference point for assessing the fitness of an analytical procedure  towards predetermined performance requirements  In development phase and during all changes within the lifecycle  linked to the purpose, not to a specific analytical technique.
  82. 82. Analytical Target Profile (ATP)  Any analytical procedure that conforms to the ATP is acceptable   USP Medicines Compendium, General Chapter <10> May be also established for existing procedures  including compendial procedures  based on (monograph) specifications, existing knowledge
  83. 83. ATP Example Assay  The procedure must be able to quantify [Analyte]  in presence of X, Y, Z  over a range of A% to B% of the nominal concentration  with an accuracy and uncertainty such that the reportable result falls  within ±1.0% of the true value  with at least a 90% probability  determined with 95% confidence
  84. 84. Three Stage Approach to Analytical Validation  Aligned with process validation terminology: Stage 2 Procedure Performance Qualification (PPQ) Stage 3 Continued Procedure Performance Verification Changes Risk assessment Knowledge management Analytical Control Strategy Stage 1 Procedure Design (Development and Understanding
  85. 85. Stage 1 – Procedure Design  According to ATP requirements  Procedure selection, development and understanding  Identification and investigation of potential analytical variables   Robustness studies (Method Design Space)   Risk assessment Analytical Control Strategy Knowledge gathering and preparation
  86. 86. Stage 2 - Procedure Performance Qualification (PPQ)  Confirmation the analytical procedure, operated in the routine environment is capable of delivering reproducible data which consistently meet the ATP    Includes analytical transfer Implementation of compendial procedures Precision study to finalize the Analytical Control Strategy   e.g. format of the reportable result (number of determinations) May / should be built on results generated in Stage 1  Iterative character of procedure development/optimisation
  87. 87. 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: 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) 653-663]
  88. 88. Continual Improvements (Changes)  Risk assessment to evaluate    Impact of the respective change Required actions to demonstrate (continued) appropriate performance Accordingly, apply  Stage 3 (if within Method Design Space)  Stage 2 (e.g. transfer)  Stage 1 (e.g. outside Method Design Space, new procedure)
  89. 89. 2010-2015 V&V Expert Panel  Gregory P. Martin, (Chair) Complectors Consulting              Kimber L. Barnett, Pfizer Inc. Christopher Burgess, Burgess Analytical Consultancy, Ltd. Paul D. Curry, Abbvie, Joachim Ermer, Sanofi-Aventis GmbH Gyongyi S. Gratzl, Ben Venue Laboratories, Inc. Elizabeth Kovacs, Apotex, Inc. David J. LeBlond, Statistical Consultant Rosario LoBrutto, Teva Pharmaceuticals USA Anne K. McCasland-Keller, Eli Lilly & Company Pauline L. McGregor, PMcG Consulting Phil Nethercote, GlaxoSmithKline David P. Thomas, Johnson & Johnson Pharmaceutical R&D M. L. Jane Weitzel, Quality Analysis Consultants  Government Liaison(s): Lucinda F. Buhse, FDA  USP Scientific Liaison(s): Todd L Cecil, Kenneth Freebern, Walter Hauck, Horacio N. Pappa, Tsion Bililign
  90. 90. Analytical Method Validations Current Practices and Industry Perspective Rajiv A. Desai, Ph.D. Dishman Pharmaceuticals and Chemicals Ltd.
  91. 91. ATP Example Impurity  Impurity: The procedure must be able to quantify [impurity] relative to [drug]  in the presence of components likely to present in the sample  over the range from reporting threshold to the specification limit.  The accuracy and precision of the procedure must be such that the reportable result falls   within ± X% of the true value for impurity levels from 0.05% to 0.15% with 80% probability with 95% confidence, and within ± Y% of the true value for impurity levels >0.15%, with 90% probability determined with 95% confidence.
  92. 92. Purpose of the Analytical measurement is to get consistent, reliable and accurate data
  93. 93. Source of Impurities in the Drug Substance and products Origin of Impurities Impurities in Drug Substances Earlier stage material From Equipment and Packaging material Residual solvents Side reactions Degradents Genotoxic Impurities Extractables Leachables
  94. 94. General Process for the Synthesis of Drug Substance Stage 1 Solvent W A + B C + ( traces of A and B ) Stage 2 Solvent X C + D E+ ( traces of C and D ) + M ( reaction between A and C ) Reagent R Stage 3 Solvent Y E + F Crude API + ( traces of E and F ) + traces of D + degradent of E Metal catalyst Stage 4 Solvent Z Crude API Final API + Traces of earlier stage material Side reactions Degradents Solvents Reagents
  95. 95. Analytical Method Validation Criteria …. - Suitability of Instrument  - Status of Instrument Qualification and calibration  - Suitability of reference standard , reagent, placebo, etc  - Suitability of documentation, written analytical procedure  approved protocol with pre-established acceptance criteria
  96. 96. USP General Chapter <1224> Transfer of Analytical Procedures 1. Comparative testing of same lot or standards 2. Co-validation between laboratories 3. Complete or partial validation of Analytical procedures by receiving laboratory and hence a transfer waiver
  97. 97. USP General Chapter <1225> Validation of Compendial Procedures As per cGMP regulations 211.194(a), the test methods with established specifications, must meet standards of accuracy and reliability As per 211.194(a)(2) users are not required to validate the accuracy and reliability of these methods. But verify their suitability under actual conditions of use.
  98. 98. Data Elements Required to be Validated Analytical Performance Charecteristics Category I Category II Quantitative Category III Category IV Limit tests Accuracy Yes * * No Precision Yes Yes No Yes No Specificity Yes Yes Yes * Yes Detection limit No No Yes * No Quantitation limit No Yes No * No Linearity Yes Yes No * No Range • Yes Yes Yes * * No May be required, depending on the nature of the specific test Category I : Procedures for Quantitation of major component or Active substance Category II : Procedures for determining Impurities Category III : Procedures for determining performance characteristics ( eg., dissolution, drug release, etc ) Category IV : Identification Tests
  99. 99. USP General Chapter <1225> Rationale for revisiting the compendial method An appropriate justification for a testing procedure Elaborating the capability of the proposed method over other types of determinations. For revisions, a comparison should be provided for the limitation of the current method and advantage offered by the new method.
  100. 100. USP General Chapter <1226> Verification of Compendial Procedures Verification for a compendial test procedure is an assessment of whether the procedure can be used for its intended purpose, under actual conditions of use for a specific drug substance or drug product. User should have the appropriate experience, knowledge and training to understand and be able to perform the compendial procedure.
  101. 101. USP General Chapter <1226> Verification of Compendial Procedures If the verification of the compendial procedure is not successful and the USP staff is unable to resolve the problem, it may be concluded that the procedure may not be suitable for use It may be necessary to develop and validate an alternate procedure. This alternate method can be submitted to USP , along with appropriate data to support the inclusion or replacement of the current compendial procedure.
  102. 102. US General Chapter <1226> Verification of Compendial Procedures Method verification should be based on an assessment of the complexity of both the procedure and the material to which the procedure is applied Verification should assess whether the compendial method is suitable for the drug substance and the drug product matrix. Taking into account the drug substance synthetic route, the method of manufacture for the drug product or both.
  103. 103. US General Chapter <1226> Verification of Compendial Procedures Drug substance from different suppliers may have different impurity profile that may not necessarily be addressed by the compendial method Excepients in the drug products can vary widely among manufacturers and may interfere directly or cause formation of impurities that are not considered by the compendial procedure.
  104. 104. US General Chapter <621> Chromatography System Suitability is an integral part of chromatography methods These are based on the concept that equipment, electronics, analytical operations and samples analysed constitute an integral system that can be evaluated as such. Factors affecting chromatography Mobile phase Composition, strength , temperature, pH, flow rate Column Flow rate, dimention, Temperature, pressure, Stationary phase
  105. 105. US General Chapter <621> Adjustments allowed in HPLC Compendial methods pH of Mobile phase : ± 0.2 units Concentration of salts in buffer : within ± 10% Ratio of components in mobile phase : ± 10% Wavelength : ± 3 nm Column length : ± 70 % Flow rate : ± 50% Column Temperature : ± 10 deg C Injection volume : Can be reduced, but not increased
  106. 106. US General Chapter <621> Adjustments allowed in GC Compendial methods Gas carrier flow rate : ± 50 % Oven temperature ± 10% : Temperature program : ± 20 % Column length ± 70 % : Injection volume and split volume : Can be adjusted, if detection and repeatability are satisfactory
  107. 107. Techniques Used for Analysis Additional testing parameters are now considered along with the conventional methods Analytical Instruments moving from Research to Quality Control NMR ICP XRD LC-MS GC-MS NIR Used mainly for low level detections of impurities Method validation parameters to be selected appropriately along with sampling and sample preparations
  108. 108. QbD and PAT Quality by Design (QbD ) is being encouraged by the Regulatory guidelines, the analysis conducted at every step of the process needs to be reliable. Testing methods adopted under the Process Analytical technology (PAT) should be able to provide real time analysis in the shortest possible time. Validation should be definitely done for analytical methods used under the QbD and PAT environment. No matter what the stage of the process and not just restricted to final product. A validated method gives assurance of process control at each stage, concept of QbD will be further reinforced.
  109. 109. Compendial Method and Non-compendial Method Compendial Method  Verification / Validation Non-compendial methods  Validation Alternate to Compendial method  Validation + Equivalence
  110. 110. Potential Genotoxic Impurity (PGI) Genotoxic Compounds have a potential to damage DNA at any level of exposure. Its scientifically proved that there are certain chemical structures which damage the DNA. The accepted levels of such chemicals is required to be maintained at a very low to avoid any cause of concern.
  111. 111. Potential Genotoxic Impurity (PGI) When can a specification of a drug substance exclude a limit of Potential Genotoxic Impurity ? 1. Is just a theoretical impurity, but not found during manufacturing. 2. Is formed or introduced in intermediate steps and is controlled in the intermediate stage and does not exceed 30% of the limit derived by TTC or any defined acceptable limits 3. Is formed or introduced in final synthesis step, it should be included. 4. However, it is possible to apply skip test if the level does not exceed 30% of the limit. Data of atleast 6 consecutive pilot scale batches or 3 consecutive production batches would support the justification Method validation becomes a very important aspect which ever stage the analysis is performed Guideline on the limits of genotoxic impurities' (EMEA/CHMP/QWP/251344/2006),
  112. 112. Potential Genotoxic Impurity (PGI) Threshold of toxicological concern (TTC) values for genotoxic impurities above 1.5 μg /day will be treated on a case-by-case basis. For short-duration treatments, the acceptability of higher levels will be in line with the principles outlined below Duration of Exposure Single dose ≤1 month ≤3 months ≤6 months ≤12 months Allowable daily intake 120 μg 60 μg 20 μg 10 μg 5 μg For more than one PGI in a drug substance, the TTC limits will be individually applied, if the impurities are structurally different. For more than on PGI, but structurally similar, it is expected that the mode of action would be same, hence a sum of the limits will be accepted.
  113. 113. Regulatory Audit Warning Letter Your firm did not validate analytical methods used to test APIs. The inspection revealed that your firm had not validated the HPLC method for assay and related substances for finished API for human use.. Your response states that XX of the APIs manufactured at your facility, are compendial products. The remaining YY % are noncompendial APIs had no method validation. You committed to complete these method validations by (Date) . However, this does not address product currently on the market, or product that will enter the market tested with an unvalidated method. Your proposal to verify “key parameters” for the first API batch produced does not provide the same level of assurance as method validation.
  114. 114. Regulatory Audit Warning Letter Inadequate Instrument Qualification and Analytical Method Validation Improvements to analytical techniques and transfer of methods to ator on-line applications emerged as important opportunities to reduce risk and increase efficiency in today’s modern manufacturing facility. A pharmaceutical company was cited for not adequately performing the required steps to support the transition to a new testing approach. There was no method comparison or equivalency study performed to show that the “changes were superior to the original approved method. The data was used for OOS closure and lot release.
  115. 115. Four Level Control on Analysis and Results