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Ibc biological assay development & validation 2011 gra presentation

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Presentation at IBC's Biological Assay Development and Validation conference 12 May 2011

Presentation at IBC's Biological Assay Development and Validation conference 12 May 2011


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  • 1. Developing Bioanalytical MethodsBalancing the Statistical Tightrope
  • 2. “Lee: can I use this number?”Process DevelopmentGSK, 1997 2
  • 3. “it’s about 40” “about 40?” “probably...” 3
  • 4. Enlightenment?
  • 5. 5
  • 6. Blooms Taxonomy the 4 stages of competence Incompetent Competent ConsciousConsciousness Unconscious 6
  • 7. A Statistical GodMe
  • 8. Using Statistics
  • 9. Why? Six Reasons1. Potency assays are key in making medicines2. Bioassays are very variable3. Statistics will help you understand your data4. Understanding your data will reveal if control exists5. Your level of control allows you to judge RISK6. Regulators globally require it 9
  • 10. The Regulator & Assay Control Regulators have been asking for this for years! QbD1. Pharmaceutical cGMPs for the 21st Century2. PAT3. ICH Q2: Validation of Analytical Procedures4. ICH Q8: Pharmaceutical Development5. ICH Q9: Quality Risk Management6. ICH Q10: Quality Pharmaceutical Systems 10
  • 11. StatisticsThe complete solution?
  • 12. Or this? Your assay? 12
  • 13. Or this? or your assay? 13
  • 14. Statistics - an Amazing Transition 14
  • 15. Bioassays will always be variableYou can improve it- by understanding it- Focusing effort in right places- This brings control- You can manage expectations- This is understood by regulators 15
  • 16. Why assay variation matters? product variation + A few unsatisfactory assay variation + batches may even inaccuracy pass specification due to a combination of assay method and process variability Many satisfactory OOS batches likely to fail (potentially costing £Ms)because of combination of assay method & process inaccuracy & variation 16
  • 17. Our Control Strategy What does the scientist need to achieve?Define i.e. selectivity, accuracy, precision linearity Identify & prioritise analytical CNX parametersMeasure Control Noise eXperimental parameters parameters parametersAnalyse Fix & control e.g., MSA, e.g., DoE Input into Precision Regression Method MethodImprove Ruggedness Robustness Method Control Strategy & reduce Risk prior toControl Validation → Routine Use & Continuous Improvement 17
  • 18. Generating Bioassay Data 18
  • 19. The Rules1. Speak with your statistician before generating data2.See Rule 1 19
  • 20. Lot’s data ≠ Value 20
  • 21. 21
  • 22. Statistics are a tool 22
  • 23. QC Which Tools? UCL Stage 4 Technology QC Tools CELLULA, Shewhart chart, YES Transfer LCL CUSUM NO TIME Stage 1: Qualification Tool Stage 3: Fishbone, Minitab Validation Tools Nested, CELLULAPrecision Stage 2:AccuracyLinearity etc. Development Tools DX8, JMP, Minitab Design
  • 24. What’s Appropriate Knowledge?• Learning takes time• Will you use it often enough?• It’s not an academic pursuit• Activities must add value  do what’s necessary 24
  • 25. Scope &Design
  • 26. Define & ScopeHow is the assay performing? Prec/TOL2-sided = 6 x 16.76 100 = 1.01 26
  • 27. Parameters (e.g. 15)pDNANaClpHTube LengthTimeSeeding DensityRatio of TransfectionTemperatureAgitation and levelVector – type, concAddition Order
  • 28. Q. How Many parameters?Q. Which parameters?Q. What ranges?A. Existing knowledgeA. Common senseA. Practical limits
  • 29. Define & ScopeDrill down - map out assay - build understanding & scope Assay Flow 29
  • 30. Define & Scope Drill down & map out assay to build understanding & scopeAttention is focusedtoward key stepsand the parametersinvolved in thesesteps Cause & Effect Diagram (Fishbone) helps think your assay through Identify & prioritise analytical CNX parameters 30
  • 31. Scope & Screen Scope ranges with simple experimentsScoping Experiments Explore mildest to most forcing conditions 31
  • 32. Revealing the Big Hitters 32
  • 33. Temptation
  • 34. Building UnderstandingOFATProvides estimatesof effects at setconditions of the NaClother factors andno interaction pDNAeffects. pH 34
  • 35. Building UnderstandingFactorial Design 2400 2600 1300 900 1800Estimates effects atdifferent conditions toestimate interactions 350 600 250 300 500Design of ExperimentsDOE 35
  • 36. Optimisation Optimise the parameters that survived the initial screeningwork towards aRobust Optimum 36
  • 37. Simulations The tools allow you to simulate scenarios using the data you’ve built upVisual simulation of expected performance relative to specification 37
  • 38. Is the Model Correct? 38
  • 39. Validate & Verify The evaluation of robustness should be considered during the development phase and should show the reliability of an analysis with respect to deliberate variations in method parameters ICH Q2B, 1994Method stretch…what if? Ideal Settings Control Space Design Space 39
  • 40. Assay Control: control the parameters inside boundaries 40
  • 41. Working within the controlboundaries will keep theassay under control Even if you go outside the control boundaries, the assay will have enough flexibility to deal with it without an OOS 41
  • 42. Summary - Data Driven Development Scope Screen Optimize Verify QC/TT Transfer to QC to validate on batches & bring into routine useExplore mildest Identify few potential Estimate & utilizeto most forcing key parameters interactions to move Rattle the cage toconditions Focus on vital few & towards optimum deliver a design narrow ranges conditions space
  • 43. 43
  • 44. PrecisionIt may be considered at three levels:1. Repeatability2. Intermediate precision3. ReproducibilityICH Q2A, 1994
  • 45. Repeatability1 analyst in 1 laboratory on 1 day injecting 6 times Summary Statistics Number of Standard Coefficient Lower 95% CI Upper 95% Values Mean Deviation of Variation for Mean CI for Mean t30 PS 6 223.27 6.43 2.88% 216.52 230.02 45
  • 46. Intermediate Precision• 1 analyst in 1 laboratory on• 1 day• injecting 6 samples• each tested 6 times As well as sample variation, this study still provides information on repeatability 46
  • 47. Intermediate PrecisionSo we compare the mean values for each sample(over replicate results per sample) Variance Components Factor df Variance % Total Sample 5 27.8535 21% Repeat 30 102.6361 79% 35 130.4896 100% Standard Mean Deviation RSD 47 216.24 11.4232 5.28%
  • 48. and the others…..?Precision within a laboratory but withdifferent analysts, on different days, withdifferent equipment…reflects the realconditions within one laboratory ICH Q2A 1995 48
  • 49. Intermediate PrecisionData collect using several analysts using several instrumentsover several days: Y 56000 55500 55000 54500 Peak Area 54000 53500 53000 52500 52000 0 5 10 15 20 25 Sample 49
  • 50. Intermediate PrecisionPotentially misleading: large analyst-to-analyst variationpresent: Y 56000 55500 55000 54500 Peak Area 54000 53500 53000 52500 52000 0 5 10 15 20 25 Sample Analyst 1 Analyst 2 Analyst 3 50
  • 51. Intermediate Precision better examined looking at multiple sources of variation within an assaywant to understandmajor sources ofvariation such assample, prep,analyst etc. 51
  • 52. Intermediate Precision 52
  • 53. Intermediate PrecisionCan also perform Unbalanced designsOne operator performs multiple injections on singlepreparation;Two operators perform single injections on multiplepreparations 53
  • 54. Reproducibility multiple laboratories; typically run as an inter- laboratory cross-over study, with each participating lab sending samples to every other lab and analysing all samples (including own) …. sent to and analysed by other lab A B CSamples from A    laboratory: B    C    54
  • 55. ReproducibilityCan use analysis of variance (ANOVA) to look fordifferences or biases between labs Alternatively look for “analytical equivalence”
  • 56. Risk ManagementThe level of effort, formality and documentation....should be commensurate with the level of risk ICH Q9Evaluation of the risk to quality should be based onscientific knowledge & ultimately link to theprotection of the patientIs the bioassay fit for purpose and under control? 56
  • 57. Before & AfterHow is the assay performing? P/TOL2-sided = 6 x 16.76 100 = 1.01 57
  • 58. Before & AfterBetter P/TOL2-sided = 6 x 6.99 100 = 0.42 58
  • 59. Risk ManagementMethod Understanding, Control and Capability (MUCC) Understand impact of variation upon risk… Risk Understanding?Capable? Management Loop Statistical Capability Process Control& Precision (SPC) Charts Control? 59
  • 60. Risk Management Understanding? Understanding?Capable? P/TOL2-sided = 6 x 16.76 Capable? 100 Control? = 1.01 Capability& Precision 60
  • 61. Risk Management P/TOL2-sided = 6 x 6.99 100 I-MR Chart of t30 PS Summary Report = 0.42 Is the process mean stable? I Chart Evaluate the % of out-of-control points. Investigate out-of-control points. 0% > 5% 225 UCL=220.77Yes No 210 0.0% t30 PS _ X=199.87 195 Comments 180 LCL=178.96 The process mean is stable. No data points are out of control 1 6 11 16 21 26 31 36 41 46 on the I chart. 61 Observation
  • 62. Summary1.Build a good basic understanding of stats but don’t need to become guru2.Involve a statistician, at least at the beginning3.Build understanding of your bioassay (QbD) – it’s a must4.Get to grips with Bioassay Variability 62
  • 63. “Lee: can I use this number?” 63
  • 64. “Yes – it’s 42 … ”  0.05 with 95% Confidencefor the statisticians in the audience 64
  • 65. AcknowledgmentsDr. Paul Nelson – Prism TC LtdPictures from “The Cartoon Guide to Statistics”Larry Gonick & Woollcott Smith 65

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