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Statistical Considerations for Defining Cut Points and Titers in Anti-Drug Antibody (ADA) Assays

Statistical Considerations for Defining Cut Points and Titers in Anti-Drug Antibody (ADA) Assays. Ken Goldberg, Non-Clinical Statistics Johnson & Johnson Pharmaceutical Research & Development, LLC, Chesterbrook, PA. Outline. Introduction Why are ADA and IR assays important? Two case studies

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Statistical Considerations for Defining Cut Points and Titers in Anti-Drug Antibody (ADA) Assays

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  1. Statistical Considerations for Defining Cut Points and Titers in Anti-Drug Antibody (ADA) Assays Ken Goldberg, Non-Clinical Statistics Johnson & Johnson Pharmaceutical Research & Development, LLC, Chesterbrook, PA

  2. Outline • Introduction • Why are ADA and IR assays important? • Two case studies 1. RIA: How to define %binding? • ECL: How to define titer cut point? • Both use a Huber 3-parameter nonlinear logistic regression Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 2

  3. Immune Response (IR) Assay • Primary question: ADA, Yes or No? • Every biologic must be evaluated.  • Safety and Efficacy concerns. • Too much IR can kill a compound. Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 3

  4. Biological Drug Products are Different than Traditional Small Molecule Drugs • Made by cells not chemists • Complicated manufacturing process • Small & simple vs large & complex chemical structures Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 4

  5. Reference: Genentech, Inc. http://www.gene.com/gene/about/views/followon-biologics.html Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 5

  6. Adverse Clinical Sequelae • Hypersensitivity & autoimmunity • Altered PK • Drug neutralization • Abnormal biodistribution • Enhanced clearance rate  Regulatory bodies require ADA evaluation for all biologics Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 6

  7. Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 7

  8. Immune Response (IR) Assay Challenges • Cut Point for confidence that screening bioassay response (eg, ECL, OD, RLU, CPM) reflects immunogenicity • Statistical issues of variance components, distributions, outliers, … Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 8

  9. Screening Cut Point Flags 5% of Naïve Samples as False Positive • Use Mean + 1.645 x SD with caution • Only for normally independently distributed data without outliers • Usually requires at least a transformation like logs • Nonparametric often easier • Simply use 95th percentile • Caution if unbalanced design Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 9

  10. ELISA Activity Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 10

  11. ELISA Cut Point Example Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 11

  12. Analysis of an RIA Cut Point Assay Validation Experiment • 6 Assay controls • 2 Analysts with 3 assays each • 2 Populations (Normal and Diabetes) • 75 Naïve Human Serum samples • Nonnormal data • Unequal variances Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 12

  13. RIA Histogram of 450 Naïve Sample Results Transformed %Binding = ln(35+%Binding). Parametric Cut Point = 10.757 ± 3.524.Transformed Cut Point = 3.823 based on adjusted mean = 3.402 and total standard deviation = 0.256. Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 13

  14. RIA Normal Probability Plot of 450 Naïve Sample Results Transformed %Binding = ln(35+%Binding). Parametric Cut Point = 10.757 ± 3.524.Transformed Cut Point = 3.823 based on adjusted mean = 3.402 and total standard deviation = 0.256. Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 14

  15. SAS Code proc mixed; * For Cut Point; class sample run analyst; model t35Pct0_100= / ddfm=sat; random sample; random sample / type=sp(exp)(tube) subject=analyst*run; repeated / group=analyst*run; proc mixed; * For Example Hypothesis Test; class sample run analyst; model t35Pct0_100 = Analyst Tube / ddfm=sat; random sample; random intercept tube / type=fa0(2) subject=analyst*run; repeated / group=analyst*run; Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 15

  16. My RIA Notation MinCPM = Minimum of the 2 Sample CPMs MaxCPM= Maximum of the 2 Sample CPMs AvgCPM = Average of the 2 Sample CPMs CV = Coefficient of Variation of the 2 Sample CPMs B0 = Average of all 6 “Validation sample 0 ng/mL” CPMs B100 = Average of all 6 “Validation sample 100 ng/mL” CPMs B250 = Average of all 6 “Validation sample 250 ng/mL” CPMs B1000 = Average of all 6 “Validation sample 1000 ng/mL” CPMs NSB = Average of all 2-6 “NSB” (Non-Specific Binding) CPMs TC = Average of all 2-6 “TC” (Total Count) CPMs Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 16

  17. Some RIA %Binding Definitions 1CV of (Response + Addend) = Standard Deviation / (Mean + Addend) x 100%. Addend chosen so that CV is not related to control concentration. Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 17

  18. How to Choose the RIA %Binding Definition? Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 18

  19. New versus Old RIA%Binding Definitions • New: (MinCPM – B0) / (B100 – B0) • Repeat if CV > 25% and (MaxCPM – B0) / (B100 – B0) > 12.0% (the Cut Point) • Old: (AvgCPM – NSB) / TC • Repeat if CV > 20% Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 19

  20. Attributes of Selected RIA %Binding Definitions Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 20

  21. RIA Validation Control Curve with Lower 1-sided 95% Prediction Limit 65 + %Binding = A+B·ConcentrationC Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 21

  22. A Logistic Curve with an Infinite Plateau is Linear wrt X C + R XH / ( MH + XH) = Substitute α = C,  = H, and R/β = MH α + R X / (R/β + X) =Multiply second term by β/β α + β R X / ( R + βX)Apply L’Hopital’s rule Lim[ α + Rβ X / (R + β X) ] = α + β X (R) Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 22

  23. RIA Naïve Sample %Binding vs Test Tube Order by Population Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 23

  24. RIA Naïve Sample %Binding vs Test Tube Order by Analyst Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 24

  25. RIA Naïve Sample %Binding vs Test Tube Order by Analyst and Run Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 25

  26. RIA Naïve Sample Means vs Test Tube Order by Population, Analyst and Run Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 26

  27. RIA Naïve Sample Mean %Binding vs CV by Analyst and Run Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 27

  28. RIA Naïve Sample Minimum %Binding vs CV by Analyst and Run Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 28

  29. RIA Naïve Sample CPM CV vs Mean by Analyst Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 29

  30. RIA Naïve Sample CPM CV vs Mean by Population and Control Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 30

  31. RIA Probability Plots of ln(35+%Binding)•100 by Analyst Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 31

  32. RIA Probability Plots of ln(35+%Binding)•100 by Population Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 32

  33. Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 33

  34. Electrochemiluminescence (ECL) BioVeris Assay • New way to determine screening cut point (Data = naïve samples) • New way to determine titer cut point (not equal to screening cut point) (Data = positive samples’ Titration series) • Estimator of Titer within-assay CV Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 34

  35. Screening Cut Point Determination ECL of Naïve Sample vs Diluent Alone with Cutoffs by Diluent ECL Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 35

  36. Titer Definition • Smallest distinct dilution in a titration series with a negative response • Response is Sample ECL mean / Diluent Control ECL mean in this case study Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 36

  37. Plot where Sample/Diluent Control ECL Ratio < 4 for 1 Selected Plate out of 24 Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 37

  38. Potential Problems with a Common Screening andTiter Cut Point • Highly diluted samples tend to be positive! • The opposite would not be a problem • Titration curve too flat at cut point • Makes the titer highly variable • Common Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 38

  39. Titer Cut Point Defined • The continuous titer inverse predicted from it has CV ≤ 30.0% with 95% confidence • 30.0% makes best case CV = worst case CV in ideal assay • Continuous titer is exact dilution giving cut point (only as a theoretical concept) Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 39

  40. Asymptotic CV • CV  Standard deviation of natural log ratio or titer • CV of dilution@ratio  CV of ratio / slope of titration curve@ratio • CV of dilution decreases as ratio and slope increase • These CVs are within-plate CVs Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 40

  41. Four Theoretical Titer Distributions Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 41

  42. Titer Cut Point Defined • A continuous (interpolated) titer inverse predicted from it has CV<30.0% with 95% confidence • Exact dilution giving cut point (eg, 1.357 ratio) is the continuous titer • Continuous titer used here only as a theoretical concept • Our cut-point 5 SD above diluent mean so false-positives of noncensored titers unlikely Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 42

  43. Summary • All biologics need ADA evaluation • Use controls to adjust for plate-to-plate variance and minimize the LOD • Define titer cut point so best case CV = worst case CV in ideal assay Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 43

  44. Pam Cawood • Gopi Shankar • Bill Pikounis Acknowledgements: • Sheng Dai • Allen Schantz Reference: Shankar, G. et al, (2008). Recommendations for the validation of immunoassays used for detection of host antibodies against biotechnology products. Journal of Pharmaceutical and Biomedical Analysis. 48:1267–1281. Statistical Considerations for Defining Cut Points and Titers in ADA Assays. Ken Goldberg. Midwest Biopharmaceutical Statistics Workshop, May 24-26, 2010. Slide 44

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