1. Bioassay Optimization and Robustness Using Design of Experiments Methodology 2015 NBC, San Francisco June 8, 2015 Kevin Guo

2. Agenda Scope Background Method life-cycle management  Method development  Method robustness Summary June 8, 20152©2015 Eli Lilly and Company

3. Scope This talk is not to show you how to select a model or how to best fit your data, calculation of Relative Potency (RP), nor testing for parallelism This is to share some ideas on how we may build a solid foundation toward your potency assay validation for the life-cycle of a product 3June 8, 2015©2015 Eli Lilly and Company

4. Agenda Scope Background Method life-cycle management  Method development  Method robustness Summary 4June 8, 2015©2015 Eli Lilly and Company

5. Purpose of developing a CMC bioassay CMC Bioassay and Relative Potency: Need to understand the Mechanism of Action (MOA) to develop a cell-based assay that is responsive to the relevant biological signal Focus on Relative Potency under GMP quality systems  Stability indicating  Lot release of drug substance (active pharmaceutical ingredient) and drug product  Help provide assurance of the quality and consistency of the product and also provide support for changes in the production process What Bioassay in CMC? Why is it special? 5June 8, 2015©2015 Eli Lilly and Company

6. Guidance for Potency Bioassays USP bioassay suite and EP 5.3 USP Design and Development of Biological Assays, Biological Assay Validation, and Analysis of Biological Assays Roadmap and Definitions 111 1030 1032, Design 1034, Analysis 1033, Validation Guidance for Potency Bioassays 6June 8, 2015©2015 Eli Lilly and Company

7. Agenda Scope Background Method life-cycle management  Method development  Method robustness Summary 7June 8, 2015©2015 Eli Lilly and Company

8. Develop, design, optimization, and qualification Robustness (Co-) Validation or method transfer Assay maintenance for product life-cycle Bioassay Life-Cycle Where does Bioassay Development fit in Product Life-Cycle? Hypothesis Generation Preclinical Candidate Development Phase 1 Phase 2 Commercialization Phase 3 Submission Launch CMC potency bioassay design, optimization, and qualification 8June 8, 2015©2015 Eli Lilly and Company

9. Types of Bioassays Animal models Biochemical assays Tissue explants Engineered cell lines Quantitative Physiological Relevance Clinical Experience “Ultimate Bioassay” 9June 8, 2015©2015 Eli Lilly and Company

10. Types of Bioassays Animal models Biochemical assays Tissue explants Engineered cell lines Quantitative Physiological Relevance Clinical Experience “Ultimate Bioassay” ELISA Antibodies (not therapeutic proteins) in early Clinical Development can be supported by ELISA  Limited mechanistic relevance (part of activity – ligand binding)  eg. no information on neutralization, effector function, etc. 10June 8, 2015©2015 Eli Lilly and Company

11. Types of Bioassays Cell-based assays are a happy medium between physiological relevance and quantitation Animal models Biochemical assays Tissue explants Engineered cell lines Quantitative Physiological Relevance Clinical Experience “Ultimate Bioassay” GOAL 11June 8, 2015©2015 Eli Lilly and Company

12. Sample Preparation Assay Assembly Signal Detection Aqueous Dilution Compound Transfer Reagent Addition Incubation Fluorescence Luminescence High Level Basic Assay Steps: Contributions to Variability Seeding cells 12June 8, 2015©2015 Eli Lilly and Company

13. How do we control the variability of the system? Assay Optimization Considerations: Temperature (growth, media for seeding & stimulation) Time of cell growth prior to stimulation Confluence of cells (seeding & stimulation) Ligand concentration/incubation time Therapeutic dilution curve Targeted integration/bicistronic vectors -> cell line stability Brand of 96-well plates Plate sealers/evaporation controls Media composition (FBS, Defined Media, antibiotics, etc) eg. Cells may require FBS for survival, but FBS may interfere with the assay outcome. Plate Layout 13June 8, 2015©2015 Eli Lilly and Company

14. Agenda Scope Background Method life-cycle management  Method development  Method robustness Summary 14June 8, 2015©2015 Eli Lilly and Company

15. Purpose of study: Screening or optimization Determining the potential influential factors Selection of the factor range, responses Choice of the study design Conducting the experiment Interpretation of results, Planning for the next step The Steps in DOE 15June 8, 2015©2015 Eli Lilly and Company

16. Choice of the study design: Screening Factorial Designs Two-level factorial design All possible combinations of low and high levels are run, giving a total of 2 k runs, where k is the number of factors. This design allows us to estimate all main effects and all of the 2-factor interactions. Two-level fractional factorial design Running a carefully selected subset of runs in order to reduce the size of a full factorial design when the number of factors is large. This design allows us to estimate all main effects and at least some of the 2-factor interactions. No. of factors2345610 No. of trials481632641024 16June 8, 2015©2015 Eli Lilly and Company

17. Choice of the study design: Screening Fractional Factorial Designs Two-level factorial design All possible combinations of low and high levels are run, giving a total of 2 k runs, where k is the number of factors. This design allows us to estimate all main effects and all of the 2-factor interactions. Two-level fractional factorial design Running a carefully selected subset of runs in order to reduce the size of a full factorial design when the number of factors is large. This design allows us to estimate all main effects and at least some of the 2-factor interactions. No. of factors2345610 No. of trials481632641024 No. of trials4488816 To determine the main effects of each of the factors, assuming no interactions: 17June 8, 2015©2015 Eli Lilly and Company

18. Choice of the study design: Screening Fractional Factorial Design Example, 6 Factors with Potential Interactions 18June 8, 2015©2015 Eli Lilly and Company

19. Designing & Analyzing Experiments for Product R&D 19 Uses of Screening Designs Process factor screening  Identify influential factors Direction and search  Provide direction toward optimal conditions Process knowledge space  Basic understanding 5- 19June 8, 2015©2015 Eli Lilly and Company

20. Designing & Analyzing Experiments for Product R&D 20 Why Screening Designs Work A few critical factors explain a large amount of the variability Lower order effects are more important than higher order interactions Parent factors of significant interactions are generally important as well 5- 20June 8, 2015©2015 Eli Lilly and Company

21. Choice of the study design: Optimization CCD: Central Composite Design with Axial Points 21June 8, 2015©2015 Eli Lilly and Company

22. Choice of the study design: Optimization CCD: Central Composite Design with Axial Points X: Temp (°C) Y: Time (hrs) (Lower temp, Shorter time) (Lower temp, Longer time) (Higher temp, Longer time) (Higher temp, Shorter time) X-axis: Temperature (°C) 22June 8, 2015©2015 Eli Lilly and Company

23. Choice of the study design: Optimization CCD: Central Composite Design, On Face X: Temp (°C) Y: Time (hrs) (Lower temp, Shorter time) (Lower temp, Longer time) (Higher temp, Longer time) (Higher temp, Shorter time) 23June 8, 2015©2015 Eli Lilly and Company

24. Temperature R8 R7 Incubation Time R5R1 Cell Count R3 R2 R4 R6 R15-18 R9 R11 R12 R13 R14 R10 Choice of the study design CCD: Central Composite Design 24June 8, 2015©2015 Eli Lilly and Company

25. Purpose of study: Screening or optimization Determining the potential influential factors Selection of the factor range, responses Choice of the study design Conducting the experiment Interpretation of results, planning for the next stage The Steps in Optimization 25June 8, 2015©2015 Eli Lilly and Company

26. A Development Study Example 26June 8, 2015©2015 Eli Lilly and Company

27. Agenda Scope Background Method life-cycle management  Method development  Method robustness Summary 27June 8, 2015©2015 Eli Lilly and Company

28. Develop, design, optimization, and qualification Robustness (Co-) Validation or method transfer Assay maintenance for product life-cycle Bioassay Life-Cycle Why do Robustness Studies? Hypothesis Generation Preclinical Candidate Development Phase 1 Phase 2 Commercialization Phase 3 Submission Launch CMC potency bioassay design, optimization, and qualification ICH Q2 (R1), VALIDATION OF ANALYTICAL PROCEDURES “The robustness of an analytical procedure is a measure of its capacity to remain unaffected by small, but deliberate variations in method parameters and provides an indication of its reliability during normal usage.” 28June 8, 2015©2015 Eli Lilly and Company

29. A 5-factor 8-run Fractional Factorial Design with 4 Center Points A Robustness Study Example 29June 8, 2015©2015 Eli Lilly and Company

30. Well Detection Area Path Length Where Is the Well? Why Robustness? Lamp, Plate & Detector Alignment 30June 8, 2015©2015 Eli Lilly and Company

31. Concluding Remarks Summary  Bioassay development should be managed from the product life- cycle perspective  Selections for DOE are plenty. Please collaborate with your statisticians to best use resources  Results from a well-thought experiment reveals more information for your next step than data from a not-as-well-planned study 31June 8, 2015©2015 Eli Lilly and Company

32. Acknowledgments Darren Kamikura, Research Scientist 32June 8, 2015©2015 Eli Lilly and Company