1. Definitive Screening Design—A Novel Statistical Tool to Leverage Information in Early Development Stages 1 Vishal C Nashine 27 OCTOBER 2015 AAPS-AM

2. Outline  Introduction  QTPP, Molecule Challenges  Risk assessment  Application of Definitive Screening Design (DSD)  Augmentation and model verification  Considerations variability to understand product performance  Summary 2

3. DP Robustness 3 Automation Mechanistic Understanding Design of Experiments QTPP Risk Assessment Robust DP Control Strategy Risk Assessment

4. Draft QTPP 4 Product AttributeTarget Dosage form Liquid/Lyo (TBD), Single use Protein Content per container TBD Dose TBD (currently projected to be 1500 mg) Concentration TBD Mode of administration IV/SC (TBD) Viscosity ≤15 cP (TBD) Container Current: Vial; Future: TBD Shelf-life TBD but likely to be ≥ 2 years at 2-8  C Compatibility with manufacturing processes TBD; Minimum 15 days at 25  C and subsequent ≥ 2 years at 2-8  C; soluble at higher concentration during UF/DF and DS storage Biocompatibility No reaction or pain upon administration (isotonic upon dilution in saline/vehicle) Degradants or impurities Below safety threshold or qualified Pharmacopoeial compliance Meets requirements and USP criteria for SVP HMW species ≤ 5% LMW species ≤ 5% Acidic variants TBD Oxidation product/s TBD Particulate formation TBD; No particulate formation during storage, handling and administration

5. Molecule Challenges in the Context of TPP  Aggregation (High conc./SC product/RTU)  Particulate formation  Oxidation (leading to aggregation)  Met, Cys, His, Tyr, Trp, and Phe  Deamidation  Isomerization  Proteolysis  Di-sulfide exchange  β-elimination  DKP formation

6. TPP Considerations: RTU Device Landscape TimeScape Features / Technical Complexity Prefilled Syringe Safety Syringe Pen Injector Auto Injector Micro Infuser “Patch Pump” Needle Free Injector Programmable Electro- Mechanical System Kindly provided by Christina Lasker 6

7. TOR = T1 + T2…..Tn 7 TOR = T1 TOR = T2 Filtration Fill/Finish Thawing of DS Inspection TOR = T3 TOR = T4 TOR = T5

8. Initial Risk Assessment 8

9. Selecting Study Factors 9

10. Typical DoE Workflow in Formulation Development 10 Screening (Main Effect) Fractional Factorial (Linear effects with or without curvature) Optimization (Effect Modeling) Response Surface (Quadratic Surface) STATISTICAL MODEL Screening (Main Effect) Definitive Screening Design (Linear effects with curvature) may require augmenting Model Validation/Verification Does it make mechanistic sense? RSD versus measurement error Continuous evaluation (Predict new experiments?) Jones, B. and Nachtsheim, C.J., J. Qual. Technol. 43, 1–15 (2011)

11. Design Comparison Orthogonal Effects 11 7 Factor – Fractional factorial 7 Factor – DSD Jones, B and Nachtsheim, CJ, J. Qual. Technol. 43, 1–15 (2011) 17 Experimental Conditions 16 Experimental Conditions

12. DSD: Design Matrix 12

13. Automation: Stability & Analytical Sample Preparation  Compounding  Vortexing, Magnetic Stir Bay, Pipette Mixing  Vial Filling: Small scale (1cc vials), large scale (3cc vials, 5cc vials)  Analytical Preparation: MTP, SEC Photosensistive Environment Sample Preparation (1, 3, 5 cc vials) Liquid Handler: 6 Tip Disposable Air Displacement Tip Liquid Handler: Piston Based Positive Displacement Tip Formulation Sample Preparation: 13

14.  TSML* is used as a tool for the prediction of k agg from non-isothermal kinetics DSF and Thermal Scanning Monomer Loss (TSML) for formulation rank ordering * Brummitt et. al. 2011 Predicting Accelerated Aggregation Rates for Monoclonal Antibody Formulations, and Challenges for Low-Temperature Predictions. J Pharm Sci 100(10):4234–4243. 14 ThT-based rank-ordering correlates with stability results ThT Nashine V., et al. AAPS. Pharm. Sci. Tech (2013), 4, 1360-1366

15. Identification of Significant Factors and Functional Relationships: %HMW 15

16. Identification of Significant Factors and Functional Relationships: (Viscosity) Identification of Significant Factors and Functional Relationships: (Viscosity) Simulating for Specifications/Measurement Variations 16

17. Identification and Verification of Significant Factors: Multiple/Orthogonal Responses 17

18. Augmentation Model Verification & Explore Wider parameter Range 18

19. Model Verification %HMW 19

20. Model Verification Viscosity 20

21. Formulation Space & Composition Selection in Context of TPP and Risk Evaluation 21

22. Summary  Successful application of QbD concepts in the early phases of biologics product development required identification and study of the relevant liabilities within the context of the anticipated TPP  DOE/simulation tools allowed quantitative relationship between design variables and QAs while estimation of variability helped identify robust product design targets and quality RA  Application of automation in sample handling as well as data analysis is critical to the success of DoE approaches  Careful selection of high-throughput tools allow efficient and effective implementation of DoE screening tools  From a mechanistic perspective DSD is an important tool to formulation development  With the end point in mind, these tools can be used to develop a comprehensive control plan to support commercialization 22

24. Acknowledgements 24 Dr. T. Carvalho R. Patel Dr. J. Tabora Dr. M. Adams Dr. R. Gandhi Dr. P. Soler A.Puri J. Levons

25. Thank You for Your Attention! 25