Risked-Based Development and CMC Question-Based Review: Asking the Right Questions for Process Understanding, Control an

1. Risked-Based Development and CMC Question-Based Review:Asking the Right Questions for Process Understanding, Control and Filing Kenneth R. Morris, Ph.D.Department of Industrial and Physical Pharmacy Purdue University OPS-SAB July 20th, 2004

2. Companies may or may not have info but it’s not always in the filing Reviewers must go through cycle of info requests and questions Companies may or may not have clear scientific rationales for choices but are not always sharing it. Reviewers must often “piece together” data and observations to discover the rationale for a spec, method, formula, process, etc. Reviewers are analyzing the data they often must tease out of the company Companies include needed data with filing and could share it prior to the filing Companies include the data analysis to produce meaningful summaries and scientific rationales Reviewers assess the rationales and summarized data presentations as satisfactory or not Current vs.QBDDesiredState

3. Use sound scientific principles in the design of the DF and Process Identify the critical attributes (CAs) for the raw materials Identify the process critical control points for the processes (PCCPs) Employ the proper analyses and PAT concepts for process understanding and control Tie it all together with the appropriate informatics to feed the information forward and backwards for QbD and continuous improvement and innovation = reduced risk Associated regulatory question rationale? Risk Based Development: a simple concept

4. Risked Based Development - RBD RBD is all about “feeding forward” (after Ali Afnan) • Exploring the characteristics of the RMs, and possible variability in RM and processing that are • expected to impact on required DF performance • Deciding on a DF based on #1 (+ business case) and selection of possible processes • Deciding what data are necessary to access the probable success of #2 (1st,principles, lit, DOE) • Collect and analyze the data (here comes PAT) • Gap analysis - refining models as development proceeds • Continuous improvement

5. Example: Solid Oral Dosage Forms Does it work? Can we make it?

6. How realistic is RBD? • As all good pharm. Scientist/Engineer know: • A formula without a process is (e.g.) a pile of powder • Even during API characterization, developing a formula implies an expected DF and process or range of choices (e.g., you don’t use compaction aids for lyophiles) • So API characteristics are among the 1st information you need to feed forward • So what’s different about the new GMP? • Models, data, and informatics – the process!

7. Accessing solubility impact at preformulation:Yalkowsky’s Modified Absorption Parameter (QSAR & Combinatorial Science, 22, 247-257 2003) • Relationship to human intestinal fraction absorbed, FA, to the absorption parameter, , of the ‘rule of unity’

8. Variability is the Enemy Variability RM Input Product Process variable ??? FIXED!! Adjustable! You CANNOT have a constant output from a fixed process and variable input - KRM Adapted from Rick Cooley, Eli Lilly, and Jon Clark CDER-FDA

9. Example: CMC-API Selection Rationale/Process for DF DevelopmentHow do you know what questions to ask? • What’s the 1st API question you’d want the answer to if designing a DF or in evaluating the appropriateness of the selected API attributes? • The 2nd?, etc… • The development scientist and the regulator are asking many of the same questions.

10. API and Excipient Selection Rationale 1st principles time

11. ID CAs

13. Process Design/Selection Rationale From RM CA selection

14. An Example: Q6A polymorph decision tree • This is great. If you understand the solid state and no polymorphs are formed, you’re done! • If there are forms, they must be understood, e.g.: What are the relative stabilities of low energy forms? • These are the “right” questions for scientist and regulators

15. An Example con’t: Q6A polymorph decision tree • We’re OK at first but when considering the product the logical 1st question should be: Based on what is known about the material AND the process, what if any changes in form would be EXPECTED? • If the answer is none based on the scientific understanding, then a confirmatory test during development should suffice • Otherwise, the next question should be: Is the observed change the Expected one? What was the rationale for selecting the processing step responsible for the change? • Then we’re back to the tree

16. An Example con’t: Q6A polymorph decision tree • Here it might be reasonable to be asked: Does the performance testing relate to the performance of interest? • If the answer is yes based on the scientific understanding, then we’re back on track • A next question might be: based on the understanding of the form’s behavior what would the expect trend in transformation be?

17. An Example con’t: Q6A polymorph decision tree Does the observed change correspond to an understood and expected transformation? • If not, the system is not as well understood as thought!

18. One Example: Mechanical Properties as a CA, the Hiestand Indices • The Bonding Index for the survival of strength after decompression: • BI = tensile strength/hardness = σT/H (>0.005) • - The Brittle Fracture Index measures the ability of a material to relieve stress by plastic deformation around a defect: • BFI = tensile strength of a compact with a defect/without = 0.5[(σT/σTo)-1] (<0.20) • The Strain Index measures the relative strain during decompression after plastic deformation: • SI = Hardness/Reduced Modulus of Elasticity = H/E’ Hiestand, E., Rationale for and the Measurement of Tableting Indices, in Pharmaceutical Powder Compaction Technology, G. Alderborn and C. Nystrom, Editors. 1996, Marcel Dekker, Inc.: New York

19. Rowe, R.C. and R.J. Roberts, Mechanical Properties, in Pharmaceutical Powder Compaction Technology, G. Alderborn and C. Nystrom, Editors. 1996, Marcel Dekker, Inc.: New York.

20. Phenacetin - fracture on decompression the importance of BI BFI = 0.4 (Moderate) BI = 0.005 (Low) SI = 0.013 (Low) BI = tensile strength/hardness = σT/H (>0.5x10-2) Courtesy of Greg Amidon, Pfizer, Previously Presented at AAPS 2002 or U.of Mich. Solids course

21. 0.040 0.025 0.035 0.030 0.020 0.025 Bonding Index 0.020 0.015 Bonding Index 0.015 0.010 0.010 0.005 0.005 0.000 Sucrose Mannitol SD Lactose CaSO4*H2O Hyd. Lactose MCC PH101 MCC PH102 MCC PH103 0.000 Aspirin PNU-A PNU-B PNU-E PNU-C PNU-D Flurbip. Acetamin. Courtesy of Greg Amidon, Pfizer, Previously Presented at AAPS 2002 or U.of Mich. Solids course Bonding Index of: Excipients Drugs BI = tensile strength/hardness = σT/H (>0.5x10-2)

22. Erythromycin - fracture on ejection the importance of the BFI BFI = 0.7 (High) BI = 0.03 (High) SI = 0.04 (High) BFI = 0.5[(σT/ σTo)-1] (<0.20) Courtesy of Greg Amidon, Pfizer, Previously Presented at AAPS 2002 or U.of Mich. Solids course

23. 1.0 0.9 0.8 0.7 0.6 0.5 Brittle Fracture Index 0.4 0.3 0.2 0.1 0.0 Sorbitol Sucrose Povidone Mannitol SD Lactose Corn Starch MCC, coarse MCC, medium MCC, med, RM Croscarmellose Ca SO4 di-H2O Hydrous Lactose Brittle Fracture Index of Excipients at a solid fraction of 0.9 BFI = tensile strength of a compact with/without a defect = 0.5[(σT/ σTo)-1] (<0.20) Courtesy of Greg Amidon, Pfizer, Previously Presented at AAPS 2002 or U.of Mich. Solids course

24. Courtesy of Greg Amidon, Pfizer, Previously Presented at AAPS 2002 or U.of Mich. 1.0 0.8 0.6 Brittle Fracture Index 0.4 0.2 0.0 0 20 40 60 80 100 % Drug Mixed with Excipient Effect of the Addition of a Non-brittle Material to a Brittle Drug (Methenamine, Flurbiprofen, Drug X (Pfizer)) Adding only 30% of a non-brittle excipient makes the mixture much less brittle.

25. Hmix C1 Component C2 Empirical Modeling of a Binary Mixture Courtesy of Greg Amidon, Pfizer, Previously Presented at AAPS 2002 or U.of Mich. log(Hmix) = log(HC2/HC1)*(%C2/100) + log(HC1) Leuenberger and others have 1st principle models to extend the concepts (Powder Technology 111 2000 145–153)

26. Use sound scientific principles in the design of the DF and Process Identify the critical attributes (CAs) for the raw materials Identify the process critical control points for the processes (PCCPs) Employ the proper analyses and PAT concepts for process understanding and control Tie it all together with the appropriate informatics to feed the information forward and backwards for QbD and continuous improvement and innovation = reduced risk Were the principles appropriately applied? How were the CAs identified and the formula designed? Risk Based Development-CMC questions

27. ID of PCCPs

28. PCCPs and Scale up with Monitoring The basic approach is captured as two simple process understanding (i.e. PAT) premises: • PCCPs are preserved throughout scale-up • the magnitude of the responses may not scale directly, but the variables being monitored reflect the “state” of the process • Monitoring material properties makes scaling less equipment dependent (as opposed to only monitoring equipment properties) • equipment differences (scale and type) may have an effect, however, differences in the material should reflect significant changes in the PCCPs

29. Equipment:Chilsonator IR220(Fitzpatrick)CDI-NIR; Texture Analyzer 3 point beam bending E = F l3/ 4x h3b Roll speed: 4 - 12 rpm VFS Speed: 200 rpm HFS Speed: 30 rpm Roll Pressure: 6560

30. Average NIR Spectrum (n = 13) Gupta, et.al., in press, J.Pharm.Sci.

31. Dry Granulation by Roller Compaction • The strength is a linear function of the density which is monitored by NIR • Semi Empirically F=(SNIR-0.17)/0.37 Gupta, et.al, in press, J.Pharm.Sci.

32. Dry Granulation by Roller Compaction • The particle sizes of the milled material is also manifest in the slope of the NIR signal (as predicted) Gupta, et.al, in press, J.Pharm.Sci.

33. Real-Time Setup • Similar trends (as seen before) were observed for Thickness, Width, Force at break and Young’s Modulus Gupta, et.al, in press, J.Pharm.Sci.

34. On-line vs. Off-line Slope Data and Post Milling PS Gupta, et.al, in press, J.Pharm.Sci.

35. Scale Up: 10% Tolmetin Compacts • Alexanderwerk’s WP 120 x 40 • Formulations:100% Avicel® PH200 (MCC), 10% Tolmetin, 30% DiTab®, 60% MCC • 8 Compactor settings studied prepared with and without vacuum Gupta, et.al, in press, J.Pharm.Sci.

36. Use sound scientific principles in the design of the DF and Process Identify the critical attributes (CAs) for the raw materials Identify the process critical control points for the processes (PCCPs) Employ the proper analyses and PAT concepts for process understanding and control Tie it all together with the appropriate informatics to feed the information forward and backwards for QbD and continuous improvement and innovation = reduced risk Were the principles appropriately applied? How were the CAs identified and the formula designed? Ditto for PCCPs What were the bases for the analyses selection? What are the supporting data for all of the above? Product Development History Risk Based Development-CMC questions

37. Summary: PAT, GMPs, RBD, PCCP • Asking the right questions at the right time • Feeding forward and back between disciplines • Designing the product and process against meaningful metrics (performance, stability etc..) • MUST start in R&D • Development of meaningful specs • Real time monitoring • Process understanding for quality and control • Known functionality (i.e., models) against which data are used to control to the mark

38. What do you get at each stage? • Early development – CMC go/no go decisions with a higher level of certainty, i.e., reduced risk • Late phase development – clear formulation and process design rationales • Control strategies based on understanding to reduce the risk • Facilitation of clear regulatory queries and logical responses • Tech transfer – more realistic processes to transfer (Gerry Migliaccio’s “leg up”) • Fewer “surprises” (analogous to forward pass) • Easier approval process and inspections

39. Acknowledgments • Dr. Gregory Amidon – Pfizer, Kalamazoo • CAMP – Consortium for the advanced manufacturing of pharmaceuticals • Abhay Gupta – graduate student in IPPH at Purdue • The Team - Headed by Jon Clark