DPQR: Advancing “Critical Path” Research

1. DPQR: Advancing “Critical Path” Research ACPS Meeting, October 19th, 2004 Mansoor A. Khan, R.Ph., Ph.D. Director, Division of Product Quality Research

2. Outline • DPQR Mission/Vision • Present teams and projects • Current needs related to critical path and cGMP initiatives • Future directions • Examples for “design space” • Questions

3. Division of Product Quality Research • Mission Advance the scientific basis of regulatory policy with comprehensive research and collaboration; focus/identify low and high-risk product development and manufacturing practices; share scientific knowledge with CDER review staff and management through laboratory support, training programs, seminars and consultations, and foster the utilization of innovative technology in the development, manufacture and regulatory assessment of product development – Stay aligned with OPS and CDER missions • Vision Be recognized leaders in providing support for guidance based on science and peer-reviewed data; well trained staff in state-of-the-art product quality laboratories that is capable of providing any information sought by reviewers, industry, or the FDA leadership. • Culture: The way we live and act – cooperation, mutual respect, synergy, professional development with life-long learning opportunities

4. Teams • 19 scientists divided as follows: • Pharm./Analytical Chemistry Team • Physical Pharmacy Team • Biopharmaceutics Team • Novel Drug Delivery Systems Team (New)

5. Pharm/Analytical Chemistry Projects (Team Leader: Dr. Patrick Faustino) • Prussian Blue (safety, efficacy and product quality studies) • Shelf-Life Extension Program (collaborative) • Isotretinoin (bioanalytical and kinetic studies (collaborative)

6. Safety and Efficacy of Prussian Blue

7. Biopharmaceutics Team (Team Leader: Dr. Donna Volpe) • Small team (Needs to grow) • BCS guidance • Levothyroxine Sodium (Stability and Bioequivalence issues-Collaborative) • Effect of cyclodextrin on permeability • Database on permeability of several drugs • Variability of permeability in caco2 cells • Liposome uptake studies

8. Clinical BA Study-Excipient EffectBCS Class I-Drug BCS Class III-Drug

9. Physical Pharmacy Team(Deputy Director: Dr. Robbe Lyon; Team Leader: Everett Jefferson) Some PAT related activities include: • Content Determination • Blend Uniformity • Moisture uptake • Polymorphic Form • Predicting Dissolution • Particle Sizing • Powder Flow

10. Acetaminophen Powder Avicel Powder 90 mg Tablet Content determination with NIR

11. Pure Acetaminophen Tablet Pure Avicel Tablet 90 mg Tablet Content Determination with Raman Spectra 785 nm Laser Excitation

12. Blend A Tablet 1 0.8 0.6 0.4 Blend Uniformity: NIR PLS Score Images and Localized Spectra Blend C Tablet API Excipient

13. Core A Core B Final Dosage: Hydration • Commercial Nitrofurantoin Capsules • Brand 1: Capsule contains 2 cores: • Core A: 25 mg nitrofurantoin anhydrous (9%) • Core B: 75 mg nitrofurantoin monohydrate (40%) • Brand 2: Capsule contains 3 cores: • Core A: 25 mg nitrofurantoin anhydrous (12.5%) • 2 x Core B: each 40 mg nitrofurantoin monohydrate (ea 20%) • Sensors: NIR Spectroscopy/ NIR Imaging

14. Nitrofurantoin Monohydrate Concentration Map Nitrofurantoin Anhydrous Concentration Map Core B Core B Core A Core A Anhydrous Conc in Core A Estimated = 8 % Actual = 9 % Monohydrate Conc in Core B Estimated = 50 % Actual = 40 % API Hydration by Chemical Imaging: NIR PLS Concentration Maps of Brand 1 Capsule Cores

15. PLS Model: NIR-Dissolution Correlation • NIR Spectra and Dissolution Values of Furosemide Tablets • 144 Tablets • Spectral Range: 1100-2300 nm • Dependent Variable: Dissolution Values at 15 min • Preprocessing • Savitzky Golay 2nd-Derivative • Validation Set (N = 72)0 • Cross-Validation Model • 3 samples from each formulation • Prediction Set (N = 72) • Remaining 3 samples from each formulation

16. Predicting Dissolution from NIR Spectra:Direct Compression (%Diss at 15 min)

17. The DPQR Today…. Analytical Methods Characterization DS Slep Cell Culture DP Stability

18. Critical Path Science Base • The science necessary to evaluate and predict safety and efficacy, and to enable manufacture is different from the science that generates the new idea for a drug, biologic, or device. • In general, NIH and academia do not perform research in this area Dr. Woodcock, May 2004

19. OPS programs and projects will support the achievement of the following attributes of drug products: • Drug quality and performance is achieved and assured through design of effective and efficient development and manufacturing processes • Regulatory specifications are based on a mechanistic understanding of how product and process factors impact product performance • Helen Winkle, ACPS, April 2004

20. “THE DESIRED STATE”/Q8(as agreed by EWG) • Product quality and performance achieved and assured by design of effective and efficient manufacturing processes • Product specifications based on mechanisticunderstanding of how formulation and process factors impact product performance • An ability to effect Continuous Improvement and Continuous "real time" assurance of quality John Berridge, Q8 Rapporteur, FDA, July 2004

21. DPQR Vision for Tomorrow.. DS Analytical Methods • Excipients • Formulation variables • Process variables • Mechanistic evaluations • Optimization & ANN procedures DP Cell Culture Slep PK/ Bioavailability Characterization NDDS Stability • Mixing • Milling • Granulation • Drying • Compression • Coating • Packaging • Nanoparticles • Liposomes • SR/MR • TDDS • Nasal • Pulmonary • Fast disintegration • Solid dispersion Chemical Physical

22. New Projects? • Novel Drug Delivery Systems including nanoparticulates; preparation, characterization, development of in-vitro procedures – in DPQR laboratories • Science-based projects with mechanistic understanding • Process engineering with real time monitoring and modeling – in-house and with collaborations • SLEP/Stability and repackaging issues • Generic Drugs; In vitro methods for determining bioequivalence of locally acting GI drugs; Stability issues with split tablets; Stability issues with Repackaging • Stents? • New CRADAS • Permeability of drugs from nanoparticles/bioavailability studies Near IR probe

23. Box, Hunter and Hunter, 1978

24. Box, Hunter, and Hunter, 1978

25. Evolutionary Operation Box, Hunter, and Hunter, 1978

26. Example of design space Osmotic push-pull system water

27. Plackett-Burman Screening Design 7-factor 2-level design Independent factors Levels used X1 = orifice size (mm) 0.35 0.64 X2 = coating level (%) 100 200 X3 =amount of NaCl in osmotic layer (mg) 1 10 X4 = amount of Polyox N10 (mg) in drug layer 40 60 X5 = amount of Polyox N80 (mg) in osmotic layer 60 80 X6 = amount of Carbopol 934P (mg) in drug layer 0 3 X7 = amount of Carbopol 974P (mg) in osmotic layer0 3 Dependent variable Y1 = cumulative % sCT released up to 3 hr Constraints Y2 (> 5 %) = % tOVM release at 1 hr Y3 (> 10 %) = % tOVM release at 2 hr Y4 (> 20 %) = % tOVM release at 3 hr

28. Factors Main Effects (Y1) X1 3.33 X2 8.65 X3 -5.14 X4 -9.25 X5 -2.26 X6 -25.16 X7 -2.60 Plackett-Burman Screening Design Y1 = 56.03+3.33X1+8.65X2–5.14X3–9.25X4–2.26X5–25.16X6- 2.60X7 Dissolution profiles Rakhi Shah et al., Clin. Res. & Reg. Affairs, (In press) 2004 A

29. Box-Behnken Optimization Design 3-factor 3-level design = 15 runs Independent factors Levels used X1 = amount of NaCl (mg) 0.1 0.5 0.9 X2 = coating level (%) 100 200 300 X3 = amount of Polyox N10 (mg) 40 50 60 Dependent variable Y5 = cumulative % sCT released up to 3 hr Constraints Y1 (16.65  10 %) = cumulative % sCT released up to 0.5 hr Y2 (33.33  10 %) = cumulative % sCT released up to 1 hr Y3 (49.95  10 %) = cumulative % sCT released up to 1.5 hr Y4 (66.66  10 %) = cumulative % sCT released up to 2 hr Drug layer: sCT+tOVM+glycyrrhetinic acid

30. Box-Behnken Optimization Design Y5 = 89.35- 2.78X1 - 1.66X2 + 1.38X3 –0.46X1X2 –0.41X2X3 –2.23X1X3 –6.21X21 –1.67 X22 + 2.23 X23 Factors X1 0.2875 X2 -0.9994 X3 1 Responses Y1 6.65 Y2 31.8 Y3 58.1 Y4 76.6 Y5 93.88 R2 = 0.94

31. Box-Behnken Optimization Design Effect of X1(NaCl), X3 (Polyox N10) on Y5 (sCT release) Contour plot Response- surface plot

32. Examples of nanoparticles • Studies conducted to characterize and evaluate a nanoparticulate formulation • Excipient induced recrystallization(excipient selection) • Droplet size analysis • Thermal analysis(DSC) • Binary phase diagrams(formation of eutectic mixtures) • Pseudo ternary phase diagram(area of spontaneous emulsion formation) • FTIR analysis( for stability evaluation) • Liquid crystalline phase determination • Dissolution studies • Turbidimetry(Time-turbidity profile for emulsification rate) Int. J. Pharm. 2002, 235, 247-265

33. Optimization by Box-Behnken Design Palamakula et al., AAPS Pharm. Sci. Tech., (2004, In press)

34. Palamakula et al., 2004, AAPS PharmSciTech

35. Questions to the advisory committee • Do you think we are missing anything important that needs to be pursued at this time? • Does a systematic study with a designed set of experiments provide opportunities for reduction of PAS documents? • Do you agree that the information on “design space” with a designed set of experiments will reduce the OOS situations? • Do you agree that the research with well-designed set of experiments on lab scale will create opportunities for continuous improvements and innovations in manufacturing?