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

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

Division of Product Quality Research • Mission Advance the scientific basis of regulatory policy 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

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

Pharm/Analytical Chemistry Projects (Team Leader: Dr. Patrick Faustino) • Prussian Blue (safety, efficacy and 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)

Safety and Efficacy of Prussian Blue Safety and Efficacy of Prussian Blue

Biopharmaceutics Team • • • (Team Leader: Dr. Donna Volpe) Small team (Needs to 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 caco 2 cells • Liposome uptake studies

Clinical BA Study-Excipient Effect BCS Class I-Drug BCS Class III-Drug Clinical BA Study-Excipient Effect BCS Class I-Drug BCS Class III-Drug

Physical Pharmacy Team (Deputy Director: Dr. Robbe Lyon; Team Leader: Everett Jefferson) Some PAT 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

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

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

Blend Uniformity: NIR PLS Score Images and Localized Spectra Blend A Tablet Blend C Blend Uniformity: NIR PLS Score Images and Localized Spectra Blend A Tablet Blend C Tablet 1 0. 8 0. 6 0. 4 API Excipient

Final Dosage: Hydration • Commercial Nitrofurantoin Capsules • Brand 1: Capsule contains 2 cores: 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 Core A Core B

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

PLS Model: NIR-Dissolution Correlation • NIR Spectra and Dissolution Values of Furosemide Tablets § 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 2 nd-Derivative • Validation Set (N = 72)0 § Cross-Validation Model § 3 samples from each formulation • Prediction Set (N = 72) § Remaining 3 samples from each formulation

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

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

Critical Path Science Base § The science necessary to evaluate and predict safety and 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

·OPS programs and projects will support the achievement of the following attributes of drug ·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

“THE DESIRED STATE”/Q 8 (as agreed by EWG) · Product quality and performance achieved “THE DESIRED STATE”/Q 8 (as agreed by EWG) · Product quality and performance achieved and assured by design of effective and efficient manufacturing processes · Product specifications based on mechanistic understanding of how formulation and process factors impact product performance · An ability to effect Continuous Improvement and Continuous "real time" assurance of quality John Berridge, Q 8 Rapporteur, FDA, July 2004

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

New Projects? • Novel Drug Delivery Systems including nanoparticulates; preparation, characterization, development of in-vitro New Projects? • Novel Drug Delivery Systems including nanoparticulates; preparation, characterization, development of in-vitro procedures – in DPQR laboratories Ne • Science-based projects with mechanistic ar IR understanding pro be • 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

Box, Hunter and Hunter, 1978 Box, Hunter and Hunter, 1978

Box, Hunter, and Hunter, 1978 Box, Hunter, and Hunter, 1978

Evolutionary Operation Box, Hunter, and Hunter, 1978 Evolutionary Operation Box, Hunter, and Hunter, 1978

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

Plackett-Burman Screening Design 7 -factor 2 -level design Independent factors X 1 = orifice Plackett-Burman Screening Design 7 -factor 2 -level design Independent factors X 1 = orifice size (mm) X 2 = coating level (%) X 3 =amount of Na. Cl in osmotic layer (mg) X 4 = amount of Polyox N 10 (mg) in drug layer X 5 = amount of Polyox N 80 (mg) in osmotic layer X 6 = amount of Carbopol 934 P (mg) in drug layer X 7 = amount of Carbopol 974 P (mg) in osmotic layer Dependent variable Y 1 = cumulative % s. CT released up to 3 hr Constraints Y 2 (> 5 %) = % t. OVM release at 1 hr Y 3 (> 10 %) = % t. OVM release at 2 hr Y 4 (> 20 %) = % t. OVM release at 3 hr Levels used 0. 35 100 1 40 60 0 0 0. 64 200 10 60 80 3 3

Plackett-Burman Screening Design Y 1 = 56. 03+3. 33 X 1+8. 65 X 2– Plackett-Burman Screening Design Y 1 = 56. 03+3. 33 X 1+8. 65 X 2– 5. 14 X 3– 9. 25 X 4– 2. 26 X 5– 25. 16 X 6 - 2. 60 X 7 Main Effects Factors (Y 1) X 3. 33 1 Dissolution profiles X 2 X 3 X 4 X 5 X 6 X 7 8. 65 -5. 14 -9. 25 -2. 26 -25. 16 -2. 60 Rakhi Shah et al. , Clin. Res. & Reg. Affairs, (In press) 2004 A

Box-Behnken Optimization Design 3 -factor 3 -level design = 15 runs Independent factors X Box-Behnken Optimization Design 3 -factor 3 -level design = 15 runs Independent factors X 1 = amount of Na. Cl (mg) X 2 = coating level (%) X 3 = amount of Polyox N 10 (mg) Levels used 0. 1 100 40 0. 5 200 50 0. 9 300 60 Dependent variable Y 5 = cumulative % s. CT released up to 3 hr Constraints Y 1 (16. 65 10 %) = Y 2 (33. 33 10 %) = Y 3 (49. 95 10 %) = Y 4 (66. 66 10 %) = cumulative % s. CT released up to 0. 5 hr cumulative % s. CT released up to 1. 5 hr cumulative % s. CT released up to 2 hr Drug layer: s. CT+t. OVM+glycyrrhetinic acid

Box-Behnken Optimization Design Y 5 = 89. 35 - 2. 78 X 1 - Box-Behnken Optimization Design Y 5 = 89. 35 - 2. 78 X 1 - 1. 66 X 2 + 1. 38 X 3 – 0. 46 X 1 X 2 – 0. 41 X 2 X 3 – 2. 23 X 1 X 3 – 6. 21 X 21 – 1. 67 X 22 + 2. 23 X 23 Factors X 1 0. 2875 X 2 -0. 9994 X 3 1 Responses Y 1 6. 65 Y 2 31. 8 Y 3 58. 1 Y 4 76. 6 Y 5 93. 88 R 2 = 0. 94

Box-Behnken Optimization Design Effect of X 1(Na. Cl), X 3 (Polyox N 10) on Box-Behnken Optimization Design Effect of X 1(Na. Cl), X 3 (Polyox N 10) on Y 5 (s. CT release) Contour plot Responsesurface plot

Examples of nanoparticles Studies conducted to characterize and evaluate a nanoparticulate formulation • Excipient 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

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

Palamakula et al. , 2004, AAPS Pharm. Sci. Tech Palamakula et al. , 2004, AAPS Pharm. Sci. Tech

Questions to the advisory committee • Do you think we are missing anything important 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 welldesigned set of experiments on lab scale will create opportunities for continuous improvements and innovations in manufacturing?