DPQR: Advancing Critical Path Research

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DPQR Advancing Critical Path Research

• ACPS Meeting, October 19th, 2004
• Mansoor A. Khan, R.Ph., Ph.D.
• Director, Division of Product Quality Research

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Outline

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

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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

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Teams

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

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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)

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Safety and Efficacy of Prussian Blue
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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

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

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Content determination with NIR
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Content Determination with Raman Spectra
785 nm Laser Excitation
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Blend Uniformity NIR PLS Score Images and
Localized Spectra
Blend C Tablet
API Excipient
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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

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API Hydration by Chemical Imaging NIR PLS
Concentration Maps of Brand 1 Capsule Cores
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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

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Predicting Dissolution from NIR SpectraDirect
Compression (Diss at 15 min)
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The DPQR Today.
Analytical Methods
Characterization
DS
Slep
Cell Culture
DP
Stability
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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

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• 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

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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 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, Q8 Rapporteur, FDA, July 2004

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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
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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/bioavaila
  bility studies

Near IR probe
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Box, Hunter and Hunter, 1978
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Box, Hunter, and Hunter, 1978
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Evolutionary Operation
Box, Hunter, and Hunter, 1978
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Example of design space
Osmotic push-pull system
water
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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
layer 0 3 Dependent variable Y1 cumulative
sCT released up to 3 hr Constraints Y2 (gt 5 )
tOVM release at 1 hr Y3 (gt 10 ) tOVM
release at 2 hr Y4 (gt 20 ) tOVM release at 3
hr
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Plackett-Burman Screening Design
Y1 56.033.33X18.65X25.14X39.25X42.26X525.1
6X6- 2.60X7
Dissolution profiles
Rakhi Shah et al., Clin. Res. Reg. Affairs, (In
press) 2004 A
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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 sCTtOVMglycyrrhetinic acid
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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
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Box-Behnken Optimization Design
Effect of X1(NaCl), X3 (Polyox N10) on Y5 (sCT
release)
Contour plot
Response- surface plot
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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
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Optimization by Box-Behnken Design
Palamakula et al., AAPS Pharm. Sci. Tech., (2004,
In press)
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Palamakula et al., 2004, AAPS PharmSciTech
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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?