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DPQR: Advancing “Critical Path” ResearchACPS Meeting, October 19th, 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 critical path and cGMP initiatives
• Future directions
• Examples for “design space”
• Questions
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
• VisionBe 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 Pharmacy Team– Biopharmaceutics Team– Novel Drug Delivery Systems Team (New)
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
A
0
50
100
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200
250
300
0 1 2 3 4 5 6 7 8 9 10
pH
Ce
sium
Bind
ing (m
g/g)
1 hr 4 hr 24 hr
A
0
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60
90
120
150
0 1 2 3 4 5 6 7 8 9 10 11 12 13
pH
Cyan
ide (
mg/g
) 1 hr
4 hr
24 hr
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
Clinical BA Study-Excipient Effect
BCS Class I-Drug BCS Class III-Drug
Time in Hours
0 2 4 6 8 10 12 14 16 18 20 22 24
Met
op
rolo
l Co
nc.
(m
cg/m
l)
0
20
40
60 Sucrose
Sorbitol
Time in Hours
0 2 4 6 8 10 12R
anit
idin
e C
on
c.
(ng
/ml)
0
100
200
300
400
500
Sucrose Solution
Sorbitol Solution
Physical Pharmacy Team(Deputy Director: Dr. Robbe Lyon; Team Leader: Everett Jefferson)
• Content Determination• Blend Uniformity• Moisture uptake• Polymorphic Form• Predicting Dissolution• Particle Sizing• Powder Flow
Some PAT related activities include:
Content determination with NIR
50
70
90
110
130
50 70 90 110 130
HPLC Assay Content (mg/tablet)
NIR
Pred
icted
Con
tent (
mg/ta
blet)
Training Set (n=140)y = 0.961x + 3.56R = 0.9801RMSEC = 3.9
Test Set (n=130)y = 0.903x + 7.88R = 0.9803RMSEP = 4.1
1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500-0.3000
-0.1000
0.1000
0.3000
0.5000
Wavelength
Inte
nsity
Acetaminophen PowderAvicel Powder90 mg Tablet
0
4000
8000
185 685 1185 1685
Raman Shift (cm-1)
Relat
ive In
tensit
y
Pure Acetaminophen TabletPure Avicel Tablet90 mg Tablet
Content Determination with Raman Spectra
785 nm Laser Excitation
50
70
90
110
130
50 70 90 110 130
HPLC Assay Content (mg/tablet)
Rama
n Pred
icted
Con
tent
(mg/t
ablet
)
Training Set (n=130)y = 0.885x + 10.68R = 0.9407RMSEC = 6.3
Test Set (n=130)y = 0.900x + 7.64R = 0.9521RMSEP = 6.0
Blend Uniformity: NIR PLS Score Images and Localized Spectra
Blend C Tablet
1100 1200 1300 1400 1500 1600 1700
0.2
0.6
1.0
1.4
Wavelength (nm)
Log
(1/R
)
Pixels
Pix
els
20 40 60 80 100
110
120
130
140
150
160
170
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190
200
1100 1200 1300 1400 1500 1600 1700
0
0.4
0.8
1.2
Wavelength (nm)
Log
(1/R
)
0.4
0.6
0.8
1
Blend A Tablet
Pixels
Pix
els
20 40 60 80 100
10
20
30
40
50
60
70
80
90
100
API
Excipient
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
Nitrofurantoin MonohydrateConcentration Map
Monohydrate Conc in Core BEstimated = 50 %Actual = 40 %
0
0.1
0.2
0.3
0.4
PixelsP
ixel
s
50 100 150 200 250 300
50
100
150
200
250
Core B Core A
Nitrofurantoin AnhydrousConcentration Map
Anhydrous Conc in Core AEstimated = 8 %Actual = 9 %
0
0.05
0.1
0.15
0.2
Pixels
Pix
els
50 100 150 200 250 300
50
100
150
200
250
Core B Core A
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
Predicting Dissolution from NIR Spectra:Direct Compression (%Diss at 15 min)
Training Set (n=72)y = 0.969x + 1.81R = 0.9844RMSEC = 5.3
0
20
40
60
80
100
0 20 40 60 80 100
Measured % Dissolution
NIR
Pre
dic
ted
% D
isso
luti
on
Training Set (n=72)y = 0.969x + 1.81R = 0.9844RMSEC = 5.3
0
20
40
60
80
100
0 20 40 60 80 100
Measured % Dissolution
NIR
Pre
dic
ted
% D
isso
luti
on
Test Set (n=72)y = 1.005x + 1.22R = 0.9784RMSEP = 6.8
DS Characterization
Analytical Methods
DPCell
CultureSlep
Stability
The DPQR Today….
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 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”/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
DS
Characterization
Analytical Methods
DPCell
Culture SlepStability
Nanoparticles Liposomes SR/MR TDDS Nasal Pulmonary Fast disintegration Solid dispersion
PK/Bioavaila
bility NDDS
Excipients Formulation variables Process variables Mechanistic evaluations Optimization & ANN procedures
•Mixing•Milling•Granulation•Drying•Compression•Coating•Packaging
DPQR Vision for Tomorrow..
Physical Chemical
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
Box, Hunter and Hunter, 1978
Box, Hunter, and Hunter, 1978
Evolutionary Operation
Box, Hunter, and Hunter, 1978
Example of design spaceExample of design spaceOsmotic push-pull system
water
Plackett-Burman Screening DesignPlackett-Burman Screening Design
Independent factors Levels usedX1 = orifice size (mm) 0.35 0.64 X2 = coating level (%) 100 200X3 =amount of NaCl in osmotic layer (mg) 1 10X4 = amount of Polyox N10 (mg) in drug layer 40 60X5 = amount of Polyox N80 (mg) in osmotic layer 60 80X6 = amount of Carbopol 934P (mg) in drug layer 0 3X7 = amount of Carbopol 974P (mg) in osmotic layer 0 3
Dependent variableY1 = cumulative % sCT released up to 3 hr
ConstraintsY2 (> 5 %) = % tOVM release at 1 hrY3 (> 10 %) = % tOVM release at 2 hrY4 (> 20 %) = % tOVM release at 3 hr
7-factor 2-level design
0
20
40
60
80
100
0 2 4
time (hr)
% s
CT
rel
ease
d Run 1
Run 2
Run 3
Run 4
Run5
Run 6
Run 7
Run 8
Run 9
Run 10
Run11
Run 12
Plackett-Burman Screening DesignPlackett-Burman Screening Design
Dissolution profiles
Factors
Main Effects (Y1)
X1 3.33X2 8.65X3 -5.14X4 -9.25X5 -2.26X6 -25.16X7 -2.60
Y1 = 56.03+3.33X1+8.65X2–5.14X3–9.25X4–2.26X5–25.16X6- 2.60X7
Rakhi Shah et al., Clin. Res. & Reg. Affairs, (In press) 2004 A
Box-Behnken Optimization DesignBox-Behnken Optimization Design
Independent factors Levels usedX1 = amount of NaCl (mg) 0.1 0.5 0.9 X2 = coating level (%) 100 200 300X3 = amount of Polyox N10 (mg) 40 50 60
Dependent variableY5 = cumulative % sCT released up to 3 hr
ConstraintsY1 (16.65 10 %) = cumulative % sCT released up to 0.5 hrY2 (33.33 10 %) = cumulative % sCT released up to 1 hrY3 (49.95 10 %) = cumulative % sCT released up to 1.5 hrY4 (66.66 10 %) = cumulative % sCT released up to 2 hr
3-factor 3-level design = 15 runs
Drug layer: sCT+tOVM+glycyrrhetinic acid
Box-Behnken Optimization DesignBox-Behnken Optimization Design
FactorsX1 0.2875X2 -0.9994X3 1
ResponsesY1 6.65Y2 31.8Y3 58.1Y4 76.6Y5 93.88
R2 = 0.94
Y5 = 89.35 - 2.78X1 - 1.66X2 + 1.38X3 –0.46X1X2 –0.41X2X3 –2.23X1X3 –6.21X2
1 –1.67 X2
2 + 2.23 X23
Box-Behnken Optimization DesignBox-Behnken Optimization DesignEffect of X1(NaCl), X3 (Polyox N10) on Y5 (sCT release)
Contour plot
Response-surface plot
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
Examples of nanoparticles
Optimization by Box-Behnken Design
Palamakula et al., AAPS Pharm. Sci. Tech., (2004, In press)
Variables in the Box-Behnken design Variables Levels used Low Medium High Independent Variables: X1 = R-limonene 18 49.5 81 X2 = Cremphor EL 7.2 32.4 57.6 X3 = Capmul GMO-50 1.8 7.2 12.6 Low High Goal Dependent Variables: Y1= Dissolution (5 min) 1.6 82.06 Maximize Y2= Dissolution (15 min) 1.3 99.69 >90
X2=Cremophor EL
X1= R-(+)-limonene
X3=Campmul GMO-50
Z= %
Dis
solu
tion
afte
r 5 m
in% dissolved
-10.0-2.0
2.0-14.0
14.0-26.0
26.0-38.0
38.0-50.0
50.0-62.0
62.0-74.0
74.0-86.0
Z=57.84
Z=68.47
Z=
3.95
Z=1
8.21Z=72.18
-1 -0.6 -0.2 0.2 0.6 1 -1-0.6
-0.20.2
0.61
0
20
40
60
80
100
Palamakula et al., 2004, AAPS PharmSciTech
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?
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