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

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

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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

Clinical BA Study-Excipient Effect

BCS Class I-Drug BCS Class III-Drug

Time in Hours

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/ml)

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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

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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

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Wavelength

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nsity

Acetaminophen PowderAvicel Powder90 mg Tablet

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Raman Shift (cm-1)

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ive In

tensit

y

Pure Acetaminophen TabletPure Avicel Tablet90 mg Tablet

Content Determination with Raman Spectra

785 nm Laser Excitation

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HPLC Assay Content (mg/tablet)

Rama

n Pred

icted

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tent

(mg/t

ablet

)



Blend Uniformity: NIR PLS Score Images and Localized Spectra

Blend C Tablet

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(1/R

)

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Blend A Tablet

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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 %

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ixel

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Core B Core A

Nitrofurantoin AnhydrousConcentration Map

Anhydrous Conc in Core AEstimated = 8 %Actual = 9 %

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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)


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Measured % Dissolution

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Measured % Dissolution

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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

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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

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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?