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Risk-Based Development for Quality by Design

Ken MorrisPurdue University

Department of Industrial and Physical Pharmacy

FDA SAB Manufacturing Sub-CommitteeSeptember, 17th, 2003

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Pharmaceutical cGMPs for the 21st Century: A Risk-Based Approach

A science and risk-based approach to product quality regulation incorporating an integrated quality systems

approach

1.Risk-based orientation 2.Science-based policies and standards3.Integrated quality systems orientation4.International cooperation5.Strong Public Health Protection

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MW Pharmaceutical cGMPs for the 21st Century: A

Risk-Based ApproachWhat’s New?

• Good science isn’t new, we all do it now

• Some technologies, techniques, and models are• Computers• Sensors• Chemometrics• Phenomenological and Fundemental Models

• The mutual FDA-Industry-Academic recognition of the technical “way forward “ in application of the state of the science

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The Issue: API, Formulation, and

Process Variables and Dosage Form Performance

Ajaz Hussain, Arden House 2003

(weak acid, rapid dissolution in SIF)

Time in Hours

0 1 2 3 4 5 6

Dru

g C

on

cen

trat

ion

in P

lasm

a (n

g/m

l)

0

200

400

600

800

1000

1200

1400

1600

1800Capsule (Ref.)

Tablet 1(wet-granulation - starch)

Tablet 2(direct compression -

calcium phosphate)

USP Paddle 50rpm, Q 70% in 30 min

(weak acid, rapid dissolution in SIF)

Time in Hours

0 1 2 3 4 5 6

Dru

g C

on

cen

trat

ion

in P

lasm

a (n

g/m

l)

0

200

400

600

800

1000

1200

1400

1600

1800Capsule (Ref.)

Tablet 1(wet-granulation - starch)

Tablet 2(direct compression -

calcium phosphate)

USP Paddle 50rpm, Q 70% in 30 min

Peak Concentration Vs. % Dissolved in vitroClarke et al. J. Pharm. Sci. 66: 1429, 1977

% Dissolved in 40 minutes

20 40 60 80 100

Pea

k C

on

cen

trat

ion

(u

g/1

00m

l)

12

14

16

18

20

22

24

26

28

30

A

B

H

I

D

F

JC

G

E

Different filler

Peak Concentration Vs. % Dissolved in vitroClarke et al. J. Pharm. Sci. 66: 1429, 1977

% Dissolved in 40 minutes

20 40 60 80 100

Pea

k C

on

cen

trat

ion

(u

g/1

00m

l)

12

14

16

18

20

22

24

26

28

30

A

B

H

I

D

F

JC

G

E

Different filler

Low Solubility - High Permeability

- Acidic compound in SIF

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MW Initial Drug Substance Characterization

Property

1. Purity

2. Solubility/dissolution

3. Partitioning

4. Stability

5. Solid state form/shape

6. Hygroscopicity

Theory-method

1. Chemistry - HPLC

2. Thermodynamics, Kinetics - traditional and automated measurement

3. Thermo - various

4. Chemistry and HPLC - SS methods

5. Crystallography SS physics - screening, prediction control

6. BET - Automated systems

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Pharmaceutical Technology Europe, 17, June 1994

“Formulations and processes are only as robust as their ability to accommodate changes in the raw materials” KRM

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Form Screening, Selection, and Control

0

2

4

6

8

10

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

UnknownORP

YPON

Unknown

ORP

YP

ON

Fre

quen

cy

Supersaturation Ratio

0

2

4

6

8

10

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

UnknownORP

YPON

Unknown

ORP

YP

ON

Fre

quen

cy

Supersaturation Ratio

Hilden et.al., Crystal Growth and Design, 2003, in press

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Ulrich Griesser, PHANTA 9/03

Cefotaxim Sodium Moisture Uptake - Ulrich Griesser, Univ. of Innsbruck, Simultaneous Multi-sample instrument

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Single Crystal Structure

+PXRD Patternexperimental

PXRD Patternsimulated

BFDH Morphology

Comb. Simple Forms Morphology

+Index Major Faces

SPO/DIFRAC Model

Average Shape

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MW Summary of Estimated Average Shapes and Areas

110 = 64%

001 = 31%

-201 = 5%

110 = 43%

011 = 29 %

200 = 15%

001 = 7%

-201 = 6%

002 = 60%

102 = 33%

100 = 4%

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MW Formulation Design and API Process

Development

Formulation element

1. Dosage form selection

2. Excipient selection

3. Stability to processing

4. Mechanical properties

1. Flow

2. Compaction

5. Initial processing

Theory-method

1. Medical processability

2. Excipient properties – interaction studies, phsico-chemical properties

3. PIT –

4. ME/MSE –

1. flow correlations,

2. heckel analysis

5. Process models – prototypes and PAT

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Avalanche testingTSI Inc.

Powder RheologyFreeman Tech.

Shear CellVirendra M. Puri,

Penn State

Powder Flow

From Heckel, Trans. AIME, 221: 1961. 14

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Development of the Heckel Equation

D1

1ln

P

01

1ln

D

A

AkPD

1

1ln

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PROCESS 1 PROCESS 2

Shape and Flow

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TREND BETWEEN MASS FLOW AND

SHAPE

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Operation

1. Particle size reduction

2. Charging

3. Blending

4. Dry granulation (RC)

5. Wet granulation

• Fluid bed

• High shear

6. Drying

7. Segregation

8. CU

9. Hardness

10.Coating

Modeling

1. Surface energy-size laws

2. Triboelectric series model

3. Cascade Model, DEM

4. Density-Strength

5. Various

• Size-Moisture-Attrition

• Water Environ Model

6. Heat/Mass transfer/FAST

7. Sinusoidal Variation

8. Partial volume analysis

9. Density response

10.Geometric Growth Compensation

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MW Particle Size Reduction Models

Rittinger’s law: The work required in crushing is proportional to the new surface created.

                   Where: P=power required, dm/dt=feed rate to crusher, Dsb = ave diameter before crushing, DSQ=ave after crushing, Kr=Rittinger’s coef.

Kick’s law: the work required for crushing a given mass of material is constant for the same reduction ratio, that is the ratio of the initial particle size to the finial particle size

              Kk=Kick’s coef.                              

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Charging:Qualitative Trends in a

Faraday Pail-Blender System

4.2

8.0

5.5

1.2

0.1

0.1 0.1

0.0

-0.2

0.3

3.7

2.7

4.0

0.1

0.3 0.7

2.01.4

3.3

-4

0

4

8

12

0% 5% 10% 20% 35% 50% 65% 80% 90% 95% 100%

Composition (% Pseudoephedrine HCl)

No

rma

lize

d M

ax

Ch

arg

e [

nC

/m2 ]

-8

-4

0

4

8

100% 95% 90% 80% 65% 50% 35% 20% 10% 5% 0%

Composition (% DiTab)

Predicted Measured

3.4

-8.3

3.9

-3.1

3.5

-3.4

-2.5

2.82.2

-7.1-7.7

-15.0

-10.0

-5.0

0.0

5.0

10.0

0% 5% 10% 50% 90% 95% 100%

Composition (% Pseudoephedrine HCl)

No

rma

lize

d M

ax C

harg

e [

nC

/m2 ]

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

100% 95% 90% 50% 10% 5% 0%

Composition (% APAP)

Predicted Measured

Purdue Triboelectric SeriesLactose, monohydrateMagnesium stearatePseudoephedrine HCl- - - - - - - - - - - - - - - - - -Di-Tab®Lactose, anhydrousMicrocrystalline cellulose (Avicel ® PH302)APAPMicrocrystalline cellulose (Avicel ® PH105)

+

-

0

+

-

0

David Engers, unpublished data Purdue

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Modeling Blending: Cascade Region

For fine grains, the boundary between the characteristic

region and the remaining powder bed is parabolic in

shape

The powder bed below the boundary

rotates with the mixer as a solid

body.

Characteristic region

Blender head space

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180kg Run

0

20

40

0 10 20 30 40 50 60 70

Rotation Number

To

lmet

in W

t%

Surface A Window Theoretical

16kg Run

Blending Scaled “Down”

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MW Dry Granulation by

Roller Compaction

Unpublished CAMP data –

A.Gupta

y = 21.54e-0.4493x

R2 = 0.9884

0

2

4

6

8

10

12

14

16

18

20

4 5 6 7 8 9 10 11 12

Roll Speed (RPM)

Fo

rce

at

bre

ak (

N)

Avicel® PH-200 compacts

VFS Speed: HFS Speed:

Roll Gap: Roll Pressure:

194 - 197 rpm 29 - 30 rpm 0.031 - 0.038" 6551 lb/in

y = 0.3672x + 0.1754

R2 = 0.9899

0.15

0.20

0.25

0.30

0.35

0.40

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Force at break/Thickness/Width (N/mm2)

Slo

pe

of

NIR

Sp

ec

tru

m

4 5 6

7 8 9

10 11 12

Roll Speed (RPM)

Avicel® PH-200 compacts

VFS Speed: HFS Speed:

Roll Pressure:

200 rpm30 rpm 6560 lb/in

The strength is a linear function of the density which is monitored by NIR

Semi Empirically

F=(SNIR-0.17)/0.37

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Avicel® PH-200 Milled Compacts

0

200

400

600

800

1000

3 4 5 6 7 8 9 10 11 12 13

Roll Speed (rpm)

Pa

rtic

le S

ize

(m

)Day1

Day2

Increaing Roll Speed

d90

d50

d10

Avicel® PH-200 Milled Compacts

0

200

400

600

800

1000

1200

2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

1 / Slope NIR Spectrum

Par

ticl

e S

ize

(m

)Day1

Day2

Increaing Roll Speed

d90

d50

d10

The particle sizes of the milled material is also manifest in the slope of the NIR signal (as predicted)

Dry Granulation by Roller

Compaction Unpublished CAMP data –

A.Gupta

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Monitoring and Modeling of Fluid Bed Granulation

Paul Findlay, Ph.D dissertation, Purdue Univ, 2003

0

2

4

6

8

10

12

14

16

18

0 20 40 60 80 100

Elapsed Time (min)

Mo

istu

re C

on

ten

t (%

w/w

)

0.300

0.350

0.400

0.450

0.500

0.550

0.600

Mea

n P

art

icle

Siz

e (m

m)

Moisture Content

Particle Size

(u,v)= 0*(u,v)

Eq

uili

bri

um

Moi

stu

re C

onte

nt

GRANULATION TIME

Eq

uili

bri

um

Moi

stu

re C

onte

nt

GRANULATION TIME

SIZ

E

(u,v)= 0*(u,v)

OptimumConditions

M=M0-Kt

M=M0’exp(-K’t)

(u,v)= 0*(u,v)

Eq

uili

bri

um

Moi

stu

re C

onte

nt

GRANULATION TIME

Eq

uili

bri

um

Moi

stu

re C

onte

nt

GRANULATION TIME

SIZ

E

(u,v)= 0*(u,v)

OptimumConditions

M=M0-Kt

M=M0’exp(-K’t)

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Wet Granulation

At the capillary stage, the water

may interact with the surface in such a way as

to change the two prominent

NIR bands (1450 and 1940 nm) differently.

Pendular

Funicular

CapillaryDroplet

Drying

OverWetting

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NIR during granulation–wet massing and Particle sizeUnpublished CAMP data, Dr. Jukka Rantanen –

X2=255 rpm

X1=110 g(=X3)

Process time (s)

0 100 200 300 400 500 600

Slo

pe

0.0002

0.0004

0.0006

0.0008

0.0010H13 (1 min)H15 (3.5 min)H14 (6 min)

MIXING WET MASSINGSPRAYING

320 m

410 m

610 m

NIR

Tre

ated

Res

pons

e

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Critical moisture

Te

mp

era

ture

Mo

istu

re C

on

ten

t

40

60

80

100

120

140

160

180

Drying Time (min)

MM

55

Re

ad

ing

45

47

49

51

53

55

57

59

61

63

65

Te

mp

era

ture

(°C

)

T

MM55

0 30252015105

K.R. Morris, S.L. Nail, G.E. Peck, S.R. Byrn, U.J. Griesser, J.G. Stowell, S.-J. Hwang, K. Park Pharm Sci Tech Today 1 6 235–245 (1998).

DRYING : NIR -Exit Temp vs. Time for APAP Granulation

KtoQQ

)t'k(EXPkQQQ 'o

Evaporative

Diffusive

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MW Full Scale Fast Drying Trials of an Ibuprofen

Granulation

75

95

115

135

155

175

195

215

235

0.00 5.00 10.00 15.00 20.00 25.00

Time (min)

NIR

Mo

nito

r (A

rbitr

ary

Va

lue

s)

Conventional Drying

Fast Drying

Morris et.al., Drug Dev. Ind. Pharm., 26 (9):985-988 (2000)

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Drying Excursions and

Dissolution

CAMP unpublished data

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Tablet CU: Testing a Model

TP CVCV

WHOLE TABS HALF TABS QUARTER TABS

Active 1 Active 2 Active 1 Active 2 Active 1 Active 2

MEAN 101.9 100.9 101.8 99.6 102.1 100.5

SD 0.7 1.6 1.4 2.8 2.4 5.1

CV (%) 0.7 1.6 1.3 2.8 2.3 5.1

T. Li, et. al., in press Pharm. Res. BioMed Anal.

CU for constant size portions of tablets must be larger than for the whole, so in spec using real time monitoring of “part” of the tablets means in spec for the whole tablet

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COATING

HPMC, Sulfanilamide and, Moisture Real-Time Measurements

-400

-300

-200

-100

0

100

200

0 20 40 60 80 100 120 140 160

Elapsed Time (min)

Su

lfa

nila

mid

e G

au

ge

-300

-100

100

300

500

700

Mo

istu

re G

au

ge

Sulfanilam ideMoisture

HPMC and Sulfanilamide Calculations (Peak Height)

0.15

0.2

0.25

0.3

0.35

0.4

0 30 60 90 120 150

Elapsed Time (min)

Abs

orba

nce

(log(

1/R

))

HPMCSulfanilamide

Unpublished CAMP data, P. Findlay,In prep for JPS

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MW Where do we stand?

Taken individually these theories and techniques look independent

Together, however, they show a concerted effort to describe contributions to the overall process of drug development.

These principles and techniques are applicable to batch and continuous processing and may be linked by multi-variate (chemometric) methods after univariate conformation.

Ultimately this give us the ability to understand how development variables interact to influence the final product and to design in the quality.

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MW The Business Case

Using existing scientific principles, monitoring and modeling capabilities one will understand more about processes and be able to detect variations quickly

• The earlier you start collecting information the more you’ll know the more comfortable everyone will be

Given this level of knowledge and communication with FDA, you will be at the lowest risk (as proposed) possible for your product/process

If your studies show up variability, the sooner you know the better. There is no such thing as what you don’t know won’t hurt you in science based development.

The companies have many of the tools to lower their risk levels RIGHT NOW This will only improve with more research.