Session V, Track 2: Oral Formulations and Componentss3.amazonaws.com/zanran_storage/€¦USP...

160
USP Workshop on Particle Size: Particle Detection and Measurement December 8-10, 2010; USP Headquarters Chair: Dr. Steven Byrn, Purdue University Session V, Track 2: Oral Formulations and Components

Transcript of Session V, Track 2: Oral Formulations and Componentss3.amazonaws.com/zanran_storage/€¦USP...

USP Workshop on Particle Size:

Particle Detection and Measurement

December 8-10, 2010; USP Headquarters

Chair: Dr. Steven Byrn,

Purdue University

Session V, Track 2: Oral Formulations

and Components

Mansoor A. Khan, R.Ph., Ph.D.Director, Division of Product Quality Research

Center for Drug Evaluation and Research

Food and Drug Administration

Dec 9, 2010

USP Workshop on Particle Size: Particle Detection and Measurement

USP Headquarters, Rockville, MD

*Impact of particle size on product

performance: An FDA Perspective

*Views expressed are my own and doesn’t necessarily represent FDAs position

3

Outline

• Importance

• Some challenges

• Particle size in ICH Q6A

• Method selection

• Real time measurements

4

Particle size• Particle size is one of the most important

physico-chemical property.

• In the case of suspensions, particle size can affect dissolution, bioavailability, settling, physical and chemical stability, appearance, dose content uniformity etc.

• Particle size is very relevant for API and excipients, as well as other products such as nanoparticles, microspheres, liposomes

5

Which method to use?

Shah RB, Khan MA. In Nanotechnology in Drug Delivery, DeVilliers M et al (Editors), Springer, 2009

Shah RB, Khan MA.. In Nano-materials for Pharmaceutical Scientists, Yin S, et al (Editors), John Wiley & Sons, 2010

6

6

Particle Size Reduction

A change in cube size from 1 mm to 100 nms results in 10 12

particles with a total surface area change from 6 sq mm to 60,000 sq mm

Rate of dissolution : dM/dT = SA x D (Cs -C/)h

Rate of diffusion = dn/dt = P X SA X dC/dX

3434

34

24

19.3Same volumes and Dv but

different surface areas

7

Challenges of particle size

• When is it important?

• Which method?

• How to validate?

• Consistency of results?

• What specs?

• What to expect when spec changes?

• How do we know what we are measuring in a mixture?

8

ICH Q6A

• A “specification” is defined as a list of tests,

reference to analytical procedures, and

appropriate acceptance criteria that are

numerical limits, ranges or other criteria

• Conformance to specifications means that the

drug substance and/or drug product, when

tested according to the listed analytical

procedure, will meet the listed acceptance

criteria

9

ICH Q6A

• Specifications are CRITICAL quality standards that are proposed and justified by the manufacturer and approved by regulatory authorities as conditions of approval.

• .. Should focus on those characteristics found to be useful in ensuring the safety and efficacy of the drug substance and the drug product.

10

Design and Development

Consideration

• The experience and data obtained during development of a new drug substance or product should form the basis for setting the specifications. It may be possible to propose excluding or replacing certain tests on this basis

• Particle size may be performed as an in-process test, or may be performed as a release test, depending on its relevance to product performance

11

Suspensions – formulation

consideration• It has

– API + excipients that include wetting,

suspending, isotonicity, preservative,

buffer, color, flavor, and others as needed

• Processing variables

– Order of mixing, homogenization,

temperature, batch size etc.

12

Quality of suspension

• Assay and potency

• Viscosity

• Aggregation

• Settling

• Dissolution

• Bioavailability

• Other qualities, eg. pH, isotonicity etc.

13

Importance of particle size

• What is changing?

• Is the particle size critical for the in-process or

batch release?

• If critical, monitor it.

• If settling, explain the reasons, eg.

• Stoke‟s Equation, R = r2(ρs – ρ0) / 9 ŋ

14

ICH Q6A (Draft) Decision Tree #3

Is the drug product a solid dosage form or liquid containing undissolved drug substance?

1. Is the particle size critical to dissolution,

solubility, or bioavailability?

2. Is the particle size critical to drug product

processability?

3. Is the particle size critical to drug product stability?

4. Is the particle size critical to drug product

content uniformity?

5. Is particle size critical for maintaining

product appearance?

No drug substance particle size acceptance criterion required for solution

dosage forms.

NO

No Acceptance Criterion Required Set Acceptance

Criterion

If YES to any

YES

If NO to all

15

Method selection

• Some helpful considerations,– Is the method widely used/well understood?

– Are some standards available for validation? Gauge R&R, robustness, and other validation parameters

– Limitation wrt size, shape, agglomeration, cohesiveness, flowability, brittleness, availability, phase of development etc.

– Availability of the equipment for the life of the product.

16

One example from laboratory

data• Objective

– To measure and compare dissolution profiles of

various size fractions of a poorly soluble drug

substance to establish performance criteria

– To perform comparative evaluation of the same

sample lot by several measurement techniques

– To develop an approach to setting particle size

specifications

17

Nitrofurantoin Drug Substance

Low Solubility (0.2-0.4 mg/ml at pH 7.4)

Dissolution is dependent on particle size

Large Particle Size Range (d10=18 ; d90=300 )

Dissolution Testing

Apparatus 2: 50 rpm, 37oC

Rainbow Monitor: UV Fiber-Optic Probe

Particle Size Techniques

Sieving (ATM Sonic Sifter)

Laser Diffraction (Malvern Mastersizer)

Light Obscuration (AccuSizer)

Image analysis (Beckman Coulter RapidVue)

Methods

18

Nitrofurantoin Dissolution

Six Sized Fractions

0

20

40

60

80

100

0 30 60 90 120 150 180

Time (min)

% D

isso

lve

d

20-32 u

32-45 u

45-63 u

63-90 u

90-150 u

150-212 u

19

Dissolution Modeling to Determine Particle

Size

V

XC

r

XXDX

dt

dXd

s

dd

2

0

31

0

32

03

20

Nitrofurantoin Dissolution

Modeling Large Sized Fractions

150-212u fraction 90-150u fraction

0.0

20.0

40.0

60.0

80.0

100.0

0 50 100 150 200

Time (min)

% D

isso

lve

d

90-150 fit

90-150 exp

0.0

20.0

40.0

60.0

80.0

100.0

0 50 100 150 200

Time (min)

% D

isso

lve

d

150-212 fit

150-212 exp

21

Comparison of Median Particle Sizes

from Calculation and Measurement

DisFit 3 = Predicted from Dissolution using 3 point distribution

DisFit 1 = Predicted from Dissolution using 1 size only (no distribution)

C om parison of d 50 D iam eters ( )

S ize

Fraction

C alculated

D isFit 3

C alculated

D isFit 1

Laser

D iffraction

Im age

A nalysis

L ight

O bscuration

20-32 64 65 38 52 48

32-45 63 64 50 63 64

45-63 71 71 66 76 85

63-90 80 94 86 95 105

90-150 88 117 114 105 134

150-212 100 140 87 99 108

22

Photomicrographs of Nitrofurantoin

90-150 Fraction

media or description particle size (nm)

SUP-0.6 mL TiO2 in PBS 1050.9

TiO2 in PBS (pH 7.4) 553.6

mouse serum 857.3

mouse serum 1161.1

ethanol 910.6

buffered solution (pH 5) 926.7

buffered solution (pH 8) 1094.1

buffered solution (pH 7) 1004.2

buffered solution (pH 6) 2008.7

PS-50 PBS (pH7.4) 14

PS-500 PBS (pH7.4) 169.6

PS-500 PBS (pH7.4) 169.6media or description particle size (nm)

water 9.6

water 10

water 116.2

water 92.6

Old lot of samples from a

manufacturer- claimed

particle size below 50 nm

DPQR results

New lot of samples

DPQR results

24

Dissolution profile vancomycin SD

Dissolutinon profile of Vancomycin SD formulation

0

20

40

60

80

100

120

0 5 10 15 20 25 30 35 40 45

Time (min)

% V

ancom

ycin

D

issolv

ed

Initial

25 0C/60%RH-1 month R

40 0C/75%RH-1 month R

DPQR study 2009

25

XRD cryomilled

carbamazepine

DPQR study 2010

26

pK changes with crystallinity

Yamashita et al., IJP, 2003, 267, 79-91

27

Plackett Burman screening factors and response for liposomes

#

Phospholipid

wt

Cholesterol wt

Stearylamine wt

Drug conc.

Particle sizing

Washing cycles

Hydration time

Entrap. EFF

Release

rate constant Size

mg mg mg mg/mL nm No. min % hr-1 nm

1 100 25 5 5 50 0 20 24.06 0.15 134.3

2 100 25 15 2.5 50 1 10 37.18 0.12 105.5

3 100 25 15 2.5 100 1 20 33.44 0.16 44.65

4 100 50 5 2.5 100 0 20 22.64 0.17 160.35

5 100 50 5 5 100 1 10 26.69 0.10 153.05

6 100 50 15 5 50 0 10 40.75 0.17 44.35

7 150 25 5 2.5 100 0 10 31.04 0.15 98.35

8 150 25 5 5 50 1 20 20.06 0.11 70.4

9 150 25 15 5 100 0 10 41.19 0.16 158.5

10 150 50 5 2.5 50 1 10 20.67 0.13 91

11 150 50 15 2.5 50 0 20 58.95 0.13 129.45

12 150 50 15 5 100 1 20 30.68 0.11 112.7

Factors Responses

28

Particle size Pareto chart

Particle sizing(200,800)

Washing cycles[One]

Stearyl amine w t(5,15)

Cholesterol w t(25,50)

Drug conc.(5,10)

Phospholipid w t(100,150)

Hydration time(10,20)

Term

0.7778648

0.7544167

-0.5724391

0.4037149

0.2242860

0.0927729

0.0056072

t Ratio

DPQR Study, 2010

29

• It has never been challenged: the impact is unknown

• The particle size distribution specifications for the raw materials are sometimes wide

• This would allow batches with really different physical properties which may impact the process

• This is true for:– Lactose 200 mesh

– Crospovidone XL

– Lactose spray-dried

– Microcrystalline cellulose

– API (impact will be assessed in the follow-up DoE)

Understanding excipient variability: Why

challenge the particle size distribution ?

30

Excipient Test Specfications Mean value Min value Max value

Lactose 200

mesh

(analysis on 31

batches)

Sieve residue 45µm

(%)

25 – 55 39 27 52

Sieve residue 100µm

(%)

Max 25 12 1 25

Sieve residue 250µm

(%)

Max 1 0.5 0 1

Lactose spray-

dried

(analysis on 24

batches)

Sieve residue 45µm

(%)

Min 80 92 87 96

Sieve residue 100µm

(%)

Max 60 57 46 60

Sieve residue 160µm

(%)

Max 20 9 3 16

Microcrystalline

cellulose

(analysis on 26

batches)

Sieve residue 71µm

(%)

Min 45 57 46 79

Sieve residue 250µm

(%)

Max 8 2 1 7

PS Distributions of the excipients used in a DOE study

31

DOE on PSD

• Impact was assessed using a 24-1 factorial design + 4 center runs adding up to 12 batches

• For each excipient a high and a low particle size distribution was used which was obtained from:– A batch with a particularly high or low PSD

– A different grade of the same material

– Sieving and retained specific fractions of a raw material

• The center runs were manufactured using batches roughly „in the middle‟ of the PSD specifications

32

Responses measured for PSD

DOE

• CU

• Dissolution

• Power consumption profiles

• Drying profiles

• Compression parameters

33

Potential on-line monitoring? Near

Infrared (NIR) studies

34

Zidan et al., Spectral and spatial characterization of protein loaded PLGA nanoparticles, 2009.

J.Pharm. Sci.,

35

36

Schematic of Lasentec FBRM

37

On-line Microparticle formation

Monitoring by FBRM and PVM

Zidan AS., Rahman Z., Khan MA. Online Monitoring of PLGA Microparticles Formation Using Lasentec

Focused Beam Reflectance (FBRM) and Particle Video Microscope (PVM). AAPS J. 2010 Mar 30.

38

Offline product

characterization

Digital

Pump

ProbeProbe

Water

FBRM

system

PVM

system

Co-precipitation vessel

Case Study: PAT Approach to determine nucleation and

growth mechanisms of a dynamic co-precipitation process

-H. Wu & M. Khan, Accepted for publication by J. Pharm. Sci., Nov. 2010

39

In-situ Real-time Nucleation induction time

measurements: FBRM and NIR process trajectory

Wu and Khan, J. Pharm. Sci., 2010

40

Product characterization: PVM, Chemical Imager

Wu and Khan, J. Pharm. Sci., Nov. 2010

41

Nucleation and growth mechanisms

At low S, heterogeneous primary nucleation mechanism;

At high S, homogeneous primary nucleation mechanism.

-H. Wu & M. Khan, J. Pharm. Sci., 2010

42

Conclusions

• Challenges for particle size consistency

need to be addressed

• Processes and product compositions that

affect the particle size and its performance

need to be understood

• An integrated PAT approach is essential

for in-depth process and product

understanding

Impact of Particle Size on Content Uniformity and Dissolution

Brian Rohrs, Ph.D.

USP Workshop on Particle SizeDecember 8-10, 2010

Acknowledgements

• Greg Amidon

• Rich Meury

• Harry King

• David Best

• Pam Secreast

• Connie Skoug

General Approach

• Define problem

• Develop model based on theoretical concepts

• Generate data to test model and demonstrate its utility

• Use the model to explore impact of parameters (e.g., particle size)

• Develop guidelines for particle size ranges

Effect of Particle Size on Content Uniformity

Content Uniformity vs. Particle Size

0 1

Content Uniformity vs. Drug Load

0 1 2 3 4

Practical Question:

You have to design a new low dose formulation.

At what particle size do you have to start worrying about

content uniformity?

0 50 100 150 200 250 300 350 400

m icrons

T esting T ablets Capsules

Stage I

T est 10

Pass if:

N one outside 85-115%

and

RSD 6 .0%

Pass if:

N M T 1 outside 85-115%

N one outside 75-125%

and

RSD 6 .0%

Stage II

T est 20

Pass if, for all 30 units:

N M T 1 outside 85-115%

N one outside 75-125%

and

RSD 7 .8%

Pass if, for all 30 units:

N M T 3 outside 85-115%

N one outside 75-125%

and

RSD 7 .8%

USP Content Uniformity Criteria

Define Probabilities

P1L = Probability for one unit that 85% x 115%

P2L = Probability for one unit that 75% x < 85% OR 115 < x 125%

P1S = Probability that RSD on 10 units 6.0%

P2S = Probability that RSD on 30 units 7.8%

50 75 100 125 150

P1L

50 75 100 125 150

P2L

S tage I C riteria

Tablets C apsules

P ass if, for 10 units :

N one outs ide 85 -115%

and

R S D 6.0%

P ass if, for 10 units :

N M T 1 outs ide 85 -115%

N one outs ide 75 -125%

and

R S D 6.0%

P 1S*P 1L

10 P 1S*(P 1L

10 + 10*P 1L

9*P 2L)

50 75 100 125 150

P1L

50 75 100 125 150

P2L

S tage II C riteria

Tablets C apsules

P ass if, for a ll 30 units :

N M T 1 outs ide 85 -115%

N one outs ide 75 -125%

and

R S D 7.8%

P ass if, for a ll 30 units :

N M T 3 outs ide 85 -115%

N one outs ide 75 -125%

and

R S D 7.8%

L2

19

L1

20

1L10

L1S1

L2

29

L1

S2

PP20

PPP1

PP10

P

3

L2

27

L1

2

L2

28

L1

L2

29

L1

30

L1

S1

3

L2

27

L1

2

L2

28

L1

S2

PP3040

PP390

PP30P

P1

PP1020PP45

P

50 75 100 125 150

50 75 100 125 150

P1L = Prob (85% x 115%)

P2L = Prob (75% x 85%) or

Prob (115% x 125%)

P1L = NORMDIST(115,100, cu,TRUE) NORMDIST(85,100, cu,TRUE)

P2L = NORMDIST(125,100, cu,TRUE) NORMDIST(75,100, cu,TRUE) – P1L

Need method to determine probabilities:

Use EXCEL function Normdist to calculate P1L & P2L

Sample and Population standard deviations related by 2 distribution:

Use EXCEL function Chidist to calculate P1S & P2S

For cu = 7.0%:

P1S = Prob RSD 6.0%

P2S = Prob RSD 7.8%

0 5 10 15 20 25 30

2

9

0 10 20 30 40 50 60

2

29

2

)1n(2

2s)1n(

Probability o f Passing Tablet C ontent U niform ity vs.

R ela tive S tandard D evia tion o f U nit D ose

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

3 3.5 4 4.5 5 5.5 6 6.5 7 7.5

S igm a

Fra

cti

on

al

Pro

ba

bil

ity

P ass a t S tage I Pass a t S tage II

Can now relate tablet population RSD to

the probability of passing USP CU criteria

Table 1. Population standard deviation (expressed as C v) to pass

U SP C U criteria at various p -va lues.

Tablets C apsules Probability

Pass C U Stage I S tage II S tage I S tage II

0 .900 4.64 6.33 4.70 6.86

0.950 4.33 6.03 4.38 6.59

0.990 3.84 5.46 3.87 6.12

0.999 3.39 4.84 3.41 5.65

0 50 100 150 200 250 300 350 400

m icrons

Yalkowsky and Bolton (Pharm Res 7 (1990) 962-966)

Related the RSD of the dose to metrics from a

log-normal particle distribution.

Where,

dm = arithmetic mean diameter

Cd = arithmetic standard deviation

D = Dose

Cv = Coefficient of variation (%RSD)

= Density

2

1

122

d

3

m

2

1

vC1d

D6100C

33

2

vln5.4

g10

100

Ce

D6d

g

2

Where,

dg’ = geometric mean diameter (d50)

σg = geometric standard deviation ~ (d84/d16)

D = Dose

Cv = Coefficient of variation of the dose (%RSD)

= Density of API

Use volume (weight) basis descriptors and rearrange

expression to estimate d50 as a function of Dose and

%RSD*

*Rohrs, et.al., J Pharm Sci 95:5 (2006) 1049-1059

Maximum Mean Volume Particle Diameter, d50

( m)

Predicted to Pass USP Content Uniformity Test (99% Confidence)

as a Function of Geometric Standard Deviation (g) and Dose (mg)

Dose, mg

0.1 1 10 100 1000

Max

imum

Geo

met

ric M

ean

Vol

ume

Par

ticle

Dia

met

er (

d50

),

m

1

10

100

1000

Mic

roni

zing

Mill

ing

Rohrs, Meury, Amidon, August 2002

g

1.0

1.5

2.0

2.5

3.0

3.5

4.0

d90

/d50

1.0

1.7

2.4

3.2

4.1

4.9

5.8

Nomograph for

passing USP Stage I

content uniformity

criteria with 99%

probability.

Maximum Mean

Volume Diameter as

a function of

Geometric Standard

Deviation and Dose

Two API lots with different particle size distributions

130 Lot 003

22.2 d50 29.2

53.0 d90 79.5

2.50 GSD 2.77

0

2

4

6

8

10

0.1 1 10 100 1000

Diameter (µm)

f(d

) in

%

Lot 130 Lot 003

Tablet API DOA Theor. Calc. Ave %RSD

Lot # Lot # dose µg dose(n=30)

55-1 130 July 03 360 343.1 1.27

55-2 130 July 03 90 85.30 2.18

55-3 130 July 03 33 31.40 3.39

55-4 130 July 03 8.2 7.70 4.75

55-5 130 July 03 3.6 3.50 12.27

55-5 130 Oct 03 3.6 3.55 6.60

63-1 130 Oct 03 90 86.20 2.29

63-2 003 Oct 03 1.3 1.55 30.54

63-3 003 Oct 03 5 4.92 5.23

63-4 003 Oct 03 15 14.22 2.35

63-5 003 Oct 03 15 14.33 3.25

CU Assay Results

CU vs. Normal Distribution

8.2 µg

3.6 µg 1.3 µg5 µg

15 µg33 µg

90 µg90 µg360 µg

Mass of single spherical particle

(Density = 1.4 g/cc)

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

50 75 100 125 150

Diameter (µm)

Num

ber

Fra

ction

0

1

2

3

4

Mass (

µg)

003 130 Mass

29

21

i

3

ii

21

v10df

D6100C

w here,

C v = C oefficient of variation (relative standard deviation) of the dose (in % )

D = D ose (in m g)

= D ensity o f the drug (in g/cc)

f i = W eight fraction o f the ith

fraction of drug

d i = D iam eter of the ith

fraction of drug (in m )

MCR Johnson (Pharm Acta Helv 47 (1972) 546-559)

related the RSD of the dose to the particle size distribution

1 10 100 1000

Dose (µg)

0

5

10

15

20

25

30

35

% R

SD

Variation in Content Uniformity

Lot 130 Lot 003 MCJ 130 MCJ 003

Maximum Mean Volume Particle Diameter, d50

( m)

Predicted to Pass USP Content Uniformity Test (99% Confidence)

as a Function of Geometric Standard Deviation (g) and Dose (mg)

Dose, mg

0.1 1 10 100 1000

Max

imum

Geo

met

ric M

ean

Vol

ume

Par

ticle

Dia

met

er (

d50

),

m

1

10

100

1000

Mic

roni

zing

Mill

ing

Rohrs, Meury, Amidon, August 2002

g

1.0

1.5

2.0

2.5

3.0

3.5

4.0

d90

/d50

1.0

1.7

2.4

3.2

4.1

4.9

5.8

Use upper part of

distribution to estimate

GSD?

Lot 130 003

d84/d16 2.50 2.77

d84/d50 2.05 2.28

d90/d50 1.97 2.19

d97/d50 1.83 1.97

0

5

10

1 10 100 1000

Diameter (µm)

f(d)

in %

Lot 130 Lot 003

Effect of Particle Size on Dissolution

Dissolution vs. Particle Size

Dissolution Rate Surface Area

Time Time

% D

issolv

ed

% D

issolv

ed

Dissolution

Tablet Aggregates GranulesPowder

Tablet Dissolution

Practical Question:

You have to design a new solid oral formulation.

For what particle size is dissolution of the primary

particles not a significant factor in the overall dissolution

of the dosage form?

th

a1

CCD2aa 0bs2

0

ha

h

C(sat)

C(bulk)

Concentration Across

Diffusion Layer

Higuchi/Heistand model for dissolution*

*Higuchi WI and Hiestand EN. 1963. J Pharm Sci 52(1):67-71

Particles in a Powder

=

+

+

+

+

+

+

etc.

Compounds for Model Validation

PNU-number, Structure Saturated

Solubility

Diffusion

Coefficient

Density

3.08

mg/ml

8.1 x 10-6

cm2/sec

1.37

g/cm3

77.4

µg/ml

6.1 x 10-6

cm2/sec

1.36

g/cm3

PNU-10858

N 2H

N 2H

N

O

NN

PNU-78875

C 3H

C 3H

HC

O

NO

N

N

N

N

Minoxidil Particle Size

Distribution

0

0.1

0.2

0.3

0.4

1 10 100 1000

Radius (microns)

No

rma

lized

Dis

trib

uti

on

45.5 µm

184 µm

247 µm

Minoxidil Dissolution Data and

Simulations

0

20

40

60

80

100

0 10 20 30 40 50 60 70

Minutes

% D

isso

lved

PNU-78875 Particle Size

Distribution

0

0.05

0.1

0.15

0.2

0.25

0.1 1 10 100

Radius (microns)

No

rmal

ized

Dis

trib

utio

n

1.55 µm

5.45 µm

12.05 µm

PNU-78875 Dissolution Data and

Simulations

0

20

40

60

80

100

0 10 20 30 40 50 60

Minutes

% D

isso

lved

Dose Number: Do

SC

VMDo

00

LRQ

rrCrDDn

S

2

3

0342

004

Q

LRPAn

eff

• Ratio of dose concentration (mass/volume) to

the saturation solubility (Cs)

• How much drug are you trying to dissolve over

how much drug you can actually get into solution

• As Do goes from 0 to 1, become more

saturated

• If Do > 1, incomplete dissolution (solids left)

PNU-78875 Dissolution vs.

Dose Number

0

20

40

60

80

100

0 10 20 30 40 50 60

Minutes

% D

isso

lved

0.09 Data

0.09 Sim

0.4 Data

0.4 Sim

0.8 Data

0.8 Sim

Powder Dissolution vs Width of Particle Size Distribution

0

20

40

60

80

100

0 15 30 45 60 75 90

M inutes

% D

iss

olv

ed

1 .5 2 3

Particle Size Distributions

d80 = 25 µm, varying σg

Dissolution (Do = 0.1)

t80 = 30 min for all σg

0

0.2

0.4

0.6

0.8

1

0.1 1 10 100 1000

M icrons

Fra

cti

on

d80 1.5 2 3

When modeled, dispersed particles dissolve

independently from one another

Practical Question:

At what particle size does dissolution of the primary

particles not become a significant factor in the overall

dissolution of the dosage form?

Define “not significant” as 80% dissolved in 15 to 30

minutes. Calculate d80 such that powder dissolution

occurs in 15 and 30 minutes. Value will be independent

of particle size distribution width if dissolution sink

conditions are met.

Particle Size (d80) such that 80% is dissolved in 15 or 30 minutes

d80(30) = 4.78*C s

0.50

d80(15) = 3.39*C s

0.50

1

10

100

1000

1 10 100 1000

Solubility (µg/m L)

Pa

rtic

le S

ize

(d

80)

t80 = 30 m in D ata t80 = 15 m in D ata

t80 = 30 m in F it t80 = 15 m in F it

Conclusions

• For Content Uniformity, large particles will have the greatest impact on failures. The nomograph can be used to give guidance on mean and width of particle size distribution such that primary particle size is not a concern.

• For Dissolution, smaller particles result in faster dissolution. Guidance for particle size such that primary particles do not significantly impact dissolution (assume sink conditions) can be determined from the supplied equations.

Impact of Particle Size on Performance

Manufacturing Performance:

Flow and Segregation

Jim PrescottSenior Consultant

400 Business Park Dr.

Tyngsboro, MA 01879

(978) 649-3300

[email protected]

USP Workshop on Particle Size: Particle Detection and Measurement

December 8-10, 2010 Rockville, MD

©2010 Jenike & Johanson, Inc.

Process

PerformanceParticle Size Distribution (PSD) Efficiency/Costs-Production time/rate

-Manpower

-Process complexity

-Repeatability/control

-Yield

-Time-to-market

Quality-Weight control

-CU/BU

- Composition

- Dissolution

Flow and

segregation

Formation (crystallization)

and Processing (milling)

Product

Performance

©2010 Jenike & Johanson, Inc.

Process

PerformancePSD

Flow/no-flow

Erratic flow

Weight control

Flow rate limits

Blend uniformity

Content

uniformity

Tabletability

Particle

Properties

Efficiency/Costs-Production time/rate

-Manpower

-Process complexity

-Repeatability/control

-Yield

-Time-to-market

Quality-Weight control

-CU/BU

- Composition

- Dissolution

©2010 Jenike & Johanson, Inc.

Flow/no-flow

Erratic flow

Weight control

Flow rate limits

Blend uniformity

Content

uniformity

Particle shape

Composition

Hardness

Moisture

Electrostatics

Particle

Properties

Process

Performance

Other Variables

Temperature

RH

T/RH cycling

Storage time

PSD

Flowability

Define “Flowability”

Powder flowability is a function of:

• The powder itself (Flow Properties)

– Physical properties, e.g. particle size distribution, shape

– Chemical properties, e.g. composition, moisture

• The powder handling equipment

– Geometry, e.g. angles, surface finish

– Throughput, e.g. paddle speed

©2010 Jenike & Johanson, Inc.

©2010 Jenike & Johanson, Inc.

Flow/no-flow

Erratic flow

Weight control

Flow rate limits

Blend uniformity

Content

uniformity

Tabletability

PSD

Particle shape

Composition

Hardness

Moisture

Electrostatics

Particle

Properties

Process

Performance

Other Variables

Temperature

RH

T/RH cycling

Storage time

Bulk

Properties

Cohesion

Wall friction

Bulk density

Permeability

Segregation

potential

Behaviors

Flow patterns

Arching

Ratholing

Segregation

Dispersion/

agglomeration

Caking

2-phase flow

Equipment

Bins

Hoppers

Chutes

Blenders

Feeders

Presses

Encapsulators

Drums

Differences in PSD

• Size differences can cause differences in

manufacturing performance

• Size differences can be the result of other

manufacturing variables that affect other

properties too

• Hard to determine the effect of size alone, since

two samples with different sizes can also have

other particle properties which are different

©2010 Jenike & Johanson, Inc.

How to Connect PSD to

Performance?

• Option 1: use properties such as size as an

empirical indicator

– “Blends with a finer size are generally more cohesive”

• Option 2: measure bulk properties as a predictor

– “Using cohesive strength data, we know a minimum

opening size for the bin is 3 inches to avoid a stable

arch”

©2010 Jenike & Johanson, Inc.

©2010 Jenike & Johanson, Inc.

Flow/no-flow

Erratic flow

Weight control

Flow rate limits

Blend uniformity

Content

uniformity

Tabletability

PSD

Particle shape

Composition

Hardness

Moisture

Electrostatics

Particle

Properties

Process

Performance

Other Variables

Temperature

RH

T/RH cycling

Storage time

Bulk

Properties

Cohesion

Wall friction

Bulk density

Permeability

Segregation

potential

Behaviors

Flow patterns

Arching

Ratholing

Segregation

Dispersion/

agglomeration

Caking

2-phase flow

Equipment

Bins

Hoppers

Chutes

Blenders

Feeders

Presses

Encapsulators

Drums

©2010 Jenike & Johanson, Inc.

Flow/no-flowPSD

Particle

Properties

Process

Performance

Bulk

Properties

Cohesion

Behaviors

Arching

Ratholing

Equipment

Bins

Hoppers

Measure

PSD

Likely direction of

Flow/no-flow

Option 1: emperical

Apply past experiences:

Finer particle size worse

Wider particle size worse

(other variables equal)

Measure

PSD

Predict

Flow/no-flow

Option 2: predictive

Measure

Cohesion

Calculate

Arching

& ratholing

parameters

Apply to

Specific

Bin Design

Fill and Discharge Sequence:

Flow Patterns in Hoppers, Bins

• Funnel flow

• Mass flow

©2010 Jenike & Johanson, Inc.

Funnel Flow

• Features

– First-in, last-out flow sequence:

material at walls discharges last

– Segregation often made worse

– More likely to yield erratic

flow problems such as arching

and ratholing

©2010 Jenike & Johanson, Inc.

Erratic Flow

©2010 Jenike & Johanson, Inc.

Funnel flow pattern led to increased processing time, operator

exposure, variable tablet weights, and decreased tableting speeds

©2010 Jenike & Johanson, Inc.

Mass Flow

• Features

– First-in, first-out flow sequence: material moves as a mass

– Segregation generally minimized

– Consistent feed density

• Mass flow occurs when the hopper walls are sufficiently smooth and steep to allow flow along them (wall friction; surface and powder dependent)

©2010 Jenike & Johanson, Inc.

Flow/no-flow

Erratic flow

Weight control

Content

uniformity

PSD

Particle

Properties

Process

Performance

Bulk

Properties

Wall Friction

Behaviors

Flow patterns

Equipment

Bins

Hoppers

Measure

PSD

No added insight

Option 1

Unknown connection

(highly material dependent)

Measure

PSD

Predict

Flow/no-flow

Erratic flow

Weight control

Content

uniformity

Option 2

Measure

Wall friction

Calculate

Required

hopper

parameters

Apply to

Specific

Bin Design

©2010 Jenike & Johanson, Inc.

Flow Properties Tests

• Use lab-scale tests to predict what will happen at the manufacturing scale

• Quantified, absolute dimensions/angles

• ASTM Standards:– D6128 Direct Shear (Jenike)

– D6773 Annular Shear Cell (Schulze)

• Critical tests include cohesive strength and wall friction Schulze RST-XS

©2010 Jenike & Johanson, Inc.

Air Counterflow

• Powder flows down by gravity

• As powder flows it expands and decreases bulk density, increases void spaces

• Air enters to satisfy the voids

• Upward flow of air acts as a body force against gravity, reducing flow rate

©2010 Jenike & Johanson, Inc.

Erratic flow

Weight control

PSD

Particle

Properties

Process

Performance

Bulk

Properties

Permeability

Behaviors

2-phase flow

Equipment

Presses

Measure

PSD

Likely direction of

Erratic flow

Weight control

Option 1

Finer particle size worse

Wider particle size worse

(other variables equal)

Measure

PSD

Predict rate limits

Erratic flow

Weight control

Option 2

Measure

Permeability

Calculate

Limiting

flow rates

Apply to

Specific

Press feeder

where: dp/dx = gas pressure gradient across the bed

k = empirically determined permeability coefficient = density of the gas= void ratio

u = superficial gas velocity through solids bedn = gas state (n=1 for laminar, n=2 for turbulent)

Dp = particle diameter assuming uniform spheres= viscosity of gas

Translating Pressure to Air Flow

©2010 Jenike & Johanson, Inc.

k(1- )(3-n)

3

(n-1)µ(2-n)

Dp(3-n)

u(n)-dp/dx =

More detailed forms followed from Darcy’s Law, including the well-known forms of Ergun (1952) and Leva (1959). Leva’s form is:

Translating Pressure to Air Flow

Because of the difficulty in determining an appropriate particle diameter, and since k must be empirically determined, this equation may be simplified by lumping the terms together into one overall material dependent coefficient.

k(1- )2

3

µ

Dp2

u-dp/dx =

Laminar flow simplification (n=1):

©2010 Jenike & Johanson, Inc.

Permeability Test

• Measures resistance to airflow through a contact bed of powder

• Required to determine settlement (deaeration), and powder flow rate limitations

• Results in relationship of permeability (K) as a function of bed density, with a curve fit constant K0

©2010 Jenike & Johanson, Inc.

Rate Limitation, Air Counterflow

• Manufacturer was feeding a blend from a bin

through a chute, into an encapsulator

• At higher feed rates, weights became erratic

• Choke point identified within the encapsulator

• Material change improved behavior

©2010 Jenike & Johanson, Inc.

Rate Limitation, Air Counterflow

• Before

– d10: 16 , d50: 125

– K0: 0.0017 fps

– Critical powder flow rate

• Calculated: 60% of target

• Actual: 75% of target

• After process changes

– d10: 26 , d50: 119

– K0: 0.0032 fps

– Critical powder flow rate

• Calculated: >110% of target

• Actual: 100% of target (max)

• Concluded: d10 was critical

©2010 Jenike & Johanson, Inc.

©2010 Jenike & Johanson, Inc.

Flow/no-flow

Erratic flow

Weight control

Flow rate limits

Blend uniformity

Content

uniformity

Tabletability

PSD

Particle shape

Composition

Hardness

Moisture

Electrostatics

Particle

Properties

Process

Performance

Other Variables

Temperature

RH

T/RH cycling

Storage time

Bulk

Properties

Cohesion

Wall friction

Bulk density

Permeability

Segregation

potential

Behaviors

Flow patterns

Arching

Ratholing

Segregation

Dispersion/

agglomeration

Caking

2-phase flow

Equipment

Bins

Hoppers

Chutes

Blenders

Feeders

Presses

Encapsulators

Drums

©2010 Jenike & Johanson, Inc.

Segregation Mechanisms

• Particle entrainment

• Air entrainment

• Sifting

©2010 Jenike & Johanson, Inc.

Segregation Mechanisms

• Particle entrainment (dusting)

– Particle entrainment requires:

• Airborne particles

• Differences in settling velocities

– Stokes’ law

• Air currents

– Results in thin layer at walls, significantly different

than bulk

©2010 Jenike & Johanson, Inc.

Particle Entrainment Filling a Bin

Segregation by Particle Entrainment

©2010 Jenike & Johanson, Inc.

Time into compression, h:mm

Assay,

% label

Segregation Mechanisms

• Particle entrainment (dusting)

• Air entrainment (fluidization)

©2010 Jenike & Johanson, Inc.

Segregation Mechanisms

• Air entrainment (fluidization)

– Air entrainment requires:

• Fine particles

• Excess air between particles

• Air counterflow

– Results in top-to-bottom differences, generally

– Tool: Geldart Chart (fluidization behaviors as a

function of particle size and particle density)

• Very fine: too cohesive to fluidize

• Very coarse: requires too much air to fluidize

©2010 Jenike & Johanson, Inc.

Segregation Mechanisms

• Particle entrainment (dusting)

• Air entrainment (fluidization)

• Sifting

©2010 Jenike & Johanson, Inc.

Sifting Segregation

©2010 Jenike & Johanson, Inc.

Segregation Mechanisms

• Sifting

– Sifting requires:

• Particle size differences (little as 1.3:1)

• “Large” particles (above 50µ)

• Free-flowing material

• Interparticle motion

– Results in center-to-perimeter (of pile) differences

– Driven by geometric differences between particles

(friction, density, momentum also play a role)

©2010 Jenike & Johanson, Inc.

Fluidization Segregation Test

©2010 Jenike & Johanson, Inc.

Air out

Air in

Column of material

Is fluidized

Column is split into

3 sections, and each

is measured for

segregation (assay/PSD)

Top

Middle

Bottom

ASTM Standard D 6941

Sifting Segregation Test

©2010 Jenike & Johanson, Inc.

Beginning Middle End

ASTM Standard D 6940

Each is measured for

segregation (assay/PSD)

Low Fluidization/Low Sifting Potential

“Product B”

Fluidization segregation test

top=red, center=green, bottom=blue

Sifting segregation test

beginning=red, middle=green, end=blue

©2010 Jenike & Johanson, Inc.

High Fluidization/High Sifting Potential

“Product C”

Fluidization segregation test

top=red, center=green, bottom=blue

Sifting segregation test

beginning=red, middle=green, end=blue

©2010 Jenike & Johanson, Inc.

High Fluidization/Low Sifting Potential

“Product D”

Fluidization segregation test

top=red, center=green, bottom=blue

Sifting segregation test

beginning=red, middle=green, end=blue

©2010 Jenike & Johanson, Inc.

Conclusions

• Particle size distribution, together with many other powder properties, can affect the performance of a manufacturing process

• By itself, particle size distribution data can provide a direct connection to manufacturability: empirically in many cases, predicatively in very few cases, and no insight in other cases

• A more powerful approach to predict manufacturability is to measure key bulk, not particle, properties

• Understanding the mechanism of how size relates to flow and segregation can allow the user to select the right parameter (e.g. D10 vs D50) and relative influence

©2010 Jenike & Johanson, Inc.

Impact of Particle Size on Performance

Manufacturing Performance:

Flow and Segregation

Jim PrescottSenior Consultant

400 Business Park Dr.

Tyngsboro, MA 01879

(978) 649-3300

[email protected]

USP Workshop on Particle Size: Particle Detection and Measurement

December 8-10, 2010 Rockville, MD

Addendum:

Other Sources of Variability

• Segregation often shows up as between-location

variation (trending)

• Within-location variations:

– sampler error

– analytical error

– weight variations

– “micro” non-uniformity of blend (e.g. API

agglomerates or API with too many coarse particles

[e.g. D90])

©2010 Jenike & Johanson, Inc.

©2010 Jenike & Johanson, Inc.

Flow/no-flow

Erratic flow

Weight control

Flow rate limits

Blend uniformity

Content

uniformity

Tabletability

PSD

Particle shape

Composition

Hardness

Moisture

Electrostatics

Particle

Properties

Process

Performance

Other Variables

Temperature

RH

T/RH cycling

Storage time

Bulk

Properties

Cohesion

Wall friction

Bulk density

Permeability

Segregation

potential

Behaviors

Flow patterns

Arching

Ratholing

Segregation

Dispersion/

agglomeration

Caking

2-phase flow

Equipment

Bins

Hoppers

Chutes

Blenders

Feeders

Presses

Encapsulators

Drums

Data on Consumer Product

©2010 Jenike & Johanson, Inc.

Prediction

• Model uses Poisson’s distribution to account for

distribution of largest particles across dosage units

©2010 Jenike & Johanson, Inc.

Cumulative Production Data

©2010 Jenike & Johanson, Inc.

“Too Coarse” API

• Can cause wide variability in results

– Erratic (frequency and locations can

– Occasional high assay values

• Can be predicted with particle size alone; bulk

properties not needed

– Can predict distribution of potency across a lot

• Critical to have “accurate” information on coarsest

particles’ size

©2010 Jenike & Johanson, Inc.

Effect of Excipient Particle Size on Product Performance

Xiaorong He, Ph.D. , M.B.A, USP Workshop on Particle Size , Dec 9 2010

Excipients - Definition

Pharmaceutical excipients are substances other than the pharmacologically active drug or prodrug which are included in the manufacturing process or are contained in a finished pharmaceutical product dosage form

– Aid in the processing of the dosage form during its manufacture

– Protect, support, or enhance stability, bioavailability, or patient acceptability

– Assist in product identification or

– Enhance any other attribute of the overall safety, effectiveness, or delivery of the drug during storage or use

13 January

2011

PLEASE INSERT Presentation title 133

Excipients are not inert!

Current Status

• About 1200 ingredients are in use currently in marketed pharmaceutical products as

excipients excluding colors and flavors

• About 250 documented in the European Pharmacopoeia

• Now 60 monographs in the international harmonization process, USP, JP and Ph Eur

• Compendial monograph primarily focused on purity and safety

• Insufficient list of tests that are related to functional performance

• Functionality Performance• USP: proposed new general information chapter <1059> - USP

33/NF 28• EP (EDQM) plans to list specific functionality related

characteristics in some of its excipient monographs

13 January

2011

PLEASE INSERT Presentation title 134

General Chapter <1059> - Proposed Excipient Functional Category

13 January

2011

PLEASE INSERT Presentation title 135

Tablets & Capsules

Oral Liquids Semisolids, topicals, and suppositories

Parenterals Aerosols

Diluent pH modifier Suppository base Pharmaceutical water

Propellent

Binder Wetting & solubilizing agent

Suspending & viscosity increasing

Diluent

Disintegrant Antimicrobial preservative

Ointment base Tonicity agent

Lubricant Antioxidant Emollient

Glidant and/or anti caking agent

Chelating & complexing

Coloring agent

Sweetening

Capsule shell

Coating agent

Ref. USP PF 33(6) page 1311-1323 & PF 35 (5) page 1228

Examples of Functional Related Characteristics

• Particle shape, size and size distribution

• Crystallinity

• Moisture content

• Specific surface area

• Density

• Solubility

• Compaction properties

• Viscosity

• Molecular weight, weight distribution

• Rheological behavior

13 January

2011

PLEASE INSERT Presentation title 136

Potential Concerns from Excipient Suppliers

• Is this necessary?

• Will this become prelude of setting tighter spec on excipients?

• Will users demand to see more data?

13 January

2011

PLEASE INSERT Presentation title 137

Part I– Effect of Excipient Particle Size on Aplivaroc Dissolution

• BCS class II; Intrinsic solubility: 3 ug/ml at pH 5.5 to 7.0 (Free base); HCl

salt is being developed

• Conventional formulation (200mg) - high Shear wet granulation

• Quality by Design approach

• Design space

• Critical formulation and process parameters

• QbD tools: fish bone diagram, PAT etc

13 January

2011

PLEASE INSERT Presentation title 138

Ref. X. He et al, J. Pharm. Sci., 97: 5274-5289, 2008

Formulation & Process DOE

Overlay Plot

A: Dis integrant

B: B

inde

r

6.0 8.0 10.0 12.0 14.0

2.0

3.0

4.0

5.0

6.0

% Disln @60 min: 85.0

%disln @ 10 min: 45.0% < 200 mesh: 20.0

CI *: 0.30

Rathole: 0.5

22

Design Space - Use DOEs to optimize formulation and process variables (dissolution, disintegration, flow, compaction, granule size distribution)

Ref. X. He et al, J. Pharm. Sci., 97: 5274-5289, 2008

Key Batches

Site of Mfg Purpose Scale Water Level Disso. @ 60 min

R&D Process DOE PMA-65 32-36% 88-92%

R&D Dev. Scale-up PMA-150 38% 77%

R&D Phase IIb PMA-150 34% 90%

Production Phase IIb PMA-600 34% 82%

Production Demo batch PMA-600 28% A: 80%, B: 86%

Production Phase 3

batch 1

PMA-600 28% 83%

Stretch critical process parameter to the edge of failureDissolution is not sensitive to lubricant level & lubrication time

Ref. X. He et al, J. Pharm. Sci., 97: 5274-5289, 2008

Fishbone Diagram of Key Differences

Drop in Dissolution

Process

Material

Environment

EquipmentPeople

Did not comil drug before dry mixing

Magnesium steaate supplied by different vendor

Used different grade of lactose (impalpable vs L1142K)

Different lot of drug substancesOversized blender (20% fill)Spray nozzle

location (center vs side)

Faster impeller speed

Longer spray timeSite differences

May favor formation of denser granules

Granule Characterization

%

<150

um

SSA

(m2/g)

PS

D10

PS

D50

PS

D90

Intra

Granule

Porosity

Inter-

granule

Porosity

Production Batches (PMA-600)

Feb 05 batch 59% 1.40 17.1 62 152.0 25.7 (1.5) 47.6 (1.7)

Mar 05 Demo

batch75% 0.91 19.3 58.6 196.0 24.7 (0.9) 51.3 (0.6)

Phase 3 Batch 1A 52% 1.00 12.6 37.4 118.0 26.0 (0.4) 50.7 (2.7)

Phase 3 Batch 1B 71% 0.89 14.3 40.8 156.0 26.8 (0.1) 49.9 (1.7)

Phase 3 Batch 2A 64% 1.00 11.5 28.4 76.7 30.1 (0.2) 48.7 (1.2)

Phase 3 Batch 2B 75% 0.90 13.9 52.1 206.0 28.8 (0.3) 49.2 (0.2)

R&D Batches (PMA-150)

Phase 2b Aug 04 83% 1.2 22.9 65.5 146.0 22.1 (1.0) 48.5 (1.1)

No significant trend was detected!!

Magnesium Stearate PSD

Supplier B - Production

X50 = 5.3 um

Supplier A - R&D

X50 = 12.8 um

Ware’s magnesium stearate is finer than RTP

Magnesium Stearate Moisture Sorption

Supplier A Supplier B

MgSt R&D Lot E01674

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 10 20 30 40 50 60 70 80 90 100

%RH

Weight (% change)

1st Adsorption

1st Desorption

2nd Adsorption

2nd Desorption

MgSt Production Lot C446858

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 10 20 30 40 50 60 70 80 90 100

%RH

Weight (% change)

1st Adsorption1st Desorption

2nd Adsorption

2nd Desorption

Magnesium stearate at production site is probably more hydrophobic than that in the R&D site

Magnesium Stearate SEM

Supplier A – R&D Supplier B – Production

Contact Angle Analysis

Drug: 143 +/- 5 degree

Production Magnesium Stearate: 142 +/- 2 degree

R&D Magnesium Stearate: 125 +/- 9 degree

Visual observations:

• Tablet floating. Floating fragments contain drug

• Floating is worse for tablets made at production(with more hydrophobic magnesium stearate)

Discussion

Risk assessment:

• Floating not occurring in the artificial stomach.

• Floating is a dissolution artifact, unlikely to occur in vivo

• Adjusted pH of the dissolution medium

Quality by Design

• Particle size – product performance

• Typically just excipient type and level

• Should evaluate and monitor lot to lot variation

Part 2 – Effect of Excipient Particle Size on Product Processibility

• Compaction

• Flow

• Content uniformity – segregation

• Other – e.g. granulation end point

13 January

2011

PLEASE INSERT Presentation title 148

Deformation Mechanism

13 January

2011

PLEASE INSERT Presentation title 149

Elastic

Plastic

Brittle

Visco-elastic

Original Compression Decompression

Particle Interactions

13 January

2011

Ref. Schubert, Chem.Ing .T ech. 51 (1979),

266 – 277.

150

Compaction Properties

13 January

2011

PLEASE INSERT Presentation title 151

Ref. Amidon, 2008 Arden House Conference

Particle Size Effect on Compaction Properties of Lysine (Plastic deforming)

13 January

2011

PLEASE INSERT Presentation title 152

Effect of Particle Size on Tablettability

Effect of Particle Size on Compactibility

• Compactibility increases with decreasing particle size – more contact points• Compressibility increases with increasing particle size at lower pressure – better packing efficiency • Large crystal – initial fragmentation

Effect of Particle Size on

Compressibility

Ref. Sun & Grant, Int. J. Pharm. 215: 221-228, 2001

Effect of Crystal Morphology on Compaction

13 January

2011

PLEASE INSERT Presentation title 153

Morphology affects slip plane (001) orientation, impact compaction

Ref. Sun & Grant, J. Pharm. Sci.,90, 569-579, 2001

Quality by Design Driven by the Regulatory Agency

The Desired State

Pharm. Dev. (ICHQ8)/

QbD (ICH Q8R)

Pharm Quality Systems(ICH Q10)

Quality RiskManagement (ICH Q9)

CGMP initiativePAT initiative

Critical path initiative

Quality by Design – Know Your Space

Knowledge Space:All that could be done

Knowledge Space:All that is known to fail to meet CQA and/or CMA

Design Space

Control Space

QbD approach - DOE• Type of key excipients• Level of key excipients

Tend to overlook batch and supplier variation of key excipients

Points to Consider

13 January

2011

PLEASE INSERT Presentation title 156

• Excipients are essentially in almost all products – may significantly impact product and process performance

• Excipient performance may be influenced by attributes not described in the monograph/COA - <1059> will address this

• Apply risk assessment tools to key excipents• For those critical to performance, may need to monitor/control

variation between batches and suppliers

Risk Assessment of Critical Excipients – Functionality Related Characteristics

U nit operations

/

Q uality attributes

D ispensing (Raw

M ateria l P roperties) G ranulation D rying

B lending

(M agnesium

Stearate)

Tableting Packaging

Dissolution Partic le s ize AP I Power

consum ption Prior know ledge

N ot critica l to

quality

Not critica l to

quality Prior know ledge

Disintegration Partic le s ize AP I water am ount and

feed rate Prior know ledge

N ot critica l to

quality

Not critica l to

quality Prior know ledge

Hardness Prior know ledge Prior know ledge Prior know ledge N ot critica l to

quality

Not critica l to

quality Prior know ledge

Assay Prior know ledge Prior know ledge Prior know ledge Prior know ledge N IR m easurem ent Prior know ledge

Content uniform ity Prior know ledge Power

consum ption

N ot critica l to

quality

N ot critica l to

quality N IR m easurem ent Prior know ledge

Degradation Prior know ledge W ater am ount and

feed rate

N ot critica l to

quality Prior know ledge Prior know ledge Prior know ledge

Stability Prior know ledge Prior know ledge C ontro l water

content Prior know ledge Prior know ledge Prior know ledge

Appearance Prior know ledge Prior know ledge N ot critica l to

quality Prior know ledge

N ot critica l to

quality Prior know ledge

Identification N IR of raw m ateria l P rior kno w ledge Prior know ledge Prior know ledge Prior know ledge Prior know ledge

W ater P rior know ledge Prior know ledge C ontro l water

content Prior know ledge Prior know ledge Prior know ledge

M icrobiology Specification of

s tarting m ateria l

Purified w ater

used Prio r know ledge Prior know ledge Prior know ledge Prior know ledge

1

low

Process understanding

Control Strategy

1

Unit operation

Qu

ality

Att

rib

ute

s

Ref. ICH Q9 potential applications, EFPIA PAT TG, 2006

Effect of Particle Size on Compaction – Brittle Materials

13 January

2011

PLEASE INSERT Presentation title 158

Alph –monohydrate lactose

NaCl

Na bicarbonate

lactose

Dical

NaCl

Saccharose

Ref. Alderborn, Pharamceutical Compaction Technology, chapter 10, 1996

Importance of Material Characterization

Molecular Level Particulate Level Dosage Form Level

Unit cell, slip planes, lattice energy, crystallinity etc

Size, shape, density, porosity etc

Flow, mechanical strength, content uniformity etc

Fast with lessQuality by Design

Process Process

Material characterizationMaterial properties = fxn (material x1, x2… Xn) * fxn (process y1, y2.. yn)

USP Workshop on Particle Size:

Particle Detection and Measurement

December 8-10, 2010; USP Headquarters

Chair: Dr. Steven Byrn,

Purdue University

Discussion – Oral Formulations and

Components