Ajit S. Narang, Ph.D.Bristol-Myers Squibb, Co.

IFPAC Annual MeetingArlington, VA

January 24-27, 2016

PAT for Wet Granulation

1

Overview Integrated Application of Mechanistic understanding Define CMAs that may be monitored and controlled

In silico modeling Predict effect of PPs on particle flow dynamics > DEM Predict process outcomes based on first principles > PBM

Utilize PAT tools to monitor process & to inform and confirm models: granule (a) size > FBRM and (b) density > DFF sensor

To Inform control strategy Batch monitoring, control, scale-up, interchangeability

Construct in silico design space Decisions and changes informed by modeling Process control & scale-up informed by material attributes

Mechanistic Understanding > A Case Study

Mix

Add BMS-582664-02, Intra-granular MCC, HPC and CCS High Shear Mixer

25 L Fuji high shear granulator

Granulation High Shear Mixer

Wet Mill

Fluid bed drying

Milling

Add EG MCC, xPVP, Cab-O-Sil

Bin Blender

Add Mg Stearate and blend

Compress into Tablets

Brivanib Alaninate tablets: intragranular 50% API, 4% HPC, 3% CCS, &23.5% MCC granulated with 46% water 3

QbD Methodology

RA• Perform product risk analysis to identify CQAs

PPs & MAs

• List relevant material attributes and process parameters that can potentially impact CQAs

Unit Ops

• Perform process risk analysis to identify attributes and parameters to be studied

DoE etc.• Conduct development studies

CQAs & CPPs

• Identify critical attributes and parameters

CS• Establish control strategy

4

Quality Attributes Potency

Content uniformity

Appearance

Impurities

Dissolution

Process Parameters Each unit operation

Interactions

In-process material attributes

Mechanistic process understanding

Mechanistic product understanding

- Basis of effect of process parameters

Formulation Ruggedness Study Design

23 split plot design

Center Point

1.5%

1.5%

4.5%

4.5%

HPC

CCS

Mg Stearate

1.5%

0.5%

2.5 kg batch size

Key Responses

Granulation: -Particle size -Flow-Compaction properties-Adhesion tendency

Tablets:-Visual defects -Dissolution-Friability -Disintegration

Design Space Studied During Process DoE

Impeller Tip Speed

44%

49%

Wat

er L

evel

10s 50s

3.6m/s 6m/s

Wet Massing Time

High Shear Granulation

Key Responses

Granulation: -Particle size -Flow-Compaction properties-Adhesion tendency

Tablets:-Visual defects -Dissolution-Friability -Disintegration

Probe Tip Enlargement

• Rotating focused-laser measures chord length• Proven technology for particles suspended in liquids• Measures particles in contact with probe sapphire tip• Probe placed directly in solution or powder flow

Lasentech FBRM C35 Probe “Chord Length”

Probe Tip Enlargement

Lasentech FBRM C35 Probe “Chord Length”

Particles being measured

C35 analog signal outputand threshold

Particle size outputfrom probe

Particle size distributionoutput from mono dispersedspheres

Wet Granulation – PAT Implementation

FBRM: In-line Real-Time PSD: Avicel

BMS Highly Confidential

Different concentrations of binder, same amount of water

0 5 10 15 20 250

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2x 10

4

Time (min)

Squ

are

Wei

ghte

d (M

ass)

Experiment D: Water - 75% HPC - 3%

0 5 10 15 20 250

0.5

1

1.5

2

2.5

3x 10

4

Time (min)

Squ

are

Wei

ghte

d (M

ass)

Experiment C: Water - 75% HPC - 4%

0 5 10 15 20 250

1

2

3

4

5

6x 10

4

Time (min)

Squ

are

Wei

ghte

d (M

ass)

Experiment E: Water - 75% HPC - 5%

0 5 10 15 20 250

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2x 10

4

Time (min)

Squ

are

Wei

ghte

d (M

ass)

Experiment D: Water - 75% HPC - 3%

0 5 10 15 20 250

1

2

3

4

5

6

7x 10

4

Time (min)S

quar

e W

eigh

ted

(Mas

s)

Experiment H: Water - 85% HPC - 3%

0 5 10 15 20 250

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2x 10

4

Time (min)

Squ

are

Wei

ghte

d (M

ass)

Experiment G: Water - 65% HPC - 3%

Different levels of water, same concentrations of binder

Chord Length Distribution by

FBRM

Test 1: 1% HPC batch

Test 3: 5% HPC batch

Test 2: 3% HPC batch

10

100

10000

5

10

15

0

200

400

600

800

Chord Length (um)

y

Time (minutes)

Squa

re W

eigh

ted

(Mas

s)

10

100

10000

5

10

15

0

200

400

600

800

Chord Length (um)

y

Time (minutes)Sq

uare

Wei

ghte

d (M

ass)

10

100

10000

5

10

15

0

200

400

600

800

Chord Length (um)

y

Time (minutes)

Squa

re W

eigh

ted

(Mas

s)1% HPC

3% HPC

5% HPC

12

BMS Highly Confidential

FBRM: In-line Real-Time PSD: Brivanib

0 5 10 15 20 25 300

1

2

3

4

5

6x 10

4

Squ

are

Wei

ghte

d (M

ass)

0 5 10 15 20 25 30

2.5

3.5

4.5

Time (min)Im

pelle

r Am

pera

ge

Brivanib E: Water - 39% HPC - 4%

0 5 10 15 20 25 300

1

2

3

4

5

6x 10

4

Squ

are

Wei

ghte

d (M

ass)

0 5 10 15 20 25 30

2.5

3.5

4.5

Time (min)

Impe

ller A

mpe

rage

Brivanib B: Water - 49% HPC - 4%

0 5 10 15 20 25 300

1

2

3

4

5

6x 10

4

Squ

are

Wei

ghte

d (M

ass)

0 5 10 15 20 25 30

2.5

3.5

4.5

Time (min)

Impe

ller A

mpe

rage

Brivanib C: Water - 44% HPC - 4%

0 5 10 15 20 25 300

1

2

3

4

5

6x 10

4

Squ

are

Wei

ghte

d (M

ass)

0 5 10 15 20 25 30

2.5

3.5

4.5

Time (min)

Impe

ller A

mpe

rage

Brivanib D: Water - 54% HPC - 4%

0 5 10 15 20 25 300

1

2

3

4

5

6x 10

4

Squ

are

Wei

ghte

d (M

ass)

0 5 10 15 20 25 30

2.5

3.5

4.5

Time (min)

Impe

ller A

mpe

rage

Brivanib A: Water - 46.5% HPC - 4%

0 – 100 μm100 - 200 μm200 - 500 μm500 - 3000 μm

QbD PSD FBRM Dissolution Porosity DFF

C35 Probe on 300 L PMA High-Shear Granulator

• Use of this probe during tech transfer provides process data to confirm process parameters chosen and verify successful transfer

- Without the probe, a single post-milling sample must be used to assess transfer

• Collection of data during transfer provides a baseline for future transfers and for troubleshooting or for change controls: for example

- Excipient or API changes- Process deviations

FBRM At Manufacturing Scale in a GMP Facility

Real-Time Release Paradigm

Identify product CQAsIdentify in-process material attributes (IPMA)Factor-response relationships

Critical Process parameters (CPP) IPMAIncoming material properties (IMP) x process IPMA

Established acceptable range of CQAsEstablished operating range of IMP, CPP, IPMATesting to assure consistent compliance

Design Space Paradigm

• Mechanistic understanding of quality drivers (Science)• Real-time measurement and control capability (Technology/PATs)

• Direct measurement• Indirect measurement

Design space+Real-time control+Limited testing

RTR Paradigm

15

RA: Dissolution: Material AttributesMechanistic Basis of Effect of Process Parameters

Identification of in-process MAs that impact DP Qas enables focus of control strategy development.

16

RA: DissolutionEffect of Material Attributes & Process Parameters

17

Mechanisms involved in HSWG can be concurrent but certain mechanisms dominate during specific phases.

Wetting & Nucleation1 Aggregation & Consolidation2

Breakage & Attrition3

(Iveson et al., 2001)

Binder droplets Nuclei

Dry powder

Daughter particles

Granule

Layering4

GranuleFines

Granules

(i) (ii)Consolidated granule

Particle Level Mechanisms

18

Wet Granulation – PorosityMercury Intrusion Porosimetry (MIP)

Autopore IVMicromeritics 19

Granule Porosity Defines Dissolution Design SpaceCumulative Porosity

0

0.05

0.1

0.15

0.2

0.25

012345678910Pore diameter (micro-m)

Pore

vol

ume

(mL

/g)

2A (30 min)2D (50 sec)1Proc81Proc101Proc131Proc13A1Proc5 HHH1Proc6 LLL

Center Point batches

Low/Low/Low of WG Process Parameters (worst case scenario)

High/High/High of WG Process Parameters (worst case scenario)

Very high wet massing time

20

Granule Porosity Defines Dissolution Design Space

0

3037

5058

64

0

47

65

7480

8590 92

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70

% d

rug

rele

ased

Time (min)

High wet massing time (20 min)High water (67%, outside design space)Lower end of design space (48% water)Center point of design space (58% water)

High/High/High of WG Process Parameters (worst case scenario)Center Point of Design Space

21

RA: Dissolution: Wet Granulation Process Parameters

DoE study enabled the identification of process parameters that impact product dissolution.

22

BMS Highly Confidential

Pillar

Sensor holder

Granulator lid

What does it measure in granulator?

Top Sensor

hs

htrt

rs

Side Sensor

Drag Flow Force (DFF) Sensor

Wall Shear Stress (WSS) Sensor

Drag Force Flow (DFF) Sensor

In-line Real-Time PAT for Granule Porosity/Density

Drag Flow Force (DFF) Sensor

Water addition

a: 1% w/w HPC

Water addition

Water addition

Lid is closed

FPM2

c: 5% w/w HPC

b: 3% w/w HPC

Start of mixing

End of mixing

Lid is open

FPM1

FPM3

FPM4

FPM1FPM2

FPM3FPM4

FPM1 FPM2FPM3 FPM4

FPM1

FPM1

FPM1

FPM2

FPM2

FPM2

FPM3

FPM3

FPM3

FPM4

FPM4

FPM4

Blade 1 Blade 2 Blade 3 Blade 4Blade 2 Blade 3Blade 1 Blade 4

d: 1% w/w HPC

f: 5% w/w HPC

e: 3% w/w HPC

g: 1% w/w HPC

i: 5% w/w HPC

h: 3% w/w HPC

Drag Flow Force (DFF) Sensor: Reproducibility

Water addition

Water addition

Water addition

a: 1% w/w HPC

c: 5% w/w HPC

b: 3% w/w HPC

DFF Sensor Peak

distribution statistics

Test 1: 1% HPC batch

Test 3: 5% HPC batch

Test 2: 3% HPC batch

00.1

0.20.3

0.40.5

0.60

5

10

15

0

10

20

30

40

50

60

70

Force (N)

y

Time (minutes)

Num

ber o

f Pea

ks

00.1

0.20.3

0.40.5

0.60

5

10

15

0

10

20

30

40

50

60

70

Force (N)

y

Time (minutes)N

umbe

r of P

eaks

00.1

0.20.3

0.40.5

0.60

5

10

15

0

10

20

30

40

50

60

70

Force (N)

y

Time (minutes)

Num

ber o

f Pea

ks1% HPC

3% HPC

5% HPC

Water addition

Water addition

In-line DFF Comparison w/FT4 Powder Rheometer

DFF sensor reports wet mass consistency, which is also reflected in the BFE measured by FT4 Powder Rheometer®

BMS Highly Confidential

Drag Flow Force (DFF) Sensor: Brivanib

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0 200 400 600 800 1000 1200 1400Si

ne fi

t am

plitu

de, N

Time, s

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0 200 400 600 800 1000 1200 1400

Peak

mag

nitu

de, N

Time, s

Peak magnitude (average over 100 blade rotations) Sine fit amplitude (average over 100 blade rotations) A B

Test 3: 5% HPC batch

Test 1: 1% HPC batch

Test 2: 3% HPC batch

DFF Sensor: Facilitates Scale-up & Equipment ChangeComparison of 10L and 60L granulators

Typical end of granulation

Control Strategy

31

Tools for Speed & Quality to Patient, Scale-up, Design Space, and Regulatory Flexibility

High Shear Wet Granulation

Discreet Element ModelingParticle Flow & Collision Frequencies & ForcesScale-up and Interchangeability of Granulators

Population Balance ModelingSimulating Particle Growth and Densification

Process Evolution as a Function of Time

Micro-GranulatorVery Small Scale (<10 g) Batch Manufacture

Mechanistic Investigations & Rank Order Analysis

Focused Beam Reflectance MeasurementReal-Time Particle Size Change

Process Evolution, Monitoring, & ControlGranule Size

Shear & Drag Force SensorReal-Time Wet Mass Consistency ChangeProcess Evolution, Monitoring, & Control

Granule Densification

Modeling & SimulationRe

al Ti

me A

nalyt

ics

DEM Simulations of HSWG

Know the difference between device makes Understand what changes upon scale change Understand high/low shear zones, profiles Evaluate and evolve scale-up/down criteria Attempt correlation between collision rates and agglomeration

rate (PBM) Quantify degree of agitation on the material Understand effect of changing fill level, speed Provide inputs for Population Balance Models

Discrete Element Method Simulations• DEM simulations determine bulk behavior

from particle level interactions• Simulations allow access to quantities

difficult to measure experimentally• Dry, spherical particles as modeled

material ~ 1.8 mm mean diameter with polydisperse size

distributions Calibration of input parameters to match Avicel

101 behavior• Parameters studied:

Effect of fill level Effect of blade speed Effect of scale

• Parameters measured: Particle velocities − Work per unit

mass Residence time distributions Stress profiles

DEM Input Parameters

34

Granulators – GEA PharmaConnect 10L

The CAD geometry of the complete GEA PharmaConnect™ 10L granulator was provided by BMS. Removing unnecessary parts, the bowl with blades was used to model the granulation of the powder. EDEM

geometry

CAD models

Granulators – Diosna 1L and 10L

The CAD models of Diosna 1L and 10L were created by DEM Solutions basing on the pictures of the devices provided by BMS.NOTE: the chopper was removed from the EDEM model

Real granulator CAD modelEDEM geometry

Calibration of Input Parameters• Calibrate material properties

using repose angle tests

• Target repose angle of Avicel 101 (35 degrees)

• Parameters varied:

1. Particle-Particle Restitution

2. Particle-Particle Static Friction

3. Particle-Particle Rolling Friction

4. Particle-Particle 37

Calibration Results

• Particle shape did not have a significant effect on repose angle• Input parameters from calibration results used to run

simulations in this study

ParticleShape

Angle(deg)

Static Friction

Rolling Friction

Coefficient of

Restitution

P-P Cohesion

P-GCohesion

Sphere 35.4 0.9 0.14 0.2 0.0 0.0

38

Instantaneous Stress ProfilesSimulation Experimental

Simulation and Experimental Comparison

• Shear stresses fluctuations follow frequency of blade rotation• DEM simulations consistent with experimental behavior

0 1 2 3 4 50

2000

4000

6000Shear Stress vs. Time

Time (sec)

Shea

r Str

ess

(Pa)

0 10 20 30 400

2

4

6 x 109 Power Spectrum - Shear Stress

Frequency (Hz)

Pow

er

Measurement direction

Blade frequency

39

-0.2 0 0.20

0.05

0.1

0.15

0.2

Vy/Vtip

Prob

abili

ty D

ensit

y

-0.2 0 0.20

0.05

0.1

0.15

0.2

Vr/Vtip

Prob

abili

ty D

ensi

ty0 0.5 10

0.05

0.1

0.15

0.2

Vθ/Vtip

Prob

abili

ty D

ensi

ty

Tangential Velocity

Radial Velocity

Vertical Velocity

Probability distribution

– Particle Velocities

Base Case Analysis 1 L Diosna, 5 m/sec, 60% fill level

40

Time Averaged Stress Profiles

Pressure Shear Stress

Base Case Analysis 1 L Diosna, 5 m/sec, 60% Fill

• Pressure within granulator is higher than shear stresses.• Higher pressure and shear stress observed near bottom plate by

the wall.41

Residence Time Distribution

Base Case Analysis 1 L Diosna, 5 m/sec, 60% fill level

High Shear Zone

r/R

y/H

Recirculation Pattern

42

40% Fill

60% Fill

Time Averaged Pressure Profile

Effect of Fill Level1 L Diosna, 5 m/sec

50% Fill

43

40% Fill

60% Fill

Time Averaged Shear Stress Profile

Effect of Fill Level1 L Diosna, 5 m/sec

50% Fill

Increasing fill level leads to higher stresses near the bottom plate and wall

Due to higher weight of particle bed

44

Effect of Fill Level - Residence Time Distributions

1 L Diosna, 5 m/secHigh Shear Zone Recirculation Patterns

40% Fill

50% Fill

60% Fill

45

Effect of Blade Speed1 L Diosna

Vtip = 2.5 m/sec

Vtip = 5.0 m/sec

• Higher pressures observed at higher tip speed Potential for higher granule consolidation

• Linear increase of pressure observed with tip speed

Time Averaged Pressure Profile

46

Effect of Blade Speed1 L Diosna

Vtip = 2.5 m/sec

Vtip = 5.0 m/sec

• Higher shear stress observed at higher tip speed Potential for higher granule attrition

• Linear increase of shear stress observed with tip speed

Time Averaged Shear Stress Profile

47

Effect of Blade Speed- Residence Time Distributions

1 L Diosna, 40% fillHigh Shear Zone Recirculation Patterns

Vtip = 2.5 m/sec

r/R

y/H

r/R

Vtip = 5.0 m/sec

48

Effect of Scale60% Fill, 5 m/sec

Time Averaged Pressure Profiles

1 L Diosna 10 L Diosna

• Higher pressures observed at 10 L scale Due to higher mass of powder bed Suggest a higher degree of granule consolidation at larger scale

49

Effect of Scale60% Fill, 5 m/sec

Time Averaged Shear Stress Profiles

1 L Diosna 10 L Diosna

• Higher shear stress observed at 10 L scale Suggest a higher degree of granule attrition at larger scale

50

Effect of Scale- Residence Time Distributions

5 m/sec, 60% fillHigh Shear Zone Recirculation Patterns

r/R r/R

51

Effect of the Blade Design

• Two blade designs were tested for Diosna 1L granulator with the blades being more radial and more tangential

• 60% material fill level was used at tip speed 5m/s

Blade Design vs. Time Averaged Velocity

• Tangential velocities normalized with respect to the tip speed

• The tangential component of the velocity is higher for radial blades near the granulator wall

-0.035

-0.03

-0.025

-0.02

-0.015

-0.01

-0.005

0

0.005

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Tang

entia

l vel

ocity

nor

mal

ized

ag

ains

t tip

spe

ed

Normalized Radial Position

Time Averaged Tangential velocity

Radial blade

Tangential blade

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Tang

entia

l Vel

ocity

noe

rmal

ized

ag

ains

t tip

spe

ed

Normalized Vertical Position

Time Averaged Tangential Velocity

Radial blade

Tangential blade

Blade Design vs. Time Averaged Velocity

• Radial velocities normalized with respect to the tip speed

• The radial component of the velocity is very similar for different blade designs

-0.05

0

0.05

0.1

0.15

0.2

0.25

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Rad

ial v

eloc

ity n

orm

aliz

ed a

gain

st

tip s

peed

Normalized Radial Position

Time Averaged Radial Velocity

Radial blade

Tangential blade

0

0.05

0.1

0.15

0.2

0.25

0.3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Rad

ial V

eloc

ity n

orm

aliz

ded

agai

nst t

ip s

peed

Normalized Vertical Position

Time Averaged RadialVelocity

Radial blade

Tangential blade

Radial vel. component

Tangential vel. component

Population Balance Modeling (PBM)

BMS Highly Confidential

0 5 10 15 20 250

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8x 10

-3

Time (s)

Nor

mal

ized

mas

s fre

quen

cy

Experiment D - 75% water, 3% HPC

0-100100-200200-500500-3000

0 5 10 15 20 250

1

2

3

4

5

6

7x 10

-3

Time (s)

Nor

mal

ized

mas

s fre

quen

cy

Experiment H - 85% water, 3% HPC

0-100100-200200-500500-3000

Different binder concn. Different water amount

0 1 2 3 4 5 6 7 8 9 100

0.005

0.01

0.015

0.02

0.025

0.03

pore size (µm)

cum

ulat

ive

pore

vol

ume

(mL/

g)

Experiment D - 75% water, 3% HPC

experimentalsimulation

0 1 2 3 4 5 6 7 8 9 100

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

pore size (µm)

cum

ulat

ive

pore

vol

ume

(mL/

g)

Experiment H - 85% water, 3% HPC

experimentalsimulation

Part

icle

Siz

eG

ranu

le P

oros

ity

FBR

MM

IP

Prediction com

pared against experimental data

generated by: E

nabl

es P

redi

ctio

n of

Multi-Dimensional PBM

G

S1+S2 L

13

1 26( )S S L Gdπ

+ + + = 1 2

L GS S L G

ε +=

+ + +

GLLlsat +

=Granule

In this project we will utilize an Seff to take into account multiple solid components (More details on PBM formulation in Telecon 2). We assume viscous regime, layering is ignored. Nucleation can be ignored due to very small time constant.

3

Aggregation and Breakage rates

• Aggregation– Net aggregation rate in each bin depends on properties and number of particles in

all smaller bins.

– Multiple properties result in multiple integrals evaluated for each bin.

– Aggregation rate kernel depends on properties of two colliding particles.

– If bins are not linearly distributed with respect to volume, new particles can form between bins (cell-average method).

• Breakage– Net breakage rate in each bin depends on properties and number of particles in all

larger bins.

– Breakage rate kernel depends on properties of parent particle.

– Breakage distribution describes daughter particles (uniform distribution assumed).

∫−∫ −−=ℜ∞

00')'()()',(')'()'()','(

21)( dxxFxFxxdxxFxxFxxxx

x

agg ββ

)()(')'()',()'()( xFxKdxxFxxbxKx breakx

breakbreak −∫=ℜ∞

Connect Material properties and Process parameters into process model

kernels

Aggregation kernels (high shear focus)

4 main types of kernels

1. Empirical (based on no physics, contains only tuning parameters)2. Semi-empirical (based on some physics, contains several tuning

parameters) (based on 3. Semi-mechanistic (Mechanistic and parametric) (based on physics,

encompasses very few tuning parameters) 4. Mechanistic (Mechanistic and non-parametric) (based on physics, no

tuning parameters)

Empirical: Constant kernel, Additive kernel, Multiplicative, Discrete (Wauters, Adetayo, which are based on certain critical properties such as size) (often no dependency on liquid content), Made.

Madec kernel above has liquid dependency but is still highly empirical)

Proposed aggregation kernel (Chaudhury, Ramachandran et al. 2013)

• Based on type 1 collisions common in high-shear as opposed to fluid bed (type 2)

• Expressed as a function of 1) liquid content (external vs. internal liquid is demarcated. Only external available for aggregation events). This is further dynamically linked with the consolidation event. 2) Impeller speed, 3) Binder viscosity, 4) contact angle (wettability) , 5) depth of liquid surface, 6) wetted area of particle

59

Proposed aggregation kernel (Chaudhury, Ramachandran et al. 2013)

0( , , , ', ', ') ( , , , ', ', ') ( , , , ', ', ')aggK s l g s s l l g g B s l g s s l l g g A s l g s s l l g g− − − = Ψ − − − × − − −

*

*

1, if ( , , , ', ', ')

0, if St St

s l g s s l l g gSt St

<Ψ − − − =

≥

*0 122

8 and 2 ln3 a

muSt Sthdλ

πµ= =

( , , ) ( ', ', ')( , , , ', ', ')( , , ) ( ', ', ')

wet wet

total total

A s l g A s s l l g gA s l g s s l l g gA s l g A s s l l g g

− − −− − − = ×

− − −

Kernel formulation

Stokes criterion

Wetted fractional area

0( , , , )( , , ) 1.5( , , , )

ext

wet

V s l g th s l gA s l g t

= ×

Awet is a function of contact angle

60

Proposed breakage kernel (Ramachandran et al. 2009)

• Empirical breakage kernel depends only on the process parameters and neglects the effect of material properties

• A more predictive model accounts for those effects inherently, instead of relying on experimental data and calibration.

• Semi-Mechanistic breakage kernel a function of particle-particle, particle-wall and particle-impeller collisions, contact area and intrinsic strength.

External stress = f(size, density, elastic modulus, Poisson ratio, coefficient of restitution, velocity)

Contact Area = f(Young’s Modulus, radius of particle, Poisson’s ratio)

Intrinsic Strength = f(viscosity, density, surface tension, contact angle, radius of particle)

Overall Kernel formulation: Kbreak=(σimpact/σintrinsic strength )

Note: Test Pandya kernel also depending on extent of breakage 61

Micro-Granulator

Disposable material use API and material requirement < 10g/batch Multiple batches simultaneously, robust statistical analyses Mechanistic investigations and rank-order trend analyses

BMS Highly Confidential

A Vision of the Future State

Formulation & Process Selection

Mechanistic Understandinge.g., Form Conversion

Formulation & Process DoEfor QbD/Design Space

Scale-up & Tech Transfer

Control Strategy & Commercial Manufacture

DE

M

PB

M

Micro-granulator

FBR

M DFF

ToolsStage of DevelopmentRapid, low cost, low API development with high quality

In-silico design space with confirmation of edges experimentally

Real-time monitoring & control enabling scale-up, RTR, no batch failures, and process trending

Acknowledgements & Contributions Population Balance Modeling

Preetanshu Pandey Rohit Ramachandran, Rutgers

Discreet Element Modeling Brenda Remy Richard LaRoche, Mark Cook,

Anna Sosnowska, and Stephen Cole, DEM Solutions

Atul Dubey, Tridiagonal Solutions

Micro-granulator Jaquan Levons, Brian Breza, Alex

Baranowski

Focused Beam Reflectance Measurement Tim Stevens, Kevin Macias Rekha Keluskar, Shruti Gour

Densification (MIP) during HSWG Li Tao, Junshu Zhao, Jinjiang Li Sherif Badawy, Avi Wolf, Summer Intern

Shear & Drag Force Sensor Valery Sheverev and Vadim Stepaniuk,

Lenterra Tim Freeman, Michael Delancy, and Doug

Millington-Smith, Freeman Tech Divyakant Desai, Sherif Badawy, Dilbir Bindra,

Kevin Macias, Tim Stevens, Brian Breza, Douglas Both, & Ganeshkumar Subramanian

BMS Highly Confidential

BMS Leadership and Project TeamDilbir Bindra, Julia Gao, Sherif Badawy, Sailesh Varia, Nancy BarbourKevin Macias, Tim Stevens, Douglas Both, Keirnan LaMarche, Judy Lin, Pankaj Shah

64

Selected Publications• Panakanti R and Narang AS (2012) Impact of excipient interactions on drug bioavailability from solid dosage

forms. Pharm Res 29: 2639.

• Narang A, Yamniuk A, Zhang L, Comezoglu SN, Bindra DS, Varia S, Doyle M, and Badawy S (2012) Reversible and pH-dependent weak drug-excipient binding does not affect oral bioavailability of high dose drugs. J PharmPharmacol 64:553-65.

• Badawy SIF, Narang AS, LaMarche K, Subramanian G, and Varia SA (2012) Mecahnistic basis for the effects of process parameters on quality attributes in high shear wet granulation. Int J Pharm 439: 324-333.

• Narang AS, Badawy S, Ye Q, Patel D, Vincent M, Raghavan K, Huang Y, Yamniuk A, Vig B, Crison J, Derbin G, Xu Y, Ramirez A, Galella M, and Rinaldi F (2015) Role of self-association and supersaturation on oral absorption of a poorly soluble weakly basic drug. Pharm Res 32: 2579-2594.

• Badawy SIF, Narang AS, LaMarche KR, Subramanian G, Varia SA, Lin J, Stevens T, and Shah PA (2015) Integrated application of quality-by-design principles to drug product development: case study of brivanb alaniante film coated tablets. J Pharm Sci (in press).

• Narang AS, Sheverev VA, Stepaniuk V, Badawy S, Stevens T, Macias K, Wolf A, Pandey P, Bindra D, and Varia S (2015) Real-time assessment of granule densification in high shear wet granulation and application to scale-up of a placebo and a Brivanib Alaninate formulation. J Pharm Sci 104: 1019-1034.

• Narang AS, Sheverev V, Freeman T, Both D, Stepaniuk V, Delancy M, Millington-Smith D, Macias K, and Subramanian G (2015) Process analytical technology for high shear wet granulation: wet mass consistency reported by in line drag flow force sensor is consistent with powder rheology measured by at line FT4 powder rheometer®. J Pharm Sci (in press).