10/11/2005

1

ENGINEERING RESEARCH CENTER FOR

STRUCTURED ORGANIC PARTICULATE SYSTEMS

RUTGERS UNIVERSITYPURDUE UNIVERSITYNEW JERSEY INSTITUTE OF TECHNOLOGYUNIVERSITY OF PUERTO RICO AT MAYAGÜEZ

PQRI workshop on “Sample Sizes for Decision Making in New

Manufacturing Paradigms”

Challenges of Statistical Analysis/Control in a Continuous Process

Fernando Muzzio, Professor IIDirector, ERC-SOPSRutgers University

2

Why Continuous Manufacturing?

• Smaller equipment• No scale up• No wasted batches• Better quality control• Meaningful PAT• More uniform processing

• Controllable segregation?• Faster development?

3

Feeder size, Screw type, feed rate

Raw material properties

Blender size, design, Blender speed, flow rate

Blender size, Blender speed, design, flow rate

Design, Feeding Speed, Tabletting speed

Tabletting (Speed, compression force, thickness)

Lubricant Mixing (Blender speed, design, flow rate, humidity, shear)

Roller compaction (Design, speed, flow rate, Gap between rolls, compaction pressure)

σ2i

σ2i

PSDBlend homogeneity (RSD)

PSD,Blend homogeneity, HydrophobicityFlow properties

PSD,Hydrophobicity, Flow properties

Direct Compression

Roller Compaction

Weight variability, Dissolution, HardnessContent Uniformity

Tablet sizeCompression speedComposition

σ2i

Milling (Mill size, Mill type, Speed, flow rate)

PSD

Ribbon density, Composition

Feeding

API Blending

Lubricant Blending

Feed frame

Tabletting

Throughput?

4

PAT for continuous secondary pharmaceutical manufacturing

Roller CompactionRibbon density (NIR, X-Ray, Ultrasound)

MillingGranule Size Distribution (Laser diffraction) Wet granulation and drying

Granule size distribution (Laser Diffraction)Moisture content (NIR)

Pneumatic transfer

BlendingBlend uniformity (NIR)Blender hold-up (Load sensor)

Gravimetric feedingFlow rate (Feeder Load sensor)

DCDG

WG

TablettingContent Uniformity (CU) (NIR)HardnessDensity

Ultrasound, X-ray

5

PAT for continuous secondary pharmaceutical manufacturing (cont.)

• Feedingo Feed rate variabilityo Feeder refill

• Blendingo Blend uniformity (NIR)o Blender hold-up (Load sensor)

• Roller compactiono Ribbon density profile (NIR, Ultrasound, X-ray)o Ribbon thickness (Gap between rollers)

• Millingo Particle size distribution (Laser diffraction)

• Wet Granulation and Dryingo Granule Size distribution (Laser diffraction)o Moisture content (NIR)

• Tablettingo Weight (Load sensor)o Hardness, density (Ultrasound)

(Load sensor)

6

Challenges

• Real time automated control is REQUIRED– Underdeveloped sensors – Lack of models

• No experience regarding performance of pharma materials in these systems

• Lack of a regulatory framework

7

Requisites of Continuous process

• More flexibility in continuous processing in terms of throughput and control

• Development of compact process models for individual unit operations

• Identification of manipulated and controlled variables

• Model based control to ensure efficient operation under closed loop conditions

• Production throughput (Capacity of Tablet press, size of tablet)

• Equal flow rate through all the units

10/11/2005

8

ENGINEERING RESEARCH CENTER FOR

STRUCTURED ORGANIC PARTICULATE SYSTEMS

RUTGERS UNIVERSITYPURDUE UNIVERSITYNEW JERSEY INSTITUTE OF TECHNOLOGYUNIVERSITY OF PUERTO RICO AT MAYAGÜEZ

Feeders

Blender

TabletPress

Delta V Control System

Multi-pointNIR

Malvern Insitec

OpticalTablet thicknessMeasurement

9

PAT Approach

• Gravity influenced flow of powder on metal chute placed right after the blender to make the NIR measurement possible

• A remote NIR probe with 5 measurement spots was used

9

Mixer outletProbe

Tablet press inletChute

10

Measurement configuration - VTT

• The probes measured from a distance of about 15 cm

• 600 µm fibers were used in both illumination and collection

• Illumination spot size ~5 mm

• Collection spot size ~ 8 mm

Probes

Chute

Mixer outlet

1111

Measurement equipment

• Multipoint NIR measurement system was used

– 5-point probe, measurement spot Ø 3 mm

– Fiber-optic light source

– NIR spectral camera

ChutePowder

Mixer

Collection fiber

bundles Real-time calculation

module

Illumination fiber

bundles

5 measurement spots

Light source

Spectral camera

Schematic of the 3x5 probe measurement system

To process control

Probe

1212

Results: 10 % APAP concentration

• Mixer was operating at 10 % APAP

• Again some peaks of high APAP concentration visible

0 20 40 60 80 100 120 140 160-5

0

5

10

15

20

25

30

35

Time [s]

Pre

dict

ed c

once

ntra

tion

[%]

Smoothed APAP concentration (ref. 10.0 %)

1313

Results: 20 % APAP concentration

• Mixer was operating at 20 % APAP• Nice ramp from 10 % concentration to 20 %• No peaks of high APAP concentration visible

0 20 40 60 80 100 120 140 1600

5

10

15

20

25

30

35

Time [s]

Pre

dict

ed c

once

ntra

tion

[%]

Smoothed APAP concentration (ref. 20.0 %)

14

SBC calibration results for caffeine

• Response spectrum (blue, scaled), shown for reference

• Regression vector (green) picks up caffeine features

14

1000 1100 1200 1300 1400 1500 1600

-50

0

50

100

150

200

250

300

Wavelength [nm]

b ve

ctor

Response and regression vectors

0 2 4 6 8

-1

0

1

2

3

4

5

6

7

8

9 RMSEC : 0.92905 cc : 0.93239 CV : 24.6356 R2 : 0.8497 #of smpl : 110

Reference concentrationPr

edic

ted

conc

entra

tion

Prediction vs. reference

gb

• Prediction scatter plot• The slope had to be adjusted (0.8927)

since the scattering properties of pure caffeine and the 0 – 8 % blend are different

1515

Results from the continuous blending trials

0 20 40 60 80 100 120 140 1600

1

2

3

4

5

6

7

Time [s]

Pre

dict

ed c

once

ntra

tion

Caffeine concentration vs. time

12345Average

Concentration measurement

16

Impulse responses

Blender speed 30 %

16

Blender speed 80 %

Continuous mixingof pure CaHPO4

Add 7g caffeinein blender inlet

Measure the timeresponse afterblender

0 50 100 150-1

-0.5

0

0.5

1

1.5

2

2.5

Time [s]

Pre

dict

ed c

once

ntra

tion

Caffeine concentration vs. time

12345Average

0 10 20 30 40 50 60 70 80-2

0

2

4

6

8

10

12

14

Time [s]

Pre

dict

ed c

once

ntra

tion

Caffeine concentration vs. time

12345Average

1717

Overall scheme for DC (In-line NIR/Raman/PSD Sensing)

Multipoint NIR / Raman / Partice size

API & excipient characterization

Process

control

• Ultimate goals• 100 % inspection• Closed-loop feedback

control• Methodology for design

and construction of continuousmanufacturing lines

• Measurements• Multipoint NIR• Particle size• Multipoint Raman

• Methodology for measurements needs

• Robust but easycalibration

• Sampling• Process and

measurementunderstanding

• Real-time computing & process connectivity

18

Approach for optimizing PAT method

20

22 )()( σσσ += xx mixingtotal

SizeSamplex _=

)()()(

.

)(

20

2

20

2

20

2

aLnxLnRSDLnxRSDxfRSD

Total

total

total

+=−

=−

=−

βσ

ασ

σβ

Experimentally measured

•Unknown parameter•Determined by optimizing R2 (result of regression)

Power law relationship between the normalized variance and sample size

Linearization:

Methodology to assess the relationship between blend uniformity (variance in concentration) and sample size

19

Dataset-1 (3% Gran APAP UV Spec)

RSD vs. sample size

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0 0.2 0.4 0.6 0.8 1

RS

D

Sample size(g)

•Confidence intervals are for the Std deviation which were normalized by the mean

20

Dataset-1 (3% Gran APAP UV Spec) (Cont.)

Ln (RSD2) vs Ln (Sample size) R2 vs. σ20

y = -0.66x - 7.913R² = 0.820

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

-4 -2 0 2

Ln(R

SD2 )

Ln (Sample size)

Series1

Linear (Series1)

00.10.20.30.40.50.60.70.80.9

0 0.0001 0.0002 0.0003 0.0004 0.0005

R2Method Error (σ2

0)

σ20 = 0 (Best case)

21

Dataset-2 (3% Gran APAP NIR)

RSD vs. sample size

0

0.05

0.1

0.15

0.2

0.25

0 0.2 0.4 0.6 0.8

RS

D

Sample size(g)

22

Dataset-2 (3% Gran APAP NIR) (Cont.)

Ln (RSD2) vs Ln (sample size)

R2 vs. σ20

y = -0.663x - 8.247R² = 0.639

-10

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

-6 -4 -2 0 2

Ln(R

SD2 )

Ln (Sample size)

Ln(Rsd^2)

Linear (Ln(Rsd^2))

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.00005 0.0001 0.00015

R2

Error (σ20)

σ20 = 0 (Best case)

23

Comparison between In-line NIR and UV Spectroscopy

0

0.02

0.04

0.06

0.08

0.1

0.12

0 0.2 0.4 0.6 0.8 1

RS

D

Sample size(g)3% NIR

•This methodology provides the number of NIR measurements to be averaged to measure blend uniformity at the desired sample size (unit dose).

Unit dose (1 Tablet)

24

Conclusions

• To minimize the required experiments for identifying the optimum “plan” for the production of a new product:

– Characterize basic material properties

– Use all the existing knowledge (experimental data and modeling techniques) that connect material properties to unit operation performance to identify appropriate equipment designs and operating conditions

25

Take Home

• In continuous processing, every component needs to operate simultaneously and at the same rate

• One bottleneck is the need to feed small rates of cohesive powders– Solution – pre-conditioning

• PAT challenges

Slide 25