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PQRI workshop on
“Sample Sizes for Decision Making in New
Manufacturing Paradigms”
Challenges of Statistical Analysis/Control in a
Continuous Process
Fernando Muzzio, Professor II
Director, ERC-SOPS
Rutgers University
ENGINEERING RESEARCH CENTER FOR
STRUCTURED ORGANIC PARTICULATE SYSTEMS
RUTGERS UNIVERSITY
PURDUE UNIVERSITY
NEW JERSEY INSTITUTE OF TECHNOLOGY
UNIVERSITY OF PUERTO RICO AT MAYAGÜEZ
10/11/2005
1
Why Continuous Manufacturing?
•
•
•
•
•
•
Smaller equipment
No scale up
No wasted batches
Better quality control
Meaningful PAT
More uniform processing
• Controllable segregation?
• Faster development?
2
Raw material properties
Roller compaction (Design, speed,
flow rate, Gap between rolls,
compaction pressure)
Feeding
Feeder size, Screw type, feed
rate
σ2i
API Blending
Ribbon density,
Composition
Blender size, design, Blender
speed, flow rate
Lubricant
Blending
PSD
Blend homogeneity (RSD)
σ2i
Blender size, Blender
speed, design, flow rate
PSD
PSD,
Blend homogeneity,
Hydrophobicity
Flow properties
Feed frame
Direct
Compression
Milling (Mill size, Mill type, Speed,
flow rate)
Design, Feeding Speed,
Tabletting speed
Tabletting
Tabletting (Speed, compression force,
thickness)
σ2i
Lubricant Mixing (Blender speed,
design, flow rate, humidity, shear)
PSD,
Hydrophobicity,
Flow properties
Roller
Compaction
Weight variability,
Dissolution, Hardness
Content Uniformity
Throughput?
Tablet size
Compression speed
Composition
3
PAT for continuous secondary
pharmaceutical manufacturing
Roller Compaction
Ribbon density
(NIR, X-Ray, Ultrasound)
Pneumatic transfer
Gravimetric feeding
Flow rate (Feeder Load sensor)
Wet granulation and drying
Granule size distribution
(Laser Diffraction)
Moisture content (NIR)
Milling
Granule Size Distribution
(Laser diffraction)
DG
DC
WG
Blending
Blend uniformity (NIR)
Blender hold-up (Load
sensor)
Tabletting
Content Uniformity (CU) (NIR)
Hardness Ultrasound, X-ray
Density
4
PAT for continuous secondary
pharmaceutical manufacturing (cont.)
• Feeding
o Feed rate variability
o Feeder refill
(Load sensor)
• Blending
o Blend uniformity (NIR)
o Blender hold-up (Load sensor)
• Roller compaction
o Ribbon density profile (NIR, Ultrasound, X-ray)
o Ribbon thickness (Gap between rollers)
• Milling
o Particle size distribution (Laser diffraction)
• Wet Granulation and Drying
o Granule Size distribution (Laser diffraction)
o Moisture content (NIR)
• Tabletting
o Weight (Load sensor)
o Hardness, density (Ultrasound)
5
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
6
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
7
Multi-point
NIR
Malvern Insitec
Feeders
Blender
Delta V
Control System
Tablet
STRUCTURED ORGANIC PARTICULATE
SYSTEMS
Press
ENGINEERING RESEARCH CENTER FOR
RUTGERS UNIVERSITY
PURDUE UNIVERSITY
NEW JERSEY INSTITUTE OF TECHNOLOGY
UNIVERSITY OF PUERTO RICO AT MAYAGÜEZ
Optical
Tablet thickness
Measurement
10/11/2005
8
PAT Approach
Probe
Mixer outlet
•
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
Tablet press inlet
Chute
9
9
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
Mixer outlet
Probes
Chute
10
Measurement equipment
Mixer
Schematic of the 3x5
probe measurement
system
Light source
Chute
Powder
Illumination
fiber
bundles
Spectral
camera
Probe
•
5 measurement
spots
Collection
fiber
bundles
Multipoint NIR measurement system was used
Real-time
calculation
module
To process
control
– 5-point probe, measurement spot Ø 3 mm
– Fiber-optic light source
– NIR spectral camera
11
11
Results: 10 % APAP concentration
Smoothed APAP concentration (ref. 10.0 %)
35
Predicted concentration [%]
30
25
20
15
10
5
0
-5
0
20
40
60
80
Time [s]
100
120
140
160
• Mixer was operating at 10 % APAP
• Again some peaks of high APAP concentration visible
12
12
Results: 20 % APAP concentration
Smoothed APAP concentration (ref. 20.0 %)
35
Predicted concentration [%]
30
25
20
15
10
5
0
0
20
•
•
•
40
60
80
Time [s]
100
120
140
160
Mixer was operating at 20 % APAP
Nice ramp from 10 % concentration to 20 %
No peaks of high APAP concentration visible
13
13
SBC calibration results for caffeine
Response and regression vectors
Prediction vs. reference
9
300
250
g
b
7
Predicted concentration
b vector
200
8
150
100
50
0
RMSEC : 0.92905
cc
: 0.93239
CV
: 24.6356
R2
: 0.8497
#of smpl : 110
6
5
4
3
2
1
0
-50
-1
1000 1100 1200 1300 1400 1500 1600
Wavelength [nm]
•
Response spectrum (blue,
scaled), shown for reference
•
Regression vector (green)
picks up caffeine features
0
2
4
6
Reference concentration
8
• 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
14
14
Results from the continuous blending trials
Caffeine concentration vs. time
7
1
2
3
4
5
Average
Predicted concentration
6
5
4
3
2
1
0
0
20
40
60
100
80
Time [s]
120
140
160
Concentration
15
measurement
15
Impulse responses
Continuous mixing
of pure CaHPO4
Add 7g caffeine
in blender inlet
Measure the time
response after
blender
Caffeine concentration vs. time
Caffeine concentration vs. time
14
2.5
Predicted concentration
2
1.5
1
0.5
0
-0.5
-1
0
10
8
6
4
2
0
50
100
Time [s]
Blender speed 30 %
16
1
2
3
4
5
Average
12
Predicted concentration
1
2
3
4
5
Average
150
-2
0
10
20
30
50
40
Time [s]
60
70
80
Blender speed 80
%
16
Overall scheme for DC (In-line NIR/Raman/PSD Sensing)
API & excipient
characterization
Multipoint NIR /
Raman /
Partice size
Process
control
• Ultimate goals
• 100 % inspection
• Closed-loop feedback
control
• Methodology for design
and construction of
continuous
manufacturing lines
• Measurements
• Multipoint NIR
• Particle size
• Multipoint Raman
• Methodology for
measurements needs
• Robust but easy
calibration
• Sampling
• Process and
measurement
understanding
• Real-time computing &
process connectivity
17
17
Approach for optimizing PAT method
Methodology to assess the relationship between
blend uniformity (variance in concentration) and sample size
x = Sample _ Size
Experimentally
measured
2
2
σ total
( x) = σ mixing
( x) + σ 02
2
− σ 02 = f ( x)
RSDtotal
Power law relationship
between the normalized
variance and sample size
2
− σ 02 = α .x β
RSDtotal
Linearization:
2
− σ 02 ) = β Ln( x) + Ln(a )
Ln( RSDTotal
•Unknown parameter
•Determined by optimizing R2 (result of
regression)
18
Dataset-1 (3% Gran APAP UV Spec)
RSD vs. sample size
0.1
0.09
0.08
RSD
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
0
0.2
0.4
0.6
0.8
1
Sample size(g)
•Confidence intervals are for the Std
deviation which were normalized by
the mean
19
Dataset-1 (3% Gran APAP UV Spec)
(Cont.)
R2 vs. σ20
Ln (RSD2) vs Ln (Sample size)
0
-4
-2
-1 0
2
Ln (RSD2)
-3
y = -0.66x - 7.913
R² = 0.820
-4
-5
-6
Series1
R2
-2
Linear
(Series1)
-7
-8
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
-9
Ln (Sample size)
0.0001 0.0002 0.0003 0.0004 0.0005
Method Error (σ20)
σ20 = 0 (Best case)
20
Dataset-2 (3% Gran APAP NIR)
RSD vs. sample size
0.25
RSD
0.2
0.15
0.1
0.05
0
0
0.2
0.4
0.6
0.8
Sample size(g)
21
Dataset-2 (3% Gran APAP NIR) (Cont.)
Ln (RSD2) vs Ln (sample
size)
0.7
2
0.6
0.5
Ln(Rsd^2)
Linear (Ln(Rsd^2))
R2
Ln (RSD2)
-6
0
-1 0
-4
-2
-2
y = -0.663x - 8.247
-3
R² = 0.639
-4
-5
-6
-7
-8
-9
-10
Ln (Sample size)
R2 vs. σ20
0.4
0.3
0.2
0.1
0
0
0.00005
0.0001
0.00015
Error (σ20)
σ20 = 0 (Best case)
22
Comparison between In-line NIR and UV Spectroscopy
0.12
Unit dose (1 Tablet)
0.1
RSD
0.08
0.06
0.04
0.02
0
0
0.2
0.4
0.6
Sample size(g)
3% NIR
0.8
1
•This methodology provides the number of NIR
measurements to be averaged to measure blend
uniformity at the desired sample size (unit dose).
23
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
24
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
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