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&
G..
C E
65
NSF ERCFOR STRUCTURED ORGANICPARTICULATE SYSTEMS
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I
E
/
IE
EC
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3
39.80%
30.60%, , 12.70%
11.20%
5.70%
$800+ Billion/yGlobal Business!
Source: IMS Health Market Prognosis, March 2010
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D D C
Cost Component Distribution
Discover 20- 25%
C D $0.8 B $2 B
A &D $60 B
Safety & Toxicology 1520%
Product DevelopmentAPI process design
Product formulation & process designClinical supply
3035%
Clinical Trials ( Phase I-III) 35-40%
& , , , 151 (200)
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Disposition of salesrevenue of 8 largestresearch-basedpharmaceuticalmanufacturers
ManufacturingCOGS27%
After TaxAccounting Profit
18%
Taxes
U.S. pharmaceuticalexpenditures in 2009~ $320 B
(IMS Health and PMPRBAnnual Report, 2010)
COGS (U.S) = ~ $90 B .. , , 136, / 2001
7%
R&D13% Sales & General
Administration
35%
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D &
D A
L
()
NHHO
O
CH3
6
D , , ,
, ,
/
G
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ExcipientsAPI
Dosage Form
/
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C A : &
D &
FDA & ()
F &
C ,
: DD D
C D C A &
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E A
& DAI
& D
I B C
E D D
( CACE 2008; CACE 2008)
C C (496)
C C (L , CACE 2009,2010)
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D
Quantitative definitionFor selected set of equipment design
parameters
Probability distributions of feed
The established range of process parameters that has
been demonstrated to provide assurance of quality.
I A
Probability distributions ofinternal process variables
Required probability of meetingproduct critical quality attributes
Design space:Multidimensional region defined byranges of operating variablescontaining all variable adjustmentsnecessary to achieve desiredprobability of meeting product CQA
CA
D
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D C :
& E.., F ,
A , ,
)C ( ,
)
CA
C I:
&
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%
BloodL
evel
H
D & D
I
D
D
D
C
D
D
C
C
D
AI (, )
D
E
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Manuf.
Design
Space
Degradant
Unstable
form
Post-
Manufacturing
Degradation
Drug
Expiry
2.0
2.5
60 min
milled
30
40
50
5% H 30% H
I D
%
13
0 5 10 15 20 25 30
0.0
0.5
1.0
1.5
%l
actam
time(days)
milled
15 min
milled
API as
received 0
10
20
0 100 200 300 400 500 600
50% H
D
D
&
()
L
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D C : G
Excipients
Binder
Lubricants
O OH
NH2 NH
O
gabapentin lactam
AI
H (70%): HC,
CC, C,
2011
/
WetGranulation
Fluid BedDrying
Blending Tabletting
API
G: D A D
D
+
(
)
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I
E
/ IE
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NSF ERC for Structured Organic Particulate SystemsNSF ERC for Structured Organic Particulate Systems
30 Faculty
16
50+ PhDstudents &postdocsIndustrial
partners26
Launched
July 2006
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CC--SOPS ObjectivesSOPS Objectives
Develop scientific foundation foroptimal design of structured organiccomposite products for
pharmaceutical, nutraceutical &agrochemical industries
Develop science and engineeringmethods for desi nin scalin
NAE 2008
17
optimizing and controlling relevantmanufacturing processes.
Demonstrate developedfundamentals on novel test beds.
Establish effective educational andtechnology transfer vehicles. Engineer
better medicines !
Personalized medicine
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Thrust D: Integrated Systems Science
Model predictive design and operation ofintegrated particulate processes
Thrust Leader: Venkat Venkatasubramanian (PU)
Thrust B projec Thrust B proThrust D projects
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D-1 Sensing MethodologiesD-2 Hardware and Software Integration
D-3 Ontological Informatics Infrastructure
D-4 Real-time Process Management
D-5 Integrated Design and Optimization
Demonstration on Test Beds
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Data, Information, ModelsData, Information, Models
1. Feeder Screw Speed (rpm) Vibration if resent
7. Pneumatic Transfer
6. Transfer Receptacle Monitor mass over time
8. Tablet Press Fill weight
Pressure RPM Feed Frame RPM Feed Frame Blade
Speed Punch Distance
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3
4
4. Roll Compactor Roll Speed Hydraulic Pressure Feed rate Roll Gap
Ribbon Content
Uniformity Ribbon Density (NIR)
5. Mill Milling Speed Particle Size
Continuous Granulation Line
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Powder Flow
Powder Level in Hopper
2. Continuous Blender Tilt Speed (rpm) Load (mass/level) Inlet Powder Flow
Content Uniformity (NIR) Density (X-ray/Microwave
/NIR)
Outlet Powder Flow
9. Tablets Weight
Tensile strength
Density
n e ow er ow
Content Uniformity Density (NIR)
Tablet Weight
Tablet Density
Feed Frame Outlet
Flow
Powder Density
Powder Segregation85
6
7
9
3. Feed Hopper / Screw Screw Speed (rpm) Vacuum Pressure
Powder Level in Hopper Density at RC Entrance
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Information Ontology ArchitectureInformation Ontology Architecture
GUI
Decisions Knowledge Models
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Information
Computationaltools
Unstructured information
Venkatasubramanian AICHE J 2009
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Real Time Process Management
21
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Process FaultsProcess Faults & Disturbances& Disturbances
2. Continuous Blender (DEM,Compartment)
No Content Uniformity
5. Mill (PBM) No Flow (screens clog/foul) Undesired Particle Size (over-milling)
RPM
6. Transfer Receptacle
8. Tablet Press (FEM) Flow Variability Incorrect Density Powder Segregation
7. Pneumatic Transfer
All Unit Operations Max/Min Limits of
Manipulated VariablesReached2
3
4
7
1Unit Operation
DEM
Neural Nets
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. Insufficient Shear Force Generating Electrostatics
No Flow RPM/Motor Malfunction Tilt Angle Cohesiveness
4. Roll Compactor (FEM)
Undesired Density Pressure RPM/Motor Malfunction Feed Screw / Roll Speed
Ratio No Content Uniformity No Flow No Compaction
PSD Variation
Mass Accumulation Bridging Whiskering Dissolution Weight Variation Picking High Friability Sticking Capping Lamination Weak (Tensile Strength)
85
6
9
Statistical
DAE
Solvers
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Roller Compaction Model Management in POPE
Johansons rolling model with timevariation of roll gap included
10/11/2005
23
ENGINEERING RESEARCH CENTER FOR
STRUCTURED ORGANIC PARTICULATE SYSTEMS
RUTGERS UNIVERSITYPURDUE UNIVERSITY
NEW JERSEY INSTITUTE OF TECHNOLOGYUNIVERSITY OF PUERTO RICO AT MAYAGEZ
Model and OperationOntology JAVA Engine and
GUI
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EEM: Alexanderwerk Roller Compactor
Roll GapFeed Screw Speed
Hydraulic Pressure
RollSpeed
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Feedback Control
Event: No powder entering roll region
Causes: No powder in hopperBlockage in hopper
Jam in nip region
Event: No powder entering roll region
Causes: No powder in hopperBlockage in hopper
Jam in nip region
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Exceptional Events Management
HydraulicPressure
Delta-V FaultDetection
Mitigation strategy isautomated wheneverpossible; otherwise, a
strategy is suggestedto the operator
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RollGap
Feed ScrewSpeed
FeedbackControl
RollSpeed
FaultDiagnosis
FaultMitigation
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Feeders
Blender
Multi-pointNIR
10/11/2005
27
ENGINEERING RESEARCH CENTER FOR
STRUCTURED ORGANIC PARTICULATE SYSTEMS
RUTGERS UNIVERSITYPURDUE UNIVERSITY
NEW JERSEY INSTITUTE OF TECHNOLOGYUNIVERSITY OF PUERTO RICO AT MAYAGEZ
TabletPress
Delta VControl System
Optical
Tablet thicknessMeasurement
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AICE 2010
383,G , F C I I
444, H , E E C
: F, B, & C
456, L , A CI C K D: HB
456, J , : I
596, G , C : I
697,L , I C I FBAED
D F 444,K , D C
()
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SummarySummary
Changes in business environment have openedexiting opportunities for development and
application of PSE methodology. Product /process design challenges: linking input
material properties & manufacturing conditions to
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o pro uc s e e erapeu c per ormance
Key process operations challenges: predicting &optimizing performance of particulate and/orheterogeneous multicomponent systems
Risk management is critical: opportunity forexploitation of quantitative Bayesian basedestimation and analysis methods