yasasvi bommireddy, prof. marcialgonzalez...
Post on 27-May-2018
213 Views
Preview:
TRANSCRIPT
0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
Average Relative Density-( )
0
50
100
150
200
250
300
Aver
age
Mai
n Co
mpr
essio
n Pr
essu
re[M
Pa]-P
• Consolidateandcompactpowders(tableting)• Achievecontinuouscontrolofpharmaceuticalsolidproducts’CQAs
(e.g.,tensilestrength,weight,disintegrationtime)• ModelforQualitybyDesign(QbD)andforProcessAnalyticalTechnology
(PAT)• Utilizemulti-scaleresultsinvolvingvaryinglengthandtimescalesto
characterizethematerialsusedintabletmanufacturing• Usesimulationtoolstoverifyandvalidatethereducedordermodels
• Predictiveconstitutivemodelsofinter-particleinteractionsforavarietyofphysicalmechanisms+Predictabilityathighlevelsofconfinementremainsanopenproblem
• Concurrentandefficientmulti-scalestrategieswhicharefully-descriptiveatthegranularscale+Basedonaparticlemechanicsdescription
MULTI-SCALERESULTS
Multi-scalecharacterizationofpowdercompactionspanningsingleparticle,singletabletfabricationandindustrialrotarytabletpress
PARTICLECHARACTERIZATION
Goals:• Assistthedevelopmentofconstitutivemodelsofinter-particleinteractions
withdirectexperimentaltestingofindividualparticlesathighlevelsofdeformationandconfinement.
• Createaprotocolformeasuringmechano-chemicalpropertiesattheparticlescale.
• Createaprotocolforpredictingtableting(oranotherunitoperation)basedonsingleparticlemeasurements.
• Adapttheprotocolfornon-sphericalparticles(chasetowardstheactualshapeofpharmaceuticalpowders).
Approach:• Singleparticleundercontrolledloadingpatterns.• Imageacquisitionofdeformedconfigurationsandprocessingto
reconstructdeformationfields.• Initialfocus:ExcipientsandAPIsusedincontinuouslineFromparticlebehaviortotableproperties:Bydevelopingmechanisticpredictivemodelsattheparticlescaleandnumericalmulti-scaletechniquesatthepowder/tabletscale(R&Deffort!)
ShimadzuMCT-510
PMMAsphericalparticles
Sidecamera recordstheshapeofaparticledeformed
underdiametricalcompression
Capabilities:+particlediameter:1-500μm+displacement range:0-100μm+displacement increment:1nm+loading/unloading cyclesChallenges:+Toposition theparticleandfocusthe camera
REDUCEDORDERMODELING INCONTINUOUSLINE
PARTICLEMECHANICSSTRATEGIESPOWDERBLENDCHARACTERIZATION
Dominantmechanisms:- Elasticdeformations- Plasticdeformations- Bonding- Strain-ratemechanisms- Frictionandfracture
- Waterintakeandswelling
cp3 NationalScienceFoundationwww.nsf.gov
CenterforStructuredOrganicParticulateSystemswww.csops.org
U.S.FoodandDrugAdministrationwww.fda.gov
CenterforParticulateProductsandProcessesengineering.purdue.edu/CP3
YasasviBommireddy,Prof.Marcial Gonzalez MechanicalEngineering
§ TestModes• Targetthickness• Targetpeakcompactionforce• Fracturetest
§ Measurements• Compactionprofile• Detachmentforce• Ejectionforce• Breakingforce
PILOTPLANTEXPERIMENTS§ UpstreamMaterial
Properties• Particlesizedistribution
(PSD)• Truedensityofblend• Massflowrate
§ VariableParametersinTabletPress• Productionrate• Fillingheightofpowder
blendindies
§ MeasurableCriticalQualityAttributes(CQAs)ofTabletsbyAT4• Weight• Thickness• Diameter• BreakingForce
CONCLUSION
- Fillingdepthandgapheight- Main&Pre-compressionForce- Ejection&TakeoffForce- Tablet&CompartmentTemp.- CompartmentHumidity- TableWeight(Mass), thickness,diameter
- Tablethardness
Powdercharacterization Microcompressiontesting Continuousmanufacturingline:tabletpressandAT4
Input DatawithUncertainty Calibration/Training ofBayesianModels Processcontrol &optimizationProduct design
CosteffectiveScience-driven
Natoli BLP-16Press
ReducedOrderModelswithuncertainty
quantificationandbuilt-indimensionalityreduction
Particle Number
F m [
N]
10 20 30 40660
670
680
690
700
710
720
730
740
Particle Number
FWH
M [
Dia
met
er]
10 20 30 402.02
2.04
2.06
2.08
2.1
2.12
2.14
2.16
680 690 700 710 720 730650
660
670
680
690
700
710
720
730
740
F m [N]
V s
[m/s
ec]
F mU:+/−5%E − F m
0%E [N]
Prob
abilit
y
−40 −20 0 20 40
0
0.01
0.02
0.03
0.04
0.05
TOFU:+/−5%E − TOF0%E [µsec]
Prob
abilit
y
−20 −10 0 10 20
0
0.05
0.1
0.15
0.2
0.25
FWHMU:+/−5%E [Diameter]
Prob
abilit
y
2.05 2.1 2.15
0
10
20
30
40
50
Particle Number
F m [
N]
10 20 30 40
680
690
700
710
720
Particle Number
FWH
M [
Dia
met
er]
10 20 30 402.02
2.04
2.06
2.08
2.1
2.12
2.14
690 700 710 720
670
680
690
700
710
720
730
F m [N]
V s
[m/s
ec]
F mU:+/−5%E − F m
0%E [N]
Prob
abilit
y
−20 −10 0 10 20−0.01
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
TOFU:+/−5%E − TOF0%E [µsec]
Prob
abilit
y
−20 −10 0 10 20
0
0.05
0.1
0.15
0.2
0.25
FWHMU:+/−5%E [Diameter]
Prob
abilit
y
2.08 2.1 2.12 2.14
0
10
20
30
40
50
60
Particle Number
F m [
N]
10 20 30 40660
670
680
690
700
710
720
730
740
Particle Number
FWH
M [
Dia
met
er]
10 20 30 402.02
2.04
2.06
2.08
2.1
2.12
2.14
2.16
680 690 700 710 720 730650
660
670
680
690
700
710
720
730
740
F m [N]
V s
[m/s
ec]
F mU:+/−5%E − F m
0%E [N]
Prob
abilit
y
−40 −20 0 20 40
0
0.01
0.02
0.03
0.04
0.05
TOFU:+/−5%E − TOF0%E [µsec]
Prob
abilit
y
−20 −10 0 10 20
0
0.05
0.1
0.15
0.2
0.25
FWHMU:+/−5%E [Diameter]
Prob
abilit
y
2.05 2.1 2.15
0
10
20
30
40
50
Particle Number
F m [
N]
10 20 30 40660
670
680
690
700
710
720
730
740
Particle Number
FWH
M [
Dia
met
er]
10 20 30 402.02
2.04
2.06
2.08
2.1
2.12
2.14
2.16
680 690 700 710 720 730650
660
670
680
690
700
710
720
730
740
F m [N]
V s
[m/s
ec]
F mU:+/−5%E − F m
0%E [N]
Prob
abilit
y
−40 −20 0 20 40
0
0.01
0.02
0.03
0.04
0.05
TOFU:+/−5%E − TOF0%E [µsec]
Prob
abilit
y
−20 −10 0 10 20
0
0.05
0.1
0.15
0.2
0.25
FWHMU:+/−5%E [Diameter]
Prob
abilit
y
2.05 2.1 2.15
0
10
20
30
40
50
Particle Number
F m [
N]
10 20 30 40660
670
680
690
700
710
720
730
740
Particle Number
FWH
M [
Dia
met
er]
10 20 30 402.02
2.04
2.06
2.08
2.1
2.12
2.14
2.16
680 690 700 710 720 730650
660
670
680
690
700
710
720
730
740
F m [N]
V s
[m/s
ec]
F mU:+/−5%E − F m
0%E [N]
Prob
abilit
y
−40 −20 0 20 40
0
0.01
0.02
0.03
0.04
0.05
TOFU:+/−5%E − TOF0%E [µsec]
Prob
abilit
y
−20 −10 0 10 20
0
0.05
0.1
0.15
0.2
0.25
FWHMU:+/−5%E [Diameter]
Prob
abilit
y
2.05 2.1 2.15
0
10
20
30
40
50
TabletstrengthDissolutionprofile,etc.
In-linemeasurementsModel-formuncertaintyMaterialproperties,etc.
Goal:• Developmentoffirst-principlespredictivemodelsto
understandtheattributesofpowderblendsandsubsequentcompacts
Leuenberger→ 𝝈𝒕,𝒑 = 𝝈𝒎𝒂𝒙 𝟏 −𝟏 − 𝝆𝟏 − 𝝆𝒄,𝝈
𝐞 𝝆/𝝆𝒄,𝝈
Goals:• ControlofCQAsoftabletsusingpredictive
modelstoachieveQbD ofthecontinuousline• DevelopmentofreliabilitystrategiesusingPAT
toolstohavearobustcontroloftheentireplant
Kawakita→ 𝐶𝐹1 − 𝜌4 𝜌⁄ =
𝐶𝐹𝑎 +
𝜋𝐷; 4⁄ 𝑎𝑏
WeightPredictionin-line→ 𝑊? = 𝜌@ABCD
E𝐷𝑃(1 −cTS)
MCTcanbeusedtocharacterizeonlyoneparticlewhilethecompactionsimulatorcanproduceonlyonetabletatatimewhilethetabletpressgenerateshundredsoftabletspermin.Hencethemodelsdescribingeachstagewouldbedifferentandofvaryingorders.
0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
Relative Density
0
20
40
60
80
100
120
Com
pacti
on P
ress
ure[
MPa
]
0 ms300 ms600 ms900 ms1200 ms
• At a time only a single partcan be compacted
• The particle size is only inmicro-meters
• The time of compaction islonger
• The size of particles andtheir uniformity play a rolein the characterization ofparticle
• At a time only a singletablet can be fabricated
• The time required can bechanged by changing thepunch speeds from veryslow to very high
• Different compositions ofblend can be changedquite easily and hencevarying ranges ofproperties can be observed
• Hundreds of tablets can beused within a minute, hencethe time required to produceone tablet is very low
• The composition of the blendcannot be changedfrequently as segregationand mixing could take placein various stages of the press
• The models would need tobe scaled up and reduced inorder to be predictive forreal time control
Sizeofcom
pacted
unit
CompactionTime
MOTIVATION
Powderblendcharacterization
Note:PilotplantislocatedatPurdueUniversity
GamlenDSeriesCompactionSimulator
top related