Integrated Product-Process Design -...
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Integrated Product-Process Design to Minimize
Expense, Time, and Material
Fernando J. Muzzio Distinguished Professor
Rutgers University March 23/2017
Presented at the 3rd FDA/PQRI Conference on
Advancing Product Quality
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CurrentBatchDevelopmentParadigm
Formula7onbatches~1kgFormula7on
ProcessDev.Batches~5kgProcess
Analyt.Dev.batches~5kgAnaly7calMethod
Scaledupbatches–30Kgto1000kgScaledupProcess
Robustnessbatches~5kgRobust/stableForm/Method/process
Valida7onbatches–30Kgto1000kg
Validated/verifiedProcess
Takes2-3yearsandusesthousandsofKgofmaterialsEachstepisperformedatrisk–Formula7onis“op7mized”withoutknowledgeofmanufacturability,processis“op7mized”withoutknowledgeofscaleability,etc.DoesnotuseDOEforanaly7caldevelopment–subop7malaccuracyandrobustnessDoesnotsupportRTR
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Integrateddevelopmentparadigm
• Performsproduct,process,andanaly7caldevelopmentsimultaneouslyandatscale,thusachievingrobustproductandprocessdesignwithlowerrisk
• Takes<2months,usesmuchlessmaterial(orderofmagnitudedecrease)• UsesDOEforAnaly7calMethoddevelopment(morereliable)
MasterDOEatmanufacturingscale–formula7onvariables,Processvariables,robustness
Formula7onDev. ProcessDev.
ProcessAnaly7calMethod,includingRTR:non-destruc7ve
transmissionspectroscopy,followedbydissolu7onandhardnesstes7ngOFTHE
SAMETABLETS
Analy7calMethodDev.
Formula7on
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Advanced Pharmaceutical Development Thegoalistomodelpharmaceu7calprocessesinsilicoandusethesetoolsforop7miza7on
IntegratedProcessModel“Flowsheets”
ReducedOrderModel
Opera9ngParameters&Design
MaterialProper9es
UnitOpsModels
e.g.,Flow,BulkDensity,AngleofRepose
y = f (x,a,t,m,n)dydt
= g(x,a,t,m,n)M
M
e.g.,Feeders
min f (x)st. h(x) = 0 g(x) ≤ 0
Op9miza9on
Predic9veModeling
20
1 1
k k
i i ii i ij i ji i i j
y x x x xβ β β β ε= = <
= + + + +∑ ∑ ∑∑
5
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A Strategy for Minimizing time and materials Maximizing process understanding (as defined as part of the collaboration with Janssen) • Identify system failure modes • Define measurements and metrics to predict
impact of failure modes for a given formulation • Build material property data base and predictive
models for new materials and surrogates in unit ops
• Use relevant failure mode knowledge to define DOEs and select PAT and control
• Perform integrated formulation and process optimization
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Key Concepts for CM Adoption (as defined as part of the Janssen-Rutgers-Gent Collaboration)
1. Materials (API) Characterization and Prediction (material sparing, faster development)
2. Methods for Improving API Processability (manufacturability, enabling direct compression)
3. Process Modeling, Control, and PAT for CM RTR
4. Rapid Development for CM (faster development, process/analytical robustness)
5. Harmonization & Standardization (regulatory clarity) 7
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Blend Behavior Flowability, Lubricity, Agglomeration, Blend Uniformity, Bulk Density,
Compactability
Individual Material Behavior Cohesion, Agglomeration, Compressibility, Bulk Density , Compactability
Particle Properties Superficial
Surface Energy, Piezoelectric,
Tribo-electrification
Mechanical Elasticity, Plasticity,
Bonding Mechanism
Crystallographical and Thermal
Form, Crystallinity, Tm, Tg, etc
Geometrical PSD, Shape, Crystal Habit, Surface Area, true density.
Particle Properties and Powder Behavior (determined for specific list of API’s and excipients as part of the Collaboration with Janssen)
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-0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
cBD, g/ml
CPS, % @ 1 5.0kPa
D1 0D50
D90
BFE, mJ
SIFRI
SE, mJ/g
Cohes ion 3kPaCohesion 6k Pa
Cohe sion 9kPaCohe sion 15k Pa
UYS 3kPaUYS 6kPa
UYS 9k Pa
UYS 15 kPa
M PS 3 kPa
MPS 6kPa
MPS 9 kPa
MPS 15k Pa
FFC 3kPaFFC 6k Pa
FFC 9 kPa
FFC 15k Pa
AIF, º @3kPaAIF @6kPa
AI F, º @ 9kPa
AI F, º @15kPa
PC 1 (37%)
PC
2 (1
9%)
Loadings Plot
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The following materials have similar flow properties :
- APAP - API 2 - API 3 - API 4
Use of Material Library to Predict Feeder Behavior (as part of the Collaboration with Janssen)
Use similar geometry, screw type, etc.
Score plot of material library with 45 materials
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Predicting feeding performance from material flow properties (as defined as part of the Collaboration with Janssen)
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• For a given new material, can we compare it to existing materials in the library?
• Once a new material is included in the material library, can we
predict its feeder performance? • Can we predict the optimal screw choice for a given new
material?
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Similarity scores of the new material
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0
1
2
3
4
5
6
7
8
Fine
zeolite
Fine
alumina
Sa7n
tone
Zeolite
LactoseMon
ohydrate
Coarsealumina
WeightedEuclideanDistance
Material similarity can be quantified by calculating weighted Euclidean distance. Smaller distance corresponds to higher similarity.
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Receivenewmaterial
Characterizenewmaterialproper7es
PerformPCAwithexis7ngmaterials
CalculateweightedEuclideandistance
Ranksimilaritybetweenmaterials
Predicttheop7malfeederscrewbasedsimilarmaterials
Prediction using similarity scores
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0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
0 1 2 3
RSDorRDM
Screwtype
FinezeoliteRDM
MaterialARDM
FinezeoliteRSD
MaterialARSD
screw type 1 fine concave screw
screw type 2 fine auger screw
screw type 3 coarse concave screw
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Prediction using PLS regression
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X Library of material
properties
X’ Scores of the
principal components
Y
PCA projection
Partial least squares
regression
Alternatively, when material with matching flow properties is difficult to find, a partial least squares (PLS) regression can be used. A PLS regression model relates material flow properties directly to feeder performance, quantified by RSD and RDM.
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Predicting feeding performance
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y=0.994xR²=0.977P<0.05
RMSECV=0.0078RMSEC=0.00150
0.02
0.04
0.06
0.08
0.1
0.12
0 0.05 0.1 0.15
Pred
ictedRSD
MeasuredRSD
FineConcaveScrew
y=0.999xR²=0.994P<0.05
RMSECV=0.0099RMSEC=0.0024
0
0.02
0.04
0.06
0.08
0.1
0.12
0 0.05 0.1 0.15
Pred
ictedRSD
MeasuredRSD
FineAugerScrew
y=0.995xR²=0.978P<0.05
RMSECV=0.0099RMSEC=0.00180
0.02
0.04
0.06
0.08
0.1
0.12
0 0.05 0.1 0.15
Pred
ictedRSD
MeasuredRSD
CoarseConcaveScrew
• PLS regression helps to answer: 1. For a new material with given properties, can we predict RSD or RDM for a certain screw? 2. For a new material, what is the optimal screw selection?
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0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0 1 2 3
RSDorRDM
ScrewType
MeasuredRSD
PredictedRSD
MeasuredRDM
PredictedRDM
Receivenewmaterial
Characterizenewmaterialproper7es
ProjectdatatoPLSRmodelstopredictfeeder
performance
Selectop7malscrewbasedonmodelpredic7on
Runconfirmatoryfeederexperiment
Addconfirmatorydataandupdatemodelknowledge
Prediction using PLSR models
screw type 1 fine concave screw
screw type 2 fine auger screw
screw type 3 coarse concave screw
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Predicting feed factor from material properties
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y=-0.9761x+9.1761R²=0.93556
y=-1.0269x+10.124R²=0.91921
y=-1.9553x+18.606R²=0.97741
0
5
10
15
20
25
30
35
-8 -6 -4 -2 0 2 4 6
Ini7alfe
edfactorkg/hr
PC1
Fineconcavescrew
Fineaugerscrew
Coarseconcavescrew
0
5
10
15
20
25
30
35
Coarsealumina
Fine
alumina
Fine
Zeo
lite
Sa7n
tone
Zeolite
Lactose
MaterialA
Ini7alfe
edfactorkg/hr
Materials
Fineconcavescrew
Fineaugerscrew
Coarseconcavescrew
• Initial feed factor reflects maximal feeding capacity for a material. • Results show that using scores of the first principal component, the
initial feed factor can be predicted based on the linear correlation. • The feed factor using different screws can also be predicted.
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M
M
EX
M
M
API
M
M
Cri9calQues9onsinDCCM(as defined as part of the Collaboration with Janssen)
Q1:Canwefeedeachingredientattherequiredflowrate?
Q2:Dotheingredientss9ckand/oragglomerate?
Q3:Canweachieveblendhomogeneity?
Q5:Areblendflowproper9esgoodenoughtosupportWeightUniformity?
Q4:Doestheblends9ckoragglomerate?
Q6,Q7:Canwemeettabletdissolu9on(Q6)andhardness(Q7)atareasonableflowrate?
Lub
Improving API processability Minimizing amounts of API needed in development
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Strategy (as defined as part of the Collaboration with Janssen)
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FindDesignSpace
FindFailureSurfaceEnvelope
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TabletPress
Lubricant
Feeders
M M
M
API
M
M
Mill
Proper9es
Blender
Density
Agglomera7on,Cohesion(surfaceenergy,PSD)
PoorMixing/segrega7on(contentUniformity)
FailtocomplyHardness
Cohesion,Adhesion
DryCoa7ng,
20
M M
EXM
EX
FailureMode Metrics
FlowRatevariability
FlowRateSetPointdivergence
TapDensityBulkDensityPermeability
ShearCellElectrosta7cs
Bonding,Plas9c,Elas9cFailtocomplyDissolu7on
Chocking,jamming,discon7nuousflow
SurfaceEnergyPSD
IGC-Droplet
LaserDiffract.
SurfaceEnergyOrContactAngle
IGC-Droplet
SurfaceEnergyPSD
FimngCompac7onModel
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UnitOp Inputs ProcessingParameters Responses OutputMaterialProper9es
Feeder(i)
RMCohesion(i)[Coh1i]RPMImpeller(RPM1)
FeederFlowRate(FFR)=f[Coh1,Cps1,SE1,E1,RPM1,
RPM2,FR,HSG,ST]Cohesion2(Coh2)=f[Coh1,Cps1,SE1,
E1,RPM1,RPM2,FR,HSG,ST]RMCompressibility(i)[Cps1i]
RMSurfaceEnergy(i)[SE1i] RPMScrew(RPM2)
FeederFlowRateVariability(σFFR)=f[Coh1,Cps1,SE1,E1,RPM1,RPM2,FR,HSG,ST]
RMElectrosta9c(i)[E1i]PowderBulkDensity2(DB2)=f[Coh1,Cps1,SE1,E1,RPM1,RPM2,FR,HSG,ST]
RefillRate(RR)RMPSD(i)[PSD1i]
RMBulkDensity(i)[BD1i]HopperSize/geometry(HSG)
Screwtype(ST)
Mill
Coh2(i) RPMBlade(RPM3)
MillHoldup BlendHomogeneity3(BH3)=f[Coh2,σFFR2,RPM3,FR5,C
%,MSSG,ST]σFFR2(i)
Composi9on(PSD,(i)[C%] MillScreen(MS)Agglomera9on3(Ag3)=f[Coh2,σFFR2,
RPM3,FR5,HSG,ST,C%]BulkDensity2(i)[BD2i]Hip:Densityhasnoeffectbeyondwhat’scapturedby
cohesion
Screwtype(ST)SpacersGeometry(SG)
Cohesion3(Coh3)=f[Coh2,σFFR2,RPM3,FR5,HSG,ST,C%]
Density3(D3)=f[Coh2,σFFR2,RPM3,FR5,HSG,ST,C%]
Blender
BlendHomogeneity3[BH3] RPMBlade(RPM4)
Holdup(inves7gatecomposi7oninblender)
Lubricity4(L4)=f[FR5,Ag3,RPM4,BG,STBP,C%]
Agglomera7on3[Ag3] BlenderResidenceTime(BRT) Compa9bility(Cpt4)=f[L4,FR5,Ag3,RPM4,BG,STBP,C%,PSD3(i)]Composi9on[C%] BlenderGeometry(BG) DispersionCoefficient(BDC)
Cohesion3[Coh3]
Screwtype/Bladeparern(STBP)
Cohesion4(Coh4)=f[Coh3,L4,RPM4,FR5,Ag3,HSG,ST,C%]
BladePasses(BBP)BulkDensity3[BD3]
Agglomera9on4(Ag4)=f[RPM4,FR5,Ag3,HSG,ST,C%]
MillHoldup
BlendHomogeneity4(BH4)=f[BH3,RPM4,FR5,Ag3,HSG,ST,
C%]BulkDensity4
(BD4)=f[BD3,L4,RPM4,FR5,Coh3,HSG,ST,C%]
Feeder,Mill,Blender:InputsandOutputs(aspartlydefinedaspartoftheJanssen-Rutgers-GentCollabora7on)
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UnitOp Inputs ProcessingParameters Responses ProductProper9es
TabletPressandFeedFrame
Lubricity[L4]RPMFeedFrame(RPM5)
Compac7onForce(CF)TabletThickness(TH5)=f[FR,L4,RPM5,
CH,ThG,FC,C%,FFG,TT,#S,PC]Compac7bility[Cpt4] Ejec7onForce(EF)
RPMTurret(RPM6=FlowRate(FR))
DwellTime(DT)WeightVariability(WV5)=f[L4,FR,Ag4,RPM5,Coh4,BH4,BD4,CH,C%,FFG,TT,
#S,PC]ChuteHeight(CH)Composi9on[C%]
TabletDensity(porosity)5(TD5)=f[Coh4,L4,RPM4,FR,Ag3,PSD(i),C%]
ThicknessGap(ThG)
Cohesion4[Coh4] FillCam[FC] ContentUniformity5(CU5)=f[BH4,
RPM5,Ag4,WV5,C%,FFG,TT,#S,PC]FeedFrameGeometry(FFG)Agglomera7on4[Ag4] TabletTooling(TT)
Hardness5(H5)=f[Cpt4,RPM5,FR,L4,WV5,C%,FFG,TT,#S,PC]
BlendHomogeneity4[BH4]#sta7ons(#S)
BulkDensity4[BD4] Dissolu9on5(Diss5)=[Cpt4,RPM5,FR,
L4,WV5,C%,FFG,TT,#S,PC]PreCompression(PC)
FeedFrameandTabletPress:InputsandOutputs(aspartlydefinedaspartoftheCollabora7onwithJanssen)
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Process Modeling and Control
Feedback control
Feedforward control
Flowsheet model
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General RTR Sensing Approach
TabletPress
Blender
Feeders
mill
API
M
M
M
M
M
M
M
M
Content/DensityBlendUniformity
NIR,Raman
LT
Thickness
Density
US
Hardness
NIR
Dissolu9on(check)
Feedforwardcontrol
Force
Weight
CompressionGap
Cross-check
Content(check)
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Goals accomplished
APAP:• Dissolu7onModelbasedonNIRspectraoftablets • HardnessModelbasedonultrasoundanddensitytodeterminemassandthickness
• RTRmethodologystrategysuccessfullydesignedandvalidated
25
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General Dissolution Prediction Methodology
Definetargetcondi7ons
0
50
100
150
0 20406080100120
Drugrelease%
Time/min
y = 0.9833x R² = 0.86308
0
50
100
150
200
250
300
0 50 100 150 200 250 300
α p
red
icte
d
α reference
Reference vs predicted
020406080
100
0 20 40 60 80100120%DrugDissolved
Time(min)
referencepredic9onf2=79.13
Iden7fydissolu7onmechanism
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Prediction of Individual Dissolution Profiles: Model-dependent Approach
27Reference:dissolu+onprofilesPredicted:NIRPCs
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Dissolution Prediction Flowchart
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Integrateddevelopmentparadigm
• Performsproduct,process,andanaly7caldevelopmentsimultaneouslyandatscale,thusachievingrobustproductandprocessdesignwithlowerrisk
• Takes<2months,usesmuchlessmaterial(orderofmagnitudedecrease)• UsesDOEforAnaly7calMethoddevelopment(morereliable)
MasterDOEatmanufacturingscale–formula7onvariables,Processvariables,robustness
Formula7onDev. ProcessDev.
ProcessAnaly7calMethod,includingRTR:non-destruc7ve
transmissionspectroscopy,followedbydissolu7onandhardnesstes7ngOFTHE
SAMETABLETS
Analy7calMethodDev.
Formula7on
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MaterialProper9es-ProcessParametersInterrela9on(asdefinedaspartoftheCollabora7onwithJanssen)
30
LubricatedBlendRawMaterial
Proper9es(RMP)
Cohesion1.CompressibilityElectrosta9csSurfaceEnergy
PSD
ProcessParameters
RPM2(Mill)
ScreenSize
ProcessParameters
RPM3(Blender)
IntermediateMaterialProper9es(IMP)
Lubricity4Compactability4.
Cohesion4Agglomera9on4
BlendHomogeneity4BulkDensity4
M
M
API
M
M
Lubricant
Feeders
Mill
ProcessParameters
RPM5(FeedFrame)
RPM6(flowRate)
ChuteHeight(CH)
ThicknessGap(ThG)
PreCompac7on(PC)
FillCam[FC]
ProductMaterialProper9es(RMP)
ThicknessWeightVariabilityDensity(porosity)ContentUniformity
HardnessDissolu9on
Product
M
EXM
EX
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Conclusions• Integratedproduct/process/analy7caldevelopment:
– Faster– Lessmaterial– Bererprocess– Bererformula7on– Bereranaly7cs
• RTRrequiresRTQA• Level3ProcesscontrolrequiresRTQA• Typically,RTQArequiresdissolu7onprodic7on
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Wayforward
1. Standardiza7onofMaterialPropertytes7ng2. Performancestandardsforequipment3. Standardiza7onoftracerselec7onforRTDstudies4. Model-basedacceleratedprocessdevelopment5. Standardiza7onofBHPATverifica7on6. Sta7s7calmethodsforacceptanceandreleasedecisions7. Failuremodetaxonomy–standardizedtes7ng8. GeneralmethodologyforRTR–dissolu7onpredic7on
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ENGINEERING RESEARCH CENTER FOR
STRUCTURED ORGANIC PARTICULATE SYSTEMS