Improving multicomponent tablet predictions – accuracy and accessibility Hikaru G. Jolliffe, Foteini Papathanasiou, Elke Prasad, Gavin Halbert, John Robertson, Cameron J. Brown, and Alastair J. Florence. Integrated Control in Powder Formulation meeting National Formulation Centre, Sedgefield. 17 th January 2019
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Improving multicomponent tabletpredictions – accuracy and accessibility
Hikaru G. Jolliffe, Foteini Papathanasiou, Elke Prasad, Gavin Halbert,John Robertson, Cameron J. Brown, and Alastair J. Florence.
Integrated Control in Powder Formulation meeting
National Formulation Centre, Sedgefield.
17th January 2019
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 74
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
1. Introduction
2. Compression model
3. Experimental work
4. Parameter estimation and predictive model
5. Conclusions
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 75
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Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 76
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 77
TKTT P1*
0* rr =
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
Compression modelGavi and Reynolds (2014) model
Tablet relative density ( ∗ ): power law• Variables: compression pressure (P)• Parameter: tablet relative density at zero P ( ∗ )• Fitted parameter: compressibility constant ( )
Mixing rules for multicomponent tablets:• kb and - gPROMS implemented (volume fraction-based)• - user-specified (also volume fraction-based)
ess bkTT e-= 0
TT
compT hd
Fp
s 2=
ii
iTmixT fss å= ,0,0 ii
ibmixb kk få= ,, ii
iTmixT KK få= ,,
SU
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 78
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 79
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 80
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 81
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
Tens
ile s
treng
th (M
Pa)
Experimental data R2 Adjusted R2 RMSE
es bae= 0.976 0.972 0.045
÷øö
çèæ=e
s ba ln 0.928 0.918 0.077
÷øö
çèæ
÷øö
çèæ=
ees ba ln 0.977 0.974 0.043
( )ba es -= 1 0.973 0.970 0.047
es ba += 0.874 0.856 0.102
Tens
ile s
treng
th (M
Pa)
Experimental data R2 Adjusted R2 RMSE
es bae= 0.981 0.979 0.651
÷øö
çèæ=e
s ba ln 0.963 0.959 0.910
÷øö
çèæ
÷øö
çèæ=
ees ba ln 0.563 0.520 0.534
( )ba es -= 1 0.987 0.986 1.403
es ba += 0.912 0.903 3.121
Parameter estimation: initial guess forTensile strength at zero porosityFit curves to find value at ε = 0
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 82
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
Key compression data for Avicel PH-101 tablets (200 mg target mass)
Parameter estimation: results for pure cellulose and lactose tablets
0
1
2
3
4
5
6
7
0 5 10 15 20
Tabl
et th
ickn
ess
(mm
)
Compaction force (kN)
200 mg (experimental)
200 mg (predicted)
250 mg (experimental)
250 mg (predicted)
Pharmatose 50M (lactose)
Avicel PH-101 (cellulose)
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 83
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
Parameter estimation: results for binary cellulose and lactose tablets
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 84
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
Parameter estimation: results for binary cellulose and lactose tablets
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 85
0
50
100
150
200
250
P T T*
Volu
me
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 86
0
1
2
3
4
5
6
0
50
100
150
200
250
300
350
400
0 5 10 15 20 25 30
Tabl
et th
ickn
ess (
mm
)
Tabl
et h
ardn
ess
(N)
Compaction force (kN)
0
1
2
3
4
5
6
0
50
100
150
200
250
300
350
400
0 5 10 15 20 25 30
Tabl
et th
ickn
ess (
mm
)
Tabl
et h
ardn
ess
(N)
Compaction force (kN)
Hardness (Gavi and Reynolds, 2014)
Hardness (MATLAB)
Hardness (measured)
Thickness (Gavi and Reynolds, 2014)
Thickness (MATLAB)
Thickness (measured)
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
0
1
2
3
4
5
6
0 5 10 15 20 25 30
Tabl
et th
ickn
ess (
mm
)
Compaction force (kN)
Hardness (Gavi and Reynolds, 2014)
Hardness (MATLAB)
Hardness (measured)
Thickness (Gavi and Reynolds, 2014)
Thickness (MATLAB)
Thickness (measured)
0 5 10 15 20 25 30Compaction force (kN)
0
50
100
150
200
250
300
350
400
0 5 10 15 20 25 30
Tabl
et h
ardn
ess (
N)
Compaction force (kN)
10
15
20
25
0 50 100 150 200 250 300 350
Mas
s-to
-gap
fact
orγ,
mm
/g
Compaction pressure (MPa)
200 mg tablets250 mg tabletsε = l imit
50-50Lactose-Cellulose
60-40Lactose-Cellulose
70-30Lactose-Cellulose
PH-101, punch gap and compaction pressure
1
2
3
4
5
6
0 5 10 15 20 25
Dist
ance
(mm
)
Compaciton force (kN)
Punch gapTablet thicknessPunch gap (measured)Thickness (measured)
PH-101 200 mg, punch gap and tablet thickness
1
2
3
4
5
6
0 5 10 15 20 25
Dist
ance
(mm
)
Compaciton force (kN)
Punch gap
Tablet thickness
Punch gap (measured)
Thickness (measured)
50-50 Lactose-Cellulose 250 mg, punch gap and tabletthickness
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 87
KEY DATAIndependent
ForceMass
Punch gap
DependentHardnessThickness
PURECOMPONENTPARAMETERS
σ0ikbiKTiAibini
Mixing rules +Compression
model
BINARY /TERNARYTABLET
PROPERTIES
σtabhtabεtab
gPROMS
MATLAB
gPROMS
Excel
OPTIMISATIONMATLAB
Tablet_optimiser.exe
Optimalexperimental
settings
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 88
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 89
Introduction gPROMS and compression model Experimental work Parameter estimation and predictivemodel Conclusions
Extensive compression data generated for a variety of materials andmaterial grades
Optimal values for key parameters ( , kb, ) found• For pure components• Good fits to experimental data
Binary tablet properties predicted using pure parameters• Various tablet compositions• Predictions improved with modified parameter weighting.
Improving multicomponent tablet predictions – accuracy and accessibilityH.G. Jolliffe – Integrated Control in Powder Formulation meeting, 17 Jan 2019, National Formulation Centre, Sedgefield, UK 90
This work was supported by:
University of Strathclyde• Foteini (Fay) Papathanasiou, MSc• The authors would like to acknowledge that this work was carried out in the CMAC National Facility supported by
UKRPIF (UK Research Partnership Fund) award from the Higher Education Funding Council for England (HEFCE)(Grant ref HH13054)