powerpoint presentation tablet issue multivariate matrix from the database then proceed to a closer...
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Multi-disciplinary Team
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Product Master
Database
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Coating
(~10 factors)
Compression
(~20 factors)
Compaction
(~15 factors)
Raw material
(~40 factors)
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Xs – Input process parameters, material attributes; the “causes” Ys – Output parameters, product attributes; the “effects”
Aim to look for “cause – effect” relationships
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API
API API
API
Multivariate analysis as first-step screening of potential cause-effect relationships related to capping/broken tablet issue
Multivariate matrix from the database
Then proceed to a closer look of the “interesting” correlations one by one:
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Looked at individual R2 from each correlation and come up with a cumulative overall parameter ranking of higher to lower risk of impacting FCT AQL results…
Screening DoE with top-ranking parameters Parameters not only determined by data analysis
but also by SME suggestion eg. Compaction force was not varied in historical
database but was considered important factor for DoE based on process understanding
Parameter
Sum
Normalized
R2 Rank Stage
API PSD <150 um 0.64 1 Mix
API Carr 0.48 2 Mix
COM AVG - Calculated (TD-BD)/BD 0.43 3 Com
COM AVG - 80 Mesh 0.40 4 Com
Precomp 0.39 5 Core
Binder LOD 0.37 6 Mix
Roller Speed (force conc) 0.36 7 Com
Mset 0.33 8 Tab
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Screening DoE (top 8 parameters. Used Binder from manufacturing process 1 for high/low trials)
Screening
DoE Test
Variable
API % PSD
< 150 mm
Gran screen
size (mm)
Compaction
force (kN)
Press
Speed
(rpm)
Compaction
gap (mm) Binder LOD Pre-comp (kN) Mset (kN)
10
20
30
40
50
60
70
80
90
100
TA
BIn
dex
-H
ard
ness/
(FR
I A%
* Wt R
SD
)A
ctu
al
10 20 30 40 50 60 70 80 90
TAB Index - Hardness/(FRIA%*WtRSD)
Predicted P=0.0029 RSq=0.8 RMSE=11.791
Actual by Predicted Plot
Response TAB Index - Hardness/(FRIA%*WtRSD)
Optimization DoE (Used Binder from manufacturing process 3-supplier driven change)
Opt DoE Test
Variable
Gran screen
size (mm)
Compaction force x gap
Roller
Speed (rpm)
Pre-comp
(kN) Mset (kN)
Based on Tablet hardness, friability, Wt%RSD and AQL from Screening DoE
Multivariate Regression modeling of DoE results further determined the top four effects
TA
V2-RM-Binder LOD
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Optimized Recommendations on Screen Size, Compaction Force, Gap Width, Roller Speed and Precompression Force
Confirmed by Confirmatory trials 1 and 2 need Compaction/Compression
range determination studies
Compaction Study (Used binder from manufacturing process 3 for Sieve Spec determination)
Compression Specification Range Determination Study (Used binder from manufacturing process 3 for Compression Spec Determination)
Full-Scale manufacturing
Based on Tablet hardness, friability, Wt%RSD and AQL from optimization DoE
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Continuous process improvement
Full scale demonstration batches (one lower strength and one higher strength) were manufactured with zero capping/broken tablet observed.
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Pre Change Ppk: 0.48 (~536640 PPM) Post Change Ppk: 1.89 (~22 PPM)
Pre Change Ppk: 0.79 (~195800 PPM) Post Change Ppk: optimal (not calculated since no defect observed)
Pre and Post process improvement Evaluation
Higher Strength
Lower Strength
After 1 year of post-process improvement, There are close to 200 batches of lower strength and 100 batches of higher strength manufactured and released. No appearance related product defects were observed. Product Robustness is significantly improved.
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Design Considerations
Formulation: Simple ingredients designed to provide consistent dissolution. Process: Moisture sensitive ingredients and dry granulation was chosen.
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Non-Critical Non-Critical Non-Critical Critical Equipment
Critical Equipment
36”pan→48”pan→66”pan Y kg →1.7 Y kg → 6.7 Y kg
Non-Critical
X kg → 7X kg
Scale up to 66” pan is a risk and requires core tablets with good mechanical strength
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Factors Responses Desirability
Press Speed (rpm) Average Hardness (n=10) Maximize in the specified range
Pre-compression Force (kN) Friability Minimize
Main Compression Force Extended Friability Minimize
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Strength Content Uniformity
Dissolution (Market 1) 80% in
X min
Dissolution (Other markets) 80% in Y min
1 (56 batches) 2.55 3.40 3.26
2 (142 batches) 3.29 4.63 4.11
3 (242 batches) 3.74 4.83 4.71
4 (157 batches) 4.16 4.80 5.13
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Thank You