A DoE/QbD Optimization Model for “High Shear Wet Granulation” Process using Face Centered...
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Transcript of A DoE/QbD Optimization Model for “High Shear Wet Granulation” Process using Face Centered...
FACTORIAL MIXTURE
BOX BEHNKEN
RESPONSE SURFACE
FACE CENTERED CENTRAL COMPOSITE
© Created & Copyrighted by Shivang Chaudhary
SHIVANG CHAUDHARY
© Copyrighted by Shivang Chaudhary
Quality Risk Manager & iP Sentinel- CIIE, IIM Ahmedabad MS (Pharmaceutics)- National Institute of Pharmaceutical Education & Research (NIPER), INDIA
PGD (Patents Law)- National academy of Legal Studies & Research (NALSAR), INDIA
+91 -9904474045, +91-7567297579 [email protected]
https://in.linkedin.com/in/shivangchaudhary
facebook.com/QbD.PAT.Pharmaceutical.Development
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A DoE/QbD CASE STUDY FOR
FOR SOLID ORAL DOSAGE FORMS DEVELOPMENT AS PER QbD
OPTIMIZATION OF CMAs & CPPs OF HIGH SHEAR WET GRANULATION PROCESS
HOW TO VERIFY DESIGN SPACE?
HOW TO CREATE OVERLAY PLOT?
HOW TO INTERPRET MODEL GRAPHS?
HOW TO DIAGNOSE RESIDUALS?
HOW TO SELECT MODEL?
HOW TO SELECT EFFECT TERMS?
RISKS
FACTORIAL MIXTURE
BOX BEHNKEN
RESPONSE SURFACE
HOW TO SELECT DESIGN?
HOW TO IDENTIFY
RISK FACTORS?
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© Created & Copyrighted by Shivang Chaudhary
FACTORS
FACE CENTERED CENTRAL COMPOSITE
LOWER HARDNESS INADEQUATE DISINTEGRATION
QUALITY COMPROMISED EFFICACY COMPROMISED
HIGH FRIABILITY INADEQUATE DISSOLUTION
SOFT GRANULES HARD GRANULES
BINDER
DISINTEGRANT
KNEADING TIME C
B
A
OPTIMIZATION OF CMAs & CPP OF HIGH SHEAR WET GRANULATION PROCESS FOR SOLID ORALS
“High”
“Medium”
“Low”
C
NO. OF LEVELS
A BINDER
KN
EA
DIN
G T
IME
NO. OF FACTORS EXPERIMENTAL DESIGN SELECTED
TOTAL NO OF EXPERIMENTAL RUNS (TRIALS)
3
3
Face Centered CENTRAL COMPOSITE DESIGN
2f fp+ (2*f)sp + cp = (22 )+ (2*3) + (6) = 8+6+6 = 20
To Optimize CMAs & CPPs of HIGH SHEAR WET GRANULATION OBJECTIVE
FACTORIAL MIXTURE
BOX BEHNKEN
RESPONSE SURFACE
© Created & Copyrighted by Shivang Chaudhary
HOW TO IDENTIFY FACTORS?
HOW TO VERIFY DESIGN SPACE?
HOW TO CREATE OVERLAY PLOT?
HOW TO INTERPRET MODEL GRAPHS?
HOW TO DIAGNOSE RESIDUALS?
HOW TO SELECT MODEL?
HOW TO SELECT EFFECT TERMS?
HOW TO SELECT
DESIGN?
OBJECTIVE of the experiment & NUMBERS of the factors involved were the primary two most important factors required to be considered during selection of any design for experimentation.
• in wet granulation process binder, disintegrant & kneading time are 3 most critical parameters which are required to be optimized with respect to hardness, friability, disintegration & dissolution.
• Here, all three factors conveniently have only three levels with very narrow nearly same Region of Operability & Region of Interest.
• Thus, Face Centered central composite design with practical alpha value of ±1 has been selected for optimization of all three factors simultaneously having only three levels & nearly the same region of interest & region of operability with little co linearity, cuboidal rather than spherical., & missing data.
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FACE CENTERED CENTRAL COMPOSITE
OPTIMIZATION OF CMAs & CPP OF HIGH SHEAR WET GRANULATION PROCESS FOR SOLID ORALS
Factors (Variables) Levels of Factors Studied -α = -1 0 +α = +1
A Binder (%w/w) 4% 7% 10% B Disintegrant (%w/w) 1% 3% 5% C Kneading Time (min) 2 min 4 min 6 min
HOW TO IDENTIFY FACTORS?
HOW TO SELECT DESIGN?
CPP CQAs
FACTORIAL MIXTURE
BOX BEHNKEN
RESPONSE SURFACE
© Created & Copyrighted by Shivang Chaudhary
HOW TO VERIFY DESIGN SPACE?
HOW TO CREATE OVERLAY PLOT?
HOW TO INTERPRET MODEL GRAPHS?
HOW TO DIAGNOSE RESIDUALS?
HOW TO SELECT MODEL?
HOW TO DESIGN
EXPERIMENTS?
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FACE CENTERED CENTRAL COMPOSITE
OPTIMIZATION OF CMAs & CPP OF HIGH SHEAR WET GRANULATION PROCESS FOR SOLID ORALS
Qualitative Formulation & other High Shear Wet Granulation processing parameters were kept constant except variable kneading time for all 20 experimental runs, i.e. Starting from Co-Sifting through 30#, Dry Mixing at 75RPM of impeller for 2 minutes, Binder addition & Wet Granulation at impeller speed of 75RPM & Chopper speed of 2000 RPM in RMG,
Drying at inlet temperature of 50°±5°C until 2% LOD, milling through 1.0mm screen at medium speed & Blending/ Lubrication at 10RPM for 5 minutes with constant 50 % occupancy of total volume. Lubricated Blend was
Compressed at compression force of 5kN & turret speed of 20 RPM in Rotary Tablet Press
CMAs
HOW TO IDENTIFY FACTORS? HOW TO SELECT
DESIGN? HOW TO SELECT
EFFECT TERMS?
FACTORIAL MIXTURE
BOX BEHNKEN
RESPONSE SURFACE
© Created & Copyrighted by Shivang Chaudhary
HOW TO VERIFY DESIGN SPACE?
HOW TO CREATE OVERLAY PLOT?
HOW TO INTERPRET MODEL GRAPHS?
HOW TO DIAGNOSE RESIDUALS?
HOW TO SELECT
MODEL?
During Selection of order of polynomial: MODEL [A mathematical relationship between factors & response assisting in calculations & predictions) for Analysis of Response; ANOVA was carried out thoroughly for
testing of SIGNIFICANCE of every possible MODEL (p<0.05) with insignificant LACK OF FIT (p>0.1) & response surface to confirm expected shape of response behavior
P-Value < 0.05 (Significant) P-Value > 0.10 (Insignificant) Lack of Fit is the variation of the data around the fitted model. If the model does not fit the actual response behavior well, this will be significant. Thus those models could not be used as a predictor of the response.
P-Value < 0.05 (Significant) P-Value > 0.10 (Insignificant) Sequential model sum of square provides a sequential comparison of models showing the statistical significance of
ADDING new model terms to those terms already in the model. Thus, the highest degree quadratic model was selected having p-value (Prob > F) that is lower than chosen level of significance (p = 0.05)
Sequential MODEL Sum of Square Tables
LACK of Fit Tests
Response 3: Disintegration Response 4: Dissolution Response 1: Hardness
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OPTIMIZATION OF CMAs & CPP OF HIGH SHEAR WET GRANULATION PROCESS FOR SOLID ORALS
Response 2: Friability
Response 3: Disintegration Response 4: Dissolution Response 1: Hardness Response 2: Friability
FACTORIAL MIXTURE
BOX BEHNKEN
RESPONSE SURFACE
© Created & Copyrighted by Shivang Chaudhary
HOW TO IDENTIFY FACTORS? HOW TO SELECT
DESIGN? HOW TO SELECT
EFFECT TERMS? HOW TO VERIFY
DESIGN SPACE? HOW TO CREATE
OVERLAY PLOT? HOW TO INTERPRET
MODEL GRAPHS? HOW TO SELECT
MODEL? HOW TO DIAGNOSE
MODEL?
Numerical Analysis of Model Variance was carried out to confirm or validate that the MODEL ASSUMPTIONS for the response behavior were met with actual response behavior or not, via testing of significance of each MODEL TERMs
with F >>1 & p<0.05 (less than 5% probability that a “Model F Value” this large could occur due to noise), insignificant LACK OF FIT (p>0.10), adequate PRECISION > 4, R2 Adj & R2 Pred in good agreement <0.2d, with
well behaved RESIDUALS analyzed by diagnostic plots as GRAPHICAL INDICATORS.
Residual (Experimental Error) Noise = (Observed Responses) Actual Data– (Predicted Responses) Model Value During RESIDUAL ANALYSIS, model predicted values were found higher than actual & lower than actual with equal probability in Actual
Vs Predicted Plot. In addition the level of error were independent of when the observation occurred in RESIDUALS Vs RUN PLOT, the size of the
observation being predicted in Residuals Vs Predicted Plot or even the factor setting involved in making the prediction in Residual Vs Factor Plot
PREDICTION EFFECT EQUATION ON INDIVIDUAL RESPONSE BY QUADRATIC MODEL
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OPTIMIZATION OF CMAs & CPP OF HIGH SHEAR WET GRANULATION PROCESS FOR SOLID ORALS
HARDNESS = +74.80+11.80A-3.10B+15.10C-
1.25AB+3.75AC+1.25BC +0.000A2-0.50B2-6.50C2
FRIABILITY= +0.11-0.050A+0.012B-0.069C
+3.750E-003AB-0.016AC-8.750E-003BC
+0.021A2+0.011B2+0.036C2
DISINTEGRATION TIME= +8.05+3.40A-2.30B+2.00C +0.50AB-0.25 AC+0.75BC
+1.14A2+2.64B2+1.14C2
DRUG DISSOLVED= +93.44-9.20A+2.70B-4.80C
+0.000AB+0.75AC-0.50BC-6.09A2-0.59B2-4.09 C2
Response 3: Disintegration Response 4: Dissolution Response 1: Hardness Response 2: Friability
FACTORIAL MIXTURE
BOX BEHNKEN
RESPONSE SURFACE
© Created & Copyrighted by Shivang Chaudhary
HOW TO IDENTIFY FACTORS? HOW TO SELECT
DESIGN? HOW TO SELECT
EFFECT TERMS? HOW TO VERIFY
DESIGN SPACE? HOW TO CREATE
OVERLAY PLOT? HOW TO SELECT
MODEL? HOW TO DIAGNOSE
RESIDUALS? HOW TO INTERPRET
MODEL GRAPHS?
Model Graphs gave a clear picture of how the response will behave at different levels of factors at a time in 2D & 3D
Contour Plots
Response Surface
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OPTIMIZATION OF CMAs & CPP OF HIGH SHEAR WET GRANULATION PROCESS FOR SOLID ORALS
Response 3: Disintegration Response 4: Dissolution Response 1: Hardness Response 2: Friability
Cube Plots
FACTORIAL MIXTURE
BOX BEHNKEN
RESPONSE SURFACE
© Created & Copyrighted by Shivang Chaudhary
HOW TO IDENTIFY FACTORS? HOW TO SELECT
DESIGN? HOW TO SELECT
EFFECT TERMS? HOW TO VERIFY
DESIGN SPACE? HOW TO SELECT
MODEL? HOW TO DIAGNOSE
RESIDUALS? HOW TO INTERPRET
MODEL GRAPHS? HOW TO DEVELOP
DESIGN SPACE?
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FACE CENTERED CENTRAL COMPOSITE
OPTIMIZATION OF CMAs & CPP OF HIGH SHEAR WET GRANULATION PROCESS FOR SOLID ORALS
Responses (Effects) Goal for Individual Responses Y1 Hardness (n) To achieve tablet hardness in the range from 65 to 85N Y2 Friability (%) To achieve minimum friability i.e. NMT 0.15% Y3 Disintegration (min) To achieve tablet DT within 10 minutes Y4 Dissolution (%) To achieve maximum dissolution in 30 minutes i.e. NLT 90%
Factors (Variables) Knowledge Space Design Space Control Space A Binder (%) 4-10 5.3-8.8 6.0-8.0 B Disintegrant (%) 1-5 2.2-4.4 3.0-4.0 c Kneading Time (min) 2-6 2.0-5.0 2.5-4.5
By Overlaying contour maps from each responses on top of each other, RSM was used to find out the IDEAL “WINDOW” of Operability-Design Space per proven acceptable ranges & Edges of Failure with respect to individual goals
© Created & Copyrighted by Shivang Chaudhary
After completion of all experiments according to DoE, Verification was required TO CONFIRM DESIGN SPACE developed by selected DESIGN MODEL, which should be rugged & robust to normal variation within a SWEET SPOT in OVERLAY PLOT,
where all the specifications for the individual responses (CQAs) met to the predefined targets (QTPP)
4.0-10.0
5.3-8.8
6.0-8.0
1.0-5.0
2.2-4.4
3.0-4.0
The OBSERVED EXPERIMENTAL RESULTS of 3 additional confirmatory runs across the entire design space were compared with PREDICTED RESULTS from Model equation by CORRELATION COEFFICIENTs. In the case of all
3 responses R2 were found to be more than 0.900, confirming right selection of DESIGN MODEL.
BINDER (%) DISINTEGRANT (%)
KNOWLEDEGE SPACE
DESIGN SPACE
CONTROL SPACE
Known Ranges of OPERABILITY
before Designing
Optimized Ranges of FEASIBILITY
after Development
Planned Ranges of CONTROLLING
during Commercialization
2.0-6.0
2.0-5.0
2.5-4.5
KNEADING TIME (min)
OPTIMIZATION OF CMAs & CPP OF HIGH SHEAR WET GRANULATION PROCESS FOR SOLID ORALS
FACTORIAL MIXTURE
BOX BEHNKEN
RESPONSE SURFACE
HOW TO IDENTIFY FACTORS? HOW TO SELECT
DESIGN? HOW TO SELECT
EFFECT TERMS? HOW TO SELECT
MODEL? HOW TO DIAGNOSE
RESIDUALS? HOW TO INTERPRET
MODEL GRAPHS? HOW TO CREATE
OVERLAY PLOT? HOW TO VERIFY
DESIGN SPACE?
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Factors (Variables) Knowledge Space Design Space Control Space A Binder (%) 4-10 5.3-8.8 6.0-8.0 B Disintegrant (%) 1-5 2.2-4.4 3.0-4.0 c Kneading Time (min) 2-6 2.0-5.0 2.5-4.5
THANK YOU SO MUCH FROM
DESIGNING IS A JOURNEY OF DISCOVERY…
© Created & Copyrighted by Shivang Chaudhary
SHIVANG CHAUDHARY
© Copyrighted by Shivang Chaudhary
Quality Risk Manager & Intellectual Property Sentinel- CIIE, IIM Ahmedabad MS (Pharmaceutics)- National Institute of Pharmaceutical Education & Research (NIPER), INDIA
PGD (Patents Law)- National academy of Legal Studies & Research (NALSAR), INDIA
+91 -9904474045, +91-7567297579 [email protected]
https://in.linkedin.com/in/shivangchaudhary
facebook.com/QbD.PAT.Pharmaceutical.Development