A DoE/QbD Optimization Model of “Hard Gelatin Encapsulation” Process using Box Behnken RSM for...
-
Upload
shivang-chaudhary -
Category
Healthcare
-
view
148 -
download
17
Transcript of A DoE/QbD Optimization Model of “Hard Gelatin Encapsulation” Process using Box Behnken RSM for...
FOR HARD GELATIN CAPSULE DEVELOPMENT AS PER QbD
OPTIMIZATION OF CMAs & CPPs OF HARD GELATIN ENCAPSULATION PROCESS
FACTORIAL MIXTURE
CENTRAL COMPOSITE
RESPONSE SURFACE
BOX BEHNKEN
© Created & Copyrighted by Shivang Chaudhary
CA
SE
STU
DY
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
A DoE/QbD CASE STUDY FOR
INADEQUATE DISINTEGRATION
QUALITY COMPROMISED EFFICACY COMPROMISED SAFETY COMPROMISED
RISKS
WEIGHT VARIATION & CONTENT NON UNIFORMITY
INAPPROPRIATE FLOW PROPERTY & FILLING RATE
INADEQUATE DISSOLUTION
FACTORIAL MIXTURE
CENTRAL COMPOSITE
RESPONSE SURFACE
BOX BEHNKEN
A GLIDANT
B ANTIADHERANT
C FILLING RATE
CA
SE
STU
DY
© Created & Copyrighted by Shivang Chaudhary
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?
HOW TO IDENTIFY
RISK FACTORS?
Factors (Variables) Levels of Factors Studied -1 0 +1
A Glidant (%w/w) 0.10%w/w 0.25%w/w 0.40%w/w B Lubricant (%w/w) 0.50%w/w 1.25%w/w 2.00%w/w C Filling Rate (SPM) 50SPM 65SPM 80SPM
NO. OF FACTORS
NO. OF LEVELS
EXPERIMENTAL DESIGN SELECTED
TOTAL NO OF EXPERIMENTAL RUNS (TRIALS) $
3
3
BOX BEHNKEN DESIGN
12MP + 3CP =15
To Optimize CMAs & CPPs of Hard Gelatin Capsule Encapsulation. OBJECTIVE
FACTORIAL MIXTURE
CENTRAL COMPOSITE
RESPONSE SURFACE
BOX BEHNKEN
OPTIMIZATION OF CMAs & CPPs OF HARD GELATIN CAPSULE ENCAPSULATION
A GLIDANT
C
FIL
LIN
G R
ATE
© Created & Copyrighted by Shivang Chaudhary
CA
SE
STU
DY
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 are the primary two most important factors required to be considered during selection of any design for experimentation.
“High”
Medium
“Low”
• In Hard Gelatin Encapsulation, 2 different CMAs & 1 CPP required to be optimized. Due to 3 factors, more no. of runs were required for optimization in the case of CCD.
• Moreover, Here Region of Interest & Region of Operability was nearly the same
• Thus, BBD is an economic alternative to CCD for optimization of 3 factors simultaneously at 3 levels providing strong coefficient estimates near the center of design space, where presumed optimum with nearly same region of interest & region of operability.
CMAs CPP CQAs
FACTORIAL MIXTURE
CENTRAL COMPOSITE
RESPONSE SURFACE
BOX BEHNKEN
OPTIMIZATION OF CMAs & CPPs OF HARD GELATIN CAPSULE ENCAPSULATION © Created & Copyrighted by Shivang Chaudhary
CA
SE
STU
DY
HOW TO IDENTIFY FACTORS?
HOW TO SELECT DESIGN?
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?
Qualitative Formulation & High Shear Wet Granulation processing parameters were kept constant for all 13 experimental runs, i.e. Starting from Co-Sifting, Dry Mixing, Binder addition & Wet Granulation, Drying, Sizing up to Blending & Lubrication in Bin Blender & it was finally grouped into 15
equal parts according to experimental runs of BBD & lubricated accordingly in Bin Blender at 10RPM for 5 minutes with constant 50 % occupancy of total volume before encapsulation
by tamping principle at different speed of encapsulation process
FACTORIAL MIXTURE
CENTRAL COMPOSITE
RESPONSE SURFACE
BOX BEHNKEN
OPTIMIZATION OF CMAs & CPPs OF HARD GELATIN CAPSULE ENCAPSULATION © Created & Copyrighted by Shivang Chaudhary
CA
SE
STU
DY
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 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), insignificant LACK OF FIT (p>0.1) with 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
R1: Weight Variation R2: Content Uniformity R3: Disintegration Time R4: Drug dissolved in 30 minutes
R1: Weight Variation R2: Content Uniformity R3: Disintegration Time R4: Drug dissolved in 30 minutes
PREDICTION EFFECT EQUATION ON INDIVIDUAL RESPONSE BY QUADRATIC MODEL
Weight Variation =+1.53-0.34A-0.10B+0.49C-0.00AC-0.075AC-0.00BC+0.37A2+0.30B2+0.92C2
Content Uniformity=+1.80-0.44A-0.14B+0.58C+0.075AB-0.050AC+0.100BC+0.44A2+0.29B2+1.11C2
Disintegration Time =+3.17+0.000A-0.50B-1.78C-0.22AB+0.025AC-0.075BC+0.054A2+0.054B2+0.40C2
%Drug Dissolved in 30 minutes =+95.67+0.37A-2.13B+7.75C+0.000AB-1.25AC-3.75BC-3.08A2-5.58B2-4.83C2
FACTORIAL MIXTURE
CENTRAL COMPOSITE
RESPONSE SURFACE
BOX BEHNKEN
OPTIMIZATION OF CMAs & CPPs OF HARD GELATIN CAPSULE ENCAPSULATION © Created & Copyrighted by Shivang Chaudhary
CA
SE
STU
DY
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 are 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
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
R1: Weight Variation R2: Content Uniformity R3: Disintegration Time R4: Drug dissolved in 30 minutes
IDENTIFICATION OF FACTORS
DESIGN OF EXPERIMMENTS
ANALYSIS OF RESPONSES
FACTORIAL MIXTURE
CENTRAL COMPOSITE
RESPONSE SURFACE
BOX BEHNKEN
DEVELOPMENT OF DESIGN SPACE
OPTIMIZATION OF CMAs & CPPs OF HARD GELATIN CAPSULE ENCAPSULATION © Created & Copyrighted by Shivang Chaudhary
CA
SE
STU
DY
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 & 4D
R1: Weight Variation
R2: Content Uniformity
R3: Disintegration Time
Contour Plots
Response Surface
Cube Plot
R4: Drug dissolved in 30 minutes
Factors (Variables) Knowledge Space Design Space Control Space A Glidant (%) 0.10-0.50 0.20-0.40 0.20-0.40 B Lubricant (%) 0.50-2.00 0.70-1.80 0.90-1.60 C Filling Rate (SPM) 50-80 56-68 58-66
Responses (Effects) Goal for Individual Responses Y1 Weight Variation Relative Standard Deviation in WV test should NMT 2.0% Y2 Content Uniformity Acceptance Value in CU test should NMT 4.0 Y3 Disintegration To achieve complete disintegration (no residue) within 5 minutes Y4 Dissolution To achieve at least 90% drug release within 30 minutes
FACTORIAL MIXTURE
CENTRAL COMPOSITE
RESPONSE SURFACE
BOX BEHNKEN
OPTIMIZATION OF CMAs & CPPs OF HARD GELATIN CAPSULE ENCAPSULATION © Created & Copyrighted by Shivang Chaudhary
CA
SE
STU
DY
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?
By Overlaying contour maps from each responses on top of each other, RSM was used to find the IDEAL “WINDOW” of Operability-Design Space per proven acceptable ranges & Edges of Failure with respect to individual goals
FACTORIAL MIXTURE
CENTRAL COMPOSITE
RESPONSE SURFACE
BOX BEHNKEN
OPTIMIZATION OF CMAs & CPPs OF HARD GELATIN CAPSULE ENCAPSULATION © Created & Copyrighted by Shivang Chaudhary
CA
SE
STU
DY
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?
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)
0.10-0.50
0.20-0.40
0.20-0.40
0.50-2.00
0.70-1.80
0.90-1.60
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.
GLIDANT (%) ANTIADHERANT (%)
KNOWLEDEGE SPACE
DESIGN SPACE
CONTROL SPACE
Known Ranges of OPERABILITY
before Designing
Optimized Ranges of FEASIBILITY
after Development
Planned Ranges of CONTROLLING
during Commercialization
50-80
56-68
58-66
FILLING RATE (SPM)
THANK YOU SO MUCH FROM
DESIGN 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