A DoE/QbD Optimization Model of “Hard Gelatin Encapsulation” Process using Box Behnken RSM for Development of Hard Gelatin Capsule

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

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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

mailto:[email protected]




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

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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

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G R

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HOW TO IDENTIFY FACTORS?






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

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HOW TO IDENTIFY FACTORS?

HOW TO SELECT DESIGN?






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


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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


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


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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


IDENTIFICATION OF FACTORS

DESIGN OF EXPERIMMENTS

ANALYSIS OF RESPONSES

FACTORIAL MIXTURE

CENTRAL COMPOSITE

RESPONSE SURFACE

BOX BEHNKEN

DEVELOPMENT OF DESIGN SPACE


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OVERLAY PLOT? HOW TO SELECT


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

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RESPONSE SURFACE

BOX BEHNKEN


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DESIGN SPACE? HOW TO SELECT



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

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RESPONSE SURFACE

BOX BEHNKEN


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EFFECT TERMS? HOW TO SELECT



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…


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]



mailto:[email protected]




A DoE/QbD Optimization Model of “Hard Gelatin Encapsulation” Process using Box Behnken RSM for Development of Hard Gelatin Capsule

Healthcare

shivang chaudhary factors

box behnken design

risk factors

important factors

levels experimental

center of design space

model graphs

ww c filling rate spm