A DoE/QbD Optimization Model for “High Shear Wet Granulation” Process using Face Centered Central Composite RSM for Development of Solid Oral Dosage Forms

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

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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

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RESPONSE SURFACE

HOW TO SELECT DESIGN?

HOW TO IDENTIFY

RISK FACTORS?

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FACTORS


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

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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 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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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

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HOW TO DESIGN

EXPERIMENTS?

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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?

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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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Response 2: Friability

Response 3: Disintegration Response 4: Dissolution Response 1: Hardness Response 2: Friability

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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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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


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DESIGN SPACE? HOW TO CREATE

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

Contour Plots

Response Surface

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Cube Plots

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DESIGN SPACE? HOW TO SELECT



MODEL GRAPHS? HOW TO DEVELOP

DESIGN SPACE?

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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


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)


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EFFECT TERMS? HOW TO SELECT



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…


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 for “High Shear Wet Granulation” Process using Face Centered Central Composite RSM for Development of Solid Oral Dosage Forms

Healthcare

factors experimental

risk factors

design space

important factors

wet granulation process

model graphs

development case study

region of operability