A DoE/QbD Optimization Model For "Tablet Compression" process using Circumscribed Central Composite RSM for Tablet Dosage Form

FOR TABLET DEVELOPMENT AS PER QbD

OPTIMIZATION OF CPPs OF TABLET COMPRESSION PROCESS

FACTORIAL MIXTURE

BOX BEHNKEN

RESPONSE SURFACE

CIRCUMSCRIBED 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

mailto:[email protected]




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?

LOWER HARDNESS INADEQUATE DISINTEGRATION

QUALITY COMPROMISED EFFICACY COMPROMISED

WEIGHT VARIATION

SAFETY COMPROMISED

HIGH FRIABILITY INADEQUATE DISSOLUTION CONTENT NONUNIFORMITY

RISKS

FACTORIAL MIXTURE

BOX BEHNKEN

RESPONSE SURFACE


COMPRESSION FORCE

TURRET SPEED B

A

HOW TO SELECT DESIGN?

HOW TO IDENTIFY

RISK FACTORS?

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OPTIMIZATION OF CRITICAL PROCESSING PARAMETERS OF TABLET COMPRESSION PROCESS

FACTORS

Factors (Variables) Levels of Factors studied -α -1 0 +1 +α

A COMPRESSION FORCE (kN) 1.17 2.00 4.00 6.00 6.83 B TURRET SPEED (RPM) 3.79 10.00 25.00 40.00 46.21

NO. OF FACTORS

NO. OF LEVELS

EXPERIMENTAL DESIGN SELECTED

TOTAL NO OF EXPERIMENTAL RUNS (TRIALS)

2

5

circumscribed CENTRAL COMPOSITE DESIGN (cCCD)

2fp + 2sp + cp = (22 )+ (2*2) + (5) = 4+4+5 = 13

To Optimize Critical Processing Parameters of Tablet Compression Process OBJECTIVE

FACTORIAL MIXTURE

BOX BEHNKEN

RESPONSE SURFACE


A

B



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 Tablet Compression, 2 Processing Parameters were critical & required to be optimized

• Moreover, the region of operability must be greater than region of interest to achieve the maximum rate of productivity to get maximum daily output at commercial manufacturing scale

• Thus, 5 Level cCCD is a time & cost effective best alternative to 3 Level FFD for optimization.

• However in 5 level cCCD, region of operability was greater than region of interest to accommodate additional star points which are at extreme levels (highest & lowest) of both factors.

“Highest”

“High”

“Medium”

“Low”

“Lowest”

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T SP

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COMPRESSON FORCE

HOW TO IDENTIFY FACTORS?

HOW TO SELECT DESIGN?

CPPs CQAs

FACTORIAL MIXTURE

BOX BEHNKEN

RESPONSE SURFACE









HOW TO DESIGN

EXPERIMENTS?

Qualitative & Quantitative 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 13 equal parts according to experimental runs of cCCD

at different combination of Compression Force & Turret Speed .

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HOW TO IDENTIFY FACTORS? HOW TO SELECT

DESIGN? HOW TO SELECT

EFFECT TERMS?

FACTORIAL MIXTURE

BOX BEHNKEN

RESPONSE SURFACE








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 1: Friability Response 2: Dissolution Response 3: Content Uniformity


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FACTORIAL MIXTURE

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RESPONSE SURFACE






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

FRIABILITY =+0.15 -0.066A +0.026B

-7.500E-003AB +0.028A2

+0.021B2

DRUG DISSOLVED IN 30 MIN =+97.20 -8.37A +0.16B -1.75AB -5.73A2

-1.48B2

CONTENT UNIFORMITY =+4.15 -0.088A +1.45B

-0.08AB +0.13A2

+0.73B2


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RESPONSE SURFACE







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


Interaction Plots

Contour Plots

Response Surface

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Factors (Variables) Knowledge Space Design Space Control Space A Compression Force (kn) 2.0-6.0 3.0-5.0 3.5-4.5 B Turret Speed (RPM) 10-40 10-30 15-25

Responses (Effects) Goal for Individual Responses Y2 Friability To achieve tablet friability NMT 0.2%w/w Y4 Dissolution Drug release should NLT 90% in 30 minutes Y6 Content uniformity Acceptance Value should in CU test should NMT 5.0

FACTORIAL MIXTURE

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RESPONSE SURFACE







DESIGN SPACE? HOW TO SELECT



MODEL GRAPHS? HOW TO DEVELOP

DESIGN SPACE?

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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

FACTORIAL MIXTURE

BOX BEHNKEN

RESPONSE SURFACE






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) were met to the predefined targets (QTPP)

2.0-6.0

3.0-5.0

3.5-4.5

10.0-40.0

10.0-30.0

15.0-25.0

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.

COMPRESSION FORCE (kN) TURRET SPEED (RPM)

KNOWLEDEGE SPACE

DESIGN SPACE

CONTROL SPACE

Known Ranges of OPERABILITY before Designing

Optimized Ranges of FEASIBILITY after Development

Planned Ranges of CONTROLLING during Commercialization

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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 "Tablet Compression" process using Circumscribed Central Composite RSM for Tablet Dosage Form

Healthcare

risk factors

important factors

design space

tablet development

levels experimental

compression force kn

development case study

doeqbd case study