A DoE/QbD Optimization Model of “Dry Mixing-Direct Compression” Process using 3^2 Full Factorial Design for Solid Oral Dosage Forms

FOR SOLID ORAL DOSAGE FORM DEVELOPMENT AS PER QbD

OPTIMIZATION OF CRITICAL PROCESSING PARAMETERS OF DRY MIXING PROCESS

PLACKETTE BURMAN

2 LEVEL FACTORIAL

3 LEVEL FULL FACTORIAL

MIXTURE RESPONSE SURFACE FACTORIAL

© 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]




INAPPROPRIATE BLENDING SPEED &/OR TIME

BLEND UNIFORMITY COMPROMISED

CONTENT UNIFORMITY COMPROMISED

PLACKETTE BURMAN

2 LEVEL FACTORIAL



BLENDING SPEED 1

2 BLENDING TIME


RISKS

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?

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Factors (Variables) Levels of Factors studied 0 1 2

A Blending Speed (in RPM) 8 10 12 B Blending Time (in minutes) 5 10 15

NO. OF FACTORS

NO. OF LEVELS

EXPERIMENTAL DESIGN SELECTED

TOTAL NO OF EXPERIMENTAL RUNS (NO OF TRIALS)

2

3

32 FULL FACTORIAL DESIGN

Lf = 32 FP = 9

To Optimize Critical Processing Parameters of Dry Mixing Process OBJECTIVE

PLACKETTE BURMAN

2 LEVEL FACTORIAL


OPTIMIZATION OF CRITICAL PROCESSING VARIABLES OF DRY MIXING/ BLENDING PROCESS


A BLENDING SPEED

B

BLE

ND

ING

TIM

E


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.

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“High”

“Medium”

“Low”

• In Dry Mixing Process, 2 Processing Parameters were critical & required to be optimized

• Moreover, It was required to investigate interactive & quadratic relationship between factors & response to find out optimum ranges

• Thus, 3 Level FFD is a time & cost effective best option for optimization of 2 factors.

• However 3 Level FFD facilitates investigation of interactive & quadratic relationship of factors & response in the terms of multiplied 2FI & squared main effects in the quadratic model equation

CPPs CQAs

PLACKETTE BURMAN

2 LEVEL FACTORIAL





HOW TO IDENTIFY FACTORS?

HOW TO SELECT DESIGN?






HOW TO DESIGN

EXPERIMENTS?

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Qualitative & Quantitative Formulation Composition & Direct Compression Processing Variables were kept constant for all 9 experiments.i.e. Drug (5%w/w); Microcrystalline Cellulose 102 (90%w/w)-diluent & PVP K29/32 (2.5%w/w)-dry binder sifted through 30#,Mg Stearate (0.5%w/w)-lubricant & Talc (2%w/w)- glidant

pre-sifted through 60# with a batch size of 2.5 kg, & dry mixed in bin blender (10 liter) with constant 50% occupancy of total volume.

PLACKETTE BURMAN

2 LEVEL FACTORIAL





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

Response 1: Average Assay in BU Response 2: %RSD in BU

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PREDICTION EFFECT EQUATION ON INDIVIDUAL RESPONSE BY QUADRATIC MODEL

Average Assay of Blend Uniformity =+99.61 +0.78A+2.32B-0.95AB-1.52A2-2.22B2

RSD Of Blend Uniformity=+1.94-0.47A-1.45B+0.53AB+1.13A2+1.98B2

PLACKETTE BURMAN

2 LEVEL FACTORIAL









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, insignificant LACK OF FIT (p>0.10), adequate PRECISION > 4, R2 Adj & R2 Pred in good agreement <0.2d as NUMERICAL INDICATORS, 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

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PLACKETTE BURMAN

2 LEVEL FACTORIAL









OVERLAY PLOT? HOW TO SELECT


RESIDUALS? HOW TO INTERPRET

MODEL GRAPHS?

Interaction Plots

Contour Plots

Response Surface

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Model Graphs gave a clear picture of how the response will behave at different levels of factors at a time in 2D & 3D

Factors (Variables) Knowledge Space Design Space Control Space A Blending Speed (RPM) 8.0-12.0 9.15-11.35 9.5-11.0 B Blending Time (minutes) 5.0-15.0 10.0-13.5 10.0-12.0

Responses (Effects) Goals for Individual Responses Y1 Avg. Assay of BU (%) To achieve average assay of BU in the range from 98 to 102%

Y2 RSD of BU(%) To achieve minimum variability in BU i.e. NMT2.0%

PLACKETTE BURMAN

2 LEVEL FACTORIAL








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 out the IDEAL “WINDOW” of operability-Design Space per proven acceptable ranges & Edges of Failure with respect to individual goals

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PLACKETTE BURMAN

2 LEVEL FACTORIAL





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)

8.0-12.0

9.15-11.35

9.5-11.0

5.0-15.0

10.0-13.5

10.0-12.0

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.

BLENDING SPEED (RPM) BLENDING TIME (MIN)

KNOWLEDEGE SPACE

DESIGN SPACE

CONTROL SPACE

Known Ranges of OPERABILITY before Designing

Optimized Ranges of FEASIBILITY after Development

Planned Ranges of CONTROLLING during Commercialization



EFFECT TERMS? HOW TO SELECT



MODEL GRAPHS? HOW TO CREATE

OVERLAY PLOT? HOW TO VERIFY

DESIGN SPACE?

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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 of “Dry Mixing-Direct Compression” Process using 3^2 Full Factorial Design for Solid Oral Dosage Forms

Healthcare