a doe/qbd optimization model of “liquid oral suspension” using box behnken rsm for development...

FACTORIAL MIXTURE

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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

FOR LIQUID ORAL DOSAGE FORM DEVELOPMENT AS PER QbD

OPTIMIZATION OF CMAs & CPPs OF SUSPENSION HOMOGENIZATION PROCESS

mailto:[email protected]




RISKS

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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?

A

B

C STIRRING TIME

HYDROCOLLOID

SURFACTANT

OPTIMIZATION OF CMAs & CPPs OF LIQUID ORAL SUSPENSION HOMOGEMIZATION

QUALITY COMPROMISED EFFICACY COMPROMISED SAFETY COMPROMISED

INADEQUATE ZETA POTENTIAL INADEQUATE VISCOSITY HIGH RATE OF SEDIMENTATION

CONTENT UNIFORMITY COMPROMISED

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

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A SURFACTANT

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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 Liquid Oral SUSPENSION, 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 were nearly the same

• Thus, BBD was selected as 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.


Factors (Variables) Levels of Factors Studied -1 0 +1

A SURFACTANT (%) 0.50%w/w 1.00%w/w 1.50%w/w B HYDROCOLLOID (%) 20%w/w 30%w/w 40%w/w C STIRRING TIME (min) 30min 45min 60min

CMAs CPP CQAs

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HOW TO IDENTIFY FACTORS?

HOW TO SELECT DESIGN?






HOW TO DESIGN

EXPERIMENTS?


Qualitative Formulation Composition & Controlled Flocculation Processing Steps were fixed for All 15 experiments i.e. starting from wetting of drug particles having constant PSD by surfactants to make the particles wetable & dispersible by reducing angle of repose & increasing zeta potential with continuous mixing for 15 minutes, controlled

flocculation through addition of electrolytes reducing forces of repulsion & allowing the particles to form loose flocks with continuous stirring for 15 minutes; finally supporting into hydrocolloid based structured vehicle with continuous stirring

keeping all the Batch Sizes constant i.e. 5 liter in a mixer (10 liter) by propeller type impeller at medium speed.

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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: SVR R2: Zeta potential R3: Viscosity R4: Content Uniformity



PREDICTION EFFECT EQUATION ON INDIVIDUAL RESPONSE BY QUADRATIC MODEL

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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




Sedimentation Volume Ratio =+0.030 -0.024A-0.089B-0.020C

+0.010AB+2.500E-003AC+2.500E-003BC+0.067A2+0.11B2+0.030C2

Zeta potential=-44.67+12.00A+5.62B+0.38C-2.25 AB-0.25AC+1.00BC-6.92A2-2.67B2-1.17C2

Viscosity =+44.67 +3.25A+8.38B+1.13C

-0.75AB-0.25AC+0.000BC -1.08A2-3.83B2+0.17C2

Content Uniformity=+1.73 -0.20A-0.50B-0.15C

+0.000AB+0.050AC+0.000BC +0.41A2+0.76B2+0.26C2

IDENTIFICATION OF FACTORS

DESIGN OF EXPERIMMENTS

ANALYSIS OF RESPONSES

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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

Contour Plots

Response Surface

Cube Plot



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DESIGN SPACE? HOW TO SELECT



MODEL GRAPHS? HOW TO DEVELOP

DESIGN SPACE?

Responses (Effects) Goal for Individual Responses Y1 Sedimentation Volume Ratio To achieve the minimum SVR i.e. NMT 0.1 Y2 Zeta Potential (mV) To achieve zeta potential of suspension in the range of -40 to -50 mv Y3 Viscosity (cps) To achieve viscosity in the range of 40 to 50 cps Y4 Content Uniformity (AV) To achieve minimum acceptance value in CU i.e. NMT 2.0

Factors (Variables) Knowledge Space Design Space Control Space A SURFACTANT (%) 0.50-1.50 0.75-1.25 0.85-1.15 B HYDROCOLLOID (%) 20.0-40.0 27.5-37.5 30.0-35.0 C STIRRING TIME (min) 30-60 37-53 40-50


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

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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)

0.50-1.50

0.75-1.25

0.85-1.15

20.0-40.0

27.5-37.5

30-35

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.

SURFACTANT (%) HYDROCOLLOID (%)

KNOWLEDEGE SPACE

DESIGN SPACE

CONTROL SPACE

Known Ranges of OPERABILITY

before Designing

Optimized Ranges of FEASIBILITY

after Development

Planned Ranges of CONTROLLING

during Commercialization

30-60

37-53

40-50

STIRRING TIME (min)


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 “liquid oral suspension” using box behnken rsm for development...

Healthcare