A DoE/QbD Ratio Optimization Model of “Glidant: Lubricant: Antiadherant” Ratio In Solid Oral Mixtures using D-Optimal Constrained Mixture Design For Development of Tablet Capsule

FOR TABLET/ CAPSULE DOSAGE FORM DEVELOPMENT AS PER QbD

OPTIMIZATION OF GLIDANT LUBRICANT ANTIADHERANT RATIO IN SOLID ORAL MIXTURES

FACTORIAL RESPONSE SURFACE MIXTURE C

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© 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

SIMPLEX LATTICE

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CONSTRAINED MIXTURE

A DoE/QbD CASE STUDY FOR

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RISKS

INADEQUATE GLIDANT INADEQUATE LUBRICANT

QUALITY COMPROMISED

INADEQUATE ANTIADHERANT

POOR FLOW OF GRANULES FROM HOPPER TO DIE

EJECTOION OF COMPLETE TABLET FROM DIE

ADHESION OF GRANULES TO PUNCES & DIES

WEIGHT VARIATION & CONTENT NONUNIFORMITY

STICKING CAN BE OBSERVED ON TABLET PERIPHERALS

PICKING CAN BE OBSERVED ON TABLET SURFACE

FACTORIAL RESPONSE SURFACE MIXTURE

ANTIADHERANT

LUBRICANT

GLIDANT 1

2

3


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

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CONSTRAINED MIXTURE

3

10

OBJECTIVE To Optimize Glidant: Lubricant: Anti-Adherant ratio of Solid Orals

OPTIMIZATION OF GLIDANT: LUBRICANT: ANTIADHERANT RATIO IN SOLID ORAL DOSAGE FORM



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.

EXPERIMENTAL DESIGN SELECTED

D-OPTIMAL CONSTRAINED MIXTURE

TOTAL NO OF EXP RUNS (TRIALS)

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Factors (Variables) Actual Levels Constrained Levels A GLIDANT (%w/w) 0.20-0.60% 0.20-0.50% B LUBRICANT (%w/w) 0.70-1.10% 0.70-1.00% C ANTIADHERANT (%w/w) 1.20-1.60% 1.20-1.50%

• During Optimization of Glidant, Antiadherant & Lubricant; ultimate response to be measured was Angle of Response which was a function of proportion of all 3 components in combination

• All 3 factors were components of a mixture, their operating ranges were not same but their total must be 2.5%w/w of formulation & there were upper bound constraints on the component proportions in the formulation mixture

• Thus, Constrained Mixture Design is selected, in opposite to Simplex Mixture, as a special class of RSM for optimization of proportions especially applicable when there are upper or lower bound constraints on the component proportions.

NO. OF COMPONENTS

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CQAs CMAs




HOW TO IDENTIFY FACTORS?

HOW TO SELECT DESIGN?






HOW TO DESIGN

EXPERIMENTS?

Here, Other Qualitative & Quantitative Formulation Composition was kept constant except flow promoters for all 10 experiments i.e. Drug (5%w/w); Microcrystalline Cellulose 101 (90%w/w) -diluent & Poly Vinyl Pyrrolidone K 29/32

(5%w/w)-binder were granulated with Purified Water as a Granulating Agent (q.s) in a Rapid Mixer Granulator. Granules were dried in fluid bed dryer at inlet temperature of 50°±5°C until %LOD reached NMT 2.0%, milled

through 1.0 mm screen in multi-mill which was distributed in 10 equal parts & blended with 60#pre-sifted flow promoters as per ratio required in D Optimal Constrained mixture

Design in bin blender at 10RPM for 10 minutes with constant.50 % occupancy.

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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 having p-value (Prob > F) that is lower than chosen level of significance (p = 0.05)

LACK of Fit Tests

Sequential MODEL Sum of Square Tables

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IDENTIFICATION OF FACTORS

PREDICTION EFFECT EQUATION OF INDIVIDUAL RESPONSE BY 3rd ORDER CUBIC MODEL

Angle of Repose = +29.15A+48.82B+40.45C-7.94 AB-23.71AC-3.54BC -230.27ABC+62.82AB(A-B)+74.89AC(A-C)-14.37BC(B-C) FULL CUBIC MODEL)

DEVELOPMENT OF DESIGN SPACE

DESIGNING OF EXPERIMMENTS

ANALYSIS OF RESPONSES








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 Value >>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

Response: Angle of Repose

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CONSTRAINED MIXTURE

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OVERLAY PLOT? HOW TO SELECT


RESIDUALS? HOW TO INTERPRET

MODEL GRAPHS?

Model Graphs gives a clear picture of how the response will behave at different levels of factors at a time in 2D, 3D & 4D Response: Angle of Repose

2 Components Mix Plots

Contour Plot Response Surface

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Responses (Effects) Goal for Individual Responses Y1 Angle of Repose To achieve angle of repose NMT 30˚ (VERY GOOD Flow property)

Factors (Variables) Knowledge Space Design Space Control Space A GLIDANT (%w/w) 0.20-0.50% 0.26-0.37 0.28-0.35 B LUBRICANT (%w/w) 0.70-1.00% 0.70-0.90 0.75-0.85 C ANTIADHERANT (%w/w) 1.20-1.50% 1.30-1.50 1.35-1.45





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 ultimate goals

SIMPLEX LATTICE

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CONSTRAINED MIXTURE

IDENTIFICATION OF FACTORS

DESIGNING OF EXPERIMMENTS

ANALYSIS OF RESPONSES

DEVELOPMENT OF DESIGN SPACE






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)

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.

0.20-0.50

0.26-0.37

0.28-0.35

0.70-1.00

0.70-0.90

0.75-0.85

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

1.20-1.50

1.30-1.50

1.35-1.45

FILLING RATE (SPM)

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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]




Healthcare

A DoE/QbD Ratio Optimization Model of “Glidant: Lubricant: Antiadherant” Ratio In Solid Oral Mixtures using D-Optimal Constrained Mixture Design For Development of Tablet Capsule