© Created & Copyrighted by Shivang Chaudhary

STEP GUIDE FOR DESIGNING OF EXPERIMENTS

FOR PHARMACEUTICAL PRODUCT DEVELOPMENT AS PER QbD

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 shivaniper@gmail.com

https://in.linkedin.com/in/shivangchaudhary

facebook.com/QbD.PAT.Pharmaceutical.Development

IDENTIFY FACTORS TO BE STUDIED & ITS LEVELS TO INDUCE A MEASURABLE SIGNIFICANT CHANGE TO THE RESPONSE

Implementatn of

CONTROLS PAT

& Development of Feedback Control system

DoE & Development of

Design Space

Quality Risk Assessment of

CMAs & CPPs Determination of

CQAs Definition of QTPP

DoE / QbD ROAD MAP

SCREENING

OPTIMIZATION

PLACKETTE BURMAN

FACTORIAL DESIGN

RESPONSE SURFACE

METHODOLOGY

MIXTURE DESIGN

3 LEVELFULL FACTORIAL

CENTRAL COMPOSITE

DESIGN

BOX BEHNKEN

DESIGN

SIMPLEX LATTICE

SIMPLEX CENTROID

CONSTRAINED MIXTURE

2 LEVEL FRACTIONAL FACTORIAL

2 LEVEL FRACTIONAL FACTORIAL

WITH CENTER POINTS

2 ≤ F ≤ 4

2 LEVEL FULL FACTORIAL

4 ≤ F ≤ 6 F≤3

L=2

2 ≤ F ≤ 6

L≥3

7 ≤ F ≤ 32

ASSESSMENT OF

QUADRATIC OR CUBIC CURVATURE IN RESPONSE

SURFACE MAP

2D CONTOUR PLOT

ADDITIONAL CENTER POINTS

VERIFICATION OF EXPERIMENTAL RESULTS BY TAKING ADDITIONAL AT LEAST 3 CHECK POINT BATCHES IN THE SAME RANGE OF DESIGN SPACE & ANALYZE R2 OF OBSERVED Vs PREDICTED VALUES PLOT

DEVELOPMENT OF OVERLAY PLOTS (DESIGN SPACE) FOR FINDING OUT OPTIMIZED RANGES OF CRITICAL FACTORS WHERE ALL THE SPECIFICATIONS OF ALL THE DESIRED RESPONSES SIMULTANEOUSLY Meet, KNOWN AS A SWEET SPOT. WORKING WITHIN DESIGN SPACE IS NOT CONSIDERED AS A CHANGE. MOVEMENT OUT

OF THE DESIGN SPACE IS CONSIDERED TO BE A CHANGE & WOULD NORMALLY INITIATE A REGULATORY POST APPROVAL CHANGES

ANOVA FOR TESTING OF SIGNIFICANCE OF SELECTED POLYNOMIAL MODEL EXPRESSING RELATIONSHIP BETWEEN FACTORS & RESPONSES WITH LEAST SQUARE REGRESSION

ASSESSMENT OF INDIVIDUAL

LINEAR EFFECTS & INTERACTION

2D INTERACTION PLOT

HALF NORMAL EFFECT PLOT

3D RESPONSE SURFACE

4D CUBE PLOT

IDENTIFY RESPONSES TO BE MEASURED

DEFINE OBJECTIVE OF EXPERIMENT

1D MAIN EFFECT PLOT

PARETO BAR CHART

OPTIMIZE SCRRENED OUT CRITICAL FORMULATION &/OR

PROCESSING PARAMETERS

VERIFICATION

F=2

F≥3

DIAGNOSTIC PLOTS FOR RESIDUAL (Obs-Pred) ANALYSIS

3 ≤ F ≤ 5

2 ≤ F ≤ 6

3 ≤ F ≤ 5

© Created & Copyrighted by Shivang Chaudhary

VALIDATION ANOVA VALIDATION

MODEL GRAPHS

MODEL SELECTION

EFFECT SELECTION

DESIGN SELECTION

SUMMARY

Where, FD: Factorial Design, FFD: Fractional Factorial Design, PBD: Plackette Burman Design, CCD: Central Composite Design, BBD: Box-Behnken Design,

MD: simplex lattice & simplex centroid Mixture Design,

PBD FFD 2k FD Xk FD CCD BBD MD

Object of Experimentation Screening × × × Optimization × × Factor Type Composition (Formulation) × Manufacturing (Processing) × Number of Factors 5 ≥ f ≥ 2 × f ≥ 6 × × × × × × Number of Levels L = 2 × × L ≥ 3 × × × Model proposed Linear model × × × Interaction model × Quadratic model × × × × Cubic model × × × × Scheffe Mixture model × × × × × ×

GUIDE

© Created & Copyrighted by Shivang Chaudhary

VALIDATION ANOVA VALIDATION

MODEL GRAPHS

SUMMARY

During Selection of any Design for experimentation, OBJECTIVE (goal) of the experiment & NUMBERS of the factors involved are the primary two most important factors to be considered

Res IV Factorial Designs are appropriate when: • Numerous unknown factors are involved • The goal is SCREENING of critical factors • Emphasis is on identifying important factor effects • Time & Resources are limited • If F>6 use Plackette Burman, if 4 ≤ F ≤ 6 then use fFD

Res V Factorial Designs are appropriate when: • The goal is EVALUATION OF MAIN EFFECTS &

INTERACTIONS between factors (2 ≤ F ≤ 4) • Have Sufficient time & resources for development • Res V Fractional or Full Factorial Design can be augmented

with axial star points to create CCD RSM for optimization.

RSM designs are appropriate when: • Vital factors are known & limited (2 ≤ F ≤ 5) • The goal is OPTIMIZATION of critical factors • Emphasis is on the fitted surface representing true behavior • detect non-linear significant curvature in response surface • Ranges of factors are defined (definite knowledge space)

Use MIXTURE Designs (SIMPLEX LATTICE or SIMPLEX CENTROID or CONSTRAINED MIXTURE) when: • Factors are components of a mixture • Emphasizing ratios of ingredients in formulations • Response is a function of proportions • Ingredients must total to 1 (100%)

PLACKETTE BURMAN or FRACTIONAL FACTORIAL

FRACTIONAL WITH CP & FULL FACTORIAL

RESPONSE SURFACE DESIGNS (CCD or BBD)

MIXTURE DESIGNS (SIMPLEX or CONSTRAINED)

MODEL SELECTION

EFFECT SELECTION GUIDE DESIGN

SELECTION

© Created & Copyrighted by Shivang Chaudhary

VALIDATION ANOVA VALIDATION

MODEL GRAPHS

SUMMARY

MODEL SELECTION GUIDE DESIGN

SELECTION EFFECT

SELECTION

EFFECT HALF NORMAL PROBABILITY PLOT

PARETO BAR CHART

Normal probability plot indicates whether the residuals (noise

factors) follow a normal distribution, which will follow a straight

line. Half-Normal Probability Plot can be used to choose significant

effects. It follows the same principle as the full normal probability

plot, except the sign of the effect is ignored in plotting.

Thus, LARGE ABSOLUTE VALUES SHOW UP AS OUTLIERS AT

NOTICEABLE GAP FROM RIGHT TO LEFT IN THE UPPER RIGHT-HAND

SECTION OF THE GRAPH. A distinct inflection point or "dogleg"

indicates the transition between significant and insignificant effects.

The Pareto chart may be more comfortable viewing as the Pareto

Chart has the significant effects selected. There are two different t

limits plotted on the graph - based on the Bonferroni corrected t and

a standard t.

EFFECTS THAT ARE NOW ABOVE THE BONFERRONI LIMIT ARE

ALMOST CERTAINLY SIGNIFICANT. Effects that are now above the

T-Value Limit are possibly significant and should be added

if they are not already selected.

, Effect Half Normal Probability plot reveals vital few from trivial many & Pareto bar chart puts all the effects in perspective during Selection of Significant Main Effects from Non significant Errors

REVEALS VITAL FEW SIGNIFICANT CRITICAL FACTORS OUT OF NONCRTICAL NONSIGNIFICANT TRIVIAL MANY

PUTS EFFECTS IN PERSPECTIVE FOR EASY UNDERSTANDING OF RELATIVE IMPORTANCE OF FACTORS

Use Stepwise Regression, Forward Selection or Backward Elimination on the basis of corresponding Coefficient values to identify important variables; when selecting variables for inclusion in the model, follow the

HIEARCHY PRINCIPLE & keep all main effects that are part of significant higher order terms or interactions, even if the main effect p-value is larger than you would like. © Created & Copyrighted by Shivang Chaudhary

VALIDATION ANOVA VALIDATION GUIDE DESIGN

SELECTION

SUMMARY

EFFECT SELECTION

LINEAR MODEL QUADRATIC MODEL

CUBIC MODEL SCHEFFE MODEL

The order of polynomial: MODEL (A mathematical relationship between factors & response assisting in calculations & predictions) for any design depends on the expected response behavior.

FIRST Order (LINEAR) terms modeling SLOPEs of a STRAIGHT LINE or FLAT PLANE

Used for SCREENING of Significant Factors for Identification of Main Effects & Interactions

SECOND Order (QUADRATIC) terms modeling CURVATUREs of an ECLIPSE.

Used for OPTIMIZATION of Factors for development of Design Space from Knowledge Space

THIRD Order (CUBIC) terms: modeling Inflected ASSYMETRY like “S” curve in response eclipse

Reduced Polynomial Scheffe Model characterized by LACK of intercept & LACK of square & cube terms

During Selection of Model for Analysis of Response; ANOVA should be carried out thoroughly for testing of SIGNIFICANCE of each MODEL TERMS (p<0.05), insignificant LACK OF FIT (p>0.1) with MODEL GRAPHS

MODEL GRAPHS

MODEL SELECTION

© Created & Copyrighted by Shivang Chaudhary

VERIFICATION GUIDE DESIGN

SELECTION

SUMMARY

EFFECT SELECTION

MODEL SELECTION

MODEL GRAPHS

ANOVA should be carried out for testing of significance of selected MODEL with F-Value >>1 & p<0.05, insignificant LACK OF FIT (p>0.10), adequate PRECISION > 4, R2 & R2 adjusted as NUMERICAL INDICATORS with

well behaved RESIDUALS analyzed by DIAGNOSTIC PLOTS as GRAPHICAL INDICATORS

RESIDUAL ANALYSIS is necessary to confirm/validate that the MODEL ASSUMPTIONS for the ANOVA are met. Thus model should predict values higher than actual & lower than actual with equal probability. In addition the

level of error should be independent of when the observation occurred in the study, or the size of the observation being predicted, or even the factor setting involved in making the prediction

MODEL DIAGNOSTICS

ACTUAL VS PREDICTED PLOT

RESIDUALS VS RUN PLOT

RESIDUALS VS PREDICTED PLOT

RESIDUAL VS FACTOR PLOT

Residuals are estimates of experimental error obtained by subtracting the observed responses from

the predicted responses calculated from the chosen model. Residual (Experimental Error) Noise= (Observed Responses) Actual Data– (Predicted Responses) Model Value

Points should be Random scatter along 45° line with

no increasing or

decreasing trend

Points should be Random scatter with no pattern

Points should be Random scatter with no megaphone “=<“ pattern

Points should be split by the zero line at either end of the range- no obvious main effects (up & down)

© Created & Copyrighted by Shivang Chaudhary

VALIDATION GUIDE DESIGN SELECTION

SUMMARY

EFFECT SELECTION

MODEL SELECTION

Interaction occurs with 2 NON-PARALLEL CROSS LINES,

indicating the effect of one factor depends on

the level of the other factor.

2D PLOT OF TWO INDEPENDENT FACTORS Vs one

response factor to predict the response at different

levels of factors, while holding the magnitude of

response and other factors as constant.

3D SURFACE PLOT OF TWO INDEPENDENT FACTORS

Vs one response factor to explore the effect of changing

levels of factors on the response, while holding the

magnitude of response & other factors constant.

4D PLOT OF THREE INDEPENDENT FACTORS

AT A TIME Vs one response factor at different levels

of factors, while holding the magnitude of response

and other factors as constant.

MODEL DIAGNOSTICS

1D INTERACTION PLOT

2D CONTOUR PLOT

3D RESPONSE SURFACE PLOT

4D CUBE PLOT

Model Graphs gives a clear picture of how the response will behave at different levels of factors at a time through predicted response equation with individual coefficients

MODEL GRAPHS

© Created & Copyrighted by Shivang Chaudhary

GUIDE DESIGN SELECTION

SUMMARY

EFFECT SELECTION

MODEL SELECTION

DIAGNOSTIC PLOTS

ANOVA VALIDATION

VERIFICATION

VERIFICATION is a confirmation of an DESIGN SPACE, which should be rugged & robust to normal variation within a SWEET SPOT, where all the specifications for the individual responses (CQAs) met to the predefined targets

(QTPP) in OVERLAY PLOT, obtained by overlaying contour surfaces of individual responses with their respective goals.

8.0-12.0

9.0-11.5

9.5-10.5

5.0-15.0

10.0-12.5

10.0-12.0

A MINIMUM OF 3 CONFIRMATORY RUNS should be conducted allowing an estimate of variability WITHIN DESIGN SPACE to have a quality product &/or stable process. The OBSERVED RESULTS of these confirmatory runs

should be compared with PREDICTED RESULTS from ANOVA predicted equation by means of CORRELATION COEFFICIENT R2 which should be not less than 0.9).

BLENDING SPEED BLENDING TIME

KNOWLEDEGE SPACE

DESIGN SPACE

CONTROL SPACE

Known Ranges of OPERABILITY before Designing

Optimized Ranges of FEASIBILITY after Development

Planned Ranges of CONTROLLING during Commercialization

© Created & Copyrighted by Shivang Chaudhary

THANK YOU SO MUCH FROM

DESIGN IS A JOURNEY OF DISCOVERY…

© Created & Copyrighted by Shivang Chaudhary

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 shivaniper@gmail.com

https://in.linkedin.com/in/shivangchaudhary

facebook.com/QbD.PAT.Pharmaceutical.Development