A DoE/QbD Screening Model for “Fluid Bed Granulation” Process Using Plackette Burman Design for Solid Oral Dosage Forms

SCREENING OF CPPs OF FLUID BED TOP SPRAY GRANULATION PROCESS FOR SOLID ORAL DOSAGE FORMS DEVELOPMENT AS PER QbD

3 LEVEL FACTORIAL

2 LEVEL FACTORIAL

PLACKETT- BURMAN

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]




A

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BINDER SPRAYING RATE

ATOMIZATION AIR PRESSURE

FLUIDIZATION AIR VELOCITY

INLET TEMPERATURE

PRODUCT TEMPERATURE

OUTLET TEMPERATURE

GUN TO BED DISTANCE

NO OF SPRAYING HEADS

FILTER BAG PORE SIZE

FILTER CLEANING FREQUENCY

BOWL CAPACITY

3 LEVEL FACTORIAL

2 LEVEL FACTORIAL

PLACKETT- BURMAN

MIXTURE RESPONSE SURFACE FACTORIAL C

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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?

RISKS

QUALITY COMPROMISED EFFICACY COMPROMISED

HIGH FRIABILITY INADEQUATE DISSOLUTION

HIGHER %FINES HIGHER %AGGLOMERATES

FACTORS

NO. OF FACTORS

NO. OF LEVELS

EXPERIMENTAL DESIGN SELECTED

TOTAL NO OF EXPERIMENTAL RUNS (NO OF TRIALS)

11

2

PLACKETTE BURMAN DESIGN

12

3 LEVEL FACTORIAL

2 LEVEL FACTORIAL

PLACKETT- BURMAN


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OBJECTIVE To Screen Out Critical Processing Parameters of Fluid Bed Top Spray Granulation Process.

HOW TO IDENTIFY FACTORS?





HOW TO SELECT EFFECT TERMS?

HOW TO SELECT

DESIGN?

Factors (Variables) Levels

-1 +1

A BINDER SPRAYING RATE (gm/min) 2 8 B ATOMIZATION AIR PRESSURE (bar) 1 3 C FLUIDIZATION AIR VELOCITY (cfm) 50 100 D INLET TEMPERATURE (˚C) 45 55 E PRODUCT TEMPERATURE (˚C) 25 35 F OUTLET TEMPERATURE (˚C) 35 45 G GUN TO BED DISTANCE (inches) 10 20 H NO OF SPRAYING HEADS 1 3 I FILTER BAG POROSITY (um) 20 40 J FILTER BAG CLEANING FREQUENCY (CPM) 2 10 K BOWL OCCUPANCY (%) 40 60

Responses (Effects) Goals for Individual Responses

Y1 %FINES To achieve minimum fines after granulation i.e. NMT 10%

Y2 % AGGLOMERATES To achieve minimum agglomerates after granulation i.e. NMT 10%

FACTORS TO BE STUDIED

RESPONSES TO BE MEASURED

SCREENING OF CRITICAL PROCESSING PARAMETERS OF FLUID BED TOP SPRAY GRANULATION PROCESS

• In Top Spray Fluid Bed Granulation, 11 different processing parameters were involved, from which real critical factors were required to be screened out to control PSD of Granules independent of interaction

• Plackett-burman was an economical SCREENING DESIGN option for numerous factors, when only main effects were concerned of interest, assuming all other interactions negligible

CQAs PPs

3 LEVEL FACTORIAL

2 LEVEL FACTORIAL

PLACKETT- BURMAN



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HOW TO IDENTIFY FACTORS?

HOW TO SELECT DESIGN?





HOW TO DESIGN

EXPERIMENTS?

Qualitative & Quantitative Formulation Composition was kept constant for all 12 experimental runs. i.e. Drug (5%w/w) & Poly Vinyl Pyrrolidone k29/32 (5%w/w)-binder dissolved in Purified Water as a Granulating Agent

(q.s) was sprayed onto fluidized bed of Microcrystalline Cellulose 102 (90%w/w) as a substrate in a Fluid Bed Processor (12 liter). Sieve Shaker method was utilized to measure both in-process critical quality attributes

i.e. % Fines (%w/w passed through 100#) & % Agglomerates (%w/w retained on 20#)

SIGNIFICANT EFFECTS: MODEL TERMS

SIGNIFICANT EFFECTS: MODEL TERMS

NEGLIGIBLE TERMS: ERROR ESTIMATES

NEGLIGIBLE TERMS: ERROR ESTIMATES

Thus, LIQUID SPRAYING RATE (A) & ATOMIZATION AIR PRESSURE (B) were the most critical factors those required to control the ultimate particle size during fluid bed granulation

3 LEVEL FACTORIAL

2 LEVEL FACTORIAL

PLACKETT- BURMAN



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HOW TO IDENTIFY FACTORS? HOW TO SELECT

DESIGN? HOW TO SELECT

EFFECT TERMS? HOW TO CREATE

OVERLAY PLOT? HOW TO INTERPRET

MODEL GRAPHS? HOW TO DIAGNOSE

RESIDUALS? HOW TO SELECT

MAIN EFFECTS?

Response 1: %AGGLOMERATES Response 2: FINES

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LOT

3 LEVEL FACTORIAL

2 LEVEL FACTORIAL

PLACKETT- BURMAN



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OVERLAY PLOT? HOW TO INTERPRET

MODEL GRAPHS? HOW TO SELECT

MODEL? HOW TO ANALYZE

MODEL?

PREDICTION EFFECT EQUATION ON INDIVIDUAL RESPONSE BY QUADRATIC MODEL

%Agglomerates = +8.42 +5.58A -1.58B %Fines = +11.42 -5.25A +1.58B

ANOVA Response 1: AGGLOMERATES ANOVA Response 2: FINES

F Value = Test For Comparing MODEL VARIANCE

(SIGNAL=Predicted value) with RESIDUAL VARIANCE

(NOISE=(Observed-Predicted value))

p-value = Probability of Falsely Detecting the Significant Effect

(also called as a Level of Significance (α))

Reject Ho&

Accept Ha Conclude that there was a

significant effect of A & B Processing

Parameters on CQA p Value =α < 0.05 for CI= 95%

F Value= (MS Model/ MS Residuals) >1

Conclude that there was only a 0.01% chance that

A "Model F-Value" this large could

occur due to noise.

Significant Signal Negligible Noise

p Value<0.05 at 95%CI for SR & AP, ensuring RIGHT DETECTION OF SIGNIFICANT EFFECTS of both factors on response & giving 95% confidence that 99% population will meet the same specification within predefined targets

F values i.e. MS Model/ MS Residuals for both the factors i.e. SR & AP were found to be far greater than 1 confirming SHARP STRONG SIGNAL (Main effect) compared to other NOISE (residual or error term)

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 par insignificant LACK OF FIT (p>0.10),

OPTIMIZATION OF CRITICAL PROCESSING PARAMETERS OF TABLET COMPRESSION PROCESS © Created & Copyrighted by Shivang Chaudhary




OVERLAY PLOT? HOW TO SELECT

MODEL? HOW TO DIAGNOSE

RESIDUALS? HOW TO DIAGNOSE

MODEL?

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3 LEVEL FACTORIAL

2 LEVEL FACTORIAL

PLACKETT- BURMAN


After Numerical Analysis of Model, RESIDUAL ANALYSIS was necessary to confirm/validate that the MODEL ASSUMPTIONS were met or not 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, of the size of the observation being predicted in Residuals Vs Predicted Plot or

even of the factor setting involved in making the prediction in Residual Vs Factor Plot

R2:

%FI

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ACTUAL VS PREDICTED PLOT

RESIDUALS VS RUN PLOT

RESIDUALS VS PREDICTED PLOT

RESIDUAL VS FACTOR PLOT

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)

OPTIMIZATION OF CRITICAL PROCESSING PARAMETERS OF TABLET COMPRESSION PROCESS © Created & Copyrighted by Shivang Chaudhary



EFFECT TERMS? HOW TO SELECT

MODEL? HOW TO DIAGNOSE

RESIDUALS? HOW TO INTERPRET

MODEL GRAPHS? HOW TO GRAPH

MODEL?

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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

Interaction Plots

Contour Plots

Response Surface

3 LEVEL FACTORIAL

2 LEVEL FACTORIAL

PLACKETT- BURMAN


Response 1: %AGGLOMERATES Response 2: FINES

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 Screening Model for “Fluid Bed Granulation” Process Using Plackette Burman Design for Solid Oral Dosage Forms