DoE

Dr. V V S Narayana Reddy Karri

Faculty

Dept. of Pharmaceutics

JSS College of Pharmacy, Ooty

(Constituent College)

JSS academy of higher education and research, Mysuru

Rocklands

Udhagamandalam.

Before knowing we should know

first about:

Null hypothesis,

P-Value and ANOVA

P

V

a

l

u

e

µ=0 (Accept null hypothesis)

The hypothesis that there is no significant (<95%) difference between specified populations

P

V

a

l

u

e

µ≠0 (Reject null hypothesis, Accept null hypothesis)

The hypothesis that there is significant (>95%) difference between specified populations

Significant >

95% 5%

95/100=0.95 05/100=0.05

p < 0.05

P<0.01

P<0.001

P Value

ANOVA

Analysis of Variance

(ANOVA): A mathematical

process for separating the

variability of a group of

observations into assignable

causes and setting up various

significance tests.

History

• At the beginning of the twentieth century, Sir

Ronald Fisher introduced the concept of applying

statistical analysis during the planning stages of

research rather than at the end of experimentation.

• When statistical thinking is applied from the design

phase, it enables to build quality into the product.

History..

• The pharmaceutical industry was late in adopting these

paradigms, compared to other sectors.

• It heavily focused on blockbuster drugs, while

formulation development was mainly performed by One

Factor At a Time (OFAT) studies, rather than

implementing Quality by Design (QbD) and modern

engineering-based manufacturing methodologies.

• E.g., OFAT (Effect of disintegrant on tablet

disintegration time)

DoE exhibits several advantages over OFAT

Maximize process knowledge, with the minimum use of resources.

Provide accurate information, in the most efficient way possible.

Identify factor interactions.

Characterize the relative significance of each factor.

Allow for the prediction of the process behavior within the design

space.

Establish a solid cause and effect relationship between CPPs and

CQAs.

Allow for multiple response optimization (unlikely OFAT, one

way and two way ANOVA). As pharmaceutical products exhibit

several CQAs, the latter require simultaneous optimization.

Make the product or process more robust.

DoE will never give an IDEAL formula/batch.

It will only provide a design space (predicted

area) to work with respect to variables.

Terms

• Factors/Cause (Independent variable)

– Also called control factors which we can control and may (interaction)

or may not depend on each other factor.

– Input variables that can be changed (excipients and concentrations)

• Response/Effect (Dependent variable)

– Measured output value (Results)

– Depends on independent variables.

• Levels

– Specific values of factors (inputs) (2 or more)

– These high and low levels can be generically coded as +1 and -1.

– For example, a 2 factor experiment will require 4 experimental runs:

• Center Points

– Points at the center value of all factor ranges.

• Replication

– Completely re-run experiment with same input levels

– Used to determine impact of measurement error.

– Replication allows an estimate of the random error independent of any

lack of fit error.

• Randomization

– A schedule for allocating treatment material and for conducting treatment

combinations in a DOE such that the conditions in one run neither depend on the

conditions of the previous run nor predict the conditions in the subsequent runs.

• Uncontrolled Factors/Variables

– Which induce variation under normal operating conditions are referred to as

“Noise Factors”. These factors, such as multiple machines, multiple shifts, raw

materials, humidity, etc., can be built into the experiment so that their variation

doesn’t get lumped into the unexplained, or experiment error.

– A key strength of Designed Experiments is the ability to determine factors and

settings that minimize the effects of the uncontrollable factors.

• Blocking

– We often need to eliminate the influence of extraneous factors when

running an experiment. We do this by “blocking”.

– Typically, a blocking factor is a source of variability that is not of

primary interest to the experimenter.

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ri/section3/pri3333.htm&hl=en-IN

• Interaction

– Effect of one input factor depends on level of another input factor

Liner (Non-interaction) Vs.

Quadratic (Interactions)

Quality by Design

• Quality by Design (QbD) is defined as a systematic approach to

development that begins with predefined objectives and

emphasizes product and process understanding and process control,

based on sound science and quality risk management.

• Quality by Design was actually suggested by the regulatory

authorities (FDA, EMA) at the beginning of the new millennium,

recognizing that “quality cannot be tested into products, i.e. quality

should be built in by design”.

QBD Example

Quality Target Product Profile (QTPP)

• The QTPP is an essential element of a QbD

approach and forms the basis of design for the

development of the product

• It describes the design criteria for the product, and

should therefore form the basis for development of

the CQAs, CPPs, and Control strategy

• The QTPP provides an understanding of what will

ensure the quality, safety, and efficacy of a specific

product for the patient and is a starting point for

identifying the CQAs.

Quality Target Product Profile (QTPP)

Considerations for QTPP include:

• Intended use in clinical setting, route of administration, dosage form, delivery

systems;

• Dosage strength(s)

• Container closure system

• Therapeutic moiety release or delivery and attributes affecting pharmacokinetic

characteristics (e.g., dissolution, aerodynamic performance) appropriate to the drug

product dosage form being developed;

• Drug product quality criteria (e.g., sterility, purity, stability and drug release)

appropriate for the intended marketed product.

QTPP Example

• Particle size is critical to the dissolution of a

solid oral product, then QTPP should include

dissolution (product profile), but not particle

size.

• Particle size will be a critical material attribute

(CMAs)

QTPP Examples

SN QTTP

General discriptions

1Color

2Cosmetic Appearance

3Rigidity Interface

4girdiness

Chemical analysis

5Assay of doxycycline

6Assay of NO

7Assay of curcumin

8CU of DOXYCYCLINE

9CU of NO

10CU of curcumin

Pharmaceutical characterization

11pH

12Buffering capacity

13Spreadability

14Viscosity

15Adhesion strength

16Gel strength

In vitro release testing using synthetic

membrane

In vitro release testing using skin

In vitro tests

22Microbial efficacy studies

23Cell viability/Toxicity

24Anti-inflamatory study

25Angiogenesis

26PDGF-ELISA TEST

In vivo testing

Histopathology

Tissue regeneration

Ulcer area measuremnt

Critical Quality Attributes (CQAs)

• Any attribute of dosage form which relates to safety and efficacy.

• A CQA is a physical, chemical, biological, or microbiological

property or characteristic that should be within an appropriate

limit, range, or distribution to ensure the desired product quality.

CQAs are generally associated with the drug substance, excipients,

intermediates (in-process materials) and drug product.

• CQAs of solid oral dosage forms are typically those aspects

affecting product purity, strength, drug release and stability. CQAs

for other delivery systems can additionally include more product

specific aspects, such as aerodynamic properties for inhaled

products, sterility for parenterals, and adhesion properties for

transdermal patches.

• For drug substances, raw materials and intermediates, the CQAs

can additionally include those properties (e.g., particle size

dTiasbtlreit bfruiatbiiolinty, abnud ldkisdsoeluntsiointy) that affect drug product CQAs.

MR Tablet

Critical Process Parameter (CPP):

• A process parameter whose variability has an

impact on a critical quality attribute (CQAs)

and therefore should be monitored or

controlled to ensure the process produces the

desired quality.

• The most influential factors are defined in ICH Q8 as

Critical Process Parameters (CPPs), i.e. process parameters

whose variability has an impact on a critical quality

attribute of the product.

• Therefore, they should be monitored and controlled to

ensure that the process produces the desired quality. The

latter is the sum of the product’s characteristics, which

should consistently comply with predefined ranges, widely

known as specifications. Within this context, ICH Q8

defines Critical Quality Attributes (CQAs) as physical,

chemical, biological, or microbiological properties or

characteristics that should be within an appropriate limit,

i.e. range or distribution to ensure the desired product

quality.

Over view of attributes

CMAs CPAs CQAs QTPP

(Excipients) (Procedure) (Responses, (Safety and

Dissolution) efficacy)

Design of Experiments

(DoE)

How much DoE is important.?

A more comprehensive review on Scopus revealed

more than 5200 results for the keywords

“Design of Experiments” and “pharmaceutical”

since 1978.

It is worth noting that a steadily increasing trend is

observed, with only two documents in 1978,

10 in 1990,

29 in 2000,

24 in 2005,

281 in 2010, and

more than 800 in 2016.

DoE

• “Design of Experiments” (DoE) is a structured, organized method

for determining the relationships between factors affecting a

process and the output of that process.

• In other words, achieving process knowledge, through the

establishment of mathematical relationships between process inputs

and its outputs.

• Design of Experiments (DoE) is the main component of the

statistical toolbox to deploy (Spread/distribute) Quality by Design

in both research and industrial settings.

DoE..

• The impact of the controlled factors on the quality

characteristics of the final product varies according to a

general pattern known as the Pareto principle, indicating

that a relatively small number of factors are responsible for

the substantial percentage of the effect. This is also known as

the 20:80 rule, stating that 20% of the causes (factors) are

responsible for 80% of the results (responses).

• DoE is an approach where the controlled input factors of the

process are systematically and purposefully varied in order to

determine their effects on the responses.

• The overall scope is the connection of the CPPs (x1, x2,.xi)

with the CQAs (yi) through mathematical functions y¼f(x),

polynomial equation.

• Such relationships enable the determination of the most

influential factors (CPPs) and identification of optimum

factor settings leading to enhanced product performance and

assuring CQAs.

Execution of an experimental

design (Seven distinct phases)

Execution of an experimental design..

1. Setting solid objectives: during this step the Quality

Target Product Profile (QTPP) should be clearly

defined, using the knowledge base deriving from

scientific literature and technical experience, critically

evaluated under the Quality Risk Management approach

(Ex: Desired dissolution and disintegration time).

2. Selection of process variables (factors) and responses

(CQAs): This choice is justified based on the DoE

objective and should assure that the whole design is

executable and appropriate for satisfying its scope. This

means that factor types either numeric (Temperature) or

categorical (color) and their levels, i.e. their values

within the design, are selected accordingly.

Execution of an experimental design..

3. Selection of an experimental design: based on

DoE objectives, screening, characterization or

optimization of process and formulation,

several types of designs can be chosen, e.g.

factorial or Response. Decisions on

randomization, blocking, and replications

should be also addressed.

4. Execution of the design: The generated design

matrix should be accurately executed, assuring

that parameters not included in the design are

identified and kept constant.

Execution of an experimental design..

5. Checking that the data are consistent with the

experimental assumptions: Related to our hypothesis and

scientific logics.

6. Analyzing the results: ANOVA and graphical tools are very

helpful in identifying main effects of the significant factors

and interactions thereof. The results are evaluated not only on

a statistical basis but also versus the established theories and

practically lead to process knowledge.

7. Use and interpretation of the results: at this step, the

evaluation of the DoE outcome can support scientifically

sound decision making on following actions, such as

executing confirmatory runs, augmenting designs, or further

proceeding with scale up and technology transfer activities.

Types of DoE

Types of DoE

Factorial Response

surface

Box behnken Central

(BB) composite

Design(CCD

Factorial designs

• They refer to parameters that can be adjusted

independently of each other, such as

compaction force, temperature, and spraying

rate. In this case, the responses are functions of

factor levels as described in Equation

Factorial designs

• Factorial designs are mainly used for screening

of factors.

• Factorial design screens unnecessary factors

to include in BB and CCD, since they have

more runs and repetabilities.

• Process characterization focuses

on discriminating the critical

process factors from the useful

many, using screening designs,

which typically include two

level factorial designs, either full

or fractional.

Response surface designs

• Once screening is completed, the selected significant factors are

further studied using more comprehensive designs aiming at

process optimization, which refers to setting the most influential

factors at levels that enhance all product CQAs simultaneously.

• Such designs typically include at least three factor levels and can

support quadratic or higher order effects. Response Surface

Methodology (RSM) techniques constitute the main tool for such

applications.

• More repeats are done to get experimental error.

• These designs are most effective when there are less than 5

factors.

• Quadratic models are used for response surface designs and at

least three levels of every factor are needed in the design.

CCD

• Four corners of the square represent the factorial

(+/- 1) design points

• Four star points represent the axial (+/- alpha)

design points

• Replicated center point (Usually 06)

What is the effect of Alpha (distance from center point)

CCD Concept

Calculating α Value (Coded)..

Calculating α Value (Actual value)

Box-Behnken Designs (BBD)

• They are very useful in the same setting as the central

composite designs.

• Their primary advantage is in addressing the issue of where

the experimental boundaries should be, and in particular to

avoid treatment combinations that are extreme.

• By extreme, we are thinking of the corner points and the

star points, which are extreme points in terms of region in

which we are doing our experiment.

• The Box-Behnken design avoids all the corner points, and

the star points.

• One way to think about this is that in the central composite

design we have a ball where all of the corner points lie on

the surface of the ball. In the Box-Behnken design the ball

is now located inside the box defined by a ‘wire frame’ that

is composed of the edges of the box.

Box-Behnken Designs (BBD)

Comparison of factorial, BBD and CCD

(For 3 Factors)

Run/Exp Starch HPMC Avicel Starch HPMC Avicel Starch HPMC Avicel

1 + + + + + + – – 0

2 + + – + + – + – 0

3 + – – + – – – + 0

4 – – + – – + + + 0

5 – + + – + + – 0 –

6 – + – – + – + 0 –

7 + – + + – + – 0 +

8 – – –

9 – – – + 0 +

10 0 0 0 0 – –

11 0 0 0 0 + –

12 0 0 0 0 – +

13 0 0 0 0 + +

14 0 0 0 0 0 0

15 0 0 0 0 0 0

16 -α 0 0 0 0 0

17 +α 0 0 0 0 0

18 0 -α 0 0 0 0

19 0 +α 0

20 0 0 -α

0 0 +α

Factorial CCD Box behnken

Types of graphs in DoE

Contour plots

• Contour: Outline or boundary line/shape

• Contour plots display the 3-dimensional

relationship in two dimensions, with x- and y-

factors (predictors) plotted on the x- and y-scales

and response values represented by contours.

• The darker regions identify higher z (response)

values

Effect of lipid and surfactant concentrations

on entrapment efficiency

Lipid Conc (mg)

Note: The body and line colors can be changed

Surfactant conc (mg)

3D Plots

• 3D surface and 3D wireframe plots are graphs that you can use to

explore the potential relationship between two variables. The

predictor variables are displayed on the x- and y-scales, and the

response (z) variable is represented by a smooth surface (3D

surface plot) or a grid (3D wireframe plot).

3D surface plots can provide a clearer concept of the response

surface than contour plots.

Overlaid contour plot

• Used to visually identify an area where the

predicted means of one or more response

variables are in an acceptable range.

• Used to visually identify the feasible variables

for multiple responses for a model.

• The plot emphasizes the region (if any) where

all responses are within their bounds. This is

known as the feasible region.

Polynomial equation

In mathematics, a polynomial is an expression consisting of variables (also

called indeterminates) and coefficients, that involves only the operations of

addition, subtraction, multiplication, and non-negative integer exponents of

variables.

Polynomial equation

Y=mx+C

Slope Intercep

F, ANOVA relation with

polynomial equation

Overall

p value

Model Summary

• Suitable mathematical models of the mixture

design such as linear, quadratic and special

cubic models were analyzed by the software.

• Significance of the model was determined by

comparisons of statistical parameters such as

SD, R2, adjusted R2, predicted R2 and

predicted residual error sum of squares

(PRESS). The best model was decided on the

basis of higher values of adjusted R2 and

predicted R2.

Model Summary/fit summary

predicted residual error sum of

squares (PRESS) Value

• PRESS value should be small for the best

model.

• PRESS demonstrates the excellence of model

fitting. The optimized formulation was

prepared for further evaluation.

Power transformations

In statistics, a power transform is a family of functions that are applied to

create a monotonic transformation of data using power functions and power

parameter λ.

95.19/25.96=3.6668

Model selection

Near value to 1

Model significance and lack of fit

FIT Lack of FIT

(Significant) (Non-Significant)

Experimental vs. predicted

• Exp indicates experimental values.

Experimental results of first to last runs (total

runs) as mean ± S.D. (n = 3).

• Pred represents predicted values predicted

through Equations of respective polynomial

equations.

Practical DoE

Experiment

Solid lipid nanoparticles (SLNs) formulation of

diallyl disulfide drug

A solid lipid nanoparticle is typically spherical with an average

diameter between 10 and 1000 nanometers. Solid lipid

nanoparticles possess a solid lipid core matrix that can

solubilize lipophilic molecules. The lipid core is stabilized by

surfactants (emulsifiers)

Contents

Solid lipid

Surfactnat

Vehicle (water, ethanol)

Drug

Method: solvent diffusion method

1. Palmitic acid and drug were dissolved

completely in ethanol in a water bath at 70 C.

2. The resultant organic solution was quickly

3. dispersed into 50 mL of an aqueous phase

containing surfactant under continuous

mechanical agitation at 400 rpm in a water bath

at 70 C for 5 min.

4. The obtained pre-emulsion (melted lipid droplet)

was subsequently transferred into an ice bath to

solidify the lipid droplets then cooled to room

temperature till SLN dispersion was obtained.

Diagrammatic representation

17-run, 3-factor, 3-level Box–

Behnken

design was employed to optimize

the SLN formulation

Factors Responses

Surfactant (X1), Particle size

amount of lipid (X2) Entrapment efficiency

volume of solvent (X3)

Acknowledgement

• Dr. K. Gowthamarajan and Dr. Rajkumar

Malayandi.

• The Principal and Staff, Krupanidhi college of

pharmacy.

• The organizing team