Analytical QBD in Nutshell

Overview and Case Study….

Agenda

2 What is AQBD ?

3 Case Study

1 Who we are ?

BUSINESSSOLUTIONS TECHNOWLOGIES

INTERNATIONAL

The SSA Spectrum

Origin:

Founded in 1999 in India by Mr. NC Narayanan

Presence:

Global footprint across 20+ Countries with headquarters in Mumbai, India

Consulting Landscape:

Business transformation across hundreds of industries including• Automobile• Pharma• FMCG• Life Science• Banking & Finance• Insurance• Plastics• Telecommunication• Packaging

Contribution To The Industry:

• Groomed over 5000 Business excellence professionals

• Help transformed 100s of organizations worldwide

• Enabled industries to provide best in class products and services

• Contributed to sustainable economic development

SSA Leadership Team

Ganesh Iyer BE, MBA (INSEAD)

MD SSA Tech

Vijay Dhonde BE,

CEO

Sashi Iyer B.Com, MBA (INSEAD)

MD SSA India

NC BE, MS (Research)

Chairman

Naveen Narayanan

BE, MBA (USA), MSc (UK)

MD SSA Int

SSA’s Pharma Offerings

Pharma Excellence

R & D Excellence

QBD / DOEDFSS / NPI

Lean

CAPA Investigati

on

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Lean Six Sigma

CAPA Investigation

Statistical Analysis

for QA / QC

Select Pharma Clientele

And Many More

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Internationalization

Differentiators

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TOP

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STRATEGIES

INTO PROJECT

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IMPLEMENTATION

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– KNOWLEDGE

TRANSFER

Premium Training & Certification

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Sigma

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Design

DFSSCAPA

Value Stream

Mapping

Problem

Solving Tools

( 7 QC Tools )

Balanced

Scorecard

Publications

Under publication

in Aug 2016

R & D EXCELLENCE - QUALITY BY DESIGN (QbD)

- NPI LEAN

Analytical Quality By Design

(AQBD) Overview

What is QBD ?

As per ICH, 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.”

Analytical Target

Profile (ATP)

identification

Identification includes the selection of method

requirements such as target analytes, analytical

technique category, and product specifications.

Critical Quality

Attributes (CQA)

Identification

Select appropriate analytical technique for

desired measurement. Define method

performance criteria ( critical Quality attributes)

Risk Assessment

using FMECAAssess risks of method operating parameters

and sample variation.

Method

Development /

Validation using

DOE

Examine potential multi-variate interactions.

Understand method robustness and ruggedness

Establish Control

Strategy

Define control space and system suitability,

meet method performance criteria

Continuous

method

monitoring and

improvement

CMM is final step in AQbD life cycle; it is a

continuous process of sharing knowledge

gained during development and implementation

of design space

AQbD ( Analytical QBD ) Roadmap

What’s Needed

QBD Approach Basics of Statistics

Target measurement

based on product QTPP

and CQA

Select Techniques for desired

measurement

Risk Assessment

using FMECA

Method Development /

Validation using DOE

Establish Control Strategy

Continual Improvement

Basic Statistics

Definition of Quality

ISO 9000 Definition of Quality: “Customer satisfaction”

Statistical Definition of Quality:

Q = f (Hitting the Target ,Reducing the variation)

Measures Of Central Tendency

Numerical value that describes the central position of

the data

Represent different ways of characterizing the central

value of a collection of data.

Simply, it is the middle point of a distribution.

Also called as measures of location

Three of these measures are:

• Mean

• Median

• Mode

Measures Of Central Tendency

Let us take the following series :7,23,4,8,3,9,9

7+23+4+8+3+9+9Mean = = 63/7=9

7

3

4

7

8

9

9

23

Medianmiddle-most value

Average

Mode most repeated value (2 times)

Curve B

Curve C

Curve A

Mean of A,B,C

Variation

Measures Of Variation

Measures Of Dispersion (Variation)

It is the spread or variability of the data set.

Three types of measures of dispersion are

• Range

• Variance and

• Standard Deviation

Range

It is the difference between the highest and lowest

observed values

Range = Value of Highest observation - Value of Lowest

observation

Example:

Let us take the following series

3,6,4,9,5,6,7,1,6,3,2,9,8,6,4,2.

Max Value = 9 Min Value = 1

Range= 9 -1=8

Observations (x1,x2..xn) n - total no. of observations

Mean ( µ )

( i - observed item)

Deviation di = Xi- µ

*

*

*

*

*

*

*

*

*

*

*

*

*

**

*

*

*

*

*

*

*

*

* *

variable

frequency

d1

d2

d3

d4 d5

Variance And Standard Deviation

Null Hypothesis (H0) :

The hypothesis to be tested, usually an assumption of

status quo (equality, i.e.. no difference).

No Significant impact on CQA / Response ( P > 0.050 )

Alternate Hypothesis (Ha) :

The condition of equality assumed in the Null hypothesis is

not true.

There is a Significant impact on CQA / Response( P < 0.05)

USE ANOVA ( Analysis of Variance ) for analysis

Types of Hypothesis

Hypothesis Testing

Statistical Hypothesis:

There is no difference

between the old machines

and the improved one.

This is called the Null

Hypothesis (Ho)

• Real Life Hypothesis: The

newly modified machine will

reduce defects.

• This is called the Alternative

Hypothesis (Ha)

Ho:

Ha:

a

a

m m

m m

=

<

b

b

• We must show that the values we observed were so unlikely to come

from the same process, that Ho must be wrong.

X (Input)

y (Output)

• Tells the relationship between the

two variables X and Y

• X is the input variable on

x(horizontal)-axis

• Y is the output variable on the

Y(vertical)-axis

Use R-Sq Value for Model

Significance

Correlation & Regression Analysis

Project Case Study

Project Scenario

To optimise Related Substance Method that is Specific, Selective,

Reproducible and Robust and is acceptable to the plant and

regulatory agencies. This project is due for technology transfer at the

manufacturing location.

• To develop the robust and reproducible method for the quantification

of Unknown impurity eluting at the tail of XYZ Peak with USP

Resolution of NLT 3.5 between main peak and unknown impurity

along with resolution of all other known impurities.

– Resolution

– Retention time

Analytical Target Profile (ATP)

• General ATP for analytical procedures is as follows:

– Target analytes selection (API and impurities)• ICH Q3 and all other regulatory guidance explained the consideration of impurities in

the API synthetic route

– Technique selection (HPLC, GC, HPTLC, Ion Chromatography, chiral

HPLC, etc.)• Analytical test item and purpose of test are also important for selecting the technique

– Method requirements selection (assay or impurity profile or residual

solvents)• Method requirements can vary from one method to another. The common ATPs for

impurity profile by HPLC method

Critical Quality Attributes (CQA)

CQA for analytical methods includes method attributes and method

parameters. Each analytical technique has different CQA.

• HPLC (UV or RID) CQA are mobile phase buffer, pH, diluent, column

selection, organic modifier, and elution method.

• GC methods CQA are gas flow, oven temperature and program,

injection temperature, sample diluent, and concentration.

• HPTLC method CQA are TLC plate, mobile phase, injection

concentration and volume, plate development time, color development

reagent, and detection method

Note : Nature of impurities and DS can define the CQA

for analytical method development such as solubility, pH

value, polarity, charged functional groups, boiling point,

and solution stability

QUALITATIVE RISK

ASSESSMENT

Mapping the Linkage : Method

attributes and Method Parameters

M1

M2

Method Attributes

P1

P2

Method

Parameters

P3

CQA1

CQA2

Critical

Quality

AttributesCQA3

P2 might not be needed in

the establishment of Design

Space

Source: CDER & FDA

Purpose:Understand & Control the variability of

Method Attributes & Critical method

Parameters to meet CQA’s

Qualitative Risk Assessment Criteria

Red Color High Risks Risks that need to be addressed by actual

studies to establish acceptable ranges

Yellow Color Medium Risks

Possibility for a change in factor level to affect

method robustness but small variations in this

factor do not adversely affect pharmaceutical

quality

Green Color Low risks Factors having wide range of acceptability

• Risk estimation helps to identify what to study as a part of analytical

method development

• Evaluation of qualitative risk is ultimately linked back to potential harm to

the patient

Qualitative Risk Assessment : Prioritization Matrix

Attributes Resolution Justification

Column type Kept constant

Column make Kept constant

Particle size of the column has impact on Resolution

Column length has impact on Resolution

% Carbon loading

Column make is constant so %

Carbon loading is constant

Internal diameter of column Kept constant

Mobile phase buffer

Kept constant (potassium

dihydrogen phosphate)

Modifier used in Buffer and its qty has impact on Resolution

Mobile phase composition

has impact on Resolution as polarity

of solvents are diff

System make No impact on resolution

Detector sensitivity (UV/PDA)

This impacts the Limit of detection

and quantitation but will have no

impact on resolution

Qualitative Risk Assessment : Prioritization Matrix

Attributes Resolution Justification

UV Lamp hours

This impacts the Limit of detection

and quantitation but will have no

impact on resolution

Type of elution (Gradient/ Isocratic) Kept constant (Gradient)

Make of reagents Kept constant

Different lots of Drug Product tested Kept constant

Analyst Constant

Column temperature

Column temperature influences the

resolution between peaks

Flow rate of system

Flow rate changes the retention time

but this may or may not impact

resolution

pH of the mobile phase buffer

Has impact on Resolution as different

peaks will have different retention

time at diff pH due to their pKa values

Detection wavelength Kept constant at 240 nm

Lot number of the column Kept constant

Sample preparation technique

(Intact/crushed) Kept constant

Organic used in Mobile phase

has impact on Resolution as polarity

of solvents are diff

QUANTITATIVE RISK

ASSESSMENT

FMECA – Identify critical factors

To study the critical factors, the team conducted a risk

assessment using a FMECA.

Output from the risk assessment study was based on

risk score which was used to identify the critical factors

required for the study

Risk priority scores included an estimate for

detectability, severity and probability

Severity Scores RatingScore Severity Description of impact on patient if failure to meet acceptance

criteria

1 Minor No impact on patient

2 Major Some impact on product, but reversible

3 Critical Impact on product but not product life threatening (rejection)

4 Catastrophic High impact on product which is irreversible and potentially

wastage

Probability Scores RatingScore Probability of not meeting

acceptance

Comment

1 Extremely low Extremely low chance of occurring, never

seen

2 Low Low chance of occurring, but could happen

3 Medium Will happen

4 High High occurrence of failure

Detectability Scores RatingScore Detectability

scores

Comment

1 Very high Failure can be detected in unit operation

2 High Failure can be detected after unit operation and before end

product testing

3 Low Will happen

4 None High occurrence of failure

Risk ScoreRisk priority number range Risk rating

1 to 17 Low

18 to 35 Medium

36 to 64 High

RPN scores were grouped into high, medium, and low risk. The boundaries

for differentiation between high, medium, and low were established by the

risk assessment team for this exercise.

Quantitative Risk Assessment: FMECA

Process

Parameter or

Material Attribute

Effect/ Suggested contingency/

Comment

Probability

(P)

Severity

(S)

Control

(C) RPN

Risk

Rating

Column

temperature Justification 4 3 2 24 Medium

Flow rate of

system To be studied 2 2 2 8 Low

Detector

sensitivity

(UV/PDA) To be kept constant at 1 mL/minute 2 2 1 4 Low

Column make Kept Constant UV Detector of All-15 4 2 1 8 Low

Particle size of

the column

based on earlier expts the make

giving best resolution is selected and

is kept constant 4 4 3 48 High

Mobile phase

composition

(aqueous and

organic)

To be studied for the impact of change

in micron over resolution 4 4 3 48 High

Modifier used in

Buffer and its qty

decided to keep the composition as

constant and vary the type of organic

(Gradient program is constant) 3 3 2 18 Medium

Type of organic

used in Mobile

phase In this case no modifier used 4 4 3 48 High

pH of the mobile

phase buffer to be studied for ACN and methanol 4 4 3 48 High

Column length To be studied 4 4 3 48 High

Response ( CQA )

Response Unit Target Comment

Resolution Numbers NLT 3.5

Retention time Mins NMT 75

Analysis needs to be

completed before 75 mins

else the impurity is not

detected

Experimental Factors

Experimental Factor UnitLow

Level

High

LevelComments/ Remarks

Column temperature deg C 25 45

Currently selected column temperature

is 30 deg C and lower range selected at

room temp and higher range at 45 deg

is within the cut off temperature of 60

deg C

Particle size of the column micron 3 5

Particle size impacts separation, lower

& higher values selected based on the

availability

Column length cm 150 250

Column length impacts separation,

lower & higher values selected based

on the availability

Acetonitrial % 50 100

100% Methanol not selected as this

may increase the back pressure and

may go beyond operating range for 3µ

column

pH of the mobile phase buffer 2 7

Current pH of the mobile phase is 3.5

and range is selected based on the

optimum operating range of the column

Constant Factors

Constant Factors Unit Level

System make HPLC Alliance -15

UV Lamp hours Hours

Same instrument will be used so

this will remain constant

throughout

Calibration of the HPLC Yes

Calibrated instrument will be

used

Volume of Mobile phase prepared mL

1000 mL (same qty of mobile

phase will be prepared each set)

Analyst Sandeep Gawas

% Carbon loading 15%

Age of the column

New Column will be used for

study and same will be used for

all expt except for the change in

column

Column type Inertsil ODS 3 L1

Different lots of Drug Product tested Batch No. A-12

Internal diameter of column mm 4.6 mm

Lot number of the column

Make of reagents AR Grade

Merck and same Lot No. from

one bottle will be used

Water Quality HPLC Grade TKA of fourth floor

Age of the sample

3 Month old

sample CRT sample

Constant FactorsConstant Factors Unit Level

Column Equilibriation time Hour 1 Hour before the injection acquisition

Detection wavelength nm Fixed at 240 nm

Injection volume µL Fixed at 20µL

Type of elution (Gradient/ Isocratic) Gradient program fixed

Previous use of the column (Product/washing solvent) New column to be used for the expt

Mobile phase buffer

potassium dihydrogen orthophosphate

1.36 gm/L

Type of filter used for Mobile Phase filtration Millipore 0.45 µ

Order of addition of diluent Fixed as per STP

Sample concentration ppm Fixed at 600 ppm as per STP

Sample preparation technique (Intact/crushed)

Crushed Method to be followed as per

STP

Sample solution stability Days

Same Sample preparation to be used

for 8 Days

Sampling (Representative sample)

Sample to be used from single

container at the start of expt

Room temperature and Humidity

deg C & %

RH 25+/- 5 deg and 65+/-5% RH

Storage of the samples In Laboratory

pH meter A197

Balance used A200

Cylinder used for Volume measurement Class A

Same Cylinder to be used through out

the expt

Sonnicator A199

Sample preparation Filter 0.45 µ Make MDI, discard volume 1 mL

Design Selection Matrix

Parameter Fractional Factorial Half Fraction Full Factorial Mixture RSM

Type of

design Screening Basic Basic

Basic +

Optimization Optimization

No. of

Responses 1-2 1-2 1-3 2-3 2 or more

Factors More than 5 4-5 3-4 3-4 3-4

Expected

outcome

• Identify significant

factors with main

effect only

• Eliminate

insignificant factors

for next level of

experiment

• Identify the

main effects &

interaction

effect

• Get prediction

equation

• Curvature with

1 center point

• Identify the

main effects &

interaction

effect

• Get prediction

equation

• Curvature with

1 center point

• Identify design

space

• Identify the

main effects &

interaction

effect

• Optimum

proportion for

mixture

• Get prediction

equation

• Identify design

space

• Identify right

factor settings

for optimum

operation

• Identify design

space

Pre-requisite

None None • None

•Composition

type

•Quantitative

All should be

quantitative

Additional

elementsNone

• Include center

point to check

curvature

• Include center

point to check

curvature

• Augmentation

done to get

precise results • None

Screening Design : Resolution V

Std

Order

Run

Order

Center

Pt Block

Column

temp

Particle

Size

%

Acetonitrile

Column

Length

pH of

MP Resolution

Retention

Time

11 1 1 2 25 5 50 150 7 6.20 75.626

9 2 1 2 25 3 50 250 7 9.60 58.120

10 3 1 2 45 3 50 150 2 2.93 71.112

12 4 1 2 45 5 50 250 2 3.25 72.119

15 5 1 2 25 5 100 150 2 2.91 62.875

14 6 1 2 45 3 100 150 7 7.57 58.276

13 7 1 2 25 3 100 250 2 3.48 49.501

16 8 1 2 45 5 100 250 7 6.92 57.980

2 9 1 1 45 3 50 150 2 2.88 58.243

6 10 1 1 45 3 100 150 7 6.79 49.321

8 11 1 1 45 5 100 250 7 6.35 62.924

3 12 1 1 25 5 50 150 7 6.44 70.626

7 14 1 1 25 5 100 150 2 2.80 58.150

4 15 1 1 45 5 50 250 2 3.09 75.721

5 16 1 1 25 3 100 250 2 3.35 72.035

Residual Analysis : Sanity check of

Experimental Trials

As seen from the residual above, the residuals are normally distributed, with

random variation and within the limits of +/-2%. Hence, it can be

concluded that the experimental error is minimum

Initial results For Resolution: ANOVA

R-Sq = 99.59% R-Sq(pred) = 97.87% R-Sq(adj) = 99.13%

Analysis of Variance for Resolution (coded units)

Source DF Seq SS Adj SS Adj MS F P

Blocks 1 0.2233 0.2233 0.2233 4.40 0.074

Main Effects 5 82.868 82.868 16.5736 326.63 0.000

Column temperatu 1 1.1396 1.1396 1.1396 22.46 0.002

Particle size of 1 3.9105 3.9105 3.9105 77.07 0.000

%Acetonitrile 1 0.7613 0.7613 0.7613 15.00 0.006

Column length 1 2.8815 2.8815 2.8815 56.79 0.000

pH of the mobile 1 74.1752 74.1752 74.1752 1461.81 0.000

2-Way Interactions 2 3.5921 3.5921 1.796 35.40 0.000

Particle size of the

column*%Acetonitrile1 0.7613 0.7613 0.7613 15.00 0.006

Particle size of the column*pH

of the mobile phase buffer1 2.8308 2.8308 2.8308 55.79 0.000

Residual Error 7 0.3552 0.3552 0.0507

Total 15 87.0385 0.2233 0.2233

P Value for Linear , Interaction term is less

than 0.05 hence very significant

R-Sq = 99.99% R-Sq(pred) = 99.95% R-Sq(adj) = 99.98%

Analysis of Variance for Retention Time (coded units)

Source DF Seq SS Adj SS Adj MS

Blocks 1 0.03 0.026 0.076

Main Effects 4 1049.66 880.170 220.043

Column temperature 1 104.15 214.096 214.096

Particle size of the column 1 131.45 8.492 8.492

%Acetonitrile 1 195.16 307.751 307.751

Column length 1 618.90 500.927 500.927

2-Way Interactions 1 10.37 10.373 10.373

Particle size of the column*%Acetonitrile 1 10.37 10.373 10.373

Residual Error 7 0.14 0.140 0.020

Total 13 1060.20

Source F P

Blocks 1.33 0.287

Main Effects 11040.61 0.000

Column temperature 10742.26 0.000

Particle size of the column 426.08 0.000

%Acetonitrile 15441.38 0.000

Column length 25133.95 0.000

2-Way Interactions 520.45 0.000

Particle size of the column*%Acetonitrile 520.45 0.000

Residual Error

Total

Initial Result For Retention Time: ANOVA

P Value for Linear , Interaction term is less

than 0.05 hence very significant

Why RSM

Most surfaces are flatter further away from optimal settings.

• Use linear models when we are far from the optimums.

• Use quadratics to approximate the surfaces near the peaks

Curved line represents the response better as compared to the straight line

R00 0512

Response Surface Methodology uses a quadratic

model (that includes the squared term).

For one X the equation is:

This model produces parabolas such as:

The Quadratic Model : Curvature

2

1 2y a b x b x=

Result Summary for Resolution and Retention Time

• Based on the results for Resolution & Retention Time and considering

that no terms have been dropped for Resolution, it was decided to

add 2 more trials with centre point setting. It would help in

analysing the response better as well and identify the curvature if

present in the design. Since column length is a discrete factor and in

future it would be advisable to use the column length at 150, it was

decided to set it as constant. Also, based on team’s domain

knowledge and expertise particle size was set at 5 micron.

• Settings for centre points run are:

– Column Length – 150 (constant)

– Particle Size – 5 micron (constant)

– pH of mobile phase – 4.5 (center)

– Column temperature – 35 Deg (center)

– % Acetronitrile – 75% (center

Experiments with Centre Points

Std

Order

Run

Order

Center

Pt

Bloc

k

Colum

n temp

Particl

e Size

%

Acetonitril

e

Column

Length

pH of

MP Resolution

Retentio

n Time

11 1 1 2 25 5 50 150 7 6.20 75.626

9 2 1 2 25 3 50 250 7 9.60 58.120

10 3 1 2 45 3 50 150 2 2.93 71.112

12 4 1 2 45 5 50 250 2 3.25 72.119

15 5 1 2 25 5 100 150 2 2.91 62.875

14 6 1 2 45 3 100 150 7 7.57 58.276

13 7 1 2 25 3 100 250 2 3.48 49.501

16 8 1 2 45 5 100 250 7 6.92 57.980

2 9 1 1 45 3 50 150 2 2.88 58.243

6 10 1 1 45 3 100 150 7 6.79 49.321

8 11 1 1 45 5 100 250 7 6.35 62.924

3 12 1 1 25 5 50 150 7 6.44 70.626

7 14 1 1 25 5 100 150 2 2.80 58.150

4 15 1 1 45 5 50 250 2 3.09 75.721

5 16 1 1 25 3 100 250 2 3.35 72.035

17 17 0 1 35 5 75 150 4.5 2.70 57.980

18 18 0 1 35 5 75 150 4.5 2.60 56.900

Resolution analysis with centre point :Anova

Analysis: Resolution

Figure below shows the results of centre point analysis conducted on the experiment results for Resolution

R-Sq = 96.01% R-Sq(pred) = 78.73% R-Sq(adj) = 93.21%

Analysis of Variance for Resolution (coded units)

Source DF Seq SS Adj SS Adj MS F P

Blocks 1 2.3717 0.2233 0.2233 0.56 0.470

Main Effects 5 89.4256 82.8680 16.5736 41.93 0.000

Column temperature 1 1.1396 1.1396 1.1396 2.88 0.120

Particle size of the column 1 7.9974 3.9105 3.9105 9.89 0.010

%Acetonitrile 1 0.7613 0.7613 0.7613 1.93 0.195

Column length 1 5.3522 2.8815 2.8815 7.29 0.022

pH of the mobile phase buffer 1 74.1752 74.1752 74.1752 187.68 0.000

Curvature 1 3.2137 3.2137 3.2137 8.13 0.017

Residual Error 10 3.9523 3.9523 0.3952

Lack of Fit 9 3.9473 3.9473 0.4386 87.72 0.083

Pure Error 1 0.0050 0.0050 0.0050

Total 17 98.9632

Prioritized Terms for Resolution

Based on the results from the above analysis, it can be clearly seen that:

Curvature effect is present which urges for a RSM model to predict the

response

The factors identified as significant are: pH of mobile phase, Particle size,

and Column length

Analysis: Retention Time

S = 1.04461 PRESS = 45.2842

R-Sq = 99.23% R-Sq(pred) = 96.01% R-Sq(adj) = 98.56%

Analysis of Variance for rt of Imp A (coded units)

Source DF Seq SS Adj SS Adj MS F P

Blocks 1 9.02 0.03 0.026 0.02 0.880

Main Effects 5 1110.61 1052.03 210.406 192.82 0.000

Column temperature 1 96.22 263.03 263.032 241.04 0.000

Particle size of the column 1 75.81 1.76 1.763 1.62 0.239

%Acetonitrile 1 195.16 365.92 365.922 335.33 0.000

Column length 1 740.11 574.45 574.448 526.43 0.000

pH of the mobile phase buffer 1 3.31 2.37 2.366 2.17 0.179

Curvature 1 5.54 5.54 5.537 5.07 0.054

Residual Error 8 8.73 8.73 1.091

Lack of Fit 7 8.15 8.15 1.164 2.00 0.498

Pure Error 1 0.58 0.58 0.583

Total 15 1133.89

Retention Time with centre point :Anova

Particle size of the column

pH of the mobile phase buffer

Column temperature

%Acetonitrile

Column length

2520151050

Term

Standardized Effect

2.31

Pareto Chart of the Standardized Effects(response is rt of Imp A, Alpha = 0.05)

Based on the results from the above ANOVA table and Pareto Chart, we can conclude

Curvature effect is present for this response

The factors identified as significant are: Column length, % Acetonitrile, and

Column temperature

Prioritized Terms for Retention Time

Significant Factors

• Out of the five factors selected for the screening design, the factors

that are significant are:

• After discussion and analysis with the team, it was decided that the

following factors will be selected for optimization design:

– Column length (Experimental): Axial Low (150), Axial High (250)

– Column Temperature: Axial Low (25), Axial High (45)

– pH: Axial Low (2), Axial High (7)

– %ACN: Axial Low (100), Axial High (100)

– Particle Size (Constant): 5 micron

• A 30 trial Central Composite design has been suggested for

optimization

Response 1 - Resolution Response 2 – Retention time

pH Column temperature

particle size %ACN

column length Column length

Optimized RSM Design

Std Run Type Col Temp %ACN pH Column Length Resolution Retention Time

1 8 Factorial 29 60 3.0 150 2.57 65.572

2 18 Factorial 41 60 3.0 150 2.55 58.917

3 27 Factorial 29 90 3.0 150 2.51 58.510

4 9 Factorial 41 90 3.0 150 2.42 53.262

5 2 Factorial 29 60 6.0 150 3.60 65.751

6 28 Factorial 41 60 6.0 150 4.42 59.190

7 4 Factorial 29 90 6.0 150 3.79 58.690

8 13 Factorial 41 90 6.0 150 3.94 53.067

9 25 Axial 25 75 4.5 150 2.55 64.070

10 19 Axial 45 75 4.5 150 2.57 54.288

11 26 Axial 35 50 4.5 150 2.54 64.877

12 29 Axial 35 100 4.5 150 2.49 53.880

13 15 Axial 35 75 2.0 150 4.95 58.822

14 6 Axial 35 75 7.0 150 5.27 58.718

15 12 Center 35 75 4.5 150 2.48 58.849

17 17 Factorial 41 60 3.0 250 3.07 74.916

18 22 Factorial 29 90 3.0 250 3.13 73.542

19 3 Factorial 41 90 3.0 250 3.03 66.358

21 16 Factorial 41 60 6.0 250 6.02 75.060

22 20 Factorial 29 90 6.0 250 4.02 73.522

23 5 Factorial 41 90 6.0 250 5.62 66.799

24 1 Axial 25 75 4.5 250 3.11 82.293

25 24 Axial 45 75 4.5 250 3.08 67.955

27 7 Axial 35 100 4.5 250 3.20 67.102

28 30 Axial 35 75 2.0 250 3.29 74.211

29 23 Axial 35 75 7.0 250 6.29 74.483

30 14 Center 35 75 4.5 250 2.24 74.311

Final Model for Resolution : ANOVA

Reduced Model (Resolution)

S = 0.453124 PRESS = 9.91394

R-Sq = 88.33% R-Sq(pred) = 74.40% R-Sq(adj) = 84.62%

Analysis of Variance for Resolution

Source DF Seq SS Adj SS Adj MS F P

Regression 7 34.2053 34.2053 4.8865 23.80 0.000

Linear 4 17.0199 17.0199 4.2550 20.72 0.000

Col Temp 1 0.4272 0.4272 0.4272 2.08 0.163

%ACN 1 0.1285 0.1285 0.1285 0.63 0.437

pH 1 13.9586 13.9586 13.9586 67.98 0.000

Column Length 1 2.5056 2.5056 2.5056 12.20 0.002

Square 1 14.4146 14.4146 14.4146 70.20 0.000

pH*pH 1 14.4146 14.4146 14.4146 70.20 0.000

Interaction 2 2.7707 2.7707 1.3854 6.75 0.005

Col Temp*pH 1 0.9702 0.9702 0.9702 4.73 0.041

pH*Column Length 1 1.8005 1.8005 1.8005 8.77 0.007

Residual Error 22 4.5171 4.5171 0.2053

Total 29 38.7223

Resolution : Model Interpretation

As per the ANOVA table above, it can be seen

that:

– The squared term pH is significant for resolution

– There is an interaction between Column temp & pH,

and Column length & pH

– The R-Sq values are 88.83% which determines that the

model is good for prediction of the response

Reduced Model (Retention Time)

S = 0.221488 PRESS = 3.08824

R-Sq = 99.95% R-Sq(pred) = 99.82% R-Sq(adj) = 99.93%

Analysis of Variance for Retention Time

Source DF Seq SS Adj SS Adj MS F P

Regression 9 1711.39 1711.39 190.15 3876.21 0.000

Linear 4 1697.67 1351.38 337.84 6886.80 0.000

Col Temp 1 136.47 277.18 277.18 5650.10 0.000

%ACN 1 98.11 245.63 245.63 5007.11 0.000

pH 1 0.07 0.07 0.07 1.33 0.264

Column Length 1 1463.02 1273.40 1273.40 25957.74 0.000

Square 2 1.04 1.15 0.58 11.75 0.001

Col Temp*Col Temp 1 0.98 0.81 0.81 16.54 0.001

%ACN*%ACN 1 0.06 0.70 0.70 14.32 0.001

Interaction 3 12.68 12.68 4.23 86.17 0.000

Col Temp*%ACN 1 0.04 2.02 2.02 41.19 0.000

Col Temp*Column Length 1 4.15 7.35 7.35 149.86 0.000

%ACN*Column Length 1 8.49 8.49 8.49 173.02 0.000

Residual Error 17 0.83 0.83 0.05

Total 26 1712.22

Final Model for Retention Time : ANOVA

As seen from the ANOVA tables above, it can be

concluded that:

– There is a squared effect of Column Temperature and %

Acetonitrile on the response

– Interaction exists between Column Temperature & %ACN,

Column Temp & Column Length, and %ACN & Column

Length

– Also, the R-Sq value of the reduced model is 99.95% which

is in indicator that the predictability of the model will be

very good

Retention Time : Model Interpretation

Optimum Settings For Validation

• Parameters

Goal Lower Target Upper

• Resolution Maximum 4 6 6

• Retention Ti Minimum 55 55 75

• Optimum settings

– Col Temp = 45

– %ACN = 100

– pH = 6.6

– Column Lengt = 150

• Predicted Responses

– Resolution = 5.0123

– Retention Ti = 52.2091

Optimization Settings

CurHigh

Low0.71144D

New

d = 0.50614

Maximum

Resoluti

y = 5.0123

d = 1.0000

Minimum

Retentio

y = 52.2091

0.71144

Desirability

Composite

150.0

250.0

2.0

7.0

50.0

100.0

25.0

45.0%ACN pH Column LCol Temp

[40.0] [100.0] [6.60] [150.0]

Validation Trials

• Validation runs were conducted to test the settings identified using

DOE. The results after validation run were as follows:

Date Analyst LNB Ref ResolutionRt of

Impurity A

30-12-2013 Sandeep SL1030-71 5.32 59.733

01-01-2014 Varsha SL1030-73 5.52 59.781

02-01-2014 Varsha SL1030-75 5.60 59.849

Design Space

• The desired profile for both the responses are:

– Response Goal Lower Target Upper

– Resolution Maximum 4 6 6

– Retention Ti Minimum 55 55 75

Design Space

• Based on the above desired response values, the design space is

identified as below

Design Space

Design Space

The design space for both the parameters (highlighted in yellow in the above figure) has been identified which is

as given below

%ACN – 50 to 70

Column length – 150 constant as it is a discrete factor

Column temperature – 35 to 45

pH value – 6.2 to 6.8

THANK YOU