The document provides an overview of analytical quality by design (AQbD) and a case study on optimizing an HPLC method for quantification of an unknown impurity in a drug product. Key aspects included: - Defining the analytical target profile (ATP) and critical quality attributes (CQA) for the method - Conducting a qualitative risk assessment to identify critical method parameters using a prioritization matrix - Performing a quantitative risk assessment using FMECA to calculate risk priority numbers and identify high, medium, and low risk factors - Selecting experimental factors (column temperature, particle size, etc.), response variables (resolution, retention time), and constant factors for the design of experiments study.

1. Analytical QBD in Nutshell
Overview and Case Study….

2. Agenda
2 What is AQBD ?
3 Case Study
1 Who we are ?

3. BUSINESS
SOLUTIONS TECHNOWLOGIES
INTERNATIONAL
The SSA Spectrum

4. 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

5. 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

6. SSA’s Pharma Offerings
Pharma
Excellence
R & D
Excellence
QBD / DOE
DFSS / NPI
Lean
CAPA
Investigati
on
Manufacturing
Excellence
Lean Six
Sigma
CAPA
Investigation
Statistical
Analysis
for QA / QC

7. Select Pharma Clientele

8. And Many More

9. China
Srilanka
Pakistan
Bangladesh
Singapore
Indonesia
Malaysia
Thailand
Hong Kong
Philippines
Vietnam
Saudi Arab
Oman
Kenya
South Africa
Nigeria
Ghana
Zimbabwe
Egypt
United Kingdom
INDIA
Internationalization

10. Differentiators
VALUE
BASED
CONSULTING
TOP
MANAGEMENT
ENGAGEMENT
CONVERTING
STRATEGIES
INTO PROJECT
LEADING
IMPLEMENTATION
INTERNALIZATION
– KNOWLEDGE
TRANSFER

11. Premium Training & Certification
Lean Six
Sigma
Quality by
Design
DFSSCAPA
Value Stream
Mapping
Problem
Solving Tools
( 7 QC Tools )
Balanced
Scorecard

12. Publications
Under publication
in Aug 2016

13. R & D EXCELLENCE
- QUALITY BY DESIGN (QbD)
- NPI LEAN

14. Analytical Quality By Design
(AQBD) Overview

15. 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.”

16. 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 FMECA
Assess 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

17. 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

18. Basic Statistics

19. Definition of Quality
 ISO 9000 Definition of Quality: “Customer satisfaction”
Statistical Definition of Quality:
Q = f (Hitting the Target ,Reducing the variation)

20. 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

21. Measures Of Central Tendency
 Let us take the following series :7,23,4,8,3,9,9
7+23+4+8+3+9+9
Mean = = 63/7=9
7
3
4
7
8
9
9
23
Median
middle-most value
Average
Mode most repeated value (2 times)

22. Curve B
Curve C
Curve A
Mean of A,B,C
Variation
Measures Of Variation

23. 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

24. 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

25. 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

26. 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

27. 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.

28. 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

29. Project Case Study

30. 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

31. 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

32. 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

33. QUALITATIVE RISK
ASSESSMENT

34. Mapping the Linkage : Method
attributes and Method Parameters
M1
M2
Method Attributes
P1
P2
Method
Parameters
P3
CQA1
CQA2
Critical
Quality
Attributes
CQA3
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

35. 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

36. 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

37. 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

38. QUANTITATIVE RISK
ASSESSMENT

39. 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

40. Severity Scores Rating
Score 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 Rating
Score 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

41. Detectability Scores Rating
Score 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 Score
Risk 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.

42. 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

43. 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

44. Experimental Factors
Experimental Factor Unit
Low
Level
High
Level
Comments/ 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

45. 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

46. Constant Factors
Constant 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

47. 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
elements
None
• Include center
point to check
curvature
• Include center
point to check
curvature
• Augmentation
done to get
precise results • None

48. 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

49. 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

50. 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*%Acetonitrile
1 0.7613 0.7613 0.7613 15.00 0.006
Particle size of the column*pH
of the mobile phase buffer
1 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

51. 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

52. 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

53. 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=  

54. 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

55. 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

56. 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

57. 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

58. 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

59. 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

60. 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

61. 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

62. 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

63. 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

64. 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

65. 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

66. 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

67. Optimization Settings
Cur
High
Low0.71144
D
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]

68. 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 Resolution
Rt 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

69. 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

70. 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

71. THANK YOU