According to Quality by Design (QbD) concept, quality should be built into product/method during pharmaceutical/analytical development. Recently, Design of Experiments (DoE) have been widely used to understand the effects of multidimensional and interactions of input factors on the output responses of pharmaceutical products and analytical methods.
5. Analytical
Development
Designof
Experiment
DOEBasics
A systematic cost-efficient way to develop
inderstanding of your product or process
and use it for optimization.
Establish cause and effect
Study interactions between variables
Analyze the results
Build a mathematic model
10. Analytical
Development
Designof
Experiment
Selectionofdesign
Fractional factorial designed experiments can be used to
efficiently screen variables to determine which have the
greatest impact on the output, whereas full factorial designed
experiments will help to reveal significant interactions amongst
variables (USP 42 (5) Stimuli to the revision process:
Analytical Control Strategy)
Fractional factorial design: some combinations of levels have
been removed.
– Reduces the total number of measurements required in a study -
still providing useful information.
Full factorial design: all combinations of levels are studied
12. Analytical
Development
Designof
Experiment
Selectionofdesign
DoE Method Variables Reference
2-level RP-HPLC-PDA
analytical protocol
for zileuton
MP composition, flow
rate, Buffer
Ganorkar et
al (2017)
Plackett-
Burrman
HPLC Quantitation
of Ambroxol
Hydrochloride
and Roxithromycin
concentration of organic
phase, MP, pH, flow rate,
column temperature
Solanki et al
(2017)
Box-
Behnken
HPTLC
Densitometry
Method for
Robustness
Determination of
Apremilast
mobile phase (MP)
composition, saturation
time, development
distance, activation time
of plate
Chaudari et
al (2017)
2-level HPLC for Related
Substances of
Omeprazole
MP, pH buffer, flow rate,
column temperature
Chauhan et
al (2013)
Plackett-
Burrman
capillary
electrophoretic
enantioresolution of
salbutamol using
dermatan sulfate
chiral selector, pH,
voltage
Furlanetto et
al (2000)
Some literature action..
17. Analytical
Development
Designof
Experiment
Example:ApplicationonHPLCmethod
STEP 2: ANOVA
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.757898
R Square 0.574409
Adjusted R
Square 0.202018
Standard
Error 50908.39
Observatio
ns 16
ANOVA
df SS MS F Significance F
Regression 7 2.8E+10 4E+09 1.542487 0.2776523
Residual 8 2.07E+10 2.59E+09
Total 15 4.87E+10
Coefficients
Standard
Error t Stat P-value Lower 95% Upper 95%
Lower
95.0%
Upper
95.0%
Intercept 4545613 12727.1 357.1602 4.23E-18 4516264.447 4574962 4516264 4574962
X Variable
1 20479.94 12727.1 1.60916 0.146248 -8868.802979 49828.68 -8868.8 49828.68
X Variable
2 -12028.8 12727.1 -0.94513 0.372257 -41377.55298 17319.93 -41377.6 17319.93
X Variable
3 -14590.3 12727.1 -1.1464 0.284761 -43939.05298 14758.43 -43939.1 14758.43
X Variable
4 -6640.06 12727.1 -0.52173 0.615993 -35988.80298 22708.68 -35988.8 22708.68
X Variable
5 18475.69 12727.1 1.451681 0.18465 -10873.05298 47824.43 -10873.1 47824.43
X Variable
6 -16521.8 12727.1 -1.29816 0.230405 -45870.55298 12826.93 -45870.6 12826.93
X Variable
7 17707.19 12727.1 1.391298 0.201604 -11641.55298 47055.93 -11641.6 47055.93
18. This is a free preview copy of the original training “Analytical Development Design of Experiment”
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