Engler and Prantl system of classification in plant taxonomy
Factorial design M Pharm 1st Yr.
1. Presented By
Mr. Sanket Chordiya
M.Pharm Ist Sem.
Pharmaceutics
Guided By
Dr. C. R. Kokare
M.Pharm , Ph. D.
Pharmaceutics
Sinhgad Technical Education Society’s
Sinhgad Institute of Pharmacy, Narhe.
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2. Overview of Presentation
Introduction
Various Terminologies
Factorial Design
Fractional Factorial Design
Software Used
Application
Key References
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3. Introduction
“Optimization is the act of achieving the best possible result
under given circumstances.”
The goal is either to minimize effort or to maximize benefit.
Various design used in optimization like factorial design,
fractional factorial design… etc.
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What is Optimization?
4. 4
Why their is need of Optimization?
Trial & Error
OFAT Approach
Knowledge of formulator & Probability
Expensive & Time Consuming
Unpredictable & Non-Reproducible
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Due to Conventional approach,
5. 5
Based on Statistical method also known as Design of
Experiment.
Less time consuming.
Predictable & Efficient.
Require fewer experiment to achieve an optimum
formulation.
Reduce the error. 9/29/2019
Systematic approach ;
6. Various Terminologies
Quality by Design (QbD)
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Systematic approach to development that
begin with predefined objective &
focused on product & process
understanding based on sound science &
Quality risk management.
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7. 7 Quality Target Product Profile
(QTPP)
It is summary of the quality characteristics of drug
product that will be achieved to ensure the desired
quality, taking into account safety and efficacy of drug
product.
To ensure the final product output remain within acceptable
quality limits. CQA are used. 9/29/2019
Critical Quality Attributes
(CQA)
9. 9
Factor : It is assigned Variable , i.e. independent variables
influencing the response.
E.g. Concentration, temperature.
Levels : Values assigned to the factor.
E.g. Low(-1), high(+1).
Response : Is the measured property of the process
E.g. dissolution rate, Hardness of tablet.
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10. 10
Effects : Change in response caused by varying levels.
Interaction : Overall effect of two or more variables.
Runs : Experiment conducted according to the selected
design.
E.g. 22 = 4 Runs
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11. Factorial Design
Introduced by “Sir Ronald Fisher” in 1926.
It involves studying the effect of each factor at each level.
The Number of experiment in factorial design is given as;
X
n
= K
Where X represents the number of level. ,n is the number
of factors. K is the Response.
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12. 12
Types Of Factorial Design
Full Factorial Design
Fractional Factorial Design
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13. 13
Full Factorial Design
FFD involve studying the effect of all possible factors at
various levels, including the interactions, with the total
number of runs.
Generally Factorial experiment with two level factors are
used.
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Merits Of full Factorial Design
More efficient than OFAT experiment.
Allow additional factors to be examined at no additional
cost.
Allow to detect interaction which is not possible in
OFAT.
Less Time Consuming.
18. 18
If there are k factors, each at Z levels, a full factorial design
has Zk runs.
(Levels)factors
[ Z
k
]
2 factors, 2 levels- 2
2
FD = 4 runs
3 factors, 2 levels- 2
3
FD = 8 runs
2 factors, 3 levels- 3
2
FD = 9 runs
3 factors, 3 levels- 3
3
FD = 27 runs
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19. 19
The simplest form of factorial design is the 2
3
factorial design.
e.g. 23 Factorial design of Sustained release Metformin
tablet
Ingredients Category
Microcrystalline cellulose Diluent
Ethyl cellulose Sustained Release polymer
PVP-K30 Binder
Magnesium Stearate Lubricant
Aerosil Glidant
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Table 1.2 All inactive Ingredients
20. 20
Among all inactive ingredients, microcrystalline cellulose, ethyl
cellulose, PVP K30 were taken as the independent factors.
Sr. No. Notation Independent factors
(mg/tab)
Levels
-1 +1
1. X1 Microcrystalline cellulose 80 100
2. X2 Ethyl cellulose 5 10
3. X3 PVP K30 3 5
Table 1.3 : Independent factors & their levels
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The experimental plan for a three-factor, two-level 2
3
design is as
follows;
Experiment
Microcrystalline
Cellulose
(mg/tab)
Ethyl-
Cellulose
(mg/tab)
Polyvinyl
Pyrrolidone
(mg/tab)
Drug release
(%) 12 hr.
1 80 5 3 80
2 100 5 3 78
3 80 10 3 65
4 100 10 3 64
5 80 5 5 72
6 100 5 5 71
7 80 10 5 62
8 100 10 5 60
Table 1.4 Statistical Data of Experiment
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The 2
3
factorial design show seven effect, i.e. three individual factor
effects, three two way interaction (X1X2,X1X3 & X2X3) & one three
way interaction (X1X2X3).
The magnitude of the main effect can be calculated by taking mean of
all experiment with high level of factor (X1,X2,X3) minus mean of all
experiment with low level of same factor.
For e.g. Effect of factor X1 = 1/4{(78+64+71+60)-(85+65+72+62)}
= 1/4 {273-279}
= -1.5
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Conclusion
Table 1.6 Magnitude of main effect & interaction of the factors.
Factor and Interaction Results
X1 -1.5
X2 -15
X3 -22
X1X2 0
X2X3 +8.0
X1X3 0
X1X2X3 +5.0
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Fractional Factorial Design
As the number of variables increases, experimental runs
also increases, To overcome these issue in a methodical
approach, Fractional Factorial Design is introduced.
It expressed as,
Xn-x ,
where X = No. of Levels
n = No. of Factors
x = Degree of Fractionation
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Resolution
Resolution III : (1+2)
Main effect aliased with 2-order interaction
Resolution IV : (1+2 or 2+2)
Main effect aliased with 3-order interactions and 2-factor
interactions aliased with other 2 factor interactions.
Resolution V : (1+4 or 2+3)
Main effect aliased with 4-order interactions and 2-factor
interactions aliased with 3-factor interactions.
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Application of Factorial Design
Formulation & Processing
Medicinal Chemistry
Study of Pharmacokinetic Parameter
Clinical Chemistry
Allow large number of variables to be
investigated in a compact trial.
37. Key References
1. Amit G. Mirani and Vandana B. Patravale, 2016. Design of
Experiments, Basic Concepts and its application in Pharmaceutical
Product Development, University College London. 118-127.
2. Gaurav Gujral, Devesh Kapoor, Manish Jaimini, 2018. An updated
Review on Design of Experiment (DOE) in Pharmaceutical, Journal
Of Drug Delivery & Therapeutics 147-152.
3. Dnyandev G. Gadhave & Chandrakant R. Kokare, 2019.
Nanostructured lipid carriers engineered for intranasal delivery of
teriflunomide in multiple sclerosis: Optimization and in vivo studies,
Drug Development and Industrial Pharmacy, 1-12.
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Key References
44
4. Rahul Kumar Garg and Indrajeet Singhvi., 2015. Optimization
Techniques: An overview for formulation development. Asian
Journal of Pharmaceutical Research. 217-221.
5. Singh B., Gupta, R.K. and Ahuja, N., 2006. Computer-
assisted optimization of pharmaceutical formulations and
processes. Pharmaceutical Product Development (Ed. NK
Jain), CBS Publishers, New Delhi. 273-318.
6. https://www.statease.com (Accessed 20th Sept 2019).
