The ppt provides detailed and comprehensive description regarding OVAT and Design of Experiment

1. OVAT AND DESIGN OF EXPERIMENT(DoE)
EXPERIMENTAL DESIGNS, SCREENING DESIGNS
Course code – GE 611
Presented by Harshada Ithape
PE/2021/306
Department of Pharmaceutics

2. Content
• Why we design a Experiment?
• OVAT
• DoE
• Process terminologies
• Types of Design
• Steps in DoE
• Case Study
• Application of DoE in pharmaceuticals
• Conclusion
• References

3. Why we design a Experiment?
• The approach of designing experiments is a multistep process and requires
defining the objective of experimental study, identification of influential
factors and response variables, establishment of cause-and-effect
relationship, mathematical modelization, and selection of the optimum
formulation.
• In simplest form, an experimental design aims at predicting the outcome by
introducing a change of conditions on the basis of planned objectives.

4. OVAT(One Variable At a Time)
• It is method consists of selecting a
starting point, or baseline set of levels, for
each factor, and then successively varying
each factor over its range with the other
factors held constant at the baseline level.
• After all tests are performed, a series of
graphs are usually constructed showing
how the response variable is affected by
varying each factor with all other factors
held constant.

5. Paracetamol Immediate Release Tablet
Ingredients Role in formulation Amount in each
tablets in mg
Comments
Paracetamol Analgesic 250 -
Microcrystalline
cellulose
Diluent 205 Concentration not very
critical however can
impact cost
Povidone Binder 20 Concentration can
impact dissolution and
impurities as well
Croscarmellose sodium Super disintegrant 15
Colloidal silicon dioxide Glidant 5 -
Magnesium stearate Lubricant 5 Can impact dissolution
Total(mg/tablet) 500

6. OVAT
Ingredients Optimum
Paracetamol 250.00
Microcrystalli
ne cellulose
205.00
Povidone 20.00
Crosscarmell
ose sodium
15.00
Collidal
silicon
diooxide
5.00
Magnesium
stearate
5.00
Total 500.00
T1 T2 T3
250.00 250.00 250.00
215.00 195.00 205.00
10.00 30.00 20.00
15.00 15.00 15.00
5.00 5.00 5.00
5.00 5.00 5.00
500.00 500.00 500.00
T4 T5 T6
250.00 250.00 250.00
210.00 200.00 205.00
20.00 20.00 20.00
10.00 20.00 15.00
5.00 5.00 5.00
5.00 5.00 5.00
500.00 500.00 500.00
T7 T8 T9
250.00 250.00 250.00
207.50 202.50 205.00
20.00 20.00 20.00
15.00 15.00 15.00
5.00 5.00 5.00
2.50 7.50 5.00
500.00 500.00 500.00

7. DoE(Design of Experiment)
• Experimental Design is a structured, organized method for
determining the relationships between factors affecting a
process and the output of that process.
• It is also known as “Design of Experiments” (DoE). In other
words, the latter is the means of achiveing process
knowledge, through the establishment of mathematical
relationships between process inputs and its outputs.

8. Advantages of Design of Experiment
• Provides more accurate and precise information.
• Multiple potential factors can be studied simultaneously, systematically and quickly –
Factors interaction can be studied.
• DoE can be helpful for screening of variables. It means out of various factors, significant
factors can be determined.
• Variables with least and/or highly significant effect of responses can be determined with
the help of statistical analysis.
• DoE makes the process and product more robust.
• Quantifies the relationship between factors (input) and responses (output) .
• Can helps to reduce the production cost .

9. Process in relation to DoE

10. Process terminology
• Factor( Input, variable): An independent variable.This is what we control and
change in an experiment. It is referred as x1, x2,x3… or A,B,C….
• Factor setting or level :A particular value for a factor
Concentration of PVP – 15 mg/tablet or 3.0%w/w
Formulation : Concentration of binder, disintergrant, lubricant
Process: Blender rpm, Blending time
• K: Number of factors or variables used in a particular study
• Factor type: Categorical or Numerical
Categorial: Type of Lactose- Cheap vs Costly
Numerical : Above examples

11. Process terminology – Factorial design
• 2k factorial designs are experiments where all factors have only two levels- typically
high and low
• The number of runs for full factorial design is denoted as n=2k where k is number of
factors
• For 3 factors the number of runs are =23=8
• Similarly 3k factorial design have three levels high , medium, low and the number of
runs for 3 factors will be =33=27

12. Run Factor A
Type of Microcrystalline cellulose
Factor B
Conc. Of disintegrant mg/tablet
Factor C
Conc. Of Binder
1 Type 2 20 30
Experimental design: The complete set of runs that we plan to do
Run Factor B Factor C
1 10 10
2 20 10
3 10 30
4 20 30
Experimental run : A particular combination of factor settings

13. Types of designs
Experimental design
type
No of factors Levels Purpose Applications
Plackett Burman
Design
7 to 32 2 Screening Used when large number of
factors are to be studied and
fractional factorial design is not
applicable
Fractional Factorial
Design
3 to 6 2 or 3 Screening or
optimization
Used when large number of
factors are to be studied
Full Factorial Design < 6 2 or 3 Screening or
optimization
To examine main and
interactive effects
Central Composite
Design
2 to 6 3 or 5 Optimization Lesser experimental runs than
FFD/FD
Box Behnken Design 3 to 5 3 or 5 Optimization Non linear effects can be
studied

14. Steps in DoE

15. Case study-
Plackett- Burman
Design

16. Result and Discussion
CONCLUSION- PB screening design was used to determine the significant main effects
among these. Crosspovidone was found to be most influencing variable. Also HPMC and
Microcrystalline Cellulose showed an effect on drug release. Hence this factor can be
considered for optimization .

17. Taguchi’s Orthogonal Array Design
• In most of the design focus is on main factors and uncontrolled variables is not
considered. However in case of Taguchi’s method both controllable and
uncontrolled variables are considered.
• In Taguchi’s method a robust design is developed wherein process response
will not only stay within specifications but also centered always at the
target(mean)

18. DoE in
Pharmaceuti
cal Industry
Formulatio
n
Developme
nt
Quality
Assurance
Regulatory
Quality
Control
Analytical
Method
Developme
nt
Business
developm
ent

19. Conclusion
• The use of DoE and statistical analysis has been widely applied to formulation
development.
• Using DoE allows formulation scientists to evaluate all formulation factors in a
systematic way and timely manner to optimised the formulation and
manufacturing process.
• When the formulation and manufacturing process of a pharmaceutical
product are optimised by a systematic approach, the scale-up and production
can be very efficient because of the robustness of the formulation and
manufacturing process.

20. References
• Design of experiments (DoE) in pharmaceutical development Stavros N. Politis, Paolo Colombo,
Gaia Colombo & Dimitrios M. Rekkas
To link to this article: http://dx.doi.org/10.1080/03639045.2017.1291672
• Application of Design of Experiments (DoE) in Pharmaceutical Product and Process Optimization
Sarwar Beg*, Suryakanta Swain† , Mahfoozur Rahman‡ , Md Saquib Hasnain§ , Syed Sarim
Imam¶ *
• DESIGN OF EXPERIMENTS IN PHARMACEUTICAL DEVELOPMENT Abhishek S. Dhoot,1 Gasper J.
Fernandes,1 Anup Naha,1 Mahalaxmi Rathnanand,1 and Lalit Kumar1
• International Journal of Pharmaceutical Sciences and Research APPLICATION OF PLACKETT–
BURMAN DESIGN OF EXPERIMENTS IN THE IDENTIFICATION OF “MAIN FACTORS” IN THE
FORMULATION OF DABIGATRAN ETEXILATE MESYLATE IMMEDIATE-RELEASE TABLETS Ashwini
Gawade * 1, Ashwin Kuchekar 1 and Sanjay Boldhane 2
DOI link: http://dx.doi.org/10.13040/IJPSR.0975-8232.12(12).6587-92
• Workshop on Design of Experiment in pharmaceutical R&D- A primer for academia by Dr.Anil
kumar Gandhi

21. • Application of design of experiments to pharmaceutical formulation
development by Richard Hwang* and Robert M. NoackInt. J. Experimental
Design and Process Optimisation, Vol. 2, No. 1, 2011
• Design of Experiments (DoE) applied to Pharmaceutical and Analytical Quality
by Design (QbD) Isa Martins Fukuda1 , Camila Francini Fidelis Pinto1 , Camila
dos Santos Moreira1 , Alessandro Morais Saviano1 , Felipe Rebello Lourenço1
https://doi.org/10.1590/s2175-97902018000001006