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FACTORIAL DESIGN
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  1. FACTORIAL DESIGN DEPARTMENT OF PHARMACEUTICS AND PHARMACEUTICAL TECHNOLOGY, L.M.COLLEGE OF PHARMACY, AHMEDABAD. Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  2. Research Process: Require intelligence planning and approach. Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  3. Research scientist Against the Person who Invest in Stock Market,.,. Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  4. Traditional Trend • Randomized fashion of research • Required More number of trials.., • Take more time • Cant predict the extension… i.e. cant say what happen if such change are made within that particular system. www.parasshah.weebly.com

  5. No of trials are more.,.,. e.g. In process of Extrusion Spheronization, Three attributes… • Binder (%) • Granulation time (Min) • Spheronization speed (RPM) Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  6. Man • Money • Material • Time And still for output ??? We are not sure !! Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  7. Optimization • Designing experiments to yield the most information from the fewest runs • Reduce the number of trial to minimum but in logical manner • Carry out research in systematic way • Identify the characteristic you want in your product Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  8. Factorial design • Fractional Factorial design • Simplex lattice design • Plackett-burman design • Central composite design • Constrained mixture design • Box Behnken design • Face centered cubic design (FCC) Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  9. Basic Terminology: In scientific language those attributes are called as variables.. Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  10. Significant variable • More important.. • Go on changing level.. • E.g. Polymer Conc, • Granulation time • Insignificant variable • Not that much importance • you can keep it at constant level also.. • E.g. Effect of lubricant on floating tablet • Independent variable may positive or negative result on • to the dependent variable Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  11. Also Known as Response variable. • Its output of our experiment. • Not limit for dependent variable. This variable depends on independent variable. • Keep as many as you wish. e.g. Angle of repose, Disintegration time, Friability, Hardness. Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  12. What is level?? Level of factor are the values or designations assigned to the factors. LEVEL • In General Factor and Level are keep below 3. • No of trial depend upon... • No of independent variable • No of level • So choosing the independent variable and no of level that is the crucial step in optimization… Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

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  14. Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  15. First order or additive linear model: Y= 10.40 + 2.08 X1 + 1.92 X2 Complete model: Y= 10.40 + 2.08 X1 + 1.92 X2 + 0.28 X1X2 Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  16. Full factorial design for 3 factors(2^3) Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  17. Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  18. Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  19. Complete synergistic model: Y= 3.45 -0.675 X1 – 0.375 X2 + 0.225 X3 + 0.05 X1X2 - 0.4 X1X3 – 0.65 X2X3 + 0.175 X1X2X3 Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  20. 32 FULL FACTORIAL DESIGN Why 3 level factorial design??? GELLAN GUM-ALGINATE BEADS Percentage entrapment efficiency (Y1), swelling ratio (Y2) and T90 (time taken for 90% drug to be released) (Y3) was selected as dependant factors Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  21. Design matrix for 32 full factorial design Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  22. The polynomial equations for three responses are shown below: • Y1= 96.48+1.63X1+2.75X2-0.83X1X2 • Y2= 6.15+0.83X1+0.50X2 • Y3= 7.50+1.98X1+0.96X2+0.83X22 Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  23. It’s a graphical representation of results. • From the Full model equation, eliminate insignificant terms gives refined equation or reduced equation. • This refined equation or Full equation is transferred in form of graphs. That is known as contour plot or response surface methodology plot. Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  24. 10 7 99 6.5 9 98 6 7. 97 8 5.5 95 96 6 94 5 93 Y3 Y2 Y1 Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  25. Maximum efficiency in estimating main effects. • Identification of interaction. • Conclusions apply to a wide range of conditions. • Maximum use of the data. • Saves time and money. Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  26. References • Gareth A. Lewis, Didier Mathieu, Roger Phan-Tan-Luu. Pharmaceutical Experimental Design. Marcel Dekker 1999. • Sanford Bolton, Charles Bon. Pharmaceutical statistics, Practical and Clinical applications. Drugs and Pharmaceutical Sciences Vol-135. • Pritesh C. Mistry. Development Of Gellan Gum Alginate Bead of Aceclofenac, 2006; LMCP THESIS. Vipul Patel/ Pharmaceutics and Pharmaceutical Technology

  27. Many THANKS for YOUR Attention Dziękuję dhanya-waad Дякую go raibhmaithagat bedankt tesekkürle Merci Thank yu köszi tack så mycket mange tak Thank you faleminderit hvala Danke díky kiitos takk Mulţumesc Obrigada nandri anugurihiitosumi Ευχαριστώ Grazie תודה dhanya-waad Muchas gracias köszönöm tack děkujivam ačiû Terima Kasih شكرًا Спасибо salamat Vipul Patel/ Pharmaceutics and Pharmaceutical Technology