Design of Experiments (DOE)

Design of Experiments

1. Seminar on Design Of Experiments (DoE) In Pharmaceutical Development Presented by: Imdad H. Mukeri M . Pharm ( Pharmaceutics) Center of Pharmaceutical Sciences, IST, JNTUH, Hyderabad-500085 10/25/2022 1 CPS, IST, JNTUH

2. TABLE OF CONTENS Introduction History of DoE Terminology & Evaluation of DoE DOE: Why to use it ? DOE: How to use it ? Steps and Guidelines for Planning and Conducting DoE Types Of DoE with example Advantages of DoE Uses of DoE Conclusion References 10/25/2022 2 CPS, IST, JNTUH

3. INTRODUCTION Design of Experiments (DOE) mathematical methodology used for planning and conducting experiments as well as analyzing and interpreting data obtained from the experiments. Simply means to make as Perfect, Effective, or Functional as possible. The design of experiments ensures Formulation quality, Saves time, Labor And money 10/25/2022 3 CPS, IST, JNTUH

4. History Of DOE   The agricultural origins 1918 – 1940s R. A. Fisher & his co-workers Profound impact on agricultural science Factorial designs, ANOVA The first industrial era, 1951 – late 1970s Box & Wilson, response surfaces Applications in the chemical & process industries The second industrial era, late 1970s – 1990 Quality improvement initiatives in many companies CQI and TQM were important ideas and became management goals Taguchi and robust parameter design, process robustness 10/25/2022 4 CPS, IST, JNTUH

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7. Evolution of DOE 10/25/2022 7 CPS, IST, JNTUH

8. DOE: Why to use it ? Reduce time to design/develop new products & processes Improve performance of existing processes Improve reliability and performance of products Achieve product & process robustness Perform evaluation of materials, design alternatives, setting component & system tolerances 10/25/2022 8 CPS, IST, JNTUH

9. DOE: How to use it ? VARIABLE / FACTOR 1. Independent Variables Quantitative : Numeric values and continuous. e.g. Time, Temperature, Amount of polymer, Plasticizer, Superdisintegrants etc. such as 1%, 2%, 3% concentration Qualitative : (also known as categorical variables) e.g. Type of polymer, component or machine. 2. Dependent Variables : Characteristics of the finished drug product are Dependent Variables Or Response Variables. e.g. Drug release profile, Percent drug entrapment, Pellet size distribution, Moisture uptake etc. 10/25/2022 9 CPS, IST, JNTUH

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11. Generally, factors that cause product variation can be categorized in three main groups External/environmental (such as temperature, humidity and dust) Internal (wear of a machine and aging of materials) Unit to unit variation (variations in material, processes and equipment) 10/25/2022 11 CPS, IST, JNTUH

12. General practical steps and guidelines for planning and conducting DOE are listed below State the objectives Response variable definition Determine factors and levels Determine Experimental design type Perform experiment Data analysis Practical conclusions and recommendations 10/25/2022 12 CPS, IST, JNTUH

13. Types Of DOE Mixture designs (used for formulation characterization and optimization) Factorial (process) designs (each factor can be adjusted independently of the others) Full factorials designs (all possible combinations of factor levels.) Fractional factorial designs (Represent a part of the relevant full design) 10/25/2022 13 CPS, IST, JNTUH

14. 1. Mixture designs Mixture designs are used for formulation characterization and optimization, applied when the overall amount of a composition is defined, e.g. for a tablet with fixed mass. Mixture designs component proportions are not independent to each other. Mixture-process designs are used to investigate interactions between formulation and process variables. Applications in pharmaceutical technology include determination of diluent proportions in solid formulations, selection of appropriate solvent-cosolvent combinations in liquid forms, etc Responses = f(component proportions) ........ Eq. (1) 10/25/2022 14 CPS, IST, JNTUH

15. 2. Factorial (process) designs Parameters that can be adjusted independently of each other, such as compaction force, temperature, spraying rate, etc. In this case, the responses are functions of factor levels as described in equation (2). Change two or more things at a time Process parameters are intentionally and simultaneously varied according to a predermined matrix of factor levels combinations. Their main difference from mixture designs is that each factor can be adjusted independently of the others. Responses = f(factor levels) ...........Eq. (2) 10/25/2022 15 CPS, IST, JNTUH

16. Factors are usually represented by capital letters (A, B, C …), while their Lower levels : (-) or -1 Intermediate level : (0). higher levels: (+) or +1, are respectively. This is obviously a coded representation of the levels, which however corresponds to actual values of the parameters, according to equation 3. Eq. (3) Xcoded=(Xactual –Xmean) / [(Xhigh-Xlow)/2].....Eq n (3) 10/25/2022 16 CPS, IST, JNTUH

17. 10/25/2022 CPS, IST, JNTUH 17 Factorials level Description One Factorial Level One factorial experiments look at only one factor having an impact on output at different factor levels. The factor can be qualitative or quantitative. In single factor experiments, ANOVA models are used to compare the mean response values at different levels of the factor Two level factorials Full 2 level factorials can support linear models, thus they are not capable for addressing more complex phenomena, requiring higher order models. Three level factorial designs Three-level designs are useful for investigating quadratic effects. The three-level design is written as a 3 k  factorial design

18. 3. Full factorials Full factorials include all possible combinations of factor levels. The number of experiments required is provided by equation 4. Change many things at a time. For example, a full two-level factorial for three factors requires 2 3 = 8 experiments. Number of experiments=Levels (Factors) .......Eq n (4) 10/25/2022 18 CPS, IST, JNTUH

19. 4. Fractional factorial designs Fractional factorial designs represent a part of the relevant full design, typically ½ or ¼ … of the full factorial. They are typically used when the number of factors exceeds 4-5, for screening purposes. Main limitation is related to confounding or alias of main effects and interactions. 10/25/2022 19 CPS, IST, JNTUH

20. Response Surface Analysis Method (RSM) RSM explores the relationships between several explanatory variables and one or more response variables. The method was introduced by G. E. P. Box and K. B. Wilson in 1951. The main idea of RSM is to use a sequence of designed experiments to obtain an optimal response. Response surface analysis is an off-line optimization technique. Usually, 2 factors are studied; but 3 or more can be studied. 10/25/2022 CPS, IST, JNTUH 20 With response surface analysis, we run a series of full factorial experiments and map the response to generate mathematical equations that describe how factors affect the response.

21. Central Composite Designs (CCDs) A Box-Wilson Central Composite Design, commonly called 'a central composite design,' contains an imbedded factorial or fractional factorial design with center points that is augmented with a group of 'star points' that allow estimation of curvature. If the distance from the center of the design space to a factorial point is ±1 unit for each factor, the distance from the center of the design space to a star point is | α| > 1. The precise value of  α  depends on certain properties desired for the design and on the number of factors involved. 10/25/2022 CPS, IST, JNTUH 21 Star points represent new extreme values (low and high) for each factor in the design Fig: Central Composite Design for Two Factors

22. Examples of DoE application in medicinal product development and pharmaceutical processes. AREA APPLICATION Applied DoE TYPE Oral Drug Delivery Dispersible tablets development Several factorial experiments at 2-3 factors, 2-3 levels Immediate release tablet platform fractional factorial design Fast dissolving pellets 2 5 – 1 fractional factorial design, 5 factors Gastroretentive dosage form 3-level-3-factor, box-behnken design Inhalation Drug Delivery Powder for inhalation (formulation and process development) Half-fractional factorial design with 5 factors at two levels Table- 1: Examples of DoE application 10/25/2022 22 CPS, IST, JNTUH

23. Examples of DoE application in medicinal product development and pharmaceutical processes. Injections Formulation for parenteral nutrition (development) Mixture design Nanopharmaceutics Solid Lipid Nanoparticles for Inhalation (process development) Two level full factorial design Table- 2: Examples of DoE application 10/25/2022 23 CPS, IST, JNTUH

24. DOE Software 1. Design Expert Software The analysis of the designs is carried out using Design Expert Software (Statease, version 9.0.1, Minneapolis, US). 10/25/2022 CPS, IST, JNTUH 24 Starting from the analysis of the full two level factorial with center points, graphical tools such as half normal and Pareto plots are helpful in identifying the most influential effects

25. 2. Analysis of Variance (ANOVA) The formal statistical analysis is carried using ANOVA and the relevant data for the two responses are shown in Tables. 8 10/25/2022 CPS, IST, JNTUH 25

26. Uses of DOE It is a multipurpose tool that can be used in various situations for identification of important input factors (input variable) and outputs (response variable). 1. Comparison: This is one factor among multiple comparisons to select the best option that uses t‒test, Z‒test, or F‒test. 2. Variable screening: These are usually two-level factorial designs intended to select important factors (variables) among many that affect performances of a system, process, or product. 10/25/2022 26 CPS, IST, JNTUH

27. Uses of DoE 3. Transfer function identification: if important input variables are identified, the relationship between the input variables and output variable can be used for further performance exploration of the system, process or product via transfer function. 4. System Optimization: the transfer function can be used for optimization by moving the experiment to optimum setting of the variables. On this way performances of the system, process or product can be improved. 5. Robust design: Deals with reduction of variation in the system, process or product without elimination of its causes. 10/25/2022 27 CPS, IST, JNTUH

28. Advantages of DOE Maximize process knowledge, with the minimum use of resources. Provide accurate information, in the most efficient way possible. Identify factor interactions. Characterize the relative significance of each factor. Allow for the prediction of the process behavior within the design space. Establish a solid cause and effect relationship between CPPs and CQAs. Allow for multiple response optimization. As pharmaceutical products exhibit several CQAs, the latter require simultaneous optimization. Make the product or process more robust 10/25/2022 28 CPS, IST, JNTUH

29. Conclusion In conclusion, Design of experiments are statistical thinking and knowledge management very useful tools in pharmaceutical development. DoE promote operational excellence within the QbD framework. Moreover, the evolution of the manufacturing science in the pharmaceutical sector. 10/25/2022 29 CPS, IST, JNTUH

30. References N. Politis S, Colombo P, Colombo G, M. Rekkas D. Design of experiments (DoE) in pharmaceutical development. Drug development and industrial pharmacy. 2017 Jun 3;43(6):889-901. Durakovic B. Design of experiments application, concepts, examples: State of the art. Periodicals of Engineering and Natural Sciences (PEN). 2017 Dec 28;5(3). https://www.slideshare.net/UpendraKartik/design-of-experiments-75405493 ( Accessed on 2022/09/26) Fang KT, Lin DK. Ch. 4. Uniform experimental designs and their applications in industry. Handbook of statistics. 2003 Jan 1;22:131-70. Christensen LB, Johnson B, Turner LA, Christensen LB. Research methods, design, and analysis. N. Politis S, Colombo P, Colombo G, M. Rekkas D. Design of experiments (DoE) in pharmaceutical development. Drug development and industrial pharmacy. 2017 Jun 3;43(6):889-901. 10/25/2022 30 CPS, IST, JNTUH

31. Thank you for your attention Any question ? 10/25/2022 Center of Pharmaceutical sciences, IST, JNTUH 31