Design of experiments formulation development exploring the best practices (doe)

Exploring the best practices of Design of experiments (DOE )
in pharmaceutical formulation development .

1. Design of Experiments Formulation development Exploring the Best Practices ©Modern &Global Pharma R&D Center – March- 2020 Maher Alabsi R&D Formulator at R&D Center- Global & Modern Pharma.

2. This presentation will cover Basics of DOE. How to Select an Experimental Design? Stages of DOE? Exercise ONE : Maher Alabsi R&D Center – March- 2020

3. Basics of DOE. Maher Alabsi R&D Center – March- 2020

4. History of DOE   : the Greek philosopher, as the Father of Scientific Method. 300 BC Aristotle 1880 OFAT 1925 1950 1960 Sir Ronald Fisher who actually started DOE the Father of DOE. famous person for RSM , Response Surface Methodology George Box Sir Ron Fisher's daughter, Joan Fisher, married George Box passed away in 2013 One Factor At a Time Japanese statistician Taguchi His contribution was in the robust design Aftermath of the world war II Japan was trying to develop better quality products Edison Fries Basics of DOE. Maher Alabsi R&D Center – March- 2020

5. History of DOE   : • The agricultural origins, 1918 – 1940s • Firstly introduced by Ronald Fisher & his co-workers showed how valid experiments could be conduct (1920s) • 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 • The modern era, economic competitiveness and globalization is driving all sectors of the economy to be more competitive. Basics of DOE. Basics of DOE. 1 st Gen. 2 nd Gen. Modern Maher Alabsi R&D Center – March- 2020

6. 1- Trail and Error method 2- On Factor at time (OFAT) The way Edison did in developing the light bulb . 1,000   unsuccessful Experiments 3- Design of Experiments (DOE) The most effective method is to apply a computer-enhanced, systematic approach to experimentation, one that considers all factors simultaneously. That approach is called design of experiments (DOE). Design of Experiments By Mark Anderson -SEPTEMBER © 1997 American Institute of Physics Experimental Design (DoE) approach One Factor at a Time (OFAT) approach Traditional Approaches Approaches to Experimentation Basics of DOE. Basics of DOE.

7. On Factor at time (OFAT) Widely taught. Straightforward. Design of Experiments (DOE) Design of Experiments By Mark Anderson -SEPTEMBER © 1997 American Institute of Physics Advantages Advantages and disadvantages of the OFAT & DOE Approaches Advantages Systematic: Thorough coverage of experimental “space” . Efficient: Able to establish solution with minimal resource (Time, Cost, Scope). Disadvantages Limited coverage of the experimental space . May miss the optimal solution . Fails to identify interaction. Inefficient use of resource. Minimum entry of Calculation 10   experiments. You may have to run experiments that you anticipate will “fail ” Disadvantages Basics of DOE. Basics of DOE.

8. What is Design of Experiments  ? Design of Experiment (DOE) is a powerful statistical technique for improving product/process designs and solving process / production problems . DOE makes controlled changes to input variables in order to gain maximum amounts of information on cause and effect relationships with a minimum sample size. When analyzing a process, experiments are often used to evaluate which process inputs have a significant impact on the process output and what the target level the inputs should be to achieve a desired result (output ). Design of Experiments (DOE) is also referred to as Designed Experiments or Experimental Design Basics of DOE. Basics of DOE. Maher Alabsi R&D Center – March- 2020

9. Why DOE   : • Reduce time to design/develop 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 . • Minimize  Consumption . Improve product quality. Reduce manufacturing costs. Increase speed to develop new medicines. Basics of DOE. Maher Alabsi R&D Center – March- 2020

10. Planning of experiment… Know the different scales of experiment: Scale Variability Control over external factors Lab Large Good Pilot Small Medium Production Very small Little 1kg 100kg Basics of DOE. Maher Alabsi R&D Center – March- 2020

11. DOE EXAMPLE : Basics of DOE. Maher Alabsi R&D Center – March- 2020

12. Terminology of DOE   o Factors : Factors are inputs to the process Factors can be classified as either controllable or uncontrollable variables . In this case, the controllable factors are Flour, Eggs, Sugar and Oven. Potential factors can be categorized using the Cause & Effect Diagram o Levels: Levels represent settings of each factor in the study Examples include the oven temperature setting, no. of spoons of sugar, no. of cups of flour , and no. of eggs . o Response: Response is output of the experiment In the case of cake baking, the taste, consistency, and appearance of the cake are measurable outcomes potentially influenced by the factors and their respective levels. Basics of DOE. Maher Alabsi R&D Center – March- 2020

13. • Factor (Inputs) One of the independent variables under investigation that can be set to a desired value • k  the number of factors or variables, the effects of which are to be estimated in an experiment • Level the numerical value or qualitative feature of a factor • Run (Experiments) the act of operating the process with the factors at certain settings • Treatment specific combination of the levels of all factors for a given test or run • Response (Outputs) the numerical result of a run Terminology of DOE DOE Vocabulary Basics of DOE. Maher Alabsi R&D Center – March- 2020

14. • Experimental error the amount of variability that may be expected in the experimental environment just by chance without any changing of the factors being investigated • Main Effect the average influence on the response as a variable changes levels • Interaction Effect the average difference in the effect on a response of one variable dependent upon the settings of another variable • MSFE Minimum Significant Factor Effect is the minimum absolute value of an effect which may be considered a significant result • Factorial experiment designed to determine the effect of all possible combinations across all levels of the factors under study • Fractional Factorial designed to examine k factors with a fraction of the runs required for a full factorial Terminology of DOE DOE Vocabulary Basics of DOE. Maher Alabsi R&D Center – March- 2020

15.  Confounding  the consequences of conducting a fractional factorial design  Blocking  a strategy for designing experiments to provide the ability to eliminate from the experimental error a contributor of variability that is known but not under investigation  Robust  the quality of a process or output being little affected by environmental or internal component variation  Noise  refers to variability, frequently uncontrollable or random variability in experimental design work  ANOVA  a mathematical procedure testing for significant differences between or among groups Terminology of DOE DOE Vocabulary Basics of DOE. Maher Alabsi R&D Center – March- 2020

16. FISHER’S FOUR DESIGN PRINCIPLES 1 . Factorial Concept - rather than one-factor -at-a-time 2 . Randomization - to avoid bias from lurking variables 3 . Blocking - to reduce noise from nuisance variables 4 . Replication - to quantify noise within an experiment Basics of DOE. FISHER’S The father of DOE Maher Alabsi R&D Center – March- 2020

17. Three R’s of DOE Randomization  sequence of experiments and/or the assignment of specimens to various treatment combinations in a purely chance manner R eplication  infers two or more runs were conducted under the same test conditions, each run following a new set-up or resetting of the conditions R epetition  obtaining more than one measurement or unit of output for each run Basics of DOE. Maher Alabsi R&D Center – March- 2020

18. أسس تصميم التجارب العلمية: التكرار Replication. التوزيع العشوائي Randomization. السيطرة على ألخطاء التجريبية Control Local. Basics of DOE. Maher Alabsi R&D Center – March- 2020

19. KEY TERMINOLOGY o Interaction o Randomization o Blocking o Replication. Basics of DOE. Maher Alabsi R&D Center – March- 2020

20. KEY TERMINOLOGY o Interaction: Sometimes factors do not behave the same when they are looked at together as when they are alone; this is called an interaction Interaction plot can be used to visualize possible interactions between two or more factors . Parallel lines in an interaction plot indicate no interaction The greater the difference in slope between the lines , the higher the degree of interaction However , the interaction plot doesn't alert you if the interaction is statistically significant Interaction plots are most often used to visualize interactions during ANOVA or DOE Basics of DOE. Maher Alabsi R&D Center – March- 2020

21. KEY TERMINOLOGY o Randomization : Randomization is a statistical tool used to minimize potential uncontrollable biases in the experiment by randomly assigning material , people , order that experimental trials are conducted , or any other factor not under the control of the experimenter When we run designed experiments, we will use experimental templates to set them up and to analyze them. We do not want to actually make the experimental runs in the order shown by the template; wherever possible, we want to randomize the experimental runs. Randomization of the run order is needed to minimize the impact of those variables outside of the experiment that we are not studying. Basics of DOE. Maher Alabsi R&D Center – March- 2020

22. KEY TERMINOLOGY o Blocking : Blocking is a technique used to increase the precision of an experiment by breaking the experiment into homogeneous segments (blocks or clusters or strata) in order to control any potential block to block variability Sometime we cannot totally randomize the experimental runs. Typically this is because it will be costly or will take a long time to complete the experiment. Blocking means to run all combinations at one level before running all treatment combinations at the next level . Experimental runs within blocks must be randomized . Basics of DOE. Maher Alabsi R&D Center – March- 2020

23. KEY TERMINOLOGY o Replication: Replication is making multiple experimental runs for each experiment combination . This is one approach to determining the common cause variation in the process so that we can test effects for statistical significance . Repetition of a basic experiment without changing any factor settings, allows the experimenter to estimate the experimental error ( noise ) in the system used to determine whether observed differences in the data are “ real ” or “ just noise ”, allows the experimenter to obtain more statistical power . Basics of DOE.

24. FD CCD e. Response Surface design (RSD) BBD RSD FFD TYPES OF DOE The most commonly designs used to obtain an optimized formulation commonly أهم التصاميم المستخدمة في الصناعات الدوائية : Basics of DOE.

25. How do you select an experimental design? FD PBD BBD OFAT RSD CCD

26. A design is selected based on The experimental objective . The number of  factors . 1- Comparative objective: If there are one or more factors to be examined and the main aim is to screen one important factor among other existent factors and its influence on the responses , then it infers to a comparative problem which can be solved by employing comparative designs. 2- Screening objective: The objective of this design is to screen the more important factors among the lesser ones. Under this objective we can select full or fraction factorial designs or Plackett -Burman design (PBD). Based on the Experimental objective Design of Experiments: Optimization and Applications in Pharmaceutical Nanotechnology Dwija Trivedi#, Veera Venkata Satyanarayana Reddy Karri#, Asha Spandana K M and Gowthamarajan-2105 Kuppusamy *

27. 3- Response surface method objective : When there is a need of investigating the interaction between the factors , quadratic effects or when the requirement involves the development of an idea in relation to the shape of response surface, in such situations, a response surface design is used . These designs are used to troubleshoot the process problems and to make a product more robust so as to not be affected by the non controllable influences. The BBD and CCD are the most popular designs under this category . Apart from all these criteria, the selection of experimental designs also depend on the number of factors to be entered , as each design has a limitation of entering the factors more or less of which will not be accepted . For instance, in BBD the minimum number of numeric factors to be entered is 3 and maximum number of numeric factors to be entered is 21 . Based on the Experimental objective Design of Experiments: Optimization and Applications in Pharmaceutical Nanotechnology Dwija Trivedi#, Veera Venkata Satyanarayana Reddy Karri#, Asha Spandana K M and Gowthamarajan-2105 Kuppusamy *

28. تصنيف و اختيار نوع التصميم في تصميم التجارب يعتمد على: ١- الهدف من التجربـــة ( experimental objective ). ٢- عدد العوامل المراد تغييرها ( number of  factors ). Design of Experiments: Optimization and Applications in Pharmaceutical Nanotechnology Dwija Trivedi#, Veera Venkata Satyanarayana Reddy Karri#, Asha Spandana K M and Gowthamarajan-2105 Kuppusamy * التصنيف على حسب الهداف ما إجراء التجارب : 1- بهدف المقارنه ( Comparative ): إذا كان هناك عامل واحد أو أكثر من العوامل التي يتعين فحصها والهدف الرئيسي هو فحص أحد العوامل المهمة من بين عوامل أخرى موجودة وتأثيرها على الاستجابات ، فإنه يتسبب في مشكلة مقارنة يمكن حلها باستخدام تصاميم مقارنة . 2- بهدف الفحص ( Screening ): الهدف من هذا التصميم هو فحص العوامل الأكثر أهمية بين العوامل الأقل أهمية. و تحت هذا الهدف يمكننا اختيار تصميمات كاملة( full or fraction factorial ) أو جزئية أو تصميم Plackett-Burman (PBD).

29. Design of Experiments: Optimization and Applications in Pharmaceutical Nanotechnology Dwija Trivedi#, Veera Venkata Satyanarayana Reddy Karri#, Asha Spandana K M and Gowthamarajan-2105 Kuppusamy * التصنيف على حسب الهداف ما إجراء التجارب : 3- منهجية استجابة سطح الهدف : عندما تكون هناك حاجة إلى دراسة التفاعل بين العوامل أو التأثيرات التربيعية أو عندما يتضمن المتطلب تطوير فكرة فيما يتعلق بشكل سطح الاستجابة ، في مثل هذه الحالات ، يتم استخدام تصميم سطح الاستجابة.  يتم استخدام هذه التصميمات لاستكشاف مشكلات العملية ولجعل المنتج أكثر قوة حتى لا يتأثر بالتأثيرات غير القابلة للتحكم. تعد BBD و CCD أكثر التصميمات شعبية ضمن هذه الفئة. بصرف النظر عن كل هذه المعايير ، يعتمد اختيار التصميمات التجريبية أيضًا على عدد العوامل التي سيتم إدخالها ، حيث أن كل تصميم له قيود على إدخال العوامل التي لن يتم قبولها بشكل أو بآخر.  على سبيل المثال ، في BBD ، يكون الحد الأدنى لعدد العوامل الرقمية المراد إدخالها هو 3 والحد الأقصى لعدد العوامل الرقمية المراد إدخالها هو 21.

30. Summary table for choosing an experimental design for comparative, screening, and response surface designs Response Surface Objective Screening Objective Comparative Objective Number of Factors - - 1-factor completely randomized design 1 Central composite   or Box-Behnken Full  or  fractional factorial Randomized block design 2-4 Screen  first to reduce number of factors Fractional factorial   or Plackett -Burman Randomized block design 5 or more Based on the number of  factors

31. التصنيف على عدد العوامل المرا د تغيرهها : Summary table for choosing an experimental design for comparative, screening, and response surface designs Response Surface Objective Screening Objective Comparative Objective Number of Factors - - 1-factor completely randomized design 1 Central composite   or Box-Behnken Full  or  fractional factorial Randomized block design 2-4 Screen  first to reduce number of factors Fractional factorial   or Plackett -Burman Randomized block design 5 or more Maher Alabsi R&D Center – March- 2020

32. The most widely used designs in pharmaceutical applications are RSM and FD , both of which serve different purposes. The best criteria F ewer runs (Experiments). Saves time . Saves money . Optimization Strategies of Experimental Designs Design of Experiments: Optimization and Applications in Pharmaceutical Nanotechnology Dwija Trivedi#, Veera Venkata Satyanarayana Reddy Karri#, Asha Spandana K M and Gowthamarajan-2105 Kuppusamy * 1. Response Surface design ( RSM) Full factorial design ½ Fraction factorial design 2. Factorial design (FD)

33. Optimization Strategies of Experimental Designs Factorial designs (FD) These designs help in screening the critical process parameters which can affect the process and product with the help of interactions between the factors. Two level factorial design (2-21 factors): Full and fractional design will explore many factors by setting each on two levels i.e. higher and lower. This design is helpful in identifying the most significant factors among many others that are involved in design. Min Run, Res V factorial designs (6-50 factors ): These class of designs containing the minimum number of trials to estimate all main effects and all two-factor interactions (Resolution V) while maintaining treatment balance within all factors. Min Run, Res IV factorial designs (5-50 factors ): These class of designs which has a minimum run (or with 2 extra runs), and resolution IV. This design allows all main effects to be estimated, clear of two-factor interactions. The two- factor interactions will be aliased with each other. Design of Experiments: Optimization and Applications in Pharmaceutical Nanotechnology Dwija Trivedi#, Veera Venkata Satyanarayana Reddy Karri#, Asha Spandana K M and Gowthamarajan-2105 Kuppusamy *

34. Optimization Strategies of Experimental Designs Irregular fraction designs (4-11 factors): It allows the estimation of main effects and two factor interactions by involving lesser number of runs and more power of resolution than the normal fractional factorial design. General factorial designs (1-12 factors ): These designs are used to design an experiment where each factors can have different number of level (2-999). The layout of the design generated by this design will include all possible combination of the factors level. Optimal design (2-30 factors): This design is similar to general factorial design which may produce a design with more number of runs. The number of runs generated depends on the model you want to estimate. These designs should be used carefully, taking into account subject matter knowledge to decide if the design is acceptable. Design of Experiments: Optimization and Applications in Pharmaceutical Nanotechnology Dwija Trivedi#, Veera Venkata Satyanarayana Reddy Karri#, Asha Spandana K M and Gowthamarajan-2105 Kuppusamy *

35. Optimization Strategies of Experimental Designs Plackett -Burman designs (up to 31 factors): These are highly confounding designs. The main useful application of this design is for validation where one can hope to find no or very little effect on the responses due to any factors. Taguchi orthogonal array designs (up to 63 factors) Response surface design (RSD) RSM quantifies the relationship between several explanatory variable and one or more responses. It helps in finding the ideal process settings to achieve optimal performance. Central composite design (CCD): The most popular design used in response surface methodology. Regular central composite designs have 5 levels for each factor, although this can be modified by choosing alpha value 1.0, a face centered CCD. The face-centered design has only three levels for each factor. This design is insensitive to missing data and has been created to estimate quadratic model. Design of Experiments: Optimization and Applications in Pharmaceutical Nanotechnology Dwija Trivedi#, Veera Venkata Satyanarayana Reddy Karri#, Asha Spandana K M and Gowthamarajan-2105 Kuppusamy *

36. Optimization Strategies of Experimental Designs Box behnken design (BBD ): This is also a popular design among response surface designs; this design has 3 levels for each factor and generates a lesser number of trials in comparison to central composite design. This design is sensitive to the missing data and provides strong coefficient estimates near the center of the design space (where the presumed optimum is), but weaker at the corners of the cube (where there are no design points). One factor at a time (OFAT ): This design is used where only one continuous factor is meant to be estimated. Categoric factor can be added to this design for each categoric combination design is duplicated. User defined: This design is user friendly and allows selecting all classes of candidate points as per requirement; vertices, centre of edges etc. One can select the number of factors and levels and can add constraints to limit the factor space to reasonable combination. One can even select the model desired to fit by using this design. Design of Experiments: Optimization and Applications in Pharmaceutical Nanotechnology Dwija Trivedi#, Veera Venkata Satyanarayana Reddy Karri#, Asha Spandana K M and Gowthamarajan-2105 Kuppusamy *

37. Optimization Strategies of Experimental Designs Mixture design This design is applied when the factors are proportion of blend. Combined designs Combined designs are optimal and user defined. While working with categoric factor in addition to continuous factors or when there are constraints on experiment optimal design, this is used to minimize the number of trials. Studies on Different Types of Nanoparticles Optimized Using DoE. Design of Experiments: Optimization and Applications in Pharmaceutical Nanotechnology Dwija Trivedi#, Veera Venkata Satyanarayana Reddy Karri#, Asha Spandana K M and Gowthamarajan-2105 Kuppusamy *

38. Examples of DoE application in medicinal product development and pharmaceutical processes Design of experiments (DoE) in pharmaceutical development - Stavros N. Politis , Paolo Colombo, Gaia Colombo & Dimitrios M. Rekk -2017

39. Examples of DoE application in medicinal product development and pharmaceutical processes Design of experiments (DoE) in pharmaceutical development - Stavros N. Politis , Paolo Colombo, Gaia Colombo & Dimitrios M. Rekk -2017

40. The Response surface design (RSD) and factorial designs (FD) are the most commonly employed designs in pharmaceutical industry . [1 ] - The Box- behnken design (BBD) is the most popular among all r esponse surface methodology (RSM) because it requires fewer runs in 3 factor experimental design than all other RSM designs [1] Which design used in pharmaceutical industry? Wang F, Chen L, Jiang S, He J, Zhang X, Peng J, J Liposome Res, 2014, 24, 171. Optimization Strategies of Experimental Designs [8] There are various designs and plots are available in DoE to obtain an optimized formulation. The most widely used designs in pharmaceutical applications are RSM and FD , both of which serve different purposes. The best criteria to select a design is that which can give an optimized formulation in fewer runs that in turn saves time as well as money.

41. Examples of DoE application in medicinal product development and pharmaceutical processes Design of experiments (DoE) in pharmaceutical development - Stavros N. Politis , Paolo Colombo, Gaia Colombo & Dimitrios M. Rekk -2017

42. لدراسة تأثير العوامل يقوم الباحث بتجربة كل عامل على حدة حيث يجري تجربة بمستويات ( Levels )عامل واحد مع تثبيت باقي العوامل و أخرى بعامل آخر .... الخ . وتسمى مثل هذه التجارب بالتجارب ذات العامل الواحد ( OFAT )( One factro experiment ) غير انه هناك بعض المشاكل في التجارب ذات العامل الواحد مثل ارتباط عامل وبعامل آخر ويعتبر هذه التأثيرات المشركة بالتفاعلات ( Interactions ). وقد تكون هذه التفاعلات ذات أهمية كبيرة في التجربة بحيث لا يمكن تجاهلها. ومن هنا فمن الأفضل ادخال كل العوامل في تجربة واحدة. وبهذه تتضح الأهداف الرئيسة للتجارب العاملية وهي تحديد أهم العوامل وأفضل مستوياتها, واكتشاف ما إذا كان هناك تفاعلات بينها. Factorial Designs (FD ) ا لتجارب العاملية Prof. Dr. Mesut Güneş ▪ Ch . 13 Design of Experiments

43. Stages of DOE Maher Alabsi R&D Center – March- 2020

44. Designed experiments are usually carried out in five stages: Planning Planning Optimization Robustness testing Verification. Stages of DOE Maher Alabsi R&D Center – March- 2020

45. Planning It is important to carefully plan for the course of experimentation before embarking upon the process of testing and data collection . A thorough and precise objective identifying the need to conduct the investigation, an assessment of time and resources available to achieve the objective and an integration of prior knowledge to the experimentation procedure are a few of the goals to keep in mind at this stage . A team composed of individuals from different disciplines related to the product or process should be used to identify possible factors to investigate and determine the most appropriate response(s) to measure. A team-approach promotes synergy that gives a richer set of factors to study and thus a more complete experiment. Carefully planned experiments always lead to increased understanding of the product or process . Maher Alabsi R&D Center – March- 2020

46. Screening Screening experiments are used to identify the important factors that affect the system under investigation out of the large pool of potential factors. These experiments are carried out in conjunction with prior knowledge of the system to eliminate unimportant factors and focus attention on the key factors that require further detailed analyses. Screening experiments are usually efficient designs requiring a few executions where the focus is not on interactions but on identifying the vital few factors . Maher Alabsi R&D Center – March- 2020

47. Optimization Once attention is narrowed down to the important factors affecting the process, the next step is to determine the best setting of these factors to achieve the desired objective . Depending on the product or process under investigation , this objective may be to either maximize , minimize or achieve a target value of the response . Maher Alabsi R&D Center – March- 2020

48. Robustness Testing Once the optimal settings of the factors have been determined , it is important to make the product or process insensitive to variations that are likely to be experienced in the application environment. These variations result from changes in factors that affect the process but are beyond the control of the analyst . Such factors as humidity, ambient temperature, variation in material , etc. are referred to as  noise factors . It is important to identify sources of such variation and take measures to ensure that the product or process is made insensitive (or robust ) to these factors . Maher Alabsi R&D Center – March- 2020

49. Verification This final stage involves validation of the best settings of the factors by conducting a few follow-up experiment runs to confirm that the system functions as desired and all objectives are met . Maher Alabsi R&D Center – March- 2020

50. Exercise ONE Maher Alabsi R&D Center – March- 2020

51. Exercise ONE : Pharm Tech, May 1998 “A Systematic Formulation Optimization Process for a Generic Pharmaceutical Tablet.” Hwang, R.; Gemoules , M; Ramlose , D. and Thomasson, C. Maher Alabsi R&D Center – March- 2020

52. Objective “ … optimizing an immediate release tablet formulation for a generic pharmaceutical product .” Develop a generic tablet with a disintegration time of 6-12 minutes, 5 minute dissolution of 40-60% and 45 minute dissolution of greater than 90%. Maher Alabsi R&D Center – March- 2020

53. API particle size Small Large API % 5 % 10 % Lactose MCC ratio. 1:3 3:1 MCC particle size Small Large MCC density Low High Disintegrant . Cornstarch, Glycolate Disintegrant % 1 % 5 % Talc 0% 5 % Mag Sterate 0.5% 1% Treatments (Factors) (Inputs) 2 levels Upper Lower limit limit 9 Factors Maher Alabsi R&D Center – March- 2020

54. Responses (Outputs ) Blend homogeneity Compression force %RSD Ejection force Tablet weight %RSD Tablet hardness Tablet friability Tablet disintegration time Tablet dissolution at 5 minutes Tablet dissolution at 45 minutes Maher Alabsi R&D Center – March- 2020

55. Statistical Design 9 factors each at two levels 16 runs Design is a 2 9-5 fractional factorial Resolution III Maher Alabsi R&D Center – March- 2020

56. The best formulation: API 7.14% Fast-Flo lactose 60.74% Avicel PH-302 30.37% Talc 1% Mag Stearate 0.75 % Maher Alabsi R&D Center – March- 2020

57. Conclusion “The formulation was successfully scaled up to a 120 kg batch size and the manufacturability and product quality were confirmed .” “This study has demonstrated the efficiency and effectiveness of using a systematic formulation optimization process … “ Maher Alabsi R&D Center – March- 2020

58. Maher Alabsi R&D Center – March- 2020 Maher Alabsi R&D Formulator at R&D Center- Global & Modern Pharma. https://www.linkedin.com/in/maher-al-absi-5579009b/