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Strategies for Formulations Development

Book Description

Strategies for Formulations Development: A Step-by-Step Guide Using JMP is based on the authors' significant practical experience partnering with scientists to develop strategies to accelerate the formulation (mixtures) development process. The authors not only explain the most important methods used to design and analyze formulation experiments, but they also present overall strategies to enhance both the efficiency and effectiveness of the development process.

With this book you will be able to:

  • Approach the development process from a strategic viewpoint with the overall end result in mind.
  • Design screening experiments to identify components that are most important to the performance of the formulation.
  • Design optimization experiments to identify the maximum response in the design space.
  • Analyze both screening and optimization experiments using graphical and numerical methods.
  • Optimize multiple criteria, such as the quality, cost, and performance of product formulations.
  • Design and analyze formulation studies that involve both formulation components and process variables using methods that reduce the required experimentation by up to 50%.
Linking dynamic graphics with powerful statistics, JMP helps construct a visually compelling narrative to interactively share findings that are coherent and actionable by colleagues and decision makers. Using this book, you can take advantage of computer generated experiment designs when classical designs do not suffice, given the physical and economic constraints of the experiential environment.

Strategies for Formulations Development: A Step-by-Step Guide Using JMP(R) is unique because it provides formulation scientists with the essential information they need in order to successfully conduct formulation studies in the chemical, biotech, and pharmaceutical industries.

Table of Contents

  1. Preface
  2. About This Book
  3. About These Authors
  4. Part 1: Fundamentals
  5. Chapter 1 Introduction to Formulations Development
  6. Overview
  7. 1.1 Examples of Formulations
  8. 1.2 How Formulation Experiments are Different
    1. Displaying Formulation Compositions Using Trilinear Coordinates
  9. 1.3 Formulation Case Studies
    1. Food Product
    2. Pharmaceutical Tablet Formulation
    3. Lubricant Formulation
    4. Pharmaceutical Tablet Compactability
  10. 1.4 Summary and Looking Forward
  11. 1.5 References
  12. Chapter 2 Basics of Experimentation and Response Surface Methodology
  13. Overview
  14. 2.1 Fundamentals of Good Experimentation
    1. Well-Defined Objectives
    2. High Quality Data
    3. How Many Formulations or Blends Do I Need to Test?
  15. 2.2 Diagnosis of the Experimental Environment
  16. 2.3 Experimentation Strategy and the Evolution of the Experimental Environment
    1. Screening Phase
    2. Optimization Phase
  17. 2.4 Roadmap for Experimenting with Formulations
  18. Part 2: Design and Analysis of Formulation Experiments
  19. Chapter 3 – Experimental Designs for Formulations
  20. Overview
  21. 3.1 Geometry of the Experimental Region
  22. 3.2 Basic Simplex Designs
  23. 3.3 Screening Designs
  24. 3.4 Response Surface Designs
  25. 3.5 Summary and Looking Forward
  26. 3.6 References
  27. Chapter 4 – Modeling Formulation Data
  28. Overview
  29. 4.1 The Model Building Process
  30. 4.2 Summary Statistics and Basic Plots
  31. 4.3 Basic Formulation Models and Interpretation of Coefficients
  32. 4.4 Model Evaluation and Criticism
  33. 4.5 Residual Analysis
  34. 4.6 Transformation of Variables
  35. 4.7 Models with More Than Three Components
  36. 4.8 Summary and Looking Forward
  37. 4.9 References
  38. Chapter 5 – Screening Formulation Components
  39. Overview
  40. 5.1 Purpose of Screening Experiments
  41. 5.2 Screening Concepts for Formulations
  42. 5.3 Simplex Screening Designs
  43. 5.4 Graphical Analysis of Simplex-Screening Designs
  44. 5.5 After the Screening Design
  45. 5.6 Estimation of the Experimental Variation
  46. 5.7 Summary and Looking Forward
  47. 5.8 References
  48. Part 3: Experimenting With Constrained Systems
  49. Chapter 6 – Experiments with Single and Multiple Component Constraints
  50. Overview
  51. 6.1 Component Constraints
  52. 6.2 Components with Lower Bounds
  53. 6.3 Three-Component Example
  54. 6.4 Computation of the Extreme Vertices
  55. 6.5 Midpoints of Long Edges
  56. 6.6 Sustained Release Tablet Development - Three Components
  57. 6.7 Four-Component Flare Experiment
    1. Computation of the Vertices
    2. Number of Blends Required
    3. Addition of the Constraint Plane Centroids
    4. Regions with Long Edges
    5. Evaluation of the Results
  58. 6.8 Graphical Display of a Four-Component Formulation Space
  59. 6.9 Identification of Clusters of Vertices
  60. 6.10 Construction of Extreme Vertices Designs for Quadratic Formulation Models
    1. Replication and Assessing Model Lack of Fit
  61. 6.11 Designs for Formulation Systems with Multicomponent Constraints
  62. 6.12 Sustained Release Tablet Formulation Study
  63. 6.13 Summary and Looking Forward
  64. 6.14 References
  65. Chapter 7 – Screening Constrained Formulation Systems
  66. Overview
  67. 7.1 Strategy for Screening Formulations
  68. 7.2 A Formulation Screening Case Study
  69. 7.3 Blending Model and Design Considerations
  70. 7.4 Analysis: Estimation of Component Effects
    1. Calculating Component Effects: Examples
  71. 7.5 Formulation Robustness
  72. 7.6 XVERT Algorithm for Computing Subsets of Extreme Vertices
    1. Eight-Component XVERT Design and Analysis
  73. 7.7 Summary and Looking Forward
  74. 7.8 References
    1. Plackett-Burman Designs for 12, 16, and 20 Runs
  75. Chapter 8 – Response Surface Modeling With Constrained Systems
  76. Overview
  77. 8.1 Design and Analysis Strategy for Response Surface Methodology
  78. 8.2 Plastic Part Optimization Study
  79. 8.3 Quadratic Blending Model Design Considerations
  80. 8.4 Example – Plastic Part Formulation
  81. 8.5 Example – Glass Formulation Optimization
  82. 8.6 Using the XVERT Algorithm to Create Designs for Quadratic Models
  83. 8.7 How to Use Computer-Aided Design of Experiments
  84. 8.8 Using JMP Custom Design
  85. 8.9 Blocking Formulation Experiments
  86. 8.10 Summary and Looking Forward
  87. 8.11 References
  88. Part 4: Further Extensions
  89. Chapter 9 - Experiments Involving Formulation and Process Variables
  90. Overview
  91. 9.1 Introduction
  92. 9.2 Additive and Interactive Models
  93. 9.3 Designs for Formulations with Process Variables
  94. 9.4 The Option of Non-Linear Models
  95. 9.5 A Recommended Strategy
  96. 9.6 An Illustration Using the Fish Patty Data
  97. 9.7 Summary and Looking Forward
  98. 9.8 References
  99. Chapter 10 – Additional and Advanced Topics
  100. Overview
  101. 10.1 Model Simplification
  102. 10.2<b xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:ns2="http://www.w3.org/2001/10/synthesis"> </b>More Advanced Model FormsMore Advanced Model Forms
    1. Common Alternative Model Forms
    2. Application of Alternative Models to the Flare Data
  103. 10.3 Response Optimization
  104. 10.4 Handling Multiple Responses
    1. The Derringer and Suich Approach
  105. 10.5 Multicollinearity in Formulation Models
    1. What Is Multicollinearity?
    2. Quantifying Multicollinearity
    3. The Impact of Multicollinearity
    4. Addressing Multicollinearity
  106. 10.6 Summary
  107. 10.7 References
  108. Index