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%.

*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.

- Preface
- About This Book
- About These Authors
- Part 1: Fundamentals
- Chapter 1 Introduction to Formulations Development
- Overview
- 1.1 Examples of Formulations
- 1.2 How Formulation Experiments are Different
- 1.3 Formulation Case Studies
- 1.4 Summary and Looking Forward
- 1.5 References
- Chapter 2 Basics of Experimentation and Response Surface Methodology
- Overview
- 2.1 Fundamentals of Good Experimentation
- 2.2 Diagnosis of the Experimental Environment
- 2.3 Experimentation Strategy and the Evolution of the Experimental Environment
- 2.4 Roadmap for Experimenting with Formulations
- Part 2: Design and Analysis of Formulation Experiments
- Chapter 3 – Experimental Designs for Formulations
- Overview
- 3.1 Geometry of the Experimental Region
- 3.2 Basic Simplex Designs
- 3.3 Screening Designs
- 3.4 Response Surface Designs
- 3.5 Summary and Looking Forward
- 3.6 References
- Chapter 4 – Modeling Formulation Data
- Overview
- 4.1 The Model Building Process
- 4.2 Summary Statistics and Basic Plots
- 4.3 Basic Formulation Models and Interpretation of Coefficients
- 4.4 Model Evaluation and Criticism
- 4.5 Residual Analysis
- 4.6 Transformation of Variables
- 4.7 Models with More Than Three Components
- 4.8 Summary and Looking Forward
- 4.9 References
- Chapter 5 – Screening Formulation Components
- Overview
- 5.1 Purpose of Screening Experiments
- 5.2 Screening Concepts for Formulations
- 5.3 Simplex Screening Designs
- 5.4 Graphical Analysis of Simplex-Screening Designs
- 5.5 After the Screening Design
- 5.6 Estimation of the Experimental Variation
- 5.7 Summary and Looking Forward
- 5.8 References
- Part 3: Experimenting With Constrained Systems
- Chapter 6 – Experiments with Single and Multiple Component Constraints
- Overview
- 6.1 Component Constraints
- 6.2 Components with Lower Bounds
- 6.3 Three-Component Example
- 6.4 Computation of the Extreme Vertices
- 6.5 Midpoints of Long Edges
- 6.6 Sustained Release Tablet Development - Three Components
- 6.7 Four-Component Flare Experiment
- 6.8 Graphical Display of a Four-Component Formulation Space
- 6.9 Identification of Clusters of Vertices
- 6.10 Construction of Extreme Vertices Designs for Quadratic Formulation Models
- 6.11 Designs for Formulation Systems with Multicomponent Constraints
- 6.12 Sustained Release Tablet Formulation Study
- 6.13 Summary and Looking Forward
- 6.14 References
- Chapter 7 – Screening Constrained Formulation Systems
- Overview
- 7.1 Strategy for Screening Formulations
- 7.2 A Formulation Screening Case Study
- 7.3 Blending Model and Design Considerations
- 7.4 Analysis: Estimation of Component Effects
- 7.5 Formulation Robustness
- 7.6 XVERT Algorithm for Computing Subsets of Extreme Vertices
- 7.7 Summary and Looking Forward
- 7.8 References
- Chapter 8 – Response Surface Modeling With Constrained Systems
- Overview
- 8.1 Design and Analysis Strategy for Response Surface Methodology
- 8.2 Plastic Part Optimization Study
- 8.3 Quadratic Blending Model Design Considerations
- 8.4 Example – Plastic Part Formulation
- 8.5 Example – Glass Formulation Optimization
- 8.6 Using the XVERT Algorithm to Create Designs for Quadratic Models
- 8.7 How to Use Computer-Aided Design of Experiments
- 8.8 Using JMP Custom Design
- 8.9 Blocking Formulation Experiments
- 8.10 Summary and Looking Forward
- 8.11 References
- Part 4: Further Extensions
- Chapter 9 - Experiments Involving Formulation and Process Variables
- Overview
- 9.1 Introduction
- 9.2 Additive and Interactive Models
- 9.3 Designs for Formulations with Process Variables
- 9.4 The Option of Non-Linear Models
- 9.5 A Recommended Strategy
- 9.6 An Illustration Using the Fish Patty Data
- 9.7 Summary and Looking Forward
- 9.8 References
- Chapter 10 – Additional and Advanced Topics
- Overview
- 10.1 Model Simplification
- 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
- 10.3 Response Optimization
- 10.4 Handling Multiple Responses
- 10.5 Multicollinearity in Formulation Models
- 10.6 Summary
- 10.7 References
- Index