Pharmaceutical Quality by Design Using JMP

Pharmaceutical Quality by Design Using JMP

by Rob Lievense
Released October 2018
Publisher(s): SAS Institute
ISBN: 9781635266184

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Book description

Solve your pharmaceutical product development and manufacturing problems using JMP .Pharmaceutical Quality by Design Using JMP : Solving Product Development and Manufacturing Problems provides broad-based techniques available in JMP to visualize data and run statistical analyses for areas common in healthcare product manufacturing. As international regulatory agencies push the concept of Quality by Design (QbD), there is a growing emphasis to optimize the processing of products. This book uses practical examples from the pharmaceutical and medical device industries to illustrate easy-to-understand ways of incorporating QbD elements using JMP. Pharmaceutical Quality by Design Using JMP opens by demonstrating the easy navigation of JMP to visualize data through the distribution function and the graph builder and then highlights the following:
  • the powerful dynamic nature of data visualization that enables users to be able to quickly extract meaningful information
  • tools and techniques designed for the use of structured, multivariate sets of experiments
  • examples of complex analysis unique to healthcare products such as particle size distributions/drug dissolution, stability of drug products over time, and blend uniformity/content uniformity.

Scientists, engineers, and technicians involved throughout the pharmaceutical and medical device product life cycles will find this book invaluable.

This book is part of the SAS Press program.

Table of contents

  1. Chapter 1: Preparing Data for Analysis
    1. Overview
    2. The Problem: Overfilling of Bulk Product Containers
    3. Collect the Data
    4. Import Data into JMP
    5. Change the Format of a JMP Table
    6. Explore Data with Distributions
    7. A Second Problem: Dealing with Discrete Characteristics of Dental Implants
    8. Get More Out of Simple Analysis with Column Formulas
    9. Practical Conclusions
    10. Exercises
  2. Chapter 2: Investigating Trends in Data over Time
    1. Overview
    2. The Problem: Fill Amounts Vary throughout Processing
    3. Visualize Trends over Time with Simple Plots in the Graph Builder
    4. More Detail for Time-Based Trends with the Control Chart Builder
    5. Dynamically Selecting Data from JMP Plots
    6. Creating Subset Tables
    7. Using Graph Builder to View Trends in Selected Data
    8. Practical Conclusions
    9. Exercises
  3. Chapter 3: Assessing How Well a Process Performs to Specifications with Capability Analyses
    1. Overview
    2. The Problems: Assessing the Capability of the Fill Process and the Dental Implant Manufacturing Processes
    3. One-Sided Capability Analysis for Fill Weight
    4. Checking Assumptions for Fill Weight Data
    5. Capability Studies from the Distribution Platform
    6. Two-Sided (Bilateral) Capability Analysis for Implant Dimensions
    7. Checking Assumptions for Implant Measures Data
    8. Capability Analysis from the Quality and Process Options
    9. Capability Analysis Summary Reports
    10. Capability Analysis for Non-normal Distributions
    11. Practical Conclusions
    12. Exercises
  4. Chapter 4: Using Random Samples to Estimate Results for the Commercial Population of a Process
    1. Overview
    2. The Problems: A Possible Difference between the Current Dissolution Results and the Historical Average
    3. Steps for a Significance Test for a Single Mean
    4. Importing Data and Preparing Tables for Analysis
    5. Practical Application of a t-test for One Mean
    6. Using a Script to Easily Repeat an Analysis
    7. Practical Application of a Hypothesis Test for One Proportion
    8. Practical Conclusions
    9. Exercises
  5. Chapter 5: Working with Two or More Groups of Variables
    1. Overview
    2. The Problems: Comparing Blend Uniformity and Content Uniformity, Average Flow of Medication, and Differences Between No-Drip Medications
    3. Comparison of Two Quantitative Variables
    4. Comparison of Two Independent Means
    5. Unequal Variance Test
    6. Matched Pairs Tests
    7. More Than Two Groups
    8. Practical Conclusions
    9. Exercises
  6. Chapter 6: Justifying Multivariate Experimental Designs to Leadership
    1. Overview
    2. The Problems: Developmental Experiments Lack Structure
    3. Why Not One Factor at a Time?
    4. Data Visualization to Justify Multivariate Experiments
    5. Using the Dynamic Model Profiler to Estimate Process Performance
    6. Practical Conclusions
    7. Exercises
  7. Chapter 7: Evaluating the Robustness of a Measurement System
    1. Overview
    2. The Problems: Determining Precision and Accuracy for Measurements of Dental Implant Physical Features
    3. Qualification of Measurement Systems through Simple Replication
    4. Analysis of Means (ANOM) for Variances of Measured Replicates
    5. Measurement Systems Analysis (MSA)
    6. Detailed Diagnostics of Measurement Systems through MSA Options
    7. Variability and Attribute Charts for Measurement Systems
    8. Practical Conclusions
    9. Exercises
  8. Chapter 8: Using Predictive Models to Reduce the Number of Process Inputs for Further Study
    1. Overview
    2. The Problem: Thin Surgical Handle Covers
    3. Data Visualization with Dynamic Distribution Plots
    4. Basic Partitioning
    5. Partitioning with Cross Validation
    6. Partitioning with Validation (JMP Pro Only)
    7. Stepwise Model Selection
    8. Practical Conclusions
    9. Exercises
  9. Chapter 9: Designing a Set of Structured, Multivariate Experiments for Materials
    1. Overview
    2. The Problem: Designing a Formulation Materials Set of Experiments
    3. The Plan
    4. Using the Custom Designer
    5. Using Model Diagnostics to Evaluate Designs
    6. Compare Designs – An Easy Way to Compare Up to Three Designs (JMP Pro Only)
    7. The Data Collection Plan
    8. Augmenting a Design
    9. Practical Conclusions
    10. Exercises
  10. Chapter 10: Using Structured Experiments for Learning about a Manufacturing Process
    1. Overview
    2. The Problems: A Thermoforming Process and a Granulation Process, Each in Need of Improvement
    3. Screening Experimental Designs for the Thermoforming Process
    4. Compare Designs for Main Effects with Different Structures (JMP Pro Only)
    5. Adding Interactions to Compare Designs (JMP Pro Only)
    6. Visualizing Design Space with Scatterplot Matrices
    7. Experimental Design for a Granulation Process with Multiple Outputs
    8. Practical Conclusions
    9. Exercises
  11. Chapter 11: Analysis of Experimental Results
    1. Overview
    2. The Problems: A Thermoforming Process and a Granulation Process, Each in Need of Improvement
    3. Execution of Experimental Designs
    4. Analysis of a Screening Design
    5. Detailed Analysis of the DSD Model
    6. Use of the Fit Model Analysis Menu Option
    7. Singularity
    8. Analysis of a Partially Reduced Model
    9. Analysis of a Response Surface Model with Multiple Outputs
    10. Examination of Fit Statistics for Individual Models
    11. Model Diagnostics through Residual Analysis
    12. Parameter Estimates
    13. Detailed Analyses of Significant Factors with Leverage Plots
    14. Visualization of the Higher-Order Terms with the Interaction Plots
    15. Examination of an Insignificant Model
    16. Dynamic Visualization of a Design Space with the Prediction Profiler
    17. Elimination of Insignificant Models to Enhance Interpretation
    18. Practical Conclusions
    19. Exercises
  12. Chapter 12: Getting Practical Value from Structured Experiments
    1. Overview
    2. The Problems: Statistical Modeling Are Needed to Gain Detail About A Thermoforming Process and a Granulation Process
    3. Identification of a Control Space from the Thermoforming DSD
    4. Verification of a Control Space with Individual Interval Estimates
    5. Using Simulations to Model Input Variability for a Granulation RSM
    6. Including Variations in Responses Within RSM Simulations
    7. Making Detailed Practical Estimations of Process Performance with a Table of Simulated Modeling Data
    8. Creating a PowerPoint Presentation from JMP Results
    9. Practical Conclusions
    10. Exercises
  13. Chapter 13: Advanced Modeling Techniques
    1. Overview
    2. The Problem: A Shift in Tablet Dissolution
    3. Preparing a Data Table to Enhance Modeling Efficiency
    4. Partition Modeling
    5. Stepwise Models
    6. Neural Network Models
    7. Advanced Predictive Modeling Techniques (Bootstrap Forest) (JMP Pro Only)
    8. Model Comparison (JMP Pro only)
    9. Practical Conclusions
    10. Exercises
  14. Chapter 14: Basic Mixture Designs for Materials Experiments
    1. Overview
    2. The Problem: Precipitants in a Liquid Drug Solution
    3. Design of Mixture Experiments
    4. Ternary Plots for Model Diagnosis
    5. Analysis of Mixture Design Results
    6. Model Profiler
    7. The Practical Application of Profiler Optimization
    8. Practical Conclusions
    9. Exercises
  15. Chapter 15: Analyzing Data with Non-linear Trends
    1. Overview
    2. The Problems: Comparing Drug Dissolution Profiles and Comparing Particle Size Distributions
    3. Formatting Data for Non-linear Modeling
    4. Making a Simple Plot of Dissolution Profiles
    5. Creating a Non-linear Model of Dissolution Profiles
    6. Equivalence Testing of Dissolution Profiles
    7. Comparisons of Dissolution Profiles with the F2 Similarity Criterion
    8. Making F2 Similarity Predictive
    9. Using Non-linear Models for Mesh Testing of Particle-Size Trends
    10. Augmenting Non-linear Plots by Using Axis Settings
    11. Making Predictions with Non-linear Models
    12. Practical Conclusions
    13. Exercises
  16. Chapter 16: Using Statistics to Support Analytical Method Development
    1. Overview
    2. The Problem: A Robust Test Method Must Be Developed
    3. Experimental Planning
    4. Design Creation Using the Definitive Screening Design (DSD)
    5. Model Analysis of the DSD
    6. Making Estimates from the Model
    7. Using Simulations to Estimate Practical Results
    8. Practical Conclusions
    9. Exercises
  17. Chapter 17: Exploring Stability Studies with JMP
    1. Overview
    2. The Problem: Transdermal Patch Stability
    3. Summarizing Stability Data
    4. Adding Initial Results and Formatting for Stability Studies
    5. Running Stability Analysis
    6. Stability—Linear Model Diagnostics
    7. Using Stability Estimates to Calculate Internal Limits
    8. Practical Conclusions

Product information

  • Title: Pharmaceutical Quality by Design Using JMP
  • Author(s): Rob Lievense
  • Release date: October 2018
  • Publisher(s): SAS Institute
  • ISBN: 9781635266184