Statistical Methods for Bioprocess Development and Commercializaton
Purchasing Options
Features
- Provides a manual for implementation of QbD principles
- Includes line charts and mathematical formulas
- Includes tables, graphs, and figures to illustrate concepts
- Offers real-world case studies as examples
Summary
Written as a single source reference that specifically covers statistics for bioprocess development, this text serves as a manual for implementation of QbD principles. It presents current statistical methods and case studies in the area of biotechnical process development and subsequent process characterization and commercialization. It covers topics in bioassay and other analytical method development, with designed experiments for critical process input screening. Other topics covered include multivariate tools for metabolomics and cell culture medium development, short term stability data analysis, process sampling procedures, and multivariate analysis methods for monitoring cell culture and fermentation processes.
Table of Contents
Process Development: Statistics in Analytical Method Development Validation and Method Transfer. Bioassay Development (selection of 4p, 5p, or linear model). D-Optimal Screening Designs for Unit Operation and Cross-Unit Operations. Contrast of Method of Steepest Assent with Simplex Search and/or other optimization method. Development and validation of qualitative methods (diagnostic type, pass/fail. Multivariate Tools for Metabolomics and Cell Culture Medium Development. First Principles Modeling: mixing, non-linear degradation kinetics,cell growth, etc. Process Characterization: Small scale qualification (specifically use of equivalency tests). Multivariate techniques for scale-down model qualification across multiple parameters or repeat measurements. Short term stability data analysis; multiple temps and containers, Arrhenius and goalpost approaches for slope contrasts. FMEA as a tool for factor selection in robustness studies. Split plot designs. I-optimal designs for RSM, review of CCD design and axial point placement. DOE across multiple unit operations. Mixed Models to estimate uncontrolled parameter variability. Process Commercialization: Calculation and presentation of Design Space. Setting Acceptance Criteria based on Design Space information using Monte Carlo Simulations. Process Controls: Process Sampling Procedures and associated acceptance criteria (acceptance based sampling plans and (understanding heterogeneity of materials). Visible Particulate characterization. Use of right/left censored data. Application of g-charts to trending of very skewed data, such as sterility data. Multivariate Analysis Methods for Monitoring Cell Culture and Fermentation Processes.
