Course "Design of Experiments and Statistical Process Control for Process Development and Validation" has been pre-approved by RAPS as eligible for up to 12 credits towards a participant's RAC recertification upon full completion.
This course will present the procedures that must be used in the application of DOE and SPC to the development, design and monitoring of manufacturing and testing processes. A practical approach with case studies and examples will be used, with theoretical information introduced only when necessary to understand an experiment. Examples from real processes and testing procedures will be used to present the student with examples that will be directly applicable to their work.
Why you should attend:
Any pharmaceutical worker who performs, supervises or reviews manufacturing or testing processes needs to understand the relationships among the process parameters and be able to monitor the performance of processes and test methods. This is particularly true for the worker in Quality Control and Quality Assurance as the recent FDA guidance document on Process Validation has assigned the responsibility for reviewing and interpreting DOE and SPC studies to the Quality Unit. The work, however, is done by the development, manufacturing, or quality systems worker who should also attend this course to learn how to design the systems and studies, and interpret the results generated.
Who will benefit:
- Lead workers in Process Development
- Regulatory Affairs
- Quality Assurance and Quality Control
- Workers who will be participating in operations or the supervision of the development, manufacturing, or testing of medicinal products will benefit from knowing the procedures and applications of DOE and SPC.
Day 1 Schedule
Dietary Design of Experiments
- Input/Output, CPP and CQA, what are they?
- Defining the CPP and CQA for a process
- Defining the design space
- Snedecor's F-test and ANOVA
One Level, One Factor Designs. Simple Comparisons.
- The simplest form of the full F-test.
- A one factor ANOVA with multiple treatments
- The least significant difference (LSD)
- The use of blocking in a one factor experiment.
Two-Level Multi-factorial Design
- Description of the experiment
- Conversion to the standard form with results of testing
- Definition of an Orthogonal Array
- Interactions in the array
- Effects (Contrasts) in the array.
Extracting Information from the Experiment
- Use of a Half-normal Plot.
- What about the second set of test data?
- Using the effects for a Pareto Chart
- Interpreting the interactions for process design
Day 2 Schedule
Statistical Process Control
Shewhart Charts for Variable Data
- Classical X-bar and R-bar charts
- Estimating control limits for early studies
- Significance of events using the "Western Electric Rules."
Shewhart Charts for Attribute Data Especially Counts.
- p and np charts for attributes.
- Handling binomially distributed data.
- c charts and u charts for non-conformities
- Handling binomially distributed data.
Considerations from Shewhart Charts
- Rational subgroups.
- Setting the process capability indices.
- Coupling as a problem
Other Types of Charts Related to Shewhart Charts
- Moving average chart
- Exponentially weighted chart
- CUSUM chart
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Founder & Principal, GXP Biotechnology, LLC
Dr. Steven Kuwahara, Ph.D. is the Founder and Principal of GXP BioTechnology LLC, a consulting firm that works in the areas covered by the GLP and GMP of drugs, biologics, and nutraceuticals. Steve has over 30 years of experience in supervising quality control laboratories, including an animal testing facility, and in performing GLP and GMP audits of internal and external testing laboratories. Steve has participated in the development of drugs and biologicals through all phases of clinical research and final product production.
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