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2-day In-person Seminar

Applied Statistics for FDA Process Validation


April 20th & 21st, 2017

Time: 9:00 AM to 6:00 PM

San Diego, CA

Venue: Four Points By Sheraton San Diego Downtown


Director : Richard (Rick) K. Burdick

**Please note the registration will be closed 2 days (48 Hours) prior to the date of the seminar.

  Price: $1,295.00
(Seminar Fee for One Delegate)

  Register for 5 attendees   Price: $3,885.00
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    • Until March 15, Early Bird Price: $1,295.00 From March 16 to April 18, Regular Price: $1,495.00
Course "Applied Statistics for FDA Process Validation" has been pre-approved by RAPS as eligible for up to 12 credits towards a participant's RAC recertification upon full completion.

In Guidance for Industry Process Validation: General Principle and Practices, process validation is defined as, ""...the collection and evaluation of data, from the process design stage through commercial production.." The guidance further delineates the 'process design stage through commercial production' into three distinct stages of the product lifecycle:

Stage 1: Process Design: The commercial manufacturing process is defined during this stage based on knowledge gained through development and scale-up activities.

Stage 2: Process Qualification: During this stage, the process design is evaluated to determine if the process is capable of reproducible commercial manufacturing.

Stage 3: Continued Process Verification: Ongoing assurance is gained during routine production that the process remains in a state of control.

The first stage of process validation is process design. The Process Validation guidance document states, "A successful validation program depends on information and knowledge from product and process development. This knowledge and understanding is the basis for establishing an approach to control of a manufacturing process that results in products with desired quality attributes:

Manufactures should:
  • Understand the sources of variation
  • Detect the presence and degree of variation
  • Understand the impact of variation on the process and ultimately on product attributes
  • Control the variation in a manner commensurate with the risk it represents to the process and product."

The second stage of process validation is process qualification. Although stage 2 has two elements, this course will focus on recommendations for the second element, PPQ. PPQ "combines the actual facility, utilities, equipment (each now qualified), and the trained personnel with the commercial manufacturing process, control procedures, and components to produce commercial batches." Additionally, the process validation guidance document that "Each manufacturer should judge whether it has gained sufficient understanding to provide a high degree of assurance in its manufacturing process to justify commercial distribution of the product. Focusing exclusively on qualification efforts without understanding the manufacturing process and associated variations may not lead to adequate assurance of quality."

The third stage of process validation is continued process verification. The process validation guidance document defines the need for this stage: "After establishing and confirming the process, manufacturers must maintain the process in a state of control over the life of the process, even as materials, equipment, production environment, personnel, and manufacturing procedures change." Manufacturers should use ongoing programs to collect and analyze product and process data to evaluate the state of control of the process. These programs may identify process or product problems or opportunities for process improvements that can be evaluated and implemented through some of the activities described in Stages 1 and 2."

This course focuses on how to establish a systematic approach to implementing statistical methodologies into a process validation program consistent with the FDA guidance. It begins with a primer on statistics, focusing on methods that will be applied in each remaining chapter. Next, it teaches the application of statistics for setting specifications and assessing measurement systems (assays), two foundational requirements for process validation. Lastly, the course applies statistic through the three stages of process validation defined by requirements in the process validation regulatory guidance documents. Methods taught through all three stages are recommended by regulatory guidance documents; references to the specific citations in the guidance documents are provided.


Why should you attend:

The Food and Drug Administration (FDA) provided a guidance for industry in 2011 that has established a framework for process validation in the pharmaceutical industry. This guidance, titled "Process Validation: General Principles and Practices" consists of a three-stage process. The three stages are 1) Process Design, 2) Process Qualification, and 3) Continued Process Verification.

This course focuses on how to establish a systematic approach to implementing statistical methodologies into a process development and validation program consistent with the FDA guidance. This course teaches the application of statistics for setting specifications, assessing measurement systems (assays), using design of experiments (DOE), developing a control plan as part of a risk management strategy, and ensuring process control/capability. All concepts are taught within the three-stage product cycle framework defined by requirements in the process validation regulatory guidance documents.

Although established for the pharmaceutical industry, it also provides a useful framework for other industries.

Analyses in this course use the point-and-click interface of JMP software by SAS.


Areas Covered in the Session:

  • apply statistics to set specifications and validate measurement systems (assays)
  • develop appropriate sample plans based on confidence and power
  • implement suitable statistical methods into a process validation program for each of the three stages
  • Stage 1, Process Design: utilize risk management tools to identify and prioritize potential critical process parameters; and define critical process parameters and operating spaces for the commercial manufacturing process using design of experiments (DOE)
  • Stage 2, Process Qualification: assess scale effects while incorporating large (pilot and/or commercial) scale data; develop process performance qualification (PPQ) acceptance criteria by characterizing intra and inter-batch variability using process design data and batch homogeneity studies; and develop an appropriate sampling plan for PPQ
  • Stage 3, Continued Process Verification: develop a control plan as part of a risk management strategy; collect and analyze product and process data; and ensure your process is in (statistical) control and capable.

Who Will Benefit:

This seminar is designed for pharmaceutical and biopharmaceutical professionals who are involved with product and/or process design, validation, or manufacturing/control.

  • Process Scientist/Engineer
  • Design Engineer
  • Product Development Engineer
  • Regulatory/Compliance Professional
  • Design Controls Engineer
  • Six Sigma Green Belt
  • Six Sigma Black Belt
  • Continuous Improvement Manager

Day 1 Schedule


Lecture 1:

Introduction to Statistics for Process Validation

  • principles of process validation
  • stages of process validation

Primer on Statistical Analysis

  • basic statistics

Lecture 2:

Primer on Statistical Analysis (cont.)

  • statistical intervals and hypothesis testing

Lecture 3:

Primer on Statistical Analysis (cont.)

  • statistical intervals and hypothesis testing
  • ANOVA

Lecture 4:

Primer on Statistical Analysis (cont.)

  • regression
  • run charts

Day 2 Schedule


Lecture 1:

Foundational Requirements for Process Validation

  • setting specifications
  • analytical methodology

Stage 1 - Process Design

  • steps to DOE
  • screening designs

Lecture 2:

Stage 1 - Process Design

  • response surface designs
  • establishing a strategy for process qualification

Lecture 3:

Stage 2 - Process Qualification

  • introduction
  • incorporation of large-scale data
  • development of PPQ acceptance criteria
  • development of sampling plans

Lecture 4:

Stage 3 - Continued Process Verification

  • statistical process control
  • process capability
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Group participations
Get fabulous discounts by participating in groups of two or more.
No Attendees Discount
1 2 Attendees 10% off
2 3 to 6 Attendees 20% off
3 7 to 10 Attendees 25% off
4 10+ Attendees 30% off

To avail the above group discounts, all the participants should register by making a single payment

Call our representative TODAY on 1800 447 9407 to have your seats confirmed!

Richard (Rick) K. Burdick


Richard (Rick) K. Burdick is an Emeritus Professor of Statistics, Arizona State University (ASU) and former Quality Engineering Director for Amgen, Inc. for 10 years. He taught at ASU for 29 years at all levels including undergraduate business students, MBAs, Master of Statistics students, and doctoral candidates in both business and engineering. He received numerous teaching awards and taught a variety of courses for adult learners. His research and consulting interests consider several CMC statistical applications including comparability studies, stability data analysis, analytical method validation, quality by design process characterization, and analytical similarity for biosimilar products. He has written over 60 journal articles and three books, including Confidence Intervals for Random and Mixed ANOVA Models with Applications to Gauge R&R Studies, (with C. M. Borror and D. C. Montgomery) and Confidence Intervals on Variance Components, (with F. A. Graybill). Burdick is a Fellow of the American Statistical Association and a member of the American Society for Quality. He has served on the USP Statistics Expert Committee since 2010. He received his Bachelor's Degree in Statistics from the University of Wyoming. He received his Masters and Doctorate degrees in Statistics from Texas A&M University.

Location: San Diego, CA Hotel: Four Points By Sheraton San Diego Downtown   1617 1st Avenue - San Diego, California, 92101 - United States

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