Description

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This seminar focuses on how to establish a systematic approach to pharmaceutical development that is defined by Quality-by-Design (QbD) principles using design of experiments (DOE). In addition, this course teaches the application of statistics for setting specifications, assessing measurement systems (assays), developing a control plan as part of a risk management strategy, and ensuring process control/capability. All concepts are taught within the product quality system framework defined by requirements in regulatory guidance documents.

Using a QbD approach for pharmaceutical development studies should include a systematic understanding of the process and using this understanding to establish a control strategy as part of a comprehensive quality risk management program.

This systematic understanding should include both identification of significant process parameters and determination of a functional relationship (mathematical model) linking these significant process parameters to the critical quality attributes (CQAs). The original guidance document on pharmaceutical development provides general guidance on how these are identified: gaining knowledge about which variation in factors explains variation in product quality characteristics of drug product. It also provides a means to achieving this knowledge: through the use of formal experimental designs. The use of DOE methodology provides a means to identify those factors that impact product quality characteristics of drug product (or significant process parameters) and determine the functional relationship that links the process parameters to the CQAs.

Although the seminar focuses on the use of DOE for QbD, multiple aspects of QbD are integrated into the course. After learning the relevant applied statistics, participants will understand how statistics can be used to help set specifications and analyze measurement systems, two foundational requirements of QbD. Next participants will learn tools to help them get value out of their designed experiments. Then, participants will learn how to generate and analyze both screening and response surface designs for QbD studies. Lastly, participants will learn how to use this information: best practices on presentation, setting control plans, constructing control charts, and evaluating process capability.

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

 

Why should you attend:

As stated in Q8, the ICH guidance document on pharmaceutical development, drug product should meet its intended product performance as well as meet the needs of patients. Although the strategy for pharmaceutical development may vary from company-to-company and/or from product-to-product, a systematic approach defined by quality by design (QbD) principles is encouraged.

Further guidance and policies have been provided to explain how the QbD approach should be integrated into the pharmaceutical quality system including process design, qualification, continued process verification, risk management, and validation. Although guidance on implementation of these requirements is prevalent, many companies have not yet implemented QbD into their quality systems; regulatory agencies have made it clear this will change. In fact, the chemistry, manufacturing, and controls (CMC) reviewers in the Office of Pharmaceutical Science (OPS) released a manual on policies and procedures (MAPP) explaining how reviewers will begin to enforce the requirements from these guidance documents. Additionally, the Director of the Center for Drug Evaluation and Research (CDER) at the FDA (May 2020) co-authored a paper in The American Association of Pharmaceutical Scientists detailing the concept and reiterating the importance of using a QbD approach to pharmaceutical development. This seminar will demonstrate how to integrate those QbD principles into a pharmaceutical quality system.

 

Areas Covered in the Session:

• implement QbD principles from discovery through product discontinuation
• apply statistics to set specifications and validate measurement systems (assays)
• utilize risk management tools to identify and prioritize potential critical process parameters
• identify critical process parameters and develop a functional relationship between those process parameters and your critical-to-quality attributes (CQAs)
• establish your design space
• develop a control plan as part of a risk management strategy
• 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

Heath Rushing

Heath Rushing is the cofounder of Adsurgo LLC and co-author of the book Design and Analysis of Experiments by Douglas Montgomery: A Supplement for using JMP. Previously, he was the JMP and Six Sigma training manager at SAS. He led a team of nine technical professionals designing and delivering applied statistics and quality continuing education courses. He created tailored courses, applications, and long-term training plans in quality and statistics across a variety of industries to include biotech, pharmaceutical, medical device, and chemical processing. Mr. Rushing has been an invited speaker on applicability of statistics for national and international conferences. As a Quality Engineer at Amgen, he championed statistical principles in every business unit. He designed and delivered a DOE course that immediately became the company standard required at multiple sites. Additionally, he developed and implemented numerous innovative statistical methods advancing corporate risk management, process capability, and validation acceptance criteria. He won the top teaching award out of 54 instructors in the Air Force Academy math department where he taught several semesters and sections of OR and statistics. Additionally, he taught Operations Research and simulation modeling at the Colorado School of Mines and designs and delivers short courses in statistics, data mining, and simulation modeling for SAS.

Please contact the event manager Marilyn below for the following: 
- Discounts for registering 5 or more participants.
- If you company requires a price quotation.
Event Manager Contact: marilyn.b.turner(at)nyeventslist.com
You can also contact us if you require a visa invitation letter, after ticket purchase. 
We can also provide a certificate of completion for this event if required.

NO REFUNDS ALLOWED ON REGISTRATIONS 
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Day 1 Schedule

Introduction to Quality by Design (QbD)

• Quality by Design (QbD) principles
• Product Quality System framework

Primer on Statistical Analysis

• basic statistics

Primer on Statistical Analysis (cont.)

• hypothesis testing

Primer on Statistical Analysis (cont.)

• ANOVA

Primer on Statistical Analysis (cont.)

• regression

Day 2 Schedule

Foundational Requirements for QbD Studies

• setting specifications
• Measurement Systems Analysis (MSA) for assays

Introduction to Design of Experiments (DOE)

• steps to DOE
• defining critical-to-quality attributes (CQAs)
• identifying and prioritizing potential process parameters

Screening Designs - Identifying Critical Process Parameters

• factorial designs

Screening Designs - Identifying Critical Process Parameters (cont.)

• fractional factorial designs
• D optimal

Response Surface Designs - Develop Functional Relationships and Establish Design Space

• Addition of center points
• Central Composite Designs (CCD)
• I optimal designs

Utilizing Systematic Understanding from QbD Studies

• presenting results
• developing a control plan as part of a risk management strategy
• process control and capability