Process Development and Validation rest on the right Design of Experiments and Statistical Process Control

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The application of DOE and SPC to the development, design and monitoring of manufacturing and testing requires the use of procedures. Why? It is because in a recent guidance document on Process Validation, the FDA has named the Quality Unit as being responsible in the review and interpretation of DOE and SPC studies.

The Quality Unit needs to take a practical orientation when it sets out doing this work. An approach in which case studies and examples are sprinkled goes a long way in helping Process Validation professionals in their work. This is exactly what a seminar from GlobalCompliancePanel, a leading provider of professional trainings for the regulatory compliance areas, will offer and help regulatory professionals in the Quality Control and Quality Assurance areas achieve their aims.

The Director of this two-day seminar is Dr. Steven Kuwahara, who Founder and Principal, GXP BioTechnology LLC. Want to understand ways by which to adapt the right approach to applying DoE and SPC to the development, design and monitoring of manufacturing and testing? Then, please register for this very useful session by logging on to Process Development and Validation rest on the right Design of Experiments and Statistical Process Control . This seminar has been pre-approved by RAPS as eligible for up to 12 credits towards a participant’s RAC recertification upon full completion.

Completely interactive

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Dr. Kuwahara will do full justice to the role and importance of practical experiments in aiding an understanding of Design of Experiments. Theoretical information will be given only when it is needed to help gain an understanding of an experiment. At this highly interactive and practical session, Dr. Kuwahara will offer examples from real processes and testing procedures. He will intersperse these with examples that will be directly applicable to the areas of work that relate to the participants of this seminar.

An understanding of the way the process parameters relate to and work with each other is necessary for any pharmaceutical worker who is involved in performing, supervising or reviewing manufacturing or testing processes. The ability to monitor the performance of processes and test methods is also needed for such a worker. While this is true for a professional in any department of pharmaceuticals; it applies more to the worker who works in Quality Control and Quality Assurance, a requirement that has become necessary following the passage of recent FDA guidance document on Process Validation.

However, it is the development, manufacturing, or quality systems worker who carries out this work. In view of this fact, a high degree of coordination is needed between these two levels of employees. At this seminar, Dr. Kuwahara will arm these two levels of employees with the knowledge of the ways of designing the systems and studies, and then interpreting the results generated.

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Dr. Kuwahara has charted out the following agenda for this seminar:

Dietary Design of Experiments

  • Introduction
  • One Level, One Factor Designs. Simple Comparisons
  • Two-Level Multi-Factorial Design
  • Extracting Information from the Experiment

Statistical Process Control

Design of Experiments and Statistical Process Control

This seminar is designed for the benefit of professionals involved in the procedures and applications of DOE and SPC, such as Directors, Managers, Supervisors, Lead workers in Process Development, Manufacturing, Regulatory Affairs, Quality Assurance and Quality Control, and workers who participate in operations or the are involved in the supervision of the development, manufacturing, or testing of medicinal products.

To join us for more information, get in touch

Applied Statistics for FDA Process Validation

The pharmaceutical industry considers Applied Statistics for FDA Process Validation to be of very high importance. In 2011, the FDA set out this guidance for the industry. as part of this guidance, called “Process Validation: General Principles and Practices”, which sets the framework for Process Validation in the pharmaceutical industry, any organization in the pharmaceutical industry has to set up a three-stage process.

These are the three stages:

I.           Process Design

II.           Process Qualification, and

III.           Continued Process Verification.

Stage 1, or what is called the Process Design stage, is the stage in which the commercial manufacturing process is defined. This definition is based on knowledge gained through development and scale-up activities.

Stage 2, called the Process Qualification, is the stage in which an evaluation is made of the process design to determine if the process is capable of reproducible commercial manufacturing.

Stage 3, the Continued Process Verification, is meant for giving ongoing assurance during routine production to ensure that the process remains in a state of control.

A seminar on the ways implementing Applied Statistics for FDA Process Validation

GlobalCompliancePanel, a leading provider of professional trainings for the regulatory compliance areas, will be organizing a two-day seminar in which the ways of using Applied Statistics for FDA Process Validation will be taught. Richard Burdick, Emeritus Professor of Statistics, Arizona State University (ASU) and former Quality Engineering Director for Amgen, Inc., will be the Director of this seminar on applied statistics for FDA Process Validation.

In order to learn Applied Statistics for FDA Process Validation in-depth, please register by visiting Applied Statistics for FDA Process Validation. This course has been pre-approved by RAPS as eligible for up to 12 credits towards a participant’s RAC recertification upon full completion.

A detailed and methodical approach to implementing statistical methodologies

This two-day course on Applied Statistics for FDA Process Validation will focus on the ways by which a systematic approach to implementing statistical methodologies into a process validation program consistent with the FDA guidance can be established.

Beginning with a primer on statistics, Dr. Burdick will explain how the methods of Applied Statistics for FDA Process Validation seminar can be applied in each remaining chapter.

Dr. Burdick will next move on to explaining the two fundamental requirements for Process Validation, namely the application of statistics for setting specifications and assessing measurement systems (assays).

He well then show how to apply statistics through the three stages of process validation as defined by requirements in the process validation regulatory guidance documents.

Given that the methods taught through all these three stages are recommended by regulatory guidance documents; this seminar on Applied Statistics for FDA Process Validation will provide references to the specific citations in the guidance documents.

This seminar on Applied Statistics for FDA Process Validation will lead participants into ways of establishing a systematic approach to implementing statistical methodologies into a process development and validation program that is consistent with the FDA guidance.

All-round learning related to Applied Statistics for FDA Process Validation

Dr. Burdick will teach participants how to:

o  Apply statistics for setting specifications

o  Assess measurement systems (assays)

o  Use Design of Experiments (DOE)

o  Develop a control plan as part of a risk management strategy, and

o  Ensure process control/capability.

All concepts at this Applied Statistics for FDA Process Validation seminar 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, this seminar on Applied Statistics for FDA Process Validation also provides a useful framework for other related industries.

In this important learning on Applied Statistics for FDA Process Validation; Dr. Burdick will cover the following areas:

o  Apply statistics to set specifications and validate measurement systems (assays)

o  Develop appropriate sample plans based on confidence and power

o  Implement suitable statistical methods into a process validation program for each of the three stages

o  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)

o  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

o  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.

Applied Statistics for product and process evaluation in design and manufacturing

Evaluating product and processes is an imperative for almost all design and/or manufacturing companies. These are the reasons for which this evaluation needs to be made:

  • Managing risks
  • Validation of processes
  • Establishing product/process specifications to QC to such specifications
  • Monitoring compliance to such specifications

risk

Lack of proper and thorough grasp of and correct implementation of statistical methods leads a company to having to face significant increases in its complaint rates, scrap rates, and time-to-market. As a result, such companies churn out poor quality in their products, leading to lowered customer satisfaction levels, severely impacting their bottom line.

A learning session to help understand statistical methods

In order to help professionals in process and manufacturing meet challenges associated with statistical methods with greater confidence, GlobalCompliancePanel, a highly reputable provider of professional trainings for the regulatory compliance areas, is organizing a highly educative two-day seminar on the topic, “Applied Statistics, with Emphasis on Verification, Validation, and Risk Management, in R&D, Manufacturing, and QA/QC”.

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John N. Zorich, Statistical Consultant & Trainer, Ohlone College & SV Polytechnic, will be the Director at this seminar, which has been pre-approved by RAPS as eligible for up to 12 credits towards a participant’s RAC recertification upon full completion.

To enroll for this seminar, participants can log on to http://www.globalcompliancepanel.com/control/globalseminars/~product_id=900537SEMINAR.

Hands on approach to statistical methods toolbox

The aim of this seminar is to offer a hands-on approach by which the participants could comprehend the ways to interpret and use a standard tool-box of statistical methods that consist of confidence intervals, t-tests, Normal K-tables, Normality tests, confidence/reliability calculations, AQL sampling plans, measurement equipment analysis, and Statistical Process Control.

The Director will equip the seminar delegates with clarity on how to accurately employ and administer statistical methods, which can be used as a launchpad for introducing new products.

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This two-day session will help participants understand the proper way of avoiding issues relating to these aspects of statistical methods. John will explain how to apply statistics to manage risk in R&D, QA/QC, and Manufacturing by giving real life examples derived mainly from the medical device design/manufacturing industry.

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John will cover the following areas at this seminar:

  • FDA, ISO 9001/13485, and MDD requirements related to statistical methods
  • How to apply statistical methods to manage product-related risks to patient, doctor, and the designing/manufacturing company
  • Design Control processes (verification, validation, risk management, design input)
  • QA/QC processes (sampling plans, monitoring of validated processes, setting of QC specifications, evaluation of measurement equipment)
  • Manufacturing processes (process validation, equipment qualification).