Handling OOE and OOT Results AND post-conference OOS Workshop

Handling OOE and OOT Results AND post-conference OOS Workshop

Prague, Czech Republic

Course No 15487

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Speakers

Dr Milan Crnogorac, Roche, Switzerland

Dr Christopher Burgess, Burgess Analytical Consultancy Limited, UK

Dr Bernd Renger, Bernd Renger Consulting, Germany

Dr Lance Smallshaw, UCB Biopharma sprl, Belgium

Dr Bianca Teodorescu, UCB Biopharma sprl, Belgium

Dr Lori McCaig, Roche/Genentech, USA

Objectives

The ECA Working Group on Analytical Quality Control was founded in 2010 in order to generate a harmonised SOP on managing analytical deviations within the laboratory including OOS, OOE and OOT results.

Version 2 of the ECA OOS SOP is already available for all ECA members since 2013.

Given the complexity of the topic, it was decided that the handling of OOT and OOE results should be addressed in a separate guideline SOP, since there is both a lack of knowledge in the industry and a lack of guidance for trend analysis from the regulators in spite of increased regulatory interest in this area.

In 2013 the ECA’s QC Working Group decided to address these issues by developing a new guideline aimed at QC and other quality groups to encourage the application of a consistent and scientifically sound approach to trend analysis as part of a QMS for assuring data integrity.

There were initially three core components:

  • Recommended approaches for detecting out of expectation (OOE) data within an analytical sequence which are based on the known process capability of the analytical procedure used.
  • Recommended approaches to detecting out of trend (OOT) data between analytical sequences where no trend is expected. These are based on standard Statistical Process Control methodology and
  • Recommended approaches for detecting out of trend (OOT) data between analytical sequences where a trend is expected as is the case for Stability Testing
.
From this foundation the current OOE-/ OOT-Laboratory Data Management Guidance was developed by an international team to provide a harmonised approach to trending.

At this ECA OOE/OOT Training Course in Prague version 1 of our OOE-/ OOT-Laboratory Data Management Guidance will be presented and participants will have the opportunity to review and discuss the contents and technical aspects of the guidance document as well as looking at the scope and application of the proposed methods within industry .

The ECA QC Working Group’s goal is to have a basic global framework for OOT/OOE within R&D, production and QC laboratories which is acceptable to the authorities and adaptable for individual companies.

Target Group

This conference is intended for technical and managerial personnel dealing with out-of-trend or out-of expectation results, including R&D, production, analytical laboratories, contract laboratories, and Quality Assurance/Quality Control personnel.

Post Conference OOS Workshop 16 November 2016, 08.30 - 16.00 h

Directly after the OOE/OOT course on Wednesday, 16 November 2016, there will be the ECA OOS Workshop with these topics:

  • OOS: US/FDA and MHRA Guidelines and European Regulatory Expectations
  • OOS Results in R&D Laboratories
  • WORKSHOP I:
  • ECA Analytical Quality Control Working Group -
  • OOS SOP Version 02
  • Strategies not to generate OOS results
  • WORKSHOP II:
  • Laboratory OOS results scenarios in QC and Development will be presented and evaluated in workshop groups
Speakers: Dr Christopher Burgess, Dr Bernd Renger

Programme

Introduction to ECA’s Analytical QC Working Group and the OOT Process

  • Overview of ECA’s Analytical QC Working Group
  • Data quality management in the Laboratory
  • Structure of the OOT/OOE guideline generation process
  • Importance of a Technical Glossary
  • Overview of the contents of the OOT/OOE Guideline
  • Aims and objectives for this Forum
Regulatory Importance of Trend Analysis under the EU GMPs
  • Regulatory concern for the control of processes
  • Overview of the cited regulatory references
  • Challenges for implementation and inspection: within the industry for the inspectorate
The Statistical Tool Box; Basics of Selection
  • What is a trend?
  • What is a control chart?
  • Data types
  • Data distributions
  • Statistical control
  • Common cause variation
  • Special cause variation
  • Process stability versus process capability
Recommendations for Process Control of Variables
  • Overview of the control of Continuous Data Monitoring for manufactured batches and for analytical test samples
  • The basis for Statistical Process Control (SPC)
  • Control Charts for Individuals
  • Control Charts for Subgroups
  • Control Charts for post mortem investigations
Example Applications for Variables I - SPC
  • Importance of individuals and means
  • Example of SPC for continuous individual data; a Moving Range (MR) Shewhart Chart
  • Setting the control limits
  • Example of SPC for continuous data for subgroups; Xbar and R
  • Process Capability
  • What if data are not normally distributed?
Example Applications for Variables II - Cusum for Investigations
  • Theory and application of Cusum analysis
  • Cusum versus EWMA charts
  • Example of a post mortem Cusum investigation
Recommended methods: Trending for Process Control of Attributes
  • Basic differences between attributes and variables
  • Control charts for attributes
  • Applications for attribute data
Examples for Trending for Process Control of Attributes
  • Theory and application of n and np charts
  • Theory and application of C and U charts
  • Example of np charting
Trending for Stability Data I; a simplified Linear Regression Approach
  • Challenges for trending stability data
  • Simplified linear regression approach
  • assumptions and limitations
  • Minimum data requirements
  • Theory and calculation of prediction intervals
  • Worked example illustrated using Excel
  • Comparison with SAS JMP; why aren’t the numbers exactly the same?
Trending for Stability Data II; a more advanced Random Coefficients Regression Model
  • Why is it sometimes necessary?
  • Basics of the RCR model
  • Advantages and disadvantages over the simplified linear regression approach
  • Evaluation of stability data
  • Examples of its application using statistical packages
Recommendations on: Out of Expectation Results (OOE)
  • Definitions for OOE
  • Unexpected variation in replicate determinations
  • Unexpected results in a Single Test or a Small Set of Tests What level of investigation is necessary and appropriate for OOE results?

INTERACTIVE WORKSHOPS


Workshop – Part I – Variables
Dr Lance Smallshaw

Workshop – Part II – Attributes
Dr Milan Crnogorac

Workshop – Part III – Stability
Dr Lori A. McCaig

Workshop – Part IV – OOE
Dr Bernd Renger

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