At this Live Online Training participants will get practical advice on how to identify OOT Results. You will get to know how to use the statistical tool box for detecting OOT data. During the training the following aspects will be discussed:
Regulatory concern for the control of processes
Data types and distributions
Variations and statistical control
Statistical tool box
Process stability versus process capability
Recommendations for process control of variables and attributes
Participants will get a worksheet document and a preparation document for pre-reading upfront. During the training feedback related to the worksheet is requested which will be discussed by the speaker. Possible solutions will be suggested and recommendations will be provided.
Background
Laboratory tests are performed on active pharmaceutical ingredients, excipients and other components, inprocess materials, and finished drug product. In these tests a trend can occur and a trend analysis has to be performed by applying techniques for detecting an underlying pattern of behaviour in a time or batch sequence which would otherwise be partly or nearly completely hidden by noise.
There are two distinct types of trend situations:
No trend is expected, for example for production or analytical process data which are known or assumed to be under statistical control.
A trend is expected, for example in stability testing.
There is a fundamental difference between these two situations in that the variance increases with time in the second situation. Trend analysis is of regulatory relevance and a key aspect in both in EU guidelines (e.g. Annex 15, EU GMP-Guide) and FDA Guidances (e.g. Guidance for Industry, Process Validation: General Principles and Practices).
The ECA Working Group on Analytical Quality Control decided to address these aspects and developed a harmonised guideline SOP on managing analytical deviations within the laboratory including OOS, OOE and OOT results. It encourages the application of a consistent and scientifically sound approach to trend analysis as part of a QMS. The current Version 2 is available for all ECA members on the ECA members area.
Target Group
This Live Online Training is recommended for all levels of technical staff and managerial personnel dealing with out-of-trend results, including analytical laboratories, contract laboratories, and Quality Assurance/Quality Control personnel.
Technical Requirements
For our Live Online Training Courses and Webinars, we use Cisco WebEx, one of the leading suppliers of online meetings.
At http://www.webex.com/test-meeting.html you can check if your system meets the necessary requirements for the participation at a WebEx meeting and at the same time install the necessary plug-in. Please just enter your name and email address for the test. If the installation is not possible because of your rights for the computer system, please contact your IT department. WebEx is a standard nowadays and the necessary installation is fast and easy.
An Introduction to the Statistical Tool Box; Distribution of Data and its Characterisation
Specifications and processes
Data types and models
Populations and samples
Data distributions
Errors and confidence intervals
Trending for Process Control of Variables
Statistical Process Control
Individual and moving range charts
X bar and R charts
Process capability indices
CUSUM & EWMA charts
Investigation using CUSUM
Workshop 1 on Variables
SPC of continuous individual and grouped monitoring data using the moving range and mean and range methods.
Scenario 1: Environmental Control in a Laboratory Refrigerator
Scenario 2: API Process Yield
Scenario 3: Stability Testing Trending
Interpretation and Conclusions
Feedback from Workshop 1
Trending for Process Control of Attributes
Basic differences between attributes and variables
Distributional requirements
AQLs & sampling for attributes [OOE attributes with a batch]
Control charts for attributes; an overview
Workshop 2 on Attributes
ATP bioluminescence is used to provide a rapid and simple method to monitor the microbiological cleanliness of a process plant at a Critical Control Point (CCP). The data, in the form of discrete Relative Light Units (RLU), measure the amount of microbial ATP.
Interpretation
Conclusions
Feedback from Workshop 2
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Seminar Programme as PDF