Practical Statistical Tools for Analytical Laboratories

Practical Statistical Tools for Analytical Laboratories

Barcelona, Spain

Course No 9194


Costs

This conference already took place.

If you have any questions, please contact us:
Tel.: +49 (0)6221 / 84 44 0 E-Mail: info@gmp-compliance.org

Speakers

Dr Christopher Burgess, Burgess Analytical Consultancy
Dr Joachim Ermer, Sanofi-Aventis

Objectives

Statistical calculations and tools are applied extensively in pharmaceutical analysis including:

Method development and validation
Transfer of analytical procedures
Setting or verification of specification limits
Data evaluation, comparison and trending

The ICH Q10 Guideline “Pharmaceutical Quality System”, the FDA Guidances on Process Validation and Methods Validation (Draft) require monitoring of “process performance and product quality” and “Trend analysis on method performance” throughout the product lifecycle. Hence the appropriate use of statistical trending and evaluation tools has become mandatory.

Consequently, a thorough understanding of statistical fundamentals is essential in order to be able to select
parameters and test methods that are ‘fit for purpose’.

Do you speak statistics?
In addition, such an understanding facilitates the communication with other technical and regulatory functions applying statistical tools in order to ensure an overall consistent approach.

Background

The course will provide the participants with recommendations, tools and examples to apply scientifically and pragmatically sound statistical principles to their day-to-day business as well as to meet future challenges described above.

The relevance of such statistical tools is also increasingly recognised by the Compendia, as reflected, for example, in the USP General Information Chapter <1010> “Interpretation and treatment of analytical data” and the recently introduced <1033> “Biological assay validation” together with USP Medicines Compendium, <10> “Assessing Validation Parameters for Reference and Acceptable Procedures”.

Statistical tools are needed, for example, to evaluate:

Distribution of data and its parameters
How to detect outliers and trends?
How to establish the total variability of the method?
How to identify method parameters that must be controlled?

Method performance and specification limits
Which accuracy and precision is needed to achieve an acceptable risk of OOS results?
Scientifically based justification and optimisation of the reportable result (single or average?)
What are the requirements for impurity methods?
Comparison of methods and data
What are the requirements for calibration models?
How to optimise the number of calibration replicates on a scientific basis?

A brief discussion of supporting software tools (e.g.
Excel, Minitab, JMP) to facilitate the generation of statistical information in a consistent manner will be undertaken.

One of the main features of this new course is the
balance of presentations and more than five hours of practical exercise workshops which will allow participants to gain ‘hands on’ practical experience in applying the statistical methods described. By means of statistical simulation tools, the participants will gain intuitive understanding of the consequences of appropriate and
inappropriate performance parameters, for example the relationship between precision and OOS results.

For this reason, the course is limited to 30 participants
so that individual attention and support can be given. In order to fully benefit from the workshops, attendees should preferably bring a notebook with Excel® 2007 or later.

Target Group

This best practice oriented course is designed for analytical laboratory managers and their colleagues charged with the day to day management and evaluation of laboratory data throughout the lifecycle, i.e. in method development, validation, transfer, specification setting, batch release and stability, continuous performance verification and change control.

QA, manufacturing and regulatory affairs professionals will benefit from participation by gaining a clear understanding of the statistical fundamentals which are important to implement scientifically sound and pragmatic tools to conform to GMP and regulatory requirements for example Product Quality Review.

Programme

(Normal) Distribution of Data and its Parameters
Data shape and its importance
Characterisation of distributions (Location and Dispersion)
Probability considerations; all measurements are subject to error
Populations and samples
Confidence intervals
What is an outlier?
Error of the error

Calculation and Evaluation of Precision Levels
System precision, repeatability, intermediate precision, reproducibility
ANOVA: Identification of relevant variance components from injection, measurement, sample preparation, intermediate conditions
Total variability: precision of the reportable result and its optimisation
Optimisation of single-point calibration
Relationship between precision and probability of OOS results
Practically relevant acceptance criteria for precision

Trending of Data
Why trend?
Evaluation; do we expect a trend or not?
Statistical Process Control principles
Types of Control charts and their application
Application to stability testing

Design of Experiments (DoE) Principles and the Investigation of Robustness
Why do we need Design of Experiments?
Basics of DoE
What is robustness?
Worked example of DoE to the investigation of robustness

Comparison of Data & Accuracy
Significance (F- and t-test) and equivalence tests
Statistical significance and practical relevance
Differences caused by random variability: observed and true bias
Applications in transfer and cross-validation

Calibration Models, Linear and non-Linear
What is a calibration model?
What is the difference between linear and non-linear models?
The principle of least squares and why it is important
Applying the principles to linear and non-linear models

Performance Requirements for Impurity Procedures
Concentration dependence of precision (Horwitz relation)
Detection and Quantitation Limits

Summary Workshop & Discussion: Appropriate Choice of Tests/Calculations
Practical objectives and data sets are provided
The participants will discuss and define appropriate tests and parameters to be calculated
The participants are given the calculation results and are asked to make an evaluation
The defined tests and results are discussed in the audience

WORKSHOP I:
Understanding the Variability (Statistical Simulations)
Range of expected data
Variability of standard deviations
Number of data and reliability of calculated standard deviations

WORKSHOP II:
Optimisation of Variability
Statistically based format of the reportable result (single or average)
Number of determinations for various levels
Probability of results outside established limits

WORKSHOP III:
Control Charts & Trending
Interactive workshop based on supplied real data sets for interpretation
Use of Minitab for control charting
Team working on evaluation and interpretation of trend data

WORKSHOP IV:
Comparison of Data (Statistical Simulations)
Significance and equivalence tests: influence of number of data and series
Differences between means and variability

WORKSHOP V:
Linearity (Statistical Simulations)
Regression range and evaluation of the intercept
Extrapolation effects

WORKSHOP VI:
Quantitation Limit
Basics to consider for calculation from linearity
How to determine appropriately from precision

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