Dr Christopher Burgess, Burgess Analytical Consultancy, UK
Dr Joachim Ermer, Ermer Quality Consulting, Germany
Statistical calculations and tools are applied extensively in pharmaceuticalanalysis including
- Procedure 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 require monitoring of “process performance and product quality” and “Trend analysis on method performance” throughout the product lifecycle. The new ICH guidelines Q2 Validation of Analytical Procedures (Revision 2) and Q14 Analytical Procedure Development request the use of “appropriate statistical methods” to evaluate calibration functions, precision, and accuracy, for example by regression analysis, confidence, prediction, and tolerance intervals. 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.
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 the General Chapter <1220> on Analytical Procedure Lifecycle.
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 course is the balance of presentations and more than four 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.
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.
Analytical Procedure Lifecycle Management Overview
- Principles of APLM
- USP <1220>
- Risk based approach
- Target Measurement Uncertainty
- Decision rules
(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 ist optimisation
- Relationship between precision and probability of OOS results
- Practically relevant acceptance criteria for precision
WORKSHOP I: Understanding the Variability (Statistical Simulations)
WORKSHOP II: Optimisation of Variability
- Scatter of results and risk of OOS
- Variability of standard deviations
- Number of data and reliability of calculated standard deviations
- Statistically based format of the reportable result (single or average)
- Number of determinations for various levels
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
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
Measurement Uncertainty without the Maths; Introduction to Monte Carlo Simulation
- Principles of Monte Carlo simulation
- Understanding variance contributions and how they Combine
- Measurement uncertainty
- Application to analytical procedures
- Examples of unit and complete procedures using Companion by Minitab
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
WORKSHOP IV: Comparison of Data (Statistical Simulations)
- Significance and equivalence tests: Impact of number of data and series
- Differences between means and variability
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
WORKSHOP V: Linearity (Statistical Simulations)
- Regression range and evaluation of the intercept
- Extrapolation effects
Performance Requirements for Impurity Procedures
- Concentration dependence of precision (Horwitz relation)
- Detection and Quantitation Limits
WORKSHOP VI: Quantitation Limit
- Determination from signal-to-noise ratio
- Appropriate consideration of types of noise
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