Lab Data Integrity, Part 2 Self Inspections and Audits to Confirm Effective Data Integrity Controls AND Practical Statistical Tools for Analytical Laboratories

Programme

Lab Data Integrity, Part 2
Self Inspections and Audits to Confirm Effective Data Integrity Controls
14-15 April 2015, Barcelona, Spain

Introduction to Course 2 & Key Learning Points from Course 1
Data integrity concerns of regulators:
FDA warning letter and EU non-compliance concerns about data integrity
FDA Compliance Program Guide 7346.832 for PAI
MHRA requirement for self inspections to focus on data integrity
Role of management in ensuring data integrity
Key learning points from Course 1

WORKSHOP I:
Identifying the Laboratory Controls to Audit for Electronic and Hybrid Systems
Group work with facilitated discussion to take the data integrity cycle and establish the controls required at each stage
This establishes what could be covered in a data integrity audit

WORKSHOP II:
Risk Assessment and Prioritisation
The data integrity cycle with the audit objectives developed in Workshop I will be applied to one of three systems (hybrid and electronic systems) to obtain a risk based approach to auditing

WORKSHOP III:
FDA Key Laboratory Data Integrity Concerns
Using some real FDA warning letters the teams will cross check that the output of Workshop II is congruent with the FDA concerns around laboratory
Attendee validation of an updated audit list

Pulling it All Together
Based on many years of the teaching team’s laboratory experience, presentation of their top 10 non-compliances based on FDA and EU regulations and audit experience will be given
There will be an opportunity to discuss and compare the output from Workshop III against this knowledge base and experience

WORKSHOP IV:
Preparing for the Data Integrity Audit
Based on the selected scenario the attendees will determine the preparation needed for a laboratory audit
Feedback and discussion with the teaching team

WORKSHOP V:
Observations and Findings During a Laboratory Audit and Planning the Closing Meeting
Each teams will be provided with an audit of a laboratory with observations
Teams will determine if there are any data integrity non-compliances with the regulations and laboratory procedures
Teams will determine if any observations are findings (non-compliances) and grade the severity of each one
Prepare for the closing meeting with the Head of the Laboratory and the business process owner of the systems

WORKSHOP VI:
Feedback to the Auditees
Teams will present the audit conclusions and the findings to the Head of the Laboratory and the business process owner of the systems
Discussion with the auditees of the findings

Review of the Course and Key Learning Points

Practical Statistical Tools for Analytical Laboratories, 16-17 April 2015, Barcelona, Spain

(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