Stability Analysis for the Pharmaceutical Industry

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Description

This 2-day course is designed for anybody concerned with stability testing who wants to know how to design trials and how to analyse them efficiently. No prior knowledge of statistics is required.

Overview

The main topics are:

  • How to analyse trials
  • Obtaining confidence intervals
  • Analysing trials using regression
  • Deciding whether batches can be combined
  • Pooling and bridging
  • Applying WHO Technical Report No. 953

Who Should Attend?

This course is for anyone working in the pharmaceutical industry who is involved with testing and analysing the stability of pharmaceutical ingredients and finished pharmaceutical products. It will help you to understand what the regulatory authorities require and how to use the correct statistical techniques to comply with ICH requirements for determining shelf-lives.

Course Programme

Introduction to Stability Testing:

  • Why is it necessary
  • Who uses it
  • What do the Regulatory Authorities require

What is the True Mean :

  • Highlighting vagaries within data using diagrams.
  • Summarising data using an average - mean or median and a measure of variability - standard deviation or range.
  • Obtaining a confidence interval for the true mean based on the sample mean and standard deviation. Estimating the sample size necessary to obtain a required width of confidence interval.

Samples and Populations: Conclusions are generally drawn about a population from data obtained from a sample.

  • What is a population
  • What is a sample
  • How should the sample be selected

Introduction to Significance Testing: The significance testing procedure using the one sample t-test for the mean and the F-test for comparison of standard deviations.

  • Formulation of hypotheses and the use of statistical tables.
  • When to choose significance tests or confidence intervals.
  • Choice of sample size and the need for a relevant design if valid conclusions are to be drawn from a significance test.

Significance Tests for Comparing Two Means:

  • The importance of designs involving two groups (e.g. control and treatment).
  • Analysis using the twosample t-test for independent samples and the one-sample t-test for paired samples.
  • Sample size required to detect a difference.

The Normal Distribution:

  • The use of the Normal Distribution.
  • Obtaining probability values and percentage limits from statistical tables.
  • The circumstances for which the distribution of data is likely to be Normal.
  • Problem solving using normal probability plots.

Outliers:

  • The assumptions behind outlier tests.
  • The importance of the Normal distribution.
  • Applying Grubbs' test.
  • The circumstances in which Grubbs' test is unsuitable.

Regression Analysis:

  • Fitting a straight line to data by least-squares regression.
  • Assessing the variability about the line using the residual standard deviation.
  • Determining the goodness of fit of the line using the correlation coefficient and percent fit.
  • Confirming that a relationship exists between two variables using the correlation coefficient.
  • Trending and extrapolation of data - obtaining predicted values and confidence intervals.
  • Assumptions behind regression analysis.
  • Regression in calibration and process improvement.
  • The importance of residual values.

Calibration:

  • Estimating the uncertainty in the response factor and the concentration of an unknown sample. Improving the precision of a calibration by changing the design.
  • Checking on validity of assumptions using residuals.
  • The effect of fixed and relative biases on error structures.

Combining Standard Deviations:

The need to improve the accuracy of an estimate of precision (standard deviation) by combining standard deviations from relevant sets of data.

Analysis of Variance and Pooling:

  • A statisticians view of the guidance as written.
  • What are its limitations and consequences"
  • Examining overall variability within a batch.
  • Understanding an Analysis of Variance table.
  • Comparing Estimating within-batch SD and batch-to-batch SD from an investigation.
  • Determining whether several groups are significantly different

Review of ICH Q1A and Q1E:

A detailed review of the technical requirements of ICH Q1A and Q1E.

Stability Testing - How to Apply the Standard:

How to implement statistical analysis as given in the WHO Technical Report No. 953 "Stability Testing of pharmaceutical ingredients and finished pharmaceutical products".

Interactive Case Exercise: Interactive case exercises, based on real industrial problems, are a feature of the course.

Tutorials: Tutorials follow each lecture. Course members, under the guidance of experienced tutors, learn how to apply newly-found knowledge by solving realistic problems from industry.

Problem Seminars: Problem Seminars and additional tutorials are held in the evenings, when extra help and guidance are available.

Free Consultancy Service: A free consultancy service is available to course members which allows for practical problems to be presented for advice and help.

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