Stability Analysis Made Easier

Free Webinar

For many, determining a shelf life for drug products in accordance with FDA guidelines can be a time-consuming and uncertain process. Watch this 20-minute webinar to learn how JMP® software from SAS — designed specifically to guide you through FDA-prescribed stability testing requirements — can make your life easier.

JMP offers built-in stability testing capabilities designed to simplify the determination of expiration times in accordance with ICH guidelines.

Join Byron Wingerd as he walks you through the regulatory requirements for stability testing and demonstrates how modern statistical software can simplify the process of compliance.

In roughly 20 minutes, you will learn about:

  • Confidence interval requirements: what they mean and how to calculate them.
  • How to apply linear degradation models in accordance with ICH guidelines to estimate expiration dates.
  • How an analysis of covariance (ANCOVA) model can be employed to determine poolability of data from different batches.
  • How built-in features in JMP® software specifically designed for stability testing simplify regulatory compliance.
  • Additional opportunities to learn more about how to integrate the techniques outlined in FDA regulations and ICH guidelines into your stability program.

Please fill out the form below to watch now.

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JMP Capabilities Ideal for Pharmaceutical Engineers and Scientists


  • Design of Experiments

    Actively manipulate factors according to a pre-specified design to quickly and easily gain useful, new understanding.

  • Statistical Process Control

    Separate common and special causes to assist your process analysis efforts, including problem investigation, out-of-control conditions and ongoing monitoring for stability.

  • Stability and Shelf Life Analysis

    Assess poolability of batches, establish expiration dating, and easily calculate confidence limits and crossing times – all in adherence with ICH guidelines.

  • Quality by Design (QbD)

    Identify critical process parameters and develop a functional relationship between those process parameters and your critical quality attributes (CQAs) using design of experiments (DOE).

  • Root Cause Analysis

    Find sources of variation in your data and identify the key drivers of process outcomes using flexible data preparation, dynamic visualization and interactive exploratory data analysis techniques.

  • Analytical Methods

    Streamline method development and validation processes, efficiently establish assay robustness and arrive at conclusions faster.


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