Why Apply Non-Linear Regression for your Stability Data?

Sponsored by: BIOVIA

Focused on:

  • Life Science
  • Manufacturing
  • Biovia
  • Pacira Pharmaceuticals
  • Stability Data

Date: 8 December


Time: 7pm London/11am Pacific Time

Stability Data Evaluation Methods, and their application with BIOVIA Discoverant

When evaluating the active pharmaceutical ingredient (API) or drug product stability under various environmental conditions, classical stability studies typically show linear degradation over time.

Many biological molecules or molecules with complex structures do not follow linear trends. According to the ICH Q1E Section 2.6 (Evaluation for Stability Data), “Regression analysis is considered an appropriate approach to evaluating the stability data for a quantitative attribute and establishing a retest period or shelf life.

The nature of the relationship between an attribute and time will determine whether data should be transformed for linear regression analysis. The relationship can be represented by a linear or non-linear function on an arithmetic or logarithmic scale. In some cases, a non-linear regression can better reflect the true relationship.”

Since the guidelines allow for non-linear modelling in stability studies, when is it appropriate to use these methods? What techniques are available for these types of analysis? This talk will focus on answering these questions and will include a demonstration of non-linear analysis using BIOVIA’s Discoverant Stability Module within InVision.

Presented by

Ron Ortiz,

Director, Manufacturing Science & Technology at Pacira Pharmaceuticals

Ronald S. Ortiz is Director for Manufacturing Science & Technology at Pacira Pharmaceuticals in San Diego, California responsible for leading the identification, planning and execution of global business and technology enhancement initiatives on the operational manufacturing side of the business.
Previously Ron was with Johnson and Johnson in the Medical Device sector instituting various supply chain initiatives.

Larry R. Fiegland, Ph.D.,

Lead Field Application Scientist at Dassault Systèmes BIOVIA

As a Lead Field Application Scientist, Larry has been part of the Product Development organization within BIOVIA since 2012 and is responsible for pre-sales activities, implementation support, and product development for the BIOVIA Discoverant solution. Larry obtained his Ph.D. from Virginia Tech under the direction of Professor John R. Morris, working on ultrahigh vacuum studies of the reaction mechanisms of ozone with saturated and unsaturated self-assembled monolayers.

Key Learning Objectives

  • When to consider non-linear modeling with Stability Data
  • New features within BIOVIA Discoverant InVision for non-linear modeling of Stability Data
  • How to automate your stability analysis


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