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Cross-Modal Multivariate Pattern Analysis
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Multivariate analysis as a method to understand variability in a complex excipient, and its contribution to

Mike Tobyn1, Ana Patricia Ferreira1, Jane Lightfoot2

  • 1a Drug Product Science & Technology , Bristol-Myers Squibb , Moreton, Wirral , UK.

Pharmaceutical Development and Technology
|October 11, 2018
PubMed
Summary

Excipient raw material variability, like in ethylcellulose aqueous dispersion (Surelease), can impact drug product performance. This study shows robust product quality despite minor detected differences in drug release.

Keywords:
Controlled release; multivariate analysis; formulation; ethylcellulose

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Area of Science:

  • Pharmaceutical Science
  • Materials Science
  • Chemical Engineering

Background:

  • Excipient variability is a critical factor in pharmaceutical risk assessment.
  • Understanding raw material variability's impact on drug product performance is essential for quality control.
  • Ethylcellulose aqueous dispersion (Surelease) is a complex, functional excipient requiring thorough evaluation.

Purpose of the Study:

  • To demonstrate an approach for assessing the impact of excipient raw material variability on drug product performance.
  • To evaluate the variability of an ethylcellulose aqueous dispersion (Surelease) using multivariate analysis.
  • To assess the robustness of a model drug product manufactured with varying Surelease batches.

Main Methods:

  • Multivariate analysis (MVA) of certificates of analysis for Surelease.
  • Analysis of batch relationships based on raw material sourcing.
  • Manufacture of a model drug product using Surelease from different raw material lots.
  • Dissolution testing of the model drug product using model-dependent and model-independent techniques.

Main Results:

  • Multivariate analysis revealed low overall variability in Surelease properties.
  • Batches manufactured with the same raw material showed closer relationships in MVA.
  • Small differences in model drug release were detected using model-dependent dissolution techniques.
  • Model-independent techniques did not detect significant differences in drug release.

Conclusions:

  • The applied techniques are suitable for detecting and understanding excipient variability.
  • Despite minor detected variability, the model drug product demonstrated robustness.
  • Risk assessment of excipients should consider both variability and product performance impact.