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The optimization research of the multi-response problems based on the SUR.

Haitao Su1, Hong Mei Yao1, Hui Zeng1

  • 1School of Economics and Management, Nanchang University, Jiangxi Province, China.

Pakistan Journal of Pharmaceutical Sciences
|March 23, 2015
PubMed
Summary

This study introduces a new method for multi-response optimization in biological medicine, using the seemingly unrelated regressions (SUR) technique. This approach effectively addresses correlated responses and identifies optimal factor levels for improved product quality and economic benefits.

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Area of Science:

  • Industrial Engineering
  • Biopharmaceutical Process Optimization
  • Statistical Modeling

Background:

  • Optimizing biological medicine products requires simultaneous consideration of multiple quality characteristics (multi-response problems).
  • Traditional methods struggle with correlated responses and regression model limitations.
  • Effective multi-response optimization significantly enhances product quality and economic benefits.

Purpose of the Study:

  • To address the limitations of traditional multi-response optimization methods in the biopharmaceutical industry.
  • To introduce and apply the Seemingly Unrelated Regressions (SUR) method for improved modeling of correlated responses.
  • To determine the optimal factor level combination for enhanced product quality in a real-world biopharmaceutical company.

Main Methods:

  • Utilized the Seemingly Unrelated Regressions (SUR) method to model relationships between response variables and control variables.
  • Developed satisfaction functions for individual responses and an overall satisfaction function.
  • Conducted empirical research using a biological medicine company (SX) to validate the methodology.

Main Results:

  • The SUR method effectively modeled the correlation between multiple response variables.
  • Satisfaction functions were confirmed, leading to the development of an overall satisfaction function.
  • The optimal factor level combination was successfully identified using the overall satisfaction function.

Conclusions:

  • The SUR method provides a robust solution for multi-response optimization problems in biopharmaceutical product design.
  • This approach effectively handles correlated responses and regression model challenges, leading to improved optimization outcomes.
  • The study successfully solved multi-response optimization issues for a biological medicine company, demonstrating practical applicability and economic value.