Applying the FDA’s PAT Initiative in Product Development

by | Mar 25, 2008 | Industry, Life Sciences & Medical, Measurement Instrumentation, Simulation | 0 comments

Interphex2008, the Pharmaceutical and Biotech manufacturing conference is going on this week in Philadelphia. Before Emerson’s Terry Blevins and Mike Boudreau left, they passed along the presentation they are giving on Thursday, March 27. It’s entitled, Application of PAT in Product Development. They are joined by University of Texas at Austin PhD graduate student, Yang Zhang and Broadley-James‘ Trish Benton. Here’s an excerpt from the abstract:

The Process Analytical Technology, PAT, initiative encourages innovation in pharmaceutical development, manufacturing, and quality assurance to enhance understanding and control of the manufacturing process. The challenge for many manufactures is to identify how best to address the opportunities that PAT offers. Broadley James, Emerson Process Management, and the University of Texas are working together to examine and quantify the potential to reduce cycle time and out-of-spec product through the use of high fidelity, dynamic simulation and multivariate analytics. The objective of this work is to show that the impact of PAT can be maximized through the integration of these tools during product development (PD).

In the presentation, Terry begins by discussing the U.S. Food and Drug Administrations’ PAT initiative, which has a framework that identifies some of the tools they discuss in the presentation. These include:

  • Multivariate data acquisition and analysis tools
  • Process and endpoint monitoring and control tools
  • Continuous improvement and knowledge management tools

Terry describes on-line process analytics including fault detection and quality parameter prediction. Tools for detection of abnormal operations vary for measured and unmeasured disturbances. For measured disturbances, principal component analysis (PCA) captures contributions that can be associated with process measurements. Deviations may be quantified using Hotelling’s T-square statistic.

The residual space that is not captured by the principal component score space reflects changes in unmeasured disturbances that can impact operations. These deviations can be measured with the Q statistic, squared prediction error (SPE).

For the quality parameter estimation, detection of deviations is addressed using projection to latent structures (PLS).

Armed with these statistical tools, Mike shows how the basis for bioreactor process modeling. In the book Mike coauthored with Greg McMillan, New Directions in Bioprocess Modeling and Control, they present a first principal bacterial model that was developed for fungal, bacterial, and mammalian cell processes. The intent of the process model is to more quickly evaluate input step techniques and control strategies in the PD stage.

The BioProcess International magazine article, PAT Tools for Accelerated Process Development and Improvement describes this collaborative effort between Emerson and Broadley-James technologists and University of Texas researchers along with how these tools can accelerate life science manufacturers’ PD phase.

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