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Processes 2018, 6(4), 27; https://doi.org/10.3390/pr6040027

The Impact of Global Sensitivities and Design Measures in Model-Based Optimal Experimental Design

1
Institute of Energy and Process Systems Engineering, TU Braunschweig, Franz-Liszt-Straße 35, 38106 Braunschweig, Germany
2
Center of Pharmaceutical Engineering (PVZ), TU Braunschweig, Franz-Liszt-Straße 35a, 38106 Braunschweig, Germany
3
International Max Planck Research School (IMPRS) for Advanced Methods in Process and Systems Engineering, Sandtorstraße 1, 39106 Magdeburg, Germany
4
Institute for Chemical and Thermal Process Engineering, TU Braunschweig, Langer Kamp 7, 38106 Braunschweig, Germany
*
Author to whom correspondence should be addressed.
Current address: Institute of Energy and Process Systems Engineering, TU Braunschweig, Franz-Liszt-Straße 35, 38106 Braunschweig, Germany.
Received: 14 February 2018 / Revised: 15 March 2018 / Accepted: 19 March 2018 / Published: 21 March 2018
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Abstract

In the field of chemical engineering, mathematical models have been proven to be an indispensable tool for process analysis, process design, and condition monitoring. To gain the most benefit from model-based approaches, the implemented mathematical models have to be based on sound principles, and they need to be calibrated to the process under study with suitable model parameter estimates. Often, the model parameters identified by experimental data, however, pose severe uncertainties leading to incorrect or biased inferences. This applies in particular in the field of pharmaceutical manufacturing, where usually the measurement data are limited in quantity and quality when analyzing novel active pharmaceutical ingredients. Optimally designed experiments, in turn, aim to increase the quality of the gathered data in the most efficient way. Any improvement in data quality results in more precise parameter estimates and more reliable model candidates. The applied methods for parameter sensitivity analyses and design criteria are crucial for the effectiveness of the optimal experimental design. In this work, different design measures based on global parameter sensitivities are critically compared with state-of-the-art concepts that follow simplifying linearization principles. The efficient implementation of the proposed sensitivity measures is explicitly addressed to be applicable to complex chemical engineering problems of practical relevance. As a case study, the homogeneous synthesis of 3,4-dihydro-1H-1-benzazepine-2,5-dione, a scaffold for the preparation of various protein kinase inhibitors, is analyzed followed by a more complex model of biochemical reactions. In both studies, the model-based optimal experimental design benefits from global parameter sensitivities combined with proper design measures. View Full-Text
Keywords: optimal experimental design; global parameter sensitivities; optimal design measures; robustification; point estimate method optimal experimental design; global parameter sensitivities; optimal design measures; robustification; point estimate method
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Schenkendorf, R.; Xie, X.; Rehbein, M.; Scholl, S.; Krewer, U. The Impact of Global Sensitivities and Design Measures in Model-Based Optimal Experimental Design. Processes 2018, 6, 27.

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