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Processes 2019, 7(2), 96; https://doi.org/10.3390/pr7020096

An Optimization-Based Framework to Define the Probabilistic Design Space of Pharmaceutical Processes with Model Uncertainty

1
Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA
2
Small Molecule Design and Development, Lilly Research Laboratories, Eli Lilly & Company, Indianapolis, IN 46285, USA
3
Sandia National Laboratories, Albuquerque, NM 87123, USA
*
Author to whom correspondence should be addressed.
Both authors contributed equally to this work.
Received: 29 December 2018 / Revised: 30 January 2019 / Accepted: 1 February 2019 / Published: 14 February 2019
(This article belongs to the Special Issue Model-Based Tools for Pharmaceutical Manufacturing Processes)
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Abstract

To increase manufacturing flexibility and system understanding in pharmaceutical development, the FDA launched the quality by design (QbD) initiative. Within QbD, the design space is the multidimensional region (of the input variables and process parameters) where product quality is assured. Given the high cost of extensive experimentation, there is a need for computational methods to estimate the probabilistic design space that considers interactions between critical process parameters and critical quality attributes, as well as model uncertainty. In this paper we propose two algorithms that extend the flexibility test and flexibility index formulations to replace simulation-based analysis and identify the probabilistic design space more efficiently. The effectiveness and computational efficiency of these approaches is shown on a small example and an industrial case study. View Full-Text
Keywords: pharmaceutical processes; flexibility analysis; probabilistic design space; global optimization pharmaceutical processes; flexibility analysis; probabilistic design space; global optimization
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Laky, D.; Xu, S.; Rodriguez, J.S.; Vaidyaraman, S.; García Muñoz, S.; Laird, C. An Optimization-Based Framework to Define the Probabilistic Design Space of Pharmaceutical Processes with Model Uncertainty. Processes 2019, 7, 96.

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