Process optimization and online product quality analysis for solid-based continuous pharmaceutical manufacturing
Date
2025
Authors
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Journal ISSN
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Publisher
University of Delaware
Abstract
At present, the pharmaceutical industry is undergoing significant transformation. The production process of small molecule drug products, which is the most widely used type of drug, needs to become more efficient, flexible, agile, and intelligent to meet the stringent quality requirement and improve global healthcare benefits. Many new technologies emerged, such as continuous production, process analytical technology, real-time release testing, which provide an effective guarantee for efficient production and quality control of small molecule drug products. Based on the intersection of small molecule drug production and process systems engineering, this thesis proposes and solves a series of current problems and challenges. ☐ First, simulation-based optimization for continuous pharmaceutical manufacturing processes has been challenging. Surrogate-based optimization approaches have been widely adopted in industrial problems due to their potential to reduce the number of simulation runs required in the optimization process. The surrogate-based optimization framework has been extended to feasibility analysis in pharmaceutical manufacturing to characterize the design space. Most surrogate-based approaches for feasibility analysis are limited to the construction of a regression model for the feasibility function. In Chapter 2, we developed a framework with the feasibility problem considered as a classification problem, and additional stages introduced to improve local exploitation and global exploration. We illustrate the efficiency of the proposed framework on three test problems and implement it in a realistic case study describing the production of solid-based drugs using wet granulation, aimed to reduce the operation cost, improve product quality, and increase process flexibility and robustness. ☐ Second, residence time distribution (RTD)-based material tracking and quality control has been a promising tool in continuous pharmaceutical manufacturing. A system’s RTD is obtained through tracer experiments. However, RTD measurements are accompanied with uncertainties because of process fluctuation and variation, measurement error, and experimental variation among different replicates. Due to the strict quality control requirements of drug manufacturing, it is essential to consider RTD uncertainty and characterize its effects on RTD-based predictions and applications. Towards this end, Chapter 3 is focused on developing approaches for RTD uncertainty quantification and propagation to analyze the effects on downstream processes. Results depict probability intervals around the upstream disturbance tracking profile and the funnel plot, facilitating better decision-making for quality control under uncertainty. ☐ Third, despite thorough efforts in tracer selection, data acquisition, and calibration model development to obtain tracer concentration profiles for RTD studies, there can exist significant noise in these profiles. This noise can make it challenging to identify the underlying signal and get a representative RTD of the system under study. Such concerns have previously indicated the importance of noise handling for RTD measurements in literature. However, the literature does not provide sufficient information on noise handling or data treatment strategies for RTD studies. To this end, we investigate the impact of varying levels of noise using different tracers on measurement of RTD profile and its applications in Chapter 4. We quantify the impact of different denoising methods (time and frequency averaging methods). Through this investigation, we see that Wavelet Transform turns out to a good method for denoising RTD profiles despite varying noise levels. The investigation is performed such that the key features of the RTD profile (which are important for RTD based applications) are preserved. Subsequently, we also investigate the impact of denoising on RTD-based applications such as out-of-specification (OOS) analysis and RTD modeling. The results show that the degree of noise levels considered in this work do not significantly impact the RTD-based applications. ☐ In summary, feasibility-driven process optimization enables economical process operation while ensuring product quality through accurate characterization of process variability and feasibility, uncertainty quantification for RTD-based material diversion accounts for and mitigates risks due to variability, statistical data pretreatment ensures noise is accurately captured and addressed. These three works equip Pharma 4.0 with tools to manage product quality, supporting flexible yet high-quality manufacturing.
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Keywords
High-quality manufacturing, Residence time distribution, Global healthcare benefits, Pharmaceutical industry