Uncertainty analysis as essential step in the establishment of the dynamic Design Space of primary drying during freeze-drying

Mortier, Séverine; Bockstal, Pieter-Jan Van; Corver, Jos; Nopens, Ingmar; Gernaey, Krist V.; Beer, Thomas De

doi:10.1016/j.ejpb.2016.03.015

Cited by 47 publications

(72 citation statements)

References 27 publications

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“…The freeze-drying process, also known as lyophilization, is used extensively in pharmaceutical manufacturing to stabilize APIs that have limited storage time in aqueous solutions, for example, therapeutic protein formulations and vaccines [10]. The primary drying process, which dominates the overall energy consumption of the lyophilization process, is considered the most critical step [44].…”

Section: Case Studymentioning

confidence: 99%

“…The mathematical model of the primary drying process used in this work is adapted from [10,44], and the overall setup is illustrated in Figure 3. The mass transfer equation of vapor, which represents the dynamics of the sublimation process at the sublimation surface, is given as:…”

Section: Mathematical Model Of the Primary Drying Processmentioning

confidence: 99%

“…T B = T i + ∆T is the temperature at the bottom of the vial. ∆T is the temperature difference across the frozen layer [10]:…”

Section: (A) (B)mentioning

confidence: 99%

“…For instance, dynamic process models support recent activities of the Food and Drug Administration (FDA) [6] and the International Council for Harmonisation (ICH) Q11 guideline [7] regarding QbD, and the quantification of process variability [8]. Although uncertainties in process models and parameters are considered, and are frequently incorporated in robust process design concepts [9][10][11][12][13], the applied algorithms are commonly based on perfect uncertainty measures, i.e., using specific probability density functions (PDFs). In addition to probability-based concepts for robust process design, scenario-based methods exist [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation

Xie

Schenkendorf

2019

Processes

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Model-based concepts have been proven to be beneficial in pharmaceutical manufacturing, thus contributing to low costs and high quality standards. However, model parameters are derived from imperfect, noisy measurement data, which result in uncertain parameter estimates and sub-optimal process design concepts. In the last two decades, various methods have been proposed for dealing with parameter uncertainties in model-based process design. Most concepts for robustification, however, ignore the batch-to-batch variations that are common in pharmaceutical manufacturing processes. In this work, a probability-box robust process design concept is proposed. Batch-to-batch variations were considered to be imprecise parameter uncertainties, and modeled as probability-boxes accordingly. The point estimate method was combined with the back-off approach for efficient uncertainty propagation and robust process design. The novel robustification concept was applied to a freeze-drying process. Optimal shelf temperature and chamber pressure profiles are presented for the robust process design under batch-to-batch variation.Processes 2019, 7, 509 2 of 14 operating mode when producing active pharmaceutical ingredients (APIs) and drugs [18], i.e., all materials are charged before the start of processing and discharged at the end of processing. Thus, slight experimental deviations or the degradation of the process equipment might result in batch-to-batch variation [18][19][20]. The source of batch-to-batch variation is difficult to predict, but can be quantified with process analytical technology (PAT) and multivariate statistical analysis [19,20]. In the literature, it is well-known that batch-to-batch variation causes severe problems in pharmaceutical manufacturing, drug quality, clinical studies, and therapeutics [6,17,21,22]. To lower batch-to-batch variation in pharmaceutical, and to improve QbD measures, analyzing the effect of measurement noise and batch-to-batch variation is essential. The adverse effect of batch-to-batch variation in pharmaceutical manufacturing is studied experimentally for various processing steps, e.g., fermentation, crystallization, and (nanomaterial) formulation [17,19,23]. In model-based process design, in turn, recent studies try to analysis and control batch-to-batch variation effects too [24,25]. For instance, in the case of model-based process design, model parameters can be derived for each batch data set separately. When each batch run is fit individually, batch-to-batch variation leads to different sets of model parameter estimates and parameter uncertainties. Please note that the variability in the model parameters is not exclusively the result of measurement noise, but the joint effect of measurement errors and slight differences in the experimental settings and the raw materials of the batches [6,17]. Thus, simulation studies should consider imprecise uncertainties [26-28] as well. These imprecise uncertainties cannot be described via a single PDF, but via a set of PDFs that is known as the a...

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Section: Case Studymentioning

confidence: 99%

Section: Mathematical Model Of the Primary Drying Processmentioning

confidence: 99%

“…T B = T i + ∆T is the temperature at the bottom of the vial. ∆T is the temperature difference across the frozen layer [10]:…”

Section: (A) (B)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation

Xie

Schenkendorf

2019

Processes

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“…[7][8][9][10][11] Numerous studies have proposed methods to build a design space for the freeze-drying process based on a mathematical model. 7,[9][10][11][12][13][14][15][16][17][18][19] Design space calculation methods have not only been proposed for primary drying stage but also for secondary drying stage and for freezing stage as well. 20,21 Scaling-up of freeze-drying process is also a critical issue.…”

mentioning

confidence: 99%

Influence of inner vapor transfer property of a freeze dryer on the design space of drying process

Nakagawa

Tamiya

2019

AIChE Journal

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A freeze dryer with a radiative heating device was constructed and the inner vapor transfer property was regulated by separating the drying chamber and the condenser with an orifice. A mathematical model was used to obtain the contour diagrams of the selected parameters (e.g., product temperature and total drying period) during drying as a function of the heating condition and chamber pressure. Furthermore, the inner vapor transfer coefficients of the freeze dryer were used to draw the operation lines on the diagrams. The combinations of contour diagrams and operation lines clearly depict the design spaces. It was confirmed that the inner vapor transfer property substantially affected the acceptable operation range, and it was revealed that this coefficient is key to quantify the performance of a freeze dryer. This coefficient could also be used to predict the occurrence of choked flow during a freeze‐drying run.

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Analysis of Transformed Upstream Bioprocess Data Provides Insights into Biological System Variation

Richelle¹,

Lee²,

Portela³

et al. 2020

Biotechnology Journal

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In recent years, multivariate data analysis (MVDA) and modeling approaches have found increasing applications for upstream bioprocess studies (e.g., monitoring, development, optimization, scale-up, etc.). Many of these studies look at variations in the concentrations of metabolites and cell-based measurements. However, these measures are subject to system inherent variations (e.g., changes in metabolic activity) but also intentional operational changes. It is proposed to perform MVDA and modeling on data representative of the underlying biological system operation, that is, the specific rates, which are per se independent of the scale, operational strategy (e.g., batch, fed-batch), and biomass content. Two industrial case studies are highlighted to showcase the approach: one is HEK medium performance comparison study and the other is CHO scale-up/-down study. It is shown that analyzing processes in this way reveals insights into behavior of the underlying biological system, which cannot to the same degree be deducted from the analysis of concentrations.

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Uncertainty analysis as essential step in the establishment of the dynamic Design Space of primary drying during freeze-drying

Cited by 47 publications

References 27 publications

Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation

Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation

Influence of inner vapor transfer property of a freeze dryer on the design space of drying process

Analysis of Transformed Upstream Bioprocess Data Provides Insights into Biological System Variation

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