Use of a multi-vial mathematical model to design freeze-drying cycles for pharmaceuticals at known risk of failure

Scutellà, Bernadette; Trelea, Ioan-Cristian; Bourlés, Erwan; Fonseca, Fernanda; Passot, Stéphanie

doi:10.4995/ids2018.2018.7421

Cited by 4 publications

(8 citation statements)

References 9 publications

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“…In this study, we analyze the robust process design of the primary drying process in the presence of imprecise parameter uncertainties due to batch-to-batch variations. Thus, we advance our preliminary work on robust process design for the primary drying process which was based on precise parameter uncertainties [45], and we extend recent model-based studies and experimental work on inter-vial heterogeneity in general [46][47][48].…”

Section: Case Studymentioning

confidence: 92%

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: 92%

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

Xie

Schenkendorf

2019

Processes

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“…In this study, we described primary drying through the model proposed by Fissore et al, 16 with the following improvements: (i) a dynamic energy balance to describe the time-varying thermal inertia of the system; (ii) the water partial pressure dynamics as dependent on the balance between the total sublimation mass flow and the condensed vapor mass flow. 14 The purpose of using the model is to describe the behavior of the vials inside the freeze-drying chamber in such a way as to reliably predict key performance indicators of the process, such as the sublimation end time and the maximum temperature reached by the product. The key model equations are described in the following.…”

Section: Process and Modelmentioning

confidence: 99%

“…The three terms Q w,v , Q r,v , and Q s,v [J s –1 ], respectively, represent the rates of heat transfer from the chamber walls to the vials, from the rails in which vials are framed (if applicable) to the vial surface, and from the shelf to the vial bottom. These rate terms are calculated from , where T̅ W [K] is the mean temperature of the chamber walls, T̅ r [K] is the mean temperature of the rails, T shelf [K] is the shelf temperature, and σ SB [W m –2 K –4 ] is the Stefan–Boltzmann constant.…”

Section: Process and Modelmentioning

confidence: 99%

“…The water partial pressure P w,c is described according to the following equation where R g [J mol –1 K –1 ] is the ideal gas constant, V c [m 3 ] is the volume of the drying chamber, and M w [kg kmol –1 ] is the molar mass of water. The water vapor flow to the condenser ( ṁ cd [kg s –1 ]) is calculated following Trelea et al where T̅ cd [K] is the mean temperature of the condenser, α cd [s kg –1 K –1 ] is an equipment-dependent parameter to be estimated, and P w,cd is the water vapor pressure at the condenser surface, calculated by replacing T B with T̅ cd in eq .…”

Section: Process and Modelmentioning

confidence: 99%

“…Several freeze-drying models are available in the literature, ranging from simplified one-dimensional models (such as the ones proposed by Sadikoglu and Liapis and by Velardi and Barresi) to multidimensional models, , up to more complex computational fluid dynamic models accounting for velocity, pressure, and temperature fields inside the freeze-drying chamber . Although proactively encouraged by the regulatory agencies, the use of freeze-drying first-principles models in industrial pharmaceutical environments (e.g., for design space description, process optimization, process control, continuous process verification − ) is still limited, not only because first-principles models may not be simple to develop but also because the experimental effort required to identify the model parameters can be very significant.…”

Section: Introductionmentioning

confidence: 99%

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Accelerating the Development and Transfer of Freeze-Drying Operations for the Manufacturing of Biopharmaceuticals by Model-Based Design of Experiments

De-Luca

Bano²,

Tomba³

et al. 2020

Ind. Eng. Chem. Res.

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In the pharmaceutical industry, freeze-drying (also known as lyophilization) is often used to increase the shelf life of heat-sensitive biopharmaceuticals such as protein-based therapeutic drugs and vaccines. The most time-and energy-consuming step of a lyophilization process is primary drying. When a new system configuration (e.g., a new equipment, product formulation, or primary packaging) is to be tested in process development or when transferring to manufacturing facilities, one needs to characterize heat and mass transfer in the new configuration through a battery of experiments that can be very demanding. In this study, we use a model-based design of experiments (MBDoE) to design optimal experiments for fast primary-drying model parameter estimation. We show that MBDoE allows estimating all key heat-and mass-transfer parameters in a statistically meaningful way using one single, optimally designed experiment. Using data from industrial equipment, we test the effectiveness of the methodology on two typical problems to be faced in pharmaceutical process development and transfer: (i) transfer of product manufacturing between two different pieces of equipment and (ii) change of vial type for a given product in a given equipment. Results demonstrate that the proposed methodology can substantially ease the experimentation, thus accelerating the development of freeze-drying operations.

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A Stochastic Modelling Approach to Describe the Effect of Drying Heterogeneity in the Lyophilisation of Pharmaceutical Vaccines

Bano

De-Luca

Tomba³

et al. 2020

Computer Aided Chemical Engineering

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Use of a multi-vial mathematical model to design freeze-drying cycles for pharmaceuticals at known risk of failure

Cited by 4 publications

References 9 publications

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

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

Accelerating the Development and Transfer of Freeze-Drying Operations for the Manufacturing of Biopharmaceuticals by Model-Based Design of Experiments

A Stochastic Modelling Approach to Describe the Effect of Drying Heterogeneity in the Lyophilisation of Pharmaceutical Vaccines

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