Investigation of Design Space for Freeze-Drying: Use of Modeling for Primary Drying Segment of a Freeze-Drying Cycle

Koganti, Venkat; Shalaev, Evgenyi; Berry, Mark; Osterberg, Thomas; Youssef, Maickel; Hiebert, David; Kanka, Frank; Nolan, Martin; Barrett, Rosemary; Scalzo, Gioval; Fitzpatrick, Gillian; Fitzgibbon, Niall; Luthra, Sumit; Zhang, Liling

doi:10.1208/s12249-011-9645-7

Cited by 91 publications

(60 citation statements)

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“…[38][39][40][41][42] shows various methods, using simple one-dimensional models, to calculate the design space of the primary drying stage of a freeze-drying process. This paper aims at developing a model-based approach to calculate the design space of the primary drying in case the product processed is characterized by a dried cake resistance dependent on the operating conditions of the process.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Framework for the Design of Freeze-Drying Cycles: Optimization of the Operating Conditions of the Primary Drying Stage

Fissore

Pisano

2015

Processes

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This paper deals with the freeze-drying process and, in particular, with the optimization of the operating conditions of the primary drying stage. When designing a freeze-drying cycle, process control aims at obtaining the values of the operating conditions (temperature of the heating fluid and pressure in the drying chamber) resulting in a product temperature lower than the limit value of the product, and in the shortest drying time. This is particularly challenging, mainly due to the intrinsic nonlinearity of the system. In this framework, deep process knowledge is required for deriving a suitable process dynamic model that can be used to calculate the design space for the primary drying stage. The design space can then be used to properly design (and optimize) the process, preserving product quality. The case of a product whose dried layer resistance, one of the key model parameters, is affected by the operating conditions is addressed in this paper, and a simple and effective method to calculate the design space in this case is presented and discussed.

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Section: Introductionmentioning

confidence: 99%

Computer-Aided Framework for the Design of Freeze-Drying Cycles: Optimization of the Operating Conditions of the Primary Drying Stage

Fissore

Pisano

2015

Processes

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“…Furthermore, the steady state model will not yield information on the residual moisture content in the dry layer [15]. PASSAGE, a commercial software package from Technalysis (Indianapolis, IN, USA), has been used to more accurately model the freezedrying process [4,15,17]. PASSAGE uses a finite element method (FEM) to solve coupled heat and mass transfer equations in a two-dimensional axisymmetric space [17].…”

Section: Introductionmentioning

confidence: 99%

“…PASSAGE, a commercial software package from Technalysis (Indianapolis, IN, USA), has been used to more accurately model the freezedrying process [4,15,17]. PASSAGE uses a finite element method (FEM) to solve coupled heat and mass transfer equations in a two-dimensional axisymmetric space [17]. This model is much more complicated than the simple steady state model, but it is much more accurate.…”

Section: Introductionmentioning

confidence: 99%

“…These parameters include chamber pressure, shelf temperature, and various product and container properties such as the K v and R p . While previous work calculated the vial heat transfer coefficient with water sublimation tests [4,17], this work performed sublimation tests with the specific formulation being tested. Upon the incorporation of these parameters into the PASSAGE model, it is capable of modeling the complete lyophilization cycle, including both primary drying and secondary drying.…”

Section: Introductionmentioning

confidence: 99%

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Finite Element Method (FEM) Modeling of Freeze-drying: Monitoring Pharmaceutical Product Robustness During Lyophilization

et al. 2015

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Abstract. Lyophilization is an approach commonly undertaken to formulate drugs that are unstable to be commercialized as ready to use (RTU) solutions. One of the important aspects of commercializing a lyophilized product is to transfer the process parameters that are developed in lab scale lyophilizer to commercial scale without a loss in product quality. This process is often accomplished by costly engineering runs or through an iterative process at the commercial scale. Here, we are highlighting a combination of computational and experimental approach to predict commercial process parameters for the primary drying phase of lyophilization. Heat and mass transfer coefficients are determined experimentally either by manometric temperature measurement (MTM) or sublimation tests and used as inputs for the finite element model (FEM)-based software called PASSAGE, which computes various primary drying parameters such as primary drying time and product temperature. The heat and mass transfer coefficients will vary at different lyophilization scales; hence, we present an approach to use appropriate factors while scaling-up from lab scale to commercial scale. As a result, one can predict commercial scale primary drying time based on these parameters. Additionally, the model-based approach presented in this study provides a process to monitor pharmaceutical product robustness and accidental process deviations during Lyophilization to support commercial supply chain continuity. The approach presented here provides a robust lyophilization scale-up strategy; and because of the simple and minimalistic approach, it will also be less capital intensive path with minimal use of expensive drug substance/active material.

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“…Passage/Freeze drying software of Technalysis Inc. 11) and SCANPT program reported by Kuu et al 12) are based on the heat and mass transfer model, and are thought to be useful tools for predicting the maximum product temperature and primary drying time. Using the novel simulation program (Kyowa FD program, Kyowa Vacuum Engineering Co.), an attempt was also made to predict the product temperature in our previous study, while the large error, 2.7°C, between the predicted and the measured maximum product temperature was observed.…”

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confidence: 99%

Optimization of Primary Drying Condition for Pharmaceutical Lyophilization Using a Novel Simulation Program with a Predictive Model for Dry Layer Resistance

Kodama

Sawada

Hosomi

et al. 2014

CHEMICAL & PHARMACEUTICAL BULLETIN

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The purpose of this study was to develop a novel simulation program to accurately predict the maximum product temperature and the primary drying time in lyophilization using the predictive model for dry layer resistance, which is the resistance of dried cake against water vapor flow. Ten percent sucrose aqueous solution was selected as a model formulation. It was demonstrated that the deviations between the predicted and measured maximum product temperature were attributed to the error of dry layer resistance at a given drying condition, which was different from the measured dry layer resistance in a preliminary lyophilization run for the simulation program. However, when the predictive model of dry layer resistance was used for the simulation program, the model remarkably enhanced the accuracy of the simulation program to predict the maximum product temperature and primary drying time under various operating conditions. Furthermore, the primary drying condition required for minimized drying at a close collapse temperature was successfully discovered through one preliminary run. Therefore, it is expected that the developed simulation program is useful for designing the lyophilization cycle without a trial and error approach.Key words simulation; dry layer mass transfer resistance; maximum product temperature; primary drying time Lyophilization is widely employed in pharmaceutical industries to enhance the stability of drug products for parenteral injection. The lyophilization cycle mainly consists of three steps: freezing, primary drying, and secondary drying. Generally, the primary drying process is carried out through sublimation, and requires up to a few days. In order to maximize the sublimation rate in primary drying, it is important that the vial heat transfer rate is as high as possible, resulting in a minimized primary drying time and high product temperature. However, the maximum product temperature has to be kept below the collapse temperature, where a lyophilized cake loses macroscopic structure and collapses, to ensure the elegant appearance of the lyophilized cake and its stability. Hence, enormous efforts have been spent to minimize the primary drying time without the collapse of lyophilized cakes by adjusting the shelf temperature and chamber pressure in pharmaceutical development.In order to minimize the trial and error experiments, the mathematical model for the prediction of the optimized product temperature is thought to be useful. The mathematical model expressed by heat and mass transfer has been investigated for the expression of the sublimation phenomenon by many researchers. [1][2][3][4][5] In the heat and mass transfer model, two parameters, heat transfer coefficient and dry layer resistance, are important for estimating the product temperature during primary drying. The heat transfer coefficient for the estimation of the heat transfer rate depends on the lyophilizer and the glass vial container and its stopper, and is experimentally determined by a water sublimation test. [6][7][8] The ...

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Investigation of Design Space for Freeze-Drying: Use of Modeling for Primary Drying Segment of a Freeze-Drying Cycle

Cited by 91 publications

References 9 publications

Computer-Aided Framework for the Design of Freeze-Drying Cycles: Optimization of the Operating Conditions of the Primary Drying Stage

Computer-Aided Framework for the Design of Freeze-Drying Cycles: Optimization of the Operating Conditions of the Primary Drying Stage

Finite Element Method (FEM) Modeling of Freeze-drying: Monitoring Pharmaceutical Product Robustness During Lyophilization

Optimization of Primary Drying Condition for Pharmaceutical Lyophilization Using a Novel Simulation Program with a Predictive Model for Dry Layer Resistance

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