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Model-based optimisation and control strategy for the primary drying phase of a lyophilisation process

(2020) PHARMACEUTICS. 12(2).
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Abstract
The standard operation of a batch freeze-dryer is protocol driven. All freeze-drying phases (i.e., freezing, primary and secondary drying) are programmed sequentially at fixed time points and within each phase critical process parameters (CPPs) are typically kept constant or linearly interpolated between two setpoints. This way of operating batch freeze-dryers is shown to be time consuming and inefficient. A model-based optimisation and real-time control strategy that includes model output uncertainty could help in accelerating the primary drying phase while controlling the risk of failure of the critical quality attributes (CQAs). In each iteration of the real-time control strategy, a design space is computed to select an optimal set of CPPs. The aim of the control strategy is to avoid product structure loss, which occurs when the sublimation interface temperature (Ti) exceeds the the collapse temperature (Tc) common during unexpected disturbances, while preventing the choked flow conditions leading to a loss of pressure control. The proposed methodology was experimentally verified when the chamber pressure and shelf fluid system were intentionally subjected to moderate process disturbances. Moreover, the end of the primary drying phase was predicted using both uncertainty analysis and a comparative pressure measurement technique. Both the prediction of Ti and end of primary drying were in agreement with the experimental data. Hence, it was confirmed that the proposed real-time control strategy is capable of mitigating the effect of moderate disturbances during batch freeze-drying.
Keywords
BIOMATH, freeze-drying, supervisory process control, primary drying, dynamic design space, uncertainty analysis, DYNAMIC DESIGN SPACE, UNCERTAINTY ANALYSIS, TEMPERATURE, STEP, PHARMACEUTICALS, FORMULATIONS, RESISTANCE, BUILD, WATER, MASS

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MLA
Vanbillemont, Brecht, et al. “Model-Based Optimisation and Control Strategy for the Primary Drying Phase of a Lyophilisation Process.” PHARMACEUTICS, vol. 12, no. 2, 2020, doi:10.3390/pharmaceutics12020181.
APA
Vanbillemont, B., Nicolaï, N., Leys, L., & De Beer, T. (2020). Model-based optimisation and control strategy for the primary drying phase of a lyophilisation process. PHARMACEUTICS, 12(2). https://doi.org/10.3390/pharmaceutics12020181
Chicago author-date
Vanbillemont, Brecht, Niels Nicolaï, Laurens Leys, and Thomas De Beer. 2020. “Model-Based Optimisation and Control Strategy for the Primary Drying Phase of a Lyophilisation Process.” PHARMACEUTICS 12 (2). https://doi.org/10.3390/pharmaceutics12020181.
Chicago author-date (all authors)
Vanbillemont, Brecht, Niels Nicolaï, Laurens Leys, and Thomas De Beer. 2020. “Model-Based Optimisation and Control Strategy for the Primary Drying Phase of a Lyophilisation Process.” PHARMACEUTICS 12 (2). doi:10.3390/pharmaceutics12020181.
Vancouver
1.
Vanbillemont B, Nicolaï N, Leys L, De Beer T. Model-based optimisation and control strategy for the primary drying phase of a lyophilisation process. PHARMACEUTICS. 2020;12(2).
IEEE
[1]
B. Vanbillemont, N. Nicolaï, L. Leys, and T. De Beer, “Model-based optimisation and control strategy for the primary drying phase of a lyophilisation process,” PHARMACEUTICS, vol. 12, no. 2, 2020.
@article{8656579,
  abstract     = {{The standard operation of a batch freeze-dryer is protocol driven. All freeze-drying phases (i.e., freezing, primary and secondary drying) are programmed sequentially at fixed time points and within each phase critical process parameters (CPPs) are typically kept constant or linearly interpolated between two setpoints. This way of operating batch freeze-dryers is shown to be time consuming and inefficient. A model-based optimisation and real-time control strategy that includes model output uncertainty could help in accelerating the primary drying phase while controlling the risk of failure of the critical quality attributes (CQAs). In each iteration of the real-time control strategy, a design space is computed to select an optimal set of CPPs. The aim of the control strategy is to avoid product structure loss, which occurs when the sublimation interface temperature (Ti) exceeds the the collapse temperature (Tc) common during unexpected disturbances, while preventing the choked flow conditions leading to a loss of pressure control. The proposed methodology was experimentally verified when the chamber pressure and shelf fluid system were intentionally subjected to moderate process disturbances. Moreover, the end of the primary drying phase was predicted using both uncertainty analysis and a comparative pressure measurement technique. Both the prediction of Ti and end of primary drying were in agreement with the experimental data. Hence, it was confirmed that the proposed real-time control strategy is capable of mitigating the effect of moderate disturbances during batch freeze-drying.}},
  articleno    = {{181}},
  author       = {{Vanbillemont, Brecht and Nicolaï, Niels and Leys, Laurens and De Beer, Thomas}},
  issn         = {{1999-4923}},
  journal      = {{PHARMACEUTICS}},
  keywords     = {{BIOMATH,freeze-drying,supervisory process control,primary drying,dynamic design space,uncertainty analysis,DYNAMIC DESIGN SPACE,UNCERTAINTY ANALYSIS,TEMPERATURE,STEP,PHARMACEUTICALS,FORMULATIONS,RESISTANCE,BUILD,WATER,MASS}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{21}},
  title        = {{Model-based optimisation and control strategy for the primary drying phase of a lyophilisation process}},
  url          = {{http://dx.doi.org/10.3390/pharmaceutics12020181}},
  volume       = {{12}},
  year         = {{2020}},
}

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