Cross-Pharma Collaboration for the Development of a Simulation Tool for the Model-Based Digital Design of Pharmaceutical Crystallization Processes (CrySiV)

Szilágyi, Botond; Wu, Wei-Lee; Eren, Ayşe; Mackey, Jaron; Kshirsagar, Shivani; Szilagyi, Emoke; Østergaard, Iben; Qu, Haiyan; Sinha, Kushal; Mlinar, Laurie; Pohlman, Daniel A.; Chen, Jie; Nere, Nandkishor K.; Boukerche, Moussa; Korde, Akshay; Ellis, Ryan J.; Lovette, Michael A.; Black, Simon; Jawor-Baczynska, Anna; Li, Huayu; Yang, Bing‐Shiou; Irdam, Erwin; Patience, Daniel B.; McKeown, Rahn; Ketchum, Megan A.; Green, Daniel A.; Reddy, Venkata Ramana; Polster, Christopher S.; Burcham, Christopher L.; Jarmer, Daniel J.; Merritt, Jeremy M.; Cote, Aaron; Sirota, E. B.; Codan, Lorenzo; Schoell, Jochen; Liu, Yiqing C.; Girard, Kevin P.; Kulkarni, Samir A.; Yang, Yihui; Quon, Justin L.

doi:10.1021/acs.cgd.1c00904

Cited by 14 publications

(13 citation statements)

References 56 publications

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“…13−15 A successful development of continuous crystallization requires additional understanding toward crystallization kinetics to ensure a robust process operation and overall economical optimality. 16,17 Mixed-suspension-mixed-product-removal (MSMPR) crystallization is a commonly used continuous concept that is analogous to a continuous stirred tank reactor. This concept has been widely used for purification and isolation of commodity chemicals and fine chemicals.…”

Section: ■ Introductionmentioning

confidence: 99%

Breakage-Facilitated Mixed-Suspension-Mixed-Product-Removal (MSMPR) Crystallization of Pharmaceutical Compounds

Liu,

Li,

2024

Crystal Growth & Design

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Continuous crystallization provides advantages of producing solid products of consistent quality attributes at a small equipment footprint. However, the intrinsic nucleation and growth rates of small-molecule pharmaceuticals constrain the design spaces of their continuous crystallization processes. In this work, a wet milling device, breaking large particles into small fragments, was incorporated into a mixed-suspension-mixed-product-removal (MSMSR) single-stage crystallizer. The MSMPR crystallization was controlled by an ultrasound feedback loop to maintain a constant total slurry volume, while the slurry was constantly being circulated through a wet mill at different milling intensities. The approach was applied to two compounds with distinct crystallization kinetic properties, including paracetamol (fast nucleation and growth) and a proprietary compound (slow nucleation with agglomeration). Experimental data showed that particle attrition provided by wet milling generated a significant number of nuclei that could promote the crystallization rate, which greatly reduced the steady-state supersaturation level. The introduction of the controllable breakage mechanism offered better particle size distribution control, higher yield, and minimized fouling tendency that resulted in a longer operation time than conventional MSMPR.

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Section: ■ Introductionmentioning

confidence: 99%

Breakage-Facilitated Mixed-Suspension-Mixed-Product-Removal (MSMPR) Crystallization of Pharmaceutical Compounds

Liu,

Li,

2024

Crystal Growth & Design

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“…[7][8][9] In all the studies listed in Table 1, model inputs (independent variables) were assumed to be perfectly known during parameter estimation and all of the experimental uncertainty was assigned to the model outputs (dependent variables). This assumption enabled modelers to use either Least Squares (LS) 15,18,30 or Weighted Least Squares (WLS) estimation, 10,12,16,17,19,23,27,28,32,34 which is applied when there are multiple dependent variables with different levels of variability.…”

Section: Introductionmentioning

confidence: 99%

“…In 6 of the 11 studies where only a subset of the parameters was estimated, the authors decided which parameters should be fixed at nominal values and which should be estimated based on their scientific or engineering judgement. 13,16,26,29,32,33 The authors for the remaining 5 studies, used formal statistical methods for subset selection with sensitivity-based methods being most popular. 19,20,23,27,31 For example, Garcıa-Munoz et al and Sen et al used a popular orthogonalization-based algorithm to rank their model parameters from most estimable to least estimable.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation and Estimability Analysis in Pharmaceutical Models with Uncertain Inputs

Moshiritabrizi

Abdi

McMullen

et al. 2023

Preprint

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A methodology is proposed to aid parameter estimation in fundamental models of pharmaceutical processes. This methodology addresses situations with insufficient data to reliably estimate all parameters, when the estimation is complicated by uncertain independent variables. The proposed method uses an augmented sensitivity matrix to rank the combined set of parameters and uncertain inputs from most estimable to least estimable. An updated mean-squared-error criterion is then used to determine the appropriate parameters and inputs that should be estimated, based on the ranked list. A model for one step in a batch pharmaceutical production process with an uncertain initial reactant concentration is used to illustrate the method, revealing that the initial reactant concentration in each batch should be estimated along with three out of six model parameters. Non-estimable parameters are fixed at their initial values to prevent overfitting. The method will aid error-in-variables parameter estimation in many situations involving limited data.

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“…In all the studies listed in Table 1, model inputs (independent variables) were assumed to be perfectly known during parameter estimation and all of the experimental uncertainty was assigned to the model outputs (dependent variables). This assumption enabled modelers to use either least squares (LS) 12,15,27 or weighted least squares (WLS) estimation, 7,9,13,14,16,20,24,25,29,31 which is applied when there are multiple dependent variables with different levels of variability. Sometimes, however, uncertainties in independent variables can be large due to measurement errors in process inputs or other difficulties in achieving the desired experimental settings.…”

Section: Introductionmentioning

confidence: 99%

“…squares (WLS) estimation, 7,9,13,14,16,20,24,25,29,31 which is applied when there are multiple dependent variables with different levels of variability.…”

mentioning

confidence: 99%

Parameter estimation and estimability analysis in pharmaceutical models with uncertain inputs

et al. 2023

View full text Add to dashboard Cite

A methodology is proposed to aid parameter estimation in fundamental models of pharmaceutical processes. This methodology addresses situations with insufficient data to reliably estimate all parameters, when the estimation is complicated by uncertain independent variables. The proposed method uses an augmented sensitivity matrix to rank the combined set of parameters and uncertain inputs from most estimable to least estimable. An updated mean‐squared‐error criterion is then used to determine the appropriate parameters and inputs that should be estimated, based on the ranked list. A model for one step in a batch pharmaceutical production process with an uncertain initial reactant concentration is used to illustrate the method, revealing that the initial reactant concentration in each batch should be estimated along with three out of six model parameters. Non‐estimable parameters are fixed at their initial values to prevent overfitting. The method will aid error‐in‐variables parameter estimation in many situations involving limited data.

show abstract

Cross-Pharma Collaboration for the Development of a Simulation Tool for the Model-Based Digital Design of Pharmaceutical Crystallization Processes (CrySiV)

Cited by 14 publications

References 56 publications

Breakage-Facilitated Mixed-Suspension-Mixed-Product-Removal (MSMPR) Crystallization of Pharmaceutical Compounds

Breakage-Facilitated Mixed-Suspension-Mixed-Product-Removal (MSMPR) Crystallization of Pharmaceutical Compounds

Parameter Estimation and Estimability Analysis in Pharmaceutical Models with Uncertain Inputs

Parameter estimation and estimability analysis in pharmaceutical models with uncertain inputs

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