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

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

doi:10.1021/acs.cgd.1c00904

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2023

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Cited by 4 publications

(2 citation statements)

References 56 publications

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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

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

Cited by 4 publications

References 56 publications

Parameter Estimation and Estimability Analysis in Pharmaceutical Models with Uncertain Inputs

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