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

Abstract

Precompetitive collaborations on new enabling technologies for research and development are becoming popular among pharmaceutical companies. The Enabling Technologies Consortium (ETC), a precompetitive collaboration of leading innovative pharmaceutical companies, identifies and executes projects, often with third-party collaborators, to develop new tools and technologies of mutual interest. Here, we report the results of one of the first ETC projects: the development of a user-friendly population balance model (PBM)-based crystallization simulator software. This project required the development of PBM software with integrated experimental data handling, kinetic parameter regression, interactive process simulation, visualization, and optimization capabilities incorporated in a computationally efficient and robust software platform. Inputs from a team of experienced scientists at 10 ETC member companies helped define a set of software features that guided a team of crystallization modelers to develop software incorporating these features. Communication, continuous testing, and feedback between the ETC and the academic team facilitated the software development. The product of this project, a software tool called CrySiV, an acronym for Crystallization Simulation and Visualization, is reported herein. Currently, CrySiV can be used for cooling, antisolvent, and combined cooling and antisolvent crystallization processes, with primary and secondary nucleation, growth, dissolution, agglomeration, and breakage of crystals. This paper describes the features and the numerical methods of the software and presents two case studies demonstrating its use for parameter estimation. In the first case study, a simulated data set is used to demonstrate the capabilities of the software to find kinetic parameters and its goodness of fit to a known solution. In the second case study, the kinetics of an antisolvent crystallization of indomethacin from a ternary solvent system are estimated, providing a practical example of the tool.

Parameter estimation and estimability analysis in pharmaceutical models with uncertain inputs

“…Sen et al 28 Methylation of heteroatom-containing molecules Batch N Szilagyi et al 29 Pharmaceutical crystallization processes for Indomethacin Batch N Diab et al 30 Kinetics, distillation, and crystallization for one step in amine production Batch N Dos Santos et al 31 Adsorption of Praziquantel enantiomers -Y authors decided which parameters should be fixed at nominal values and which should be estimated based on their scientific or engineering judgment. 10,13,23,26,29,30 The authors for the remaining five studies, used formal statistical methods for subset selection with sensitivitybased methods being most popular. 16,17,20,24,28 For example, Garcıa-…”

“…Notice, however, that in 11 of the 25 studies shown in Table 1 (see right‐most column), the authors determined that only a subset of the model parameters should be estimated from the available data, either to avoid numerical problems or parameter overfitting. 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 judgment 10,13,23,26,29,30 . The authors for the remaining five studies, used formal statistical methods for subset selection with sensitivity‐based methods being most popular 16,17,20,24,28 .…”

“…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.…”

Parameter Estimation and Estimability Analysis in Pharmaceutical Models with Uncertain Inputs

“…[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.…”

“…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.…”